diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..b78ea69 --- /dev/null +++ b/.env.example @@ -0,0 +1,25 @@ +# ============================================================================== +# NBA Analytics Environment Configuration +# ============================================================================== + +# ------------------------------------------------------------------------------ +# 1. PostgreSQL Source Connection (Required for pipeline extraction) +# ------------------------------------------------------------------------------ +POSTGRES_URL=postgresql://username:password@host:5432/nba_source_db + +# ------------------------------------------------------------------------------ +# 2. Cloudflare R2 / S3-Compatible Object Storage (Required for DB sync & CI/CD) +# ------------------------------------------------------------------------------ +# Cloudflare R2 endpoint format: https://.r2.cloudflarestorage.com +S3_ENDPOINT_URL=https://.r2.cloudflarestorage.com +S3_BUCKET_NAME=nba-analytics +S3_ACCESS_KEY_ID=your_r2_access_key_id +S3_SECRET_ACCESS_KEY=your_r2_secret_access_key +S3_REGION=auto +S3_DB_KEY=dbt_nba.duckdb + +# ------------------------------------------------------------------------------ +# 3. dbt CLI Configuration (Allows running `uv run dbt ...` directly from root) +# ------------------------------------------------------------------------------ +DBT_PROJECT_DIR=dbt_nba +DBT_PROFILES_DIR=dbt_nba diff --git a/.gitea/workflows/pipeline.yaml b/.gitea/workflows/pipeline.yaml new file mode 100644 index 0000000..ddb1902 --- /dev/null +++ b/.gitea/workflows/pipeline.yaml @@ -0,0 +1,42 @@ +name: NBA Data Pipeline + +on: + schedule: + - cron: '0 6 * * *' + workflow_dispatch: + +jobs: + build-and-sync: + runs-on: ubuntu-latest + steps: + - name: Checkout Code + uses: actions/checkout@v4 + + - name: Setup uv + uses: astral-sh/setup-uv@v4 + with: + version: "latest" + + - name: Install DuckDB CLI + run: | + curl -fsSL https://github.com/duckdb/duckdb/releases/latest/download/duckdb_cli-linux-amd64.zip -o duckdb.zip + unzip -o duckdb.zip + sudo mv duckdb /usr/local/bin/ + rm -f duckdb.zip + + - name: Install gettext (envsubst) + run: | + sudo apt-get update && sudo apt-get install -y gettext-base + + - name: Run ETL Pipeline & Upload to Cloudflare R2 + env: + POSTGRES_URL: ${{ secrets.POSTGRES_URL }} + S3_ENDPOINT_URL: ${{ secrets.S3_ENDPOINT_URL }} + S3_BUCKET_NAME: ${{ secrets.S3_BUCKET_NAME }} + S3_ACCESS_KEY_ID: ${{ secrets.S3_ACCESS_KEY_ID }} + S3_SECRET_ACCESS_KEY: ${{ secrets.S3_SECRET_ACCESS_KEY }} + S3_REGION: ${{ secrets.S3_REGION || 'auto' }} + S3_DB_KEY: ${{ secrets.S3_DB_KEY || 'dbt_nba.duckdb' }} + run: | + chmod +x ./scripts/pipeline.sh ./scripts/db_storage.py + ./scripts/pipeline.sh diff --git a/.gitignore b/.gitignore index d8518a5..e6029fc 100644 --- a/.gitignore +++ b/.gitignore @@ -176,3 +176,22 @@ cython_debug/ .docs docs + +# Database files (stored in S3 / Cloudflare R2, not in Git) +*.duckdb +*.duckdb.wal +*.duckdb.tmp + +# dbt artifacts +dbt_nba/target/ +dbt_nba/dbt_packages/ +dbt_nba/logs/ + +# Environments & secrets +.env +.env.* +!.env.example + +AGENTS.md +.agents +.claude \ No newline at end of file diff --git a/data/shot_chart.png b/data/shot_chart.png new file mode 100644 index 0000000..47076ed Binary files /dev/null and b/data/shot_chart.png differ diff --git a/data/shot_chart.svg b/data/shot_chart.svg new file mode 100644 index 0000000..dd2648a --- /dev/null +++ b/data/shot_chart.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/dbt_nba/.gitignore b/dbt_nba/.gitignore new file mode 100644 index 0000000..49f147c --- /dev/null +++ b/dbt_nba/.gitignore @@ -0,0 +1,4 @@ + +target/ +dbt_packages/ +logs/ diff --git a/dbt_nba/.user.yml b/dbt_nba/.user.yml new file mode 100644 index 0000000..1845032 --- /dev/null +++ b/dbt_nba/.user.yml @@ -0,0 +1 @@ +id: 428675f2-8539-4353-aae8-d37a4d91a7f7 diff --git a/dbt_nba/README.md b/dbt_nba/README.md new file mode 100644 index 0000000..7874ac8 --- /dev/null +++ b/dbt_nba/README.md @@ -0,0 +1,15 @@ +Welcome to your new dbt project! + +### Using the starter project + +Try running the following commands: +- dbt run +- dbt test + + +### Resources: +- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction) +- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers +- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support +- Find [dbt events](https://events.getdbt.com) near you +- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices diff --git a/dbt_nba/analyses/.gitkeep b/dbt_nba/analyses/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/dbt_nba/dbt_project.yml b/dbt_nba/dbt_project.yml new file mode 100644 index 0000000..bb6233f --- /dev/null +++ b/dbt_nba/dbt_project.yml @@ -0,0 +1,65 @@ +name: 'dbt_nba' +version: '1.0.0' +config-version: 2 + +profile: 'dbt_nba' + +model-paths: ["models"] +analysis-paths: ["analyses"] +test-paths: ["tests"] +seed-paths: ["seeds"] +macro-paths: ["macros"] +snapshot-paths: ["snapshots"] + +clean-targets: + - "target" + - "dbt_packages" + +models: + dbt_nba: + +materialized: view + + staging: + +materialized: view + +schema: staging + +tags: ['staging'] + + intermediate: + +materialized: table + +schema: intermediate + +tags: ['intermediate'] + + marts: + +materialized: table + +schema: marts + +tags: ['marts'] + + dimensions: + +materialized: table + +tags: ['dimension'] + + facts: + +materialized: incremental + +tags: ['fact'] + +tests: + dbt_nba: + +severity: warn + +store_failures: true + +schema: test_results + +seeds: + dbt_nba: + +schema: seeds + +vars: + start_date: '2002-10-01' + end_date: '2025-11-10' + min_games_played: 10 + min_minutes_per_game: 15 + clutch_time_minutes: 5 + close_game_margin: 5 + regular_season_start_month: 10 + regular_season_end_month: 4 + playoff_start_month: 4 + playoff_end_month: 6 diff --git a/dbt_nba/macros/.gitkeep b/dbt_nba/macros/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/dbt_nba/models/intermediate/_int_models.yml b/dbt_nba/models/intermediate/_int_models.yml new file mode 100644 index 0000000..ef10d0b --- /dev/null +++ b/dbt_nba/models/intermediate/_int_models.yml @@ -0,0 +1,58 @@ +--- +version: 2 +models: + - name: int_player_performance + description: "Unified model containing basic and advanced stats for each player in every game. Grain: one row per player per game." + tests: + - dbt_utils.unique_combination_of_columns: + combination_of_columns: + - game_id + - player_id + columns: + - name: game_id + tests: + - not_null + - name: player_id + tests: + - not_null + + - name: int_team_performance + description: "Unified model containing basic and advanced stats for each team in every game. Grain: one row per team per game." + tests: + - dbt_utils.unique_combination_of_columns: + combination_of_columns: + - game_id + - team + columns: + - name: game_id + tests: + - not_null + - name: team + tests: + - not_null + + - name: int_games_enriched + description: "Game-level model enriched with detailed performance stats for both the home and visitor teams. Grain: one row per game." + columns: + - name: game_id + tests: + - unique + - not_null + + - name: int_player_shots_enriched + description: > + Unified shot-level dataset combining field goal attempts (from shot charts) + with free throw attempts (derived from stg_player_game_basic_stats). + Grain: one row per shot attempt (FG or FT). + columns: + - name: shot_id + tests: + - not_null + - name: player_id + tests: + - not_null + - name: shot_source + tests: + - not_null + - accepted_values: + values: ['shot_chart', 'box_score_ft'] diff --git a/dbt_nba/models/intermediate/int_games_enriched.sql b/dbt_nba/models/intermediate/int_games_enriched.sql new file mode 100644 index 0000000..96521e1 --- /dev/null +++ b/dbt_nba/models/intermediate/int_games_enriched.sql @@ -0,0 +1,81 @@ +{{ + config( + materialized='table', + schema='intermediate', + tags=["intermediate"] + ) +}} + +WITH games AS ( + SELECT + g.*, + home_map.team_abbr AS home_team_abbr, + visitor_map.team_abbr AS visitor_team_abbr, + winning_map.team_abbr AS winning_team_abbr + FROM {{ ref('stg_games') }} AS g + LEFT JOIN {{ ref('team_maps') }} AS home_map ON g.home_team = home_map.full_name + LEFT JOIN {{ ref('team_maps') }} AS visitor_map ON g.visitor_team = visitor_map.full_name + LEFT JOIN {{ ref('team_maps') }} AS winning_map ON g.winning_team = winning_map.full_name + WHERE (g.season_start_year >= home_map.start_year AND g.season_start_year < home_map.end_year) + AND (g.season_start_year >= visitor_map.start_year AND g.season_start_year < visitor_map.end_year) + AND (g.season_start_year >= winning_map.start_year AND g.season_start_year < winning_map.end_year) +), + +arena_locations AS ( + SELECT arena_name, arena_city + FROM ( + SELECT + arena_name, + city AS arena_city, + ROW_NUMBER() OVER (PARTITION BY arena_name ORDER BY city) as rn + FROM {{ ref('arena_maps') }} + ) AS sub + WHERE rn = 1 +), + +team_performance AS ( + SELECT * FROM {{ ref('int_team_performance') }} +), + +final AS ( + SELECT + g.game_id, + g.game_date, + g.season_start_year, + g.is_playoff, + g.arena, + al.arena_city, + g.home_team_abbr AS home_team, + g.visitor_team_abbr AS visitor_team, + g.winning_team_abbr AS winning_team, + g.home_points, + g.visitor_points, + g.point_differential, + g.total_points, + g.is_overtime, + home_stats.offensive_rating AS home_offensive_rating, + home_stats.defensive_rating AS home_defensive_rating, + home_stats.net_rating AS home_net_rating, + home_stats.pace AS home_pace, + home_stats.effective_fg_pct AS home_effective_fg_pct, + home_stats.turnover_rate AS home_turnover_rate, + home_stats.offensive_tier AS home_offensive_tier, + home_stats.defensive_tier AS home_defensive_tier, + visitor_stats.offensive_rating AS visitor_offensive_rating, + visitor_stats.defensive_rating AS visitor_defensive_rating, + visitor_stats.net_rating AS visitor_net_rating, + visitor_stats.pace AS visitor_pace, + visitor_stats.effective_fg_pct AS visitor_effective_fg_pct, + visitor_stats.turnover_rate AS visitor_turnover_rate, + visitor_stats.offensive_tier AS visitor_offensive_tier, + visitor_stats.defensive_tier AS visitor_defensive_tier, + (home_stats.pace + visitor_stats.pace) / 2 AS matchup_pace + FROM games g + LEFT JOIN arena_locations al ON g.arena = al.arena_name + LEFT JOIN team_performance AS home_stats + ON g.game_id = home_stats.game_id AND g.home_team_abbr = home_stats.team + LEFT JOIN team_performance AS visitor_stats + ON g.game_id = visitor_stats.game_id AND g.visitor_team_abbr = visitor_stats.team +) + +SELECT * FROM final diff --git a/dbt_nba/models/intermediate/int_player_performance.sql b/dbt_nba/models/intermediate/int_player_performance.sql new file mode 100644 index 0000000..ca91d7d --- /dev/null +++ b/dbt_nba/models/intermediate/int_player_performance.sql @@ -0,0 +1,97 @@ +{{ + config( + materialized='table', + schema='intermediate', + tags=["intermediate"] + ) +}} + +WITH basic_stats AS ( + SELECT + s.*, + map.team_abbr AS team_conformed + FROM {{ ref('stg_player_game_basic_stats') }} AS s + LEFT JOIN {{ ref('team_maps') }} AS map + ON s.team = map.team_abbr + LEFT JOIN {{ ref('stg_games') }} AS g + ON s.game_id = g.game_id + WHERE did_play = TRUE + AND (g.season_start_year >= map.start_year AND g.season_start_year < map.end_year) +), + +adv_stats AS ( + SELECT + s.*, + map.team_abbr AS team_conformed + FROM {{ ref('stg_player_game_adv_stats_extended') }} AS s + LEFT JOIN {{ ref('team_maps') }} AS map + ON s.team = map.team_abbr + LEFT JOIN {{ ref('stg_games') }} AS g + ON s.game_id = g.game_id + WHERE (g.season_start_year >= map.start_year AND g.season_start_year < map.end_year) +), + +games AS ( + SELECT + g.game_id, + g.game_date, + g.season_start_year, + g.is_playoff, + home_map.team_abbr AS home_team_abbr, + winning_map.team_abbr AS winning_team_abbr + FROM {{ ref('stg_games') }} g + LEFT JOIN {{ ref('team_maps') }} AS home_map ON g.home_team = home_map.full_name + LEFT JOIN {{ ref('team_maps') }} AS winning_map ON g.winning_team = winning_map.full_name + WHERE (g.season_start_year >= home_map.start_year AND g.season_start_year < home_map.end_year) + AND (g.season_start_year >= winning_map.start_year AND g.season_start_year < winning_map.end_year) +), + +final AS ( + SELECT + b.game_id, + b.player_id, + b.player_name, + b.team_conformed AS team, + g.game_date, + g.season_start_year, + g.is_playoff, + CASE WHEN b.team_conformed = g.winning_team_abbr THEN 'W' ELSE 'L' END AS game_result, + CASE WHEN b.team_conformed = g.home_team_abbr THEN 'HOME' ELSE 'AWAY' END AS team_location, + b.minutes_played, + b.points, + b.assists, + b.total_rebounds, + b.steals, + b.blocks, + b.turnovers, + b.plus_minus, + a.net_rating, + a.box_plus_minus, + b.field_goals_made, + b.field_goals_attempted, + b.field_goal_pct, + b.three_pointers_made, + b.three_pointers_attempted, + b.three_point_pct, + a.true_shooting_pct, + a.effective_fg_pct, + a.usage_pct, + a.offensive_rating, + a.defensive_rating, + a.usage_tier, + a.impact_tier, + a.shooting_efficiency_tier, + a.minutes_based_role, + b.is_double_double, + b.is_triple_double, + a.is_versatile, + a.is_defensive_specialist, + a.is_three_and_d + FROM basic_stats AS b + LEFT JOIN adv_stats AS a + ON b.game_id = a.game_id AND b.player_id = a.player_id + LEFT JOIN games AS g + ON b.game_id = g.game_id +) + +SELECT * FROM final diff --git a/dbt_nba/models/intermediate/int_player_shots_enriched.sql b/dbt_nba/models/intermediate/int_player_shots_enriched.sql new file mode 100644 index 0000000..15a0b6d --- /dev/null +++ b/dbt_nba/models/intermediate/int_player_shots_enriched.sql @@ -0,0 +1,214 @@ +{{ + config( + materialized='table', + schema='intermediate', + tags=["intermediate"] + ) +}} + +WITH shot_charts AS ( + SELECT * FROM {{ ref('stg_player_shot_charts') }} +), + +games AS ( + SELECT + g.game_id, + g.game_date, + g.season_start_year, + g.is_playoff, + g.arena, + g.home_points, + g.visitor_points, + g.point_differential, + g.is_overtime, + home_map.team_abbr AS home_team_abbr, + visitor_map.team_abbr AS visitor_team_abbr, + winning_map.team_abbr AS winning_team_abbr + FROM {{ ref('stg_games') }} AS g + LEFT JOIN {{ ref('team_maps') }} AS home_map ON g.home_team = home_map.full_name + LEFT JOIN {{ ref('team_maps') }} AS visitor_map ON g.visitor_team = visitor_map.full_name + LEFT JOIN {{ ref('team_maps') }} AS winning_map ON g.winning_team = winning_map.full_name + WHERE (g.season_start_year >= home_map.start_year AND g.season_start_year < home_map.end_year) + AND (g.season_start_year >= visitor_map.start_year AND g.season_start_year < visitor_map.end_year) + AND (g.season_start_year >= winning_map.start_year AND g.season_start_year < winning_map.end_year) +), + +shots_conformed AS ( + SELECT + sc.*, + COALESCE(tm.team_abbr, sc.team_abbr_raw) AS team_conformed, + COALESCE(opp.team_abbr, sc.opponent_abbr_raw) AS opponent_conformed + FROM shot_charts AS sc + LEFT JOIN {{ ref('team_maps') }} AS tm + ON sc.team_abbr_raw = tm.team_abbr + AND sc.season_start_year >= tm.start_year + AND sc.season_start_year < tm.end_year + LEFT JOIN {{ ref('team_maps') }} AS opp + ON sc.opponent_abbr_raw = opp.team_abbr + AND sc.season_start_year >= opp.start_year + AND sc.season_start_year < opp.end_year +), + +shots_with_game AS ( + SELECT + sc.*, + g.game_id, + g.is_playoff, + g.arena, + g.is_overtime AS game_had_overtime, + CASE + WHEN sc.team_conformed = g.home_team_abbr THEN 'HOME' + WHEN sc.team_conformed = g.visitor_team_abbr THEN 'AWAY' + ELSE NULL + END AS team_location, + CASE + WHEN sc.team_conformed = g.winning_team_abbr THEN 'W' + ELSE 'L' + END AS game_result + FROM shots_conformed AS sc + LEFT JOIN games AS g + ON CAST(sc.game_date AS DATE) = CAST(g.game_date AS DATE) + AND ( + (sc.team_conformed = g.home_team_abbr AND sc.opponent_conformed = g.visitor_team_abbr) + OR + (sc.team_conformed = g.visitor_team_abbr AND sc.opponent_conformed = g.home_team_abbr) + ) +), + +fg_shots AS ( + SELECT + shot_id, + game_id, + player_id, + team_conformed AS team, + opponent_conformed AS opponent, + game_date, + game_date_raw, + season_start_year, + is_playoff, + team_location, + game_result, + game_had_overtime, + quarter_raw, + quarter_number, + is_overtime_shot, + time_remaining_raw, + seconds_remaining_in_quarter, + shot_x_coordinate, + shot_y_coordinate, + is_made, + shot_made_flag, + shot_missed_flag, + shot_type_raw, + shot_point_value, + is_three_pointer, + is_free_throw, + distance_ft, + shot_distance_zone, + points_generated, + team_had_lead, + team_score_at_shot, + opponent_score_at_shot, + score_margin_at_shot, + is_clutch_shot, + CASE WHEN game_id IS NOT NULL THEN TRUE ELSE FALSE END AS has_game_match, + 'shot_chart' AS shot_source, + created_at, + updated_at, + dbt_loaded_at + FROM shots_with_game +), + +-- Free throw expansion using DuckDB's generate_series + UNNEST +matched_player_games AS ( + SELECT DISTINCT + game_id, player_id, team, opponent, game_date, game_date_raw, + season_start_year, is_playoff, team_location, game_result, game_had_overtime + FROM fg_shots + WHERE has_game_match = TRUE +), + +ft_source AS ( + SELECT + mpg.*, + bs.free_throws_made, + bs.free_throws_attempted + FROM matched_player_games AS mpg + INNER JOIN {{ ref('stg_player_game_basic_stats') }} AS bs + ON mpg.game_id = bs.game_id AND mpg.player_id = bs.player_id + WHERE bs.did_play = TRUE AND bs.free_throws_attempted > 0 +), + +ft_made_expanded AS ( + SELECT ft.*, n.n AS ft_seq, TRUE AS ft_is_made + FROM ft_source AS ft + CROSS JOIN LATERAL (SELECT UNNEST(generate_series(1, GREATEST(ft.free_throws_made, 0))) AS n) AS n + WHERE ft.free_throws_made > 0 +), + +ft_missed_expanded AS ( + SELECT ft.*, n.n AS ft_seq, FALSE AS ft_is_made + FROM ft_source AS ft + CROSS JOIN LATERAL (SELECT UNNEST(generate_series(1, GREATEST(ft.free_throws_attempted - ft.free_throws_made, 0))) AS n) AS n + WHERE ft.free_throws_attempted > ft.free_throws_made +), + +ft_shots AS ( + SELECT + -(ABS(hash( + ft.game_id || '|' || ft.player_id || '|FT_' + || CASE WHEN ft.ft_is_made THEN 'MADE' ELSE 'MISS' END + || '|' || CAST(ft.ft_seq AS TEXT) + )) % 9223372036854775807 + 1) AS shot_id, + ft.game_id, + ft.player_id, + ft.team, + ft.opponent, + ft.game_date, + ft.game_date_raw, + ft.season_start_year, + ft.is_playoff, + ft.team_location, + ft.game_result, + ft.game_had_overtime, + 'Free Throw' AS quarter_raw, + 0 AS quarter_number, + FALSE AS is_overtime_shot, + NULL AS time_remaining_raw, + NULL::INT AS seconds_remaining_in_quarter, + NULL::BIGINT AS shot_x_coordinate, + NULL::BIGINT AS shot_y_coordinate, + ft.ft_is_made AS is_made, + CASE WHEN ft.ft_is_made THEN 1 ELSE 0 END AS shot_made_flag, + CASE WHEN ft.ft_is_made THEN 0 ELSE 1 END AS shot_missed_flag, + 'free-throw' AS shot_type_raw, + 1 AS shot_point_value, + FALSE AS is_three_pointer, + TRUE AS is_free_throw, + 15 AS distance_ft, + 'Free Throw (15 ft)' AS shot_distance_zone, + CASE WHEN ft.ft_is_made THEN 1 ELSE 0 END AS points_generated, + NULL::BOOLEAN AS team_had_lead, + NULL::BIGINT AS team_score_at_shot, + NULL::BIGINT AS opponent_score_at_shot, + NULL::INT AS score_margin_at_shot, + FALSE AS is_clutch_shot, + TRUE AS has_game_match, + 'box_score_ft' AS shot_source, + CURRENT_TIMESTAMP AS created_at, + CURRENT_TIMESTAMP AS updated_at, + CURRENT_TIMESTAMP AS dbt_loaded_at + FROM ( + SELECT * FROM ft_made_expanded + UNION ALL + SELECT * FROM ft_missed_expanded + ) AS ft +), + +final AS ( + SELECT * FROM fg_shots + UNION ALL + SELECT * FROM ft_shots +) + +SELECT * FROM final diff --git a/dbt_nba/models/intermediate/int_team_performance.sql b/dbt_nba/models/intermediate/int_team_performance.sql new file mode 100644 index 0000000..03d87d1 --- /dev/null +++ b/dbt_nba/models/intermediate/int_team_performance.sql @@ -0,0 +1,85 @@ +{{ + config( + materialized='table', + schema='intermediate', + tags=["intermediate"] + ) +}} + +WITH basic_stats AS ( + SELECT + s.*, + map.team_abbr AS team_conformed + FROM {{ ref('stg_team_game_basic_stats') }} AS s + LEFT JOIN {{ ref('team_maps') }} AS map + ON s.team = map.team_abbr + LEFT JOIN {{ ref('stg_games') }} AS g + ON s.game_id = g.game_id + WHERE (g.season_start_year >= map.start_year AND g.season_start_year < map.end_year) +), + +adv_stats AS ( + SELECT + s.*, + map.team_abbr AS team_conformed + FROM {{ ref('stg_team_game_adv_stats') }} AS s + LEFT JOIN {{ ref('team_maps') }} AS map + ON s.team = map.team_abbr + LEFT JOIN {{ ref('stg_games') }} AS g + ON s.game_id = g.game_id + WHERE (g.season_start_year >= map.start_year AND g.season_start_year < map.end_year) +), + +games AS ( + SELECT + g.game_id, + g.game_date, + g.season_start_year, + g.is_playoff, + home_map.team_abbr AS home_team_abbr, + visitor_map.team_abbr AS visitor_team_abbr, + winning_map.team_abbr AS winning_team_abbr + FROM {{ ref('stg_games') }} g + LEFT JOIN {{ ref('team_maps') }} AS home_map ON g.home_team = home_map.full_name + LEFT JOIN {{ ref('team_maps') }} AS visitor_map ON g.visitor_team = visitor_map.full_name + LEFT JOIN {{ ref('team_maps') }} AS winning_map ON g.winning_team = winning_map.full_name + WHERE (g.season_start_year >= home_map.start_year AND g.season_start_year < home_map.end_year) + AND (g.season_start_year >= visitor_map.start_year AND g.season_start_year < visitor_map.end_year) + AND (g.season_start_year >= winning_map.start_year AND g.season_start_year < winning_map.end_year) +), + +final AS ( + SELECT + b.game_id, + b.team_conformed AS team, + g.game_date, + g.season_start_year, + g.is_playoff, + CASE WHEN b.team_conformed = g.winning_team_abbr THEN 'W' ELSE 'L' END AS game_result, + CASE + WHEN b.team_conformed = g.home_team_abbr THEN g.visitor_team_abbr + ELSE g.home_team_abbr + END AS opponent_team, + b.points, + a.offensive_rating, + a.defensive_rating, + a.net_rating, + b.pace, + b.effective_fg_pct, + b.turnover_rate, + b.offensive_rebound_rate, + b.free_throw_rate, + a.offensive_tier, + a.defensive_tier, + a.shot_selection_style, + a.ball_movement_style, + a.ball_security_tier, + a.defensive_activity + FROM basic_stats AS b + LEFT JOIN adv_stats AS a + ON b.game_id = a.game_id AND b.team_conformed = a.team_conformed + LEFT JOIN games AS g + ON b.game_id = g.game_id +) + +SELECT * FROM final diff --git a/dbt_nba/models/marts/dimensions/dim_arenas.sql b/dbt_nba/models/marts/dimensions/dim_arenas.sql new file mode 100644 index 0000000..46a2def --- /dev/null +++ b/dbt_nba/models/marts/dimensions/dim_arenas.sql @@ -0,0 +1,21 @@ +{{ + config( + materialized='table', + schema='marts', + tags=["dimension"] + ) +}} + +WITH source AS ( + SELECT arena_name, city, state_or_country, location + FROM {{ ref('arena_mappings') }} +) + +SELECT + {{ dbt_utils.generate_surrogate_key(['arena_name', 'city']) }} AS arena_key, + arena_name, + city AS arena_city, + state_or_country AS arena_state_or_country, + location AS arena_location +FROM source +ORDER BY arena_name, arena_city diff --git a/dbt_nba/models/marts/dimensions/dim_dates.sql b/dbt_nba/models/marts/dimensions/dim_dates.sql new file mode 100644 index 0000000..5feb50a --- /dev/null +++ b/dbt_nba/models/marts/dimensions/dim_dates.sql @@ -0,0 +1,44 @@ +{{ + config( + materialized='table', + schema='marts', + tags=["dimension"] + ) +}} + +{% set start_date_query %} +select min(game_date)::date from {{ ref('stg_games') }} +{% endset %} +{% set start_date = dbt_utils.get_single_value(start_date_query) %} + +{% set end_date_query %} +select max(game_date)::date from {{ ref('stg_games') }} +{% endset %} +{% set end_date = dbt_utils.get_single_value(end_date_query) %} + +WITH date_spine AS ( + SELECT UNNEST(generate_series( + '{{ start_date }}'::date, + '{{ end_date }}'::date, + INTERVAL '1 day' + ))::date AS date_day +) + +SELECT + CAST(strftime(date_day, '%Y%m%d') AS INTEGER) AS date_key, + date_day AS full_date, + EXTRACT(YEAR FROM date_day)::int AS year, + EXTRACT(QUARTER FROM date_day)::int AS quarter_of_year, + EXTRACT(MONTH FROM date_day)::int AS month_of_year, + strftime(date_day, '%B') AS month_name, + EXTRACT(DAY FROM date_day)::int AS day_of_month, + EXTRACT(ISODOW FROM date_day)::int AS day_of_week, + strftime(date_day, '%A') AS day_of_week_name, + EXTRACT(DOY FROM date_day)::int AS day_of_year, + EXTRACT(WEEK FROM date_day)::int AS week_of_year, + CASE + WHEN EXTRACT(ISODOW FROM date_day) IN (6, 7) THEN true + ELSE false + END AS is_weekend +FROM date_spine +ORDER BY full_date diff --git a/dbt_nba/models/marts/dimensions/dim_player_game_archetypes.sql b/dbt_nba/models/marts/dimensions/dim_player_game_archetypes.sql new file mode 100644 index 0000000..178aaf2 --- /dev/null +++ b/dbt_nba/models/marts/dimensions/dim_player_game_archetypes.sql @@ -0,0 +1,36 @@ +{{ + config( + materialized='table', + schema='marts', + tags=["dimension"] + ) +}} + +WITH distinct_archetypes AS ( + SELECT DISTINCT + usage_tier, + impact_tier, + shooting_efficiency_tier, + minutes_based_role, + is_double_double, + is_triple_double, + is_versatile, + is_defensive_specialist, + is_three_and_d + FROM {{ ref('int_player_performance') }} +) + +SELECT + {{ dbt_utils.generate_surrogate_key([ + 'usage_tier', + 'impact_tier', + 'shooting_efficiency_tier', + 'minutes_based_role', + 'is_double_double', + 'is_triple_double', + 'is_versatile', + 'is_defensive_specialist', + 'is_three_and_d' + ]) }} AS archetype_key, + * +FROM distinct_archetypes diff --git a/dbt_nba/models/marts/dimensions/dim_players.sql b/dbt_nba/models/marts/dimensions/dim_players.sql new file mode 100644 index 0000000..4573f61 --- /dev/null +++ b/dbt_nba/models/marts/dimensions/dim_players.sql @@ -0,0 +1,26 @@ +{{ + config( + materialized='table', + schema='marts', + tags=["dimension"] + ) +}} + +WITH player_game_stats AS ( + SELECT + stats.player_id, + stats.player_name, + games.game_date, + ROW_NUMBER() OVER (PARTITION BY stats.player_id ORDER BY games.game_date DESC) as rn + FROM {{ ref('stg_player_game_basic_stats') }} AS stats + LEFT JOIN {{ ref('stg_games') }} AS games + ON stats.game_id = games.game_id +) + +SELECT + {{ dbt_utils.generate_surrogate_key(['player_id']) }} AS player_key, + player_id, + player_name +FROM player_game_stats +WHERE rn = 1 +ORDER BY player_name diff --git a/dbt_nba/models/marts/dimensions/dim_seasons.sql b/dbt_nba/models/marts/dimensions/dim_seasons.sql new file mode 100644 index 0000000..c9af45a --- /dev/null +++ b/dbt_nba/models/marts/dimensions/dim_seasons.sql @@ -0,0 +1,19 @@ +{{ + config( + materialized='table', + schema='marts', + tags=["dimension"] + ) +}} + +WITH all_seasons AS ( + SELECT DISTINCT season_start_year + FROM {{ ref('int_games_enriched') }} +) + +SELECT + {{ dbt_utils.generate_surrogate_key(['season_start_year']) }} AS season_key, + season_start_year, + season_start_year || '-' || SUBSTR(CAST(season_start_year + 1 AS VARCHAR), 3, 2) AS season_display +FROM all_seasons +ORDER BY season_start_year diff --git a/dbt_nba/models/marts/dimensions/dim_shot_zones.sql b/dbt_nba/models/marts/dimensions/dim_shot_zones.sql new file mode 100644 index 0000000..43dcad2 --- /dev/null +++ b/dbt_nba/models/marts/dimensions/dim_shot_zones.sql @@ -0,0 +1,32 @@ +{{ + config( + materialized='table', + schema='marts', + tags=["dimension"] + ) +}} + +WITH zones AS ( + SELECT * FROM ( + VALUES + ('At Rim (0-3 ft)', 0, 3, 1, 'Paint', 'Interior', 'Field Goal'), + ('Short Range (4-10 ft)', 4, 10, 2, 'Paint', 'Interior', 'Field Goal'), + ('Mid Range (11-16 ft)', 11, 16, 3, 'Mid Range', 'Mid Range', 'Field Goal'), + ('Long Mid Range (17-23 ft)', 17, 23, 4, 'Mid Range', 'Mid Range', 'Field Goal'), + ('Three Point (24-27 ft)', 24, 27, 5, 'Perimeter', 'Three Point', 'Field Goal'), + ('Deep Three (28+ ft)', 28, 50, 6, 'Perimeter', 'Three Point', 'Field Goal'), + ('Free Throw (15 ft)', 15, 15, 7, 'Free Throw', 'Free Throw', 'Free Throw') + ) AS t(shot_distance_zone, min_distance_ft, max_distance_ft, zone_order, zone_group, zone_category, shot_class) +) + +SELECT + {{ dbt_utils.generate_surrogate_key(['shot_distance_zone']) }} AS shot_zone_key, + shot_distance_zone, + min_distance_ft, + max_distance_ft, + zone_order, + zone_group, + zone_category, + shot_class +FROM zones +ORDER BY zone_order diff --git a/dbt_nba/models/marts/dimensions/dim_teams.sql b/dbt_nba/models/marts/dimensions/dim_teams.sql new file mode 100644 index 0000000..e464b01 --- /dev/null +++ b/dbt_nba/models/marts/dimensions/dim_teams.sql @@ -0,0 +1,19 @@ +{{ + config( + materialized='table', + schema='marts', + tags=["dimension"] + ) +}} + +WITH team_mappings AS ( + SELECT team_abbr, full_name + FROM {{ ref('team_maps') }} +) + +SELECT + {{ dbt_utils.generate_surrogate_key(['team_abbr']) }} AS team_key, + team_abbr, + full_name AS team_full_name +FROM team_mappings +ORDER BY team_abbr diff --git a/dbt_nba/models/marts/facts/fct_game_results.sql b/dbt_nba/models/marts/facts/fct_game_results.sql new file mode 100644 index 0000000..6ddc977 --- /dev/null +++ b/dbt_nba/models/marts/facts/fct_game_results.sql @@ -0,0 +1,50 @@ +{{ + config( + materialized='incremental', + schema='marts', + unique_key='game_key', + tags=["fact"] + ) +}} + +WITH games_enriched AS ( + SELECT * + FROM {{ ref('int_games_enriched') }} + {% if is_incremental() %} + WHERE game_date >= (SELECT MAX(game_date) FROM {{ this }}) - INTERVAL '30 days' + {% endif %} +) + +SELECT + {{ dbt_utils.generate_surrogate_key(['ge.game_id']) }} AS game_key, + d.date_key, + s.season_key, + a.arena_key, + home_team.team_key AS home_team_key, + visitor_team.team_key AS visitor_team_key, + winning_team.team_key AS winning_team_key, + ge.game_id, + ge.home_points, + ge.visitor_points, + ge.point_differential, + ge.total_points, + ge.is_playoff, + ge.is_overtime, + CASE WHEN ge.home_points > ge.visitor_points THEN TRUE ELSE FALSE END AS is_home_team_winner, + ge.home_net_rating, + ge.visitor_net_rating, + ge.matchup_pace, + CASE + WHEN ge.point_differential <= 5 THEN 'Clutch Game' + WHEN ge.point_differential <= 10 THEN 'Competitive' + WHEN ge.point_differential <= 20 THEN 'Decisive' + ELSE 'Blowout' + END AS game_competitiveness_tier, + CAST(ge.game_date AS DATE) AS game_date +FROM games_enriched AS ge +LEFT JOIN {{ ref('dim_dates') }} AS d ON CAST(ge.game_date AS DATE) = d.full_date +LEFT JOIN {{ ref('dim_seasons') }} AS s ON ge.season_start_year = s.season_start_year +LEFT JOIN {{ ref('dim_arenas') }} AS a ON ge.arena = a.arena_name AND ge.arena_city = a.arena_city +LEFT JOIN {{ ref('dim_teams') }} AS home_team ON ge.home_team = home_team.team_abbr +LEFT JOIN {{ ref('dim_teams') }} AS visitor_team ON ge.visitor_team = visitor_team.team_abbr +LEFT JOIN {{ ref('dim_teams') }} AS winning_team ON ge.winning_team = winning_team.team_abbr diff --git a/dbt_nba/models/marts/facts/fct_player_game_shooting.sql b/dbt_nba/models/marts/facts/fct_player_game_shooting.sql new file mode 100644 index 0000000..a1b6f90 --- /dev/null +++ b/dbt_nba/models/marts/facts/fct_player_game_shooting.sql @@ -0,0 +1,175 @@ +{{ + config( + materialized='incremental', + schema='marts', + unique_key='player_game_shooting_key', + tags=["fact"] + ) +}} + +WITH shots AS ( + SELECT * + FROM {{ ref('int_player_shots_enriched') }} + WHERE has_game_match = TRUE + {% if is_incremental() %} + AND game_date >= (SELECT MAX(game_date) FROM {{ this }}) - INTERVAL '30 days' + {% endif %} +), + +game_agg AS ( + SELECT + game_id, player_id, team, opponent, + MIN(game_date) AS game_date, + MIN(season_start_year) AS season_start_year, + BOOL_OR(is_playoff) AS is_playoff, + MIN(team_location) AS team_location, + MIN(game_result) AS game_result, + + SUM(points_generated) AS total_points, + COUNT(*) AS total_shot_attempts, + SUM(shot_made_flag) AS total_shots_made, + + COUNT(*) FILTER (WHERE shot_source = 'shot_chart') AS fg_attempts, + SUM(shot_made_flag) FILTER (WHERE shot_source = 'shot_chart') AS fg_makes, + CASE WHEN COUNT(*) FILTER (WHERE shot_source = 'shot_chart') > 0 + THEN SUM(shot_made_flag) FILTER (WHERE shot_source = 'shot_chart')::DECIMAL + / COUNT(*) FILTER (WHERE shot_source = 'shot_chart')::DECIMAL + ELSE 0 + END AS fg_pct, + SUM(points_generated) FILTER (WHERE shot_source = 'shot_chart') AS fg_points, + + COUNT(*) FILTER (WHERE shot_source = 'box_score_ft') AS ft_attempts, + SUM(shot_made_flag) FILTER (WHERE shot_source = 'box_score_ft') AS ft_makes, + CASE WHEN COUNT(*) FILTER (WHERE shot_source = 'box_score_ft') > 0 + THEN SUM(shot_made_flag) FILTER (WHERE shot_source = 'box_score_ft')::DECIMAL + / COUNT(*) FILTER (WHERE shot_source = 'box_score_ft')::DECIMAL + ELSE 0 + END AS ft_pct, + SUM(points_generated) FILTER (WHERE shot_source = 'box_score_ft') AS ft_points, + + COUNT(*) FILTER (WHERE is_three_pointer = FALSE AND shot_source = 'shot_chart') AS two_point_attempts, + SUM(shot_made_flag) FILTER (WHERE is_three_pointer = FALSE AND shot_source = 'shot_chart') AS two_point_makes, + CASE WHEN COUNT(*) FILTER (WHERE is_three_pointer = FALSE AND shot_source = 'shot_chart') > 0 + THEN SUM(shot_made_flag) FILTER (WHERE is_three_pointer = FALSE AND shot_source = 'shot_chart')::DECIMAL + / COUNT(*) FILTER (WHERE is_three_pointer = FALSE AND shot_source = 'shot_chart')::DECIMAL + ELSE 0 + END AS two_point_fg_pct, + + COUNT(*) FILTER (WHERE is_three_pointer = TRUE) AS three_point_attempts, + SUM(shot_made_flag) FILTER (WHERE is_three_pointer = TRUE) AS three_point_makes, + CASE WHEN COUNT(*) FILTER (WHERE is_three_pointer = TRUE) > 0 + THEN SUM(shot_made_flag) FILTER (WHERE is_three_pointer = TRUE)::DECIMAL + / COUNT(*) FILTER (WHERE is_three_pointer = TRUE)::DECIMAL + ELSE 0 + END AS three_point_fg_pct, + + COUNT(*) FILTER (WHERE shot_distance_zone = 'At Rim (0-3 ft)') AS at_rim_attempts, + SUM(shot_made_flag) FILTER (WHERE shot_distance_zone = 'At Rim (0-3 ft)') AS at_rim_makes, + CASE WHEN COUNT(*) FILTER (WHERE shot_distance_zone = 'At Rim (0-3 ft)') > 0 + THEN SUM(shot_made_flag) FILTER (WHERE shot_distance_zone = 'At Rim (0-3 ft)')::DECIMAL + / COUNT(*) FILTER (WHERE shot_distance_zone = 'At Rim (0-3 ft)')::DECIMAL + ELSE 0 + END AS at_rim_fg_pct, + + COUNT(*) FILTER (WHERE is_clutch_shot = TRUE) AS clutch_fg_attempts, + SUM(shot_made_flag) FILTER (WHERE is_clutch_shot = TRUE) AS clutch_fg_makes, + CASE WHEN COUNT(*) FILTER (WHERE is_clutch_shot = TRUE) > 0 + THEN SUM(shot_made_flag) FILTER (WHERE is_clutch_shot = TRUE)::DECIMAL + / COUNT(*) FILTER (WHERE is_clutch_shot = TRUE)::DECIMAL + ELSE 0 + END AS clutch_fg_pct, + + CASE WHEN COUNT(*) FILTER (WHERE shot_source = 'shot_chart') > 0 + THEN COUNT(*) FILTER (WHERE is_three_pointer = TRUE)::DECIMAL + / COUNT(*) FILTER (WHERE shot_source = 'shot_chart')::DECIMAL + ELSE 0 + END AS three_point_rate, + + CASE WHEN COUNT(*) FILTER (WHERE shot_source = 'shot_chart') > 0 + THEN COUNT(*) FILTER (WHERE shot_distance_zone = 'At Rim (0-3 ft)')::DECIMAL + / COUNT(*) FILTER (WHERE shot_source = 'shot_chart')::DECIMAL + ELSE 0 + END AS at_rim_rate, + + CASE WHEN COUNT(*) FILTER (WHERE shot_source = 'shot_chart') > 0 + THEN COUNT(*) FILTER (WHERE shot_distance_zone IN ('Mid Range (11-16 ft)', 'Long Mid Range (17-23 ft)'))::DECIMAL + / COUNT(*) FILTER (WHERE shot_source = 'shot_chart')::DECIMAL + ELSE 0 + END AS mid_range_rate, + + AVG(distance_ft) FILTER (WHERE shot_source = 'shot_chart') AS avg_fg_distance_ft, + + CASE WHEN ( + COUNT(*) FILTER (WHERE shot_source = 'shot_chart') + + 0.44 * COUNT(*) FILTER (WHERE shot_source = 'box_score_ft') + ) > 0 + THEN SUM(points_generated)::DECIMAL / ( + 2.0 * ( + COUNT(*) FILTER (WHERE shot_source = 'shot_chart') + + 0.44 * COUNT(*) FILTER (WHERE shot_source = 'box_score_ft') + ) + ) + ELSE 0 + END AS true_shooting_pct + + FROM shots + GROUP BY game_id, player_id, team, opponent +), + +final AS ( + SELECT + {{ dbt_utils.generate_surrogate_key(['ga.game_id', 'ga.player_id']) }} AS player_game_shooting_key, + p.player_key, + t.team_key, + opp_t.team_key AS opponent_key, + d.date_key, + s.season_key, + ga.game_id, + ga.is_playoff, + ga.team_location, + ga.game_result, + ga.total_points, + ga.total_shot_attempts, + ga.total_shots_made, + ga.fg_attempts, + ga.fg_makes, + ga.fg_pct, + ga.fg_points, + ga.ft_attempts, + ga.ft_makes, + ga.ft_pct, + ga.ft_points, + ga.true_shooting_pct, + ga.two_point_attempts, + ga.two_point_makes, + ga.two_point_fg_pct, + ga.three_point_attempts, + ga.three_point_makes, + ga.three_point_fg_pct, + ga.at_rim_attempts, + ga.at_rim_makes, + ga.at_rim_fg_pct, + ga.clutch_fg_attempts, + ga.clutch_fg_makes, + ga.clutch_fg_pct, + ga.three_point_rate, + ga.at_rim_rate, + ga.mid_range_rate, + ga.avg_fg_distance_ft, + CASE + WHEN ga.three_point_rate >= 0.50 THEN 'Perimeter Heavy' + WHEN ga.at_rim_rate >= 0.50 THEN 'Rim Attacker' + WHEN ga.mid_range_rate >= 0.40 THEN 'Mid Range Heavy' + WHEN ga.three_point_rate >= 0.35 AND ga.at_rim_rate >= 0.30 THEN 'Modern (Rim & Three)' + ELSE 'Balanced' + END AS shot_profile_type, + CAST(ga.game_date AS DATE) AS game_date + FROM game_agg AS ga + LEFT JOIN {{ ref('dim_players') }} AS p ON ga.player_id = p.player_id + LEFT JOIN {{ ref('dim_teams') }} AS t ON ga.team = t.team_abbr + LEFT JOIN {{ ref('dim_teams') }} AS opp_t ON ga.opponent = opp_t.team_abbr + LEFT JOIN {{ ref('dim_dates') }} AS d ON CAST(ga.game_date AS DATE) = d.full_date + LEFT JOIN {{ ref('dim_seasons') }} AS s ON ga.season_start_year = s.season_start_year +) + +SELECT * FROM final diff --git a/dbt_nba/models/marts/facts/fct_player_game_stats.sql b/dbt_nba/models/marts/facts/fct_player_game_stats.sql new file mode 100644 index 0000000..5852346 --- /dev/null +++ b/dbt_nba/models/marts/facts/fct_player_game_stats.sql @@ -0,0 +1,71 @@ +{{ + config( + materialized='incremental', + schema='marts', + unique_key='player_game_key', + tags=["fact"] + ) +}} + +WITH player_performance AS ( + SELECT * FROM {{ ref('int_player_performance') }} +), + +game_details AS ( + SELECT game_id, arena AS arena_name, arena_city + FROM {{ ref('int_games_enriched') }} +), + +final AS ( + SELECT + {{ dbt_utils.generate_surrogate_key(['pp.game_id', 'pp.player_id']) }} AS player_game_key, + p.player_key, + t.team_key, + d.date_key, + s.season_key, + a.arena_key, + arch.archetype_key, + pp.game_id, + pp.minutes_played, + pp.points, + pp.assists, + pp.total_rebounds, + pp.steals, + pp.blocks, + pp.turnovers, + pp.plus_minus, + pp.net_rating, + pp.box_plus_minus, + pp.field_goals_made, + pp.field_goals_attempted, + pp.three_pointers_made, + pp.three_pointers_attempted, + pp.true_shooting_pct, + pp.effective_fg_pct, + pp.usage_pct, + pp.offensive_rating, + pp.defensive_rating, + CAST(pp.game_date AS DATE) AS game_date + FROM player_performance AS pp + LEFT JOIN game_details AS gd ON pp.game_id = gd.game_id + LEFT JOIN {{ ref('dim_player_game_archetypes') }} AS arch + ON pp.usage_tier = arch.usage_tier + AND pp.impact_tier = arch.impact_tier + AND pp.shooting_efficiency_tier = arch.shooting_efficiency_tier + AND pp.minutes_based_role = arch.minutes_based_role + AND pp.is_double_double = arch.is_double_double + AND pp.is_triple_double = arch.is_triple_double + AND pp.is_versatile = arch.is_versatile + AND pp.is_defensive_specialist = arch.is_defensive_specialist + AND pp.is_three_and_d = arch.is_three_and_d + LEFT JOIN {{ ref('dim_players') }} AS p ON pp.player_id = p.player_id + LEFT JOIN {{ ref('dim_teams') }} AS t ON pp.team = t.team_abbr + LEFT JOIN {{ ref('dim_dates') }} AS d ON CAST(pp.game_date AS DATE) = d.full_date + LEFT JOIN {{ ref('dim_seasons') }} AS s ON pp.season_start_year = s.season_start_year + LEFT JOIN {{ ref('dim_arenas') }} AS a ON gd.arena_name = a.arena_name AND gd.arena_city = a.arena_city + {% if is_incremental() %} + WHERE pp.game_date >= (SELECT MAX(game_date) FROM {{ this }}) - INTERVAL '30 days' + {% endif %} +) + +SELECT * FROM final diff --git a/dbt_nba/models/marts/facts/fct_player_shots.sql b/dbt_nba/models/marts/facts/fct_player_shots.sql new file mode 100644 index 0000000..6705d20 --- /dev/null +++ b/dbt_nba/models/marts/facts/fct_player_shots.sql @@ -0,0 +1,62 @@ +{{ + config( + materialized='incremental', + schema='marts', + unique_key='shot_key', + tags=["fact"] + ) +}} + +WITH shots_enriched AS ( + SELECT * + FROM {{ ref('int_player_shots_enriched') }} + WHERE has_game_match = TRUE + {% if is_incremental() %} + AND game_date >= (SELECT MAX(game_date) FROM {{ this }}) - INTERVAL '30 days' + {% endif %} +), + +final AS ( + SELECT + {{ dbt_utils.generate_surrogate_key(['se.shot_id', 'se.shot_source']) }} AS shot_key, + p.player_key, + t.team_key, + opp_t.team_key AS opponent_key, + d.date_key, + s.season_key, + se.game_id, + se.shot_id, + se.shot_source, + se.is_playoff, + se.team_location, + se.game_result, + se.quarter_number, + se.is_overtime_shot, + se.seconds_remaining_in_quarter, + se.shot_x_coordinate, + se.shot_y_coordinate, + se.is_made, + se.shot_made_flag, + se.shot_missed_flag, + se.shot_type_raw AS shot_type, + se.shot_point_value, + se.is_three_pointer, + se.is_free_throw, + se.distance_ft, + se.shot_distance_zone, + se.points_generated, + se.team_had_lead, + se.team_score_at_shot, + se.opponent_score_at_shot, + se.score_margin_at_shot, + se.is_clutch_shot, + CAST(se.game_date AS DATE) AS game_date + FROM shots_enriched AS se + LEFT JOIN {{ ref('dim_players') }} AS p ON se.player_id = p.player_id + LEFT JOIN {{ ref('dim_teams') }} AS t ON se.team = t.team_abbr + LEFT JOIN {{ ref('dim_teams') }} AS opp_t ON se.opponent = opp_t.team_abbr + LEFT JOIN {{ ref('dim_dates') }} AS d ON CAST(se.game_date AS DATE) = d.full_date + LEFT JOIN {{ ref('dim_seasons') }} AS s ON se.season_start_year = s.season_start_year +) + +SELECT * FROM final diff --git a/dbt_nba/models/marts/facts/fct_quarter_scoring.sql b/dbt_nba/models/marts/facts/fct_quarter_scoring.sql new file mode 100644 index 0000000..1fe824b --- /dev/null +++ b/dbt_nba/models/marts/facts/fct_quarter_scoring.sql @@ -0,0 +1,57 @@ +{{ + config( + materialized='incremental', + schema='marts', + unique_key='quarter_scoring_key', + tags=["fact"] + ) +}} + +WITH games AS ( + SELECT game_id, game_date, season_start_year + FROM {{ ref('int_games_enriched') }} + {% if is_incremental() %} + WHERE game_date >= (SELECT MAX(game_date) FROM {{ this }}) - INTERVAL '30 days' + {% endif %} +), + +unpivoted_scores AS ( + {{ dbt_utils.unpivot( + relation=ref('stg_line_scores'), + cast_to='INTEGER', + exclude=['game_id', 'team', 'total_points', 'first_half_points', 'second_half_points', 'regulation_points', 'overtime_points', 'had_ot1', 'had_ot2', 'had_ot3', 'q2_momentum', 'q3_momentum', 'q4_momentum', 'max_quarter_score', 'min_quarter_score', 'best_quarter', 'created_at', 'updated_at', 'dbt_loaded_at'], + field_name='period_name', + value_name='points_scored' + ) }} + WHERE game_id IN (SELECT game_id FROM games) +), + +final AS ( + SELECT + {{ dbt_utils.generate_surrogate_key(['team_scores.game_id', 'team_map.team_abbr', 'team_scores.period_name']) }} AS quarter_scoring_key, + d.date_key, + s.season_key, + t.team_key, + opp.team_key AS opponent_key, + team_scores.game_id, + REPLACE(UPPER(team_scores.period_name), '_POINTS', '') AS period, + team_scores.points_scored, + opponent_scores.points_scored AS opponent_points_scored, + (team_scores.points_scored - opponent_scores.points_scored) AS period_point_differential, + CAST(g.game_date AS DATE) AS game_date + FROM unpivoted_scores AS team_scores + INNER JOIN unpivoted_scores AS opponent_scores + ON team_scores.game_id = opponent_scores.game_id + AND team_scores.period_name = opponent_scores.period_name + AND team_scores.team != opponent_scores.team + INNER JOIN games AS g ON team_scores.game_id = g.game_id + LEFT JOIN {{ ref('team_maps') }} AS team_map ON team_scores.team = team_map.team_abbr + LEFT JOIN {{ ref('team_maps') }} AS opponent_map ON opponent_scores.team = opponent_map.team_abbr + LEFT JOIN {{ ref('dim_teams') }} AS t ON team_map.team_abbr = t.team_abbr + LEFT JOIN {{ ref('dim_teams') }} AS opp ON opponent_map.team_abbr = opp.team_abbr + LEFT JOIN {{ ref('dim_dates') }} AS d ON CAST(g.game_date AS DATE) = d.full_date + LEFT JOIN {{ ref('dim_seasons') }} AS s ON g.season_start_year = s.season_start_year + WHERE team_scores.points_scored IS NOT NULL +) + +SELECT * FROM final diff --git a/dbt_nba/models/marts/facts/fct_team_game_stats.sql b/dbt_nba/models/marts/facts/fct_team_game_stats.sql new file mode 100644 index 0000000..704026e --- /dev/null +++ b/dbt_nba/models/marts/facts/fct_team_game_stats.sql @@ -0,0 +1,54 @@ +{{ + config( + materialized='incremental', + schema='marts', + tags=["fact"], + unique_key='team_game_key' + ) +}} + +WITH team_performance AS ( + SELECT * FROM {{ ref('int_team_performance') }} +), + +game_details AS ( + SELECT + game_id, game_date, season_start_year, + home_team, visitor_team, arena AS arena_name, arena_city + FROM {{ ref('int_games_enriched') }} +), + +final AS ( + SELECT + {{ dbt_utils.generate_surrogate_key(['tp.game_id', 'tp.team']) }} AS team_game_key, + t.team_key, + d.date_key, + s.season_key, + a.arena_key, + opp.team_key AS opponent_key, + tp.game_id, + (tp.team = gd.home_team) AS is_home_team, + tp.points, + tp.offensive_rating, + tp.defensive_rating, + tp.net_rating, + tp.pace, + tp.effective_fg_pct, + tp.turnover_rate, + tp.offensive_tier, + tp.defensive_tier, + CAST(gd.game_date AS DATE) AS game_date + FROM team_performance AS tp + LEFT JOIN game_details AS gd ON tp.game_id = gd.game_id + LEFT JOIN {{ ref('dim_teams') }} AS t ON tp.team = t.team_abbr + LEFT JOIN {{ ref('dim_teams') }} AS opp + ON CASE WHEN tp.team = gd.home_team THEN gd.visitor_team ELSE gd.home_team END = opp.team_abbr + LEFT JOIN {{ ref('dim_dates') }} AS d ON CAST(gd.game_date AS DATE) = d.full_date + LEFT JOIN {{ ref('dim_seasons') }} AS s ON gd.season_start_year = s.season_start_year + LEFT JOIN {{ ref('dim_arenas') }} AS a ON gd.arena_name = a.arena_name AND gd.arena_city = a.arena_city + {% if is_incremental() %} + WHERE gd.game_date >= (SELECT MAX(game_date) FROM {{ this }}) - INTERVAL '30 days' + {% endif %} +) + +SELECT * FROM final diff --git a/dbt_nba/models/schema.yml b/dbt_nba/models/schema.yml new file mode 100644 index 0000000..d54a10c --- /dev/null +++ b/dbt_nba/models/schema.yml @@ -0,0 +1,129 @@ +version: 2 + +sources: + - name: raw_nba + description: Raw NBA data in DuckDB + schema: main + tables: + - name: games + description: Core game information including scores, teams, and venues + columns: + - name: game_id + tests: + - unique + - not_null + - name: line_scores + description: Quarter-by-quarter scoring breakdown + columns: + - name: game_id + tests: + - not_null + - name: player_game_basic_stats + description: Basic player statistics for each game + columns: + - name: game_id + tests: + - not_null + - name: player_id + tests: + - not_null + - name: player_game_adv_stats + description: Advanced player statistics for each game + columns: + - name: game_id + tests: + - not_null + - name: player_id + tests: + - not_null + - name: team_game_basic_stats + description: Basic team statistics for each game + columns: + - name: game_id + tests: + - not_null + - name: team + tests: + - not_null + - name: team_game_adv_stats + description: Advanced team statistics for each game + columns: + - name: game_id + tests: + - not_null + - name: team + tests: + - not_null + - name: player_shot_charts + description: Individual shot-level data from NBA player shot charts + columns: + - name: id + tests: + - unique + - not_null + +models: + - name: stg_games + description: Staged games data with cleaned fields and derived metrics + columns: + - name: game_id + tests: + - unique + - not_null + + - name: stg_line_scores + description: Staged quarter-by-quarter scoring with momentum metrics + columns: + - name: game_id + tests: + - not_null + + - name: stg_player_game_basic_stats + description: Staged player basic stats with calculated metrics + columns: + - name: game_id + tests: + - not_null + - name: player_id + tests: + - not_null + + - name: stg_player_game_adv_stats + description: Staged player advanced stats with performance tiers + columns: + - name: game_id + tests: + - not_null + - name: player_id + tests: + - not_null + + - name: stg_player_game_adv_stats_extended + description: Extended player advanced stats with dynamic per-season tiering + columns: + - name: game_id + tests: + - not_null + - name: player_id + tests: + - not_null + + - name: stg_team_game_basic_stats + description: Staged team basic stats with four factors + columns: + - name: game_id + tests: + - not_null + - name: team + tests: + - not_null + + - name: stg_team_game_adv_stats + description: Staged team advanced stats with play style indicators + columns: + - name: game_id + tests: + - not_null + - name: team + tests: + - not_null diff --git a/dbt_nba/models/staging/stg_games.sql b/dbt_nba/models/staging/stg_games.sql new file mode 100644 index 0000000..cfc90ca --- /dev/null +++ b/dbt_nba/models/staging/stg_games.sql @@ -0,0 +1,82 @@ +{{ + config( + materialized='view', + schema='staging' + ) +}} + +WITH overtime_games AS ( + SELECT + game_id, + MAX(CASE WHEN overtime_points > 0 THEN 1 ELSE 0 END) AS had_overtime + FROM {{ ref('stg_line_scores') }} + GROUP BY 1 +), + +source_data AS ( + SELECT * FROM {{ source('raw_nba', 'games') }} + WHERE deleted_at IS NULL +), + +cleaned AS ( + SELECT + g.game_id, + g.home_team, + g.visitor_team, + g.date AS game_date, + EXTRACT(YEAR FROM g.date) AS season_year, + CASE + WHEN EXTRACT(MONTH FROM g.date) >= 10 THEN EXTRACT(YEAR FROM g.date) + ELSE EXTRACT(YEAR FROM g.date) - 1 + END AS season_start_year, + EXTRACT(MONTH FROM g.date) AS game_month, + EXTRACT(DAY FROM g.date) AS game_day, + strftime(g.date, '%A') AS game_day_of_week, + g.is_playoff, + g.start_time_et, + CASE + WHEN g.start_time_et LIKE '%7:%p%' OR g.start_time_et LIKE '%8:%p%' THEN 'Prime Time' + WHEN g.start_time_et LIKE '%12:%p%' OR g.start_time_et LIKE '%1:%p%' + OR g.start_time_et LIKE '%2:%p%' OR g.start_time_et LIKE '%3:%p%' THEN 'Afternoon' + ELSE 'Late Night' + END AS game_time_slot, + g.arena, + g.home_pts AS home_points, + g.visitor_pts AS visitor_points, + CASE + WHEN g.home_pts > g.visitor_pts THEN g.home_team + WHEN g.visitor_pts > g.home_pts THEN g.visitor_team + ELSE NULL + END AS winning_team, + CASE + WHEN g.home_pts < g.visitor_pts THEN g.home_team + WHEN g.visitor_pts < g.home_pts THEN g.visitor_team + ELSE NULL + END AS losing_team, + CASE + WHEN g.home_pts > g.visitor_pts THEN 'HOME' + WHEN g.visitor_pts > g.home_pts THEN 'AWAY' + ELSE NULL + END AS winner_location, + ABS(g.home_pts - g.visitor_pts) AS point_differential, + g.home_pts + g.visitor_pts AS total_points, + g.game_duration, + CASE + WHEN ot.had_overtime = 1 THEN TRUE + ELSE FALSE + END AS is_overtime, + g.box_score_url, + g.created_at, + g.updated_at, + CURRENT_TIMESTAMP AS dbt_loaded_at + FROM source_data AS g + LEFT JOIN overtime_games AS ot + ON g.game_id = ot.game_id + WHERE + g.game_id IS NOT NULL + AND g.date IS NOT NULL + AND g.home_team IS NOT NULL + AND g.visitor_team IS NOT NULL +) + +SELECT * FROM cleaned diff --git a/dbt_nba/models/staging/stg_line_scores.sql b/dbt_nba/models/staging/stg_line_scores.sql new file mode 100644 index 0000000..42ba8e2 --- /dev/null +++ b/dbt_nba/models/staging/stg_line_scores.sql @@ -0,0 +1,51 @@ +{{ + config( + materialized='view', + schema='staging' + ) +}} + +WITH source_data AS ( + SELECT * + FROM {{ source('raw_nba', 'line_scores') }} + WHERE deleted_at IS NULL +), + +cleaned_and_transformed AS ( + SELECT + game_id, + team, + COALESCE(q1, 0) AS q1_points, + COALESCE(q2, 0) AS q2_points, + COALESCE(q3, 0) AS q3_points, + COALESCE(q4, 0) AS q4_points, + COALESCE(ot1, 0) AS ot1_points, + COALESCE(ot2, 0) AS ot2_points, + COALESCE(ot3, 0) AS ot3_points, + total AS total_points, + COALESCE(q1, 0) + COALESCE(q2, 0) AS first_half_points, + COALESCE(q3, 0) + COALESCE(q4, 0) AS second_half_points, + COALESCE(q1, 0) + COALESCE(q2, 0) + COALESCE(q3, 0) + COALESCE(q4, 0) AS regulation_points, + COALESCE(ot1, 0) + COALESCE(ot2, 0) + COALESCE(ot3, 0) AS overtime_points, + COALESCE(ot1, 0) > 0 AS had_ot1, + COALESCE(ot2, 0) > 0 AS had_ot2, + COALESCE(ot3, 0) > 0 AS had_ot3, + COALESCE(q2, 0) - COALESCE(q1, 0) AS q2_momentum, + COALESCE(q3, 0) - COALESCE(q2, 0) AS q3_momentum, + COALESCE(q4, 0) - COALESCE(q3, 0) AS q4_momentum, + GREATEST(COALESCE(q1, 0), COALESCE(q2, 0), COALESCE(q3, 0), COALESCE(q4, 0)) AS max_quarter_score, + LEAST(COALESCE(q1, 0), COALESCE(q2, 0), COALESCE(q3, 0), COALESCE(q4, 0)) AS min_quarter_score, + CASE + WHEN GREATEST(COALESCE(q1, 0), COALESCE(q2, 0), COALESCE(q3, 0), COALESCE(q4, 0)) = COALESCE(q1, 0) THEN 'Q1' + WHEN GREATEST(COALESCE(q1, 0), COALESCE(q2, 0), COALESCE(q3, 0), COALESCE(q4, 0)) = COALESCE(q2, 0) THEN 'Q2' + WHEN GREATEST(COALESCE(q1, 0), COALESCE(q2, 0), COALESCE(q3, 0), COALESCE(q4, 0)) = COALESCE(q3, 0) THEN 'Q3' + ELSE 'Q4' + END AS best_quarter, + created_at, + updated_at, + CURRENT_TIMESTAMP AS dbt_loaded_at + FROM source_data + WHERE game_id IS NOT NULL AND team IS NOT NULL +) + +SELECT * FROM cleaned_and_transformed diff --git a/dbt_nba/models/staging/stg_player_game_adv_stats.sql b/dbt_nba/models/staging/stg_player_game_adv_stats.sql new file mode 100644 index 0000000..482ef5c --- /dev/null +++ b/dbt_nba/models/staging/stg_player_game_adv_stats.sql @@ -0,0 +1,106 @@ +{{ + config( + materialized='view', + schema='staging' + ) +}} + +WITH source_data AS ( + SELECT * FROM {{ source('raw_nba', 'player_game_adv_stats') }} + WHERE deleted_at IS NULL +), + +with_minutes AS ( + SELECT + *, + CASE + WHEN mp IS NOT NULL AND mp != '' AND mp LIKE '%:%' THEN + CAST(SPLIT_PART(mp, ':', 1) AS DECIMAL) + + (CAST(SPLIT_PART(mp, ':', 2) AS DECIMAL) / 60.0) + WHEN mp IS NOT NULL AND mp != '' THEN CAST(mp AS DECIMAL) + ELSE 0 + END AS minutes_played_calc + FROM source_data +), + +cleaned AS ( + SELECT + game_id, + player_id, + team, + player_name, + mp AS minutes_played_str, + minutes_played_calc AS minutes_played, + COALESCE(ts_percent, 0) AS true_shooting_pct, + COALESCE(efg_percent, 0) AS effective_fg_pct, + COALESCE(three_p_ar, 0) AS three_point_attempt_rate, + COALESCE(f_tr, 0) AS free_throw_rate, + COALESCE(orb_percent, 0) AS offensive_rebound_pct, + COALESCE(drb_percent, 0) AS defensive_rebound_pct, + COALESCE(trb_percent, 0) AS total_rebound_pct, + COALESCE(ast_percent, 0) AS assist_pct, + COALESCE(stl_percent, 0) AS steal_pct, + COALESCE(blk_percent, 0) AS block_pct, + COALESCE(tov_percent, 0) AS turnover_pct, + COALESCE(usg_percent, 0) AS usage_pct, + COALESCE(o_rtg, 0) AS offensive_rating, + COALESCE(d_rtg, 0) AS defensive_rating, + COALESCE(bpm, 0) AS box_plus_minus, + COALESCE(o_rtg, 0) - COALESCE(d_rtg, 0) AS net_rating, + CASE + WHEN minutes_played_calc < 5 THEN 'Insufficient Minutes' + WHEN COALESCE(ts_percent, 0) >= 0.60 THEN 'Elite' + WHEN COALESCE(ts_percent, 0) >= 0.55 THEN 'Good' + WHEN COALESCE(ts_percent, 0) >= 0.50 THEN 'Average' + ELSE 'Below Average' + END AS shooting_efficiency_tier, + CASE + WHEN minutes_played_calc < 5 THEN 'Garbage Time' + WHEN minutes_played_calc < 15 THEN 'Limited Minutes' + WHEN (minutes_played_calc >= 30 AND COALESCE(usg_percent, 0) >= 22) OR + (minutes_played_calc >= 25 AND COALESCE(usg_percent, 0) >= 25) THEN 'Primary Option' + WHEN minutes_played_calc >= 20 AND COALESCE(usg_percent, 0) >= 20 THEN 'Secondary Option' + WHEN minutes_played_calc >= 15 THEN 'Role Player' + ELSE 'Limited Minutes' + END AS usage_tier, + CASE + WHEN minutes_played_calc < 5 THEN 'Insufficient Minutes' + WHEN COALESCE(bpm, 0) >= 10 THEN 'Elite Impact' + WHEN COALESCE(bpm, 0) >= 5 THEN 'High Impact' + WHEN COALESCE(bpm, 0) >= 0 THEN 'Positive Impact' + WHEN COALESCE(bpm, 0) >= -5 THEN 'Negative Impact' + ELSE 'Very Negative Impact' + END AS impact_tier, + CASE + WHEN minutes_played_calc >= 32 THEN 'Starter/Key Player' + WHEN minutes_played_calc >= 20 THEN 'Rotation Player' + WHEN minutes_played_calc >= 10 THEN 'Bench Player' + WHEN minutes_played_calc >= 5 THEN 'Deep Bench' + ELSE 'Garbage Time' + END AS minutes_based_role, + CASE + WHEN minutes_played_calc >= 15 + AND COALESCE(ast_percent, 0) >= 20 + AND COALESCE(trb_percent, 0) >= 15 THEN TRUE + ELSE FALSE + END AS is_versatile, + CASE + WHEN minutes_played_calc >= 15 + AND (COALESCE(stl_percent, 0) >= 2.5 OR COALESCE(blk_percent, 0) >= 4) + AND COALESCE(d_rtg, 0) < 105 THEN TRUE + ELSE FALSE + END AS is_defensive_specialist, + CASE + WHEN minutes_played_calc >= 15 + AND COALESCE(three_p_ar, 0) >= 0.4 + AND COALESCE(d_rtg, 0) < 110 THEN TRUE + ELSE FALSE + END AS is_three_and_d, + created_at, + updated_at, + CURRENT_TIMESTAMP AS dbt_loaded_at + FROM with_minutes + WHERE game_id IS NOT NULL AND player_id IS NOT NULL +) + +SELECT * FROM cleaned diff --git a/dbt_nba/models/staging/stg_player_game_adv_stats_extended.sql b/dbt_nba/models/staging/stg_player_game_adv_stats_extended.sql new file mode 100644 index 0000000..b919891 --- /dev/null +++ b/dbt_nba/models/staging/stg_player_game_adv_stats_extended.sql @@ -0,0 +1,169 @@ +{{ + config( + materialized='table', + schema='staging', + tags=["staging"] + ) +}} + +{% set minutes_insufficient = 5 %} + +WITH source_data AS ( + SELECT * FROM {{ source('raw_nba', 'player_game_adv_stats') }} + WHERE deleted_at IS NULL +), + +games AS ( + SELECT game_id, season_start_year + FROM {{ ref('stg_games') }} +), + +with_minutes AS ( + SELECT + sd.*, + g.season_start_year, + CASE + WHEN sd.mp IS NOT NULL AND sd.mp != '' AND sd.mp LIKE '%:%' THEN + CAST(SPLIT_PART(sd.mp, ':', 1) AS DECIMAL) + + (CAST(SPLIT_PART(sd.mp, ':', 2) AS DECIMAL) / 60.0) + WHEN sd.mp IS NOT NULL AND sd.mp != '' AND regexp_matches(sd.mp, '^[0-9\.]+$') THEN + CAST(sd.mp AS DECIMAL) + ELSE 0 + END AS minutes_played + FROM source_data AS sd + LEFT JOIN games AS g ON sd.game_id = g.game_id +), + +season_thresholds AS ( + SELECT * FROM {{ ref('stg_season_thresholds') }} +), + +cleaned AS ( + SELECT + wm.game_id, + wm.player_id, + wm.team, + wm.player_name, + wm.mp AS minutes_played_str, + wm.minutes_played, + COALESCE(wm.ts_percent, 0) AS true_shooting_pct, + COALESCE(wm.efg_percent, 0) AS effective_fg_pct, + COALESCE(wm.three_p_ar, 0) AS three_point_attempt_rate, + COALESCE(wm.f_tr, 0) AS free_throw_rate, + COALESCE(wm.orb_percent, 0) AS offensive_rebound_pct, + COALESCE(wm.drb_percent, 0) AS defensive_rebound_pct, + COALESCE(wm.trb_percent, 0) AS total_rebound_pct, + COALESCE(wm.ast_percent, 0) AS assist_pct, + COALESCE(wm.stl_percent, 0) AS steal_pct, + COALESCE(wm.blk_percent, 0) AS block_pct, + COALESCE(wm.tov_percent, 0) AS turnover_pct, + COALESCE(wm.usg_percent, 0) AS usage_pct, + COALESCE(wm.o_rtg, 0) AS offensive_rating, + COALESCE(wm.d_rtg, 0) AS defensive_rating, + COALESCE(wm.bpm, 0) AS box_plus_minus, + COALESCE(wm.o_rtg, 0) - COALESCE(wm.d_rtg, 0) AS net_rating, + + -- Minutes-based role + CASE + WHEN wm.minutes_played >= t.min_p95 THEN 'Elite Minutes (Top 5%)' + WHEN wm.minutes_played >= t.min_p90 THEN 'Elite Minutes (Top 10%)' + WHEN wm.minutes_played >= t.min_q3 THEN 'Starter' + WHEN wm.minutes_played >= t.min_median THEN 'Key Rotation' + WHEN wm.minutes_played >= t.min_q1 THEN 'Regular Rotation' + WHEN wm.minutes_played >= t.min_p5 THEN 'Deep Bench' + WHEN wm.minutes_played >= {{ minutes_insufficient }} THEN 'Garbage Time' + ELSE 'Insufficient Minutes' + END AS minutes_based_role, + + CASE + WHEN wm.minutes_played < {{ minutes_insufficient }} THEN 'Insufficient' + WHEN wm.minutes_played < t.min_q1 THEN 'Q1' + WHEN wm.minutes_played < t.min_median THEN 'Q2' + WHEN wm.minutes_played < t.min_q3 THEN 'Q3' + ELSE 'Q4' + END AS minutes_quartile, + + -- Usage Tier + CASE + WHEN wm.minutes_played < {{ minutes_insufficient }} THEN 'Insufficient Minutes' + WHEN COALESCE(wm.usg_percent, 0) >= t.usg_p90 + AND wm.minutes_played >= t.min_q1 THEN 'Heliocentric Option' + WHEN COALESCE(wm.usg_percent, 0) >= t.usg_q3 + AND wm.minutes_played >= t.min_q3 THEN 'Primary Option' + WHEN COALESCE(wm.usg_percent, 0) >= t.usg_median + AND wm.minutes_played >= t.min_median THEN 'Secondary Option' + WHEN COALESCE(wm.usg_percent, 0) >= t.usg_q1 + AND wm.minutes_played >= t.min_q1 THEN 'Role Player' + WHEN wm.minutes_played >= t.min_q1 + AND COALESCE(wm.usg_percent, 0) >= t.usg_p5 THEN 'Connector/Specialist' + WHEN wm.minutes_played >= t.min_p5 THEN 'Low Usage Player' + ELSE 'Limited Role' + END AS usage_tier, + + CASE + WHEN COALESCE(wm.usg_percent, 0) < t.usg_q1 THEN 'Q1' + WHEN COALESCE(wm.usg_percent, 0) < t.usg_median THEN 'Q2' + WHEN COALESCE(wm.usg_percent, 0) < t.usg_q3 THEN 'Q3' + ELSE 'Q4' + END AS usage_quartile, + + -- Impact Tier + CASE + WHEN wm.minutes_played < {{ minutes_insufficient }} THEN 'Insufficient Minutes' + WHEN COALESCE(wm.bpm, 0) >= t.bpm_p90 THEN 'Elite Impact' + WHEN COALESCE(wm.bpm, 0) >= t.bpm_q3 THEN 'High Impact' + WHEN COALESCE(wm.bpm, 0) >= t.bpm_median THEN 'Positive Impact' + WHEN COALESCE(wm.bpm, 0) >= t.bpm_q1 THEN 'Neutral Impact' + WHEN COALESCE(wm.bpm, 0) >= t.bpm_p5 THEN 'Negative Impact' + ELSE 'Very Negative Impact' + END AS impact_tier, + + CASE + WHEN COALESCE(wm.bpm, 0) < t.bpm_q1 THEN 'Q1' + WHEN COALESCE(wm.bpm, 0) < t.bpm_median THEN 'Q2' + WHEN COALESCE(wm.bpm, 0) < t.bpm_q3 THEN 'Q3' + ELSE 'Q4' + END AS bpm_quartile, + + -- Shooting efficiency tier + CASE + WHEN wm.minutes_played < {{ minutes_insufficient }} THEN 'Insufficient Minutes' + WHEN COALESCE(wm.ts_percent, 0) >= t.ts_q3 THEN 'Elite' + WHEN COALESCE(wm.ts_percent, 0) >= t.ts_median THEN 'Good' + WHEN COALESCE(wm.ts_percent, 0) >= t.ts_q1 THEN 'Average' + ELSE 'Below Average' + END AS shooting_efficiency_tier, + + -- Boolean flags + wm.minutes_played >= t.min_p90 AS is_extreme_minutes, + wm.minutes_played >= t.min_q3 AS is_starter_minutes, + wm.minutes_played >= t.min_q1 AS is_meaningful_minutes, + + wm.minutes_played >= t.min_q1 + AND COALESCE(wm.ast_percent, 0) >= 20 + AND COALESCE(wm.trb_percent, 0) >= 13 AS is_versatile, + + wm.minutes_played >= t.min_q1 + AND (COALESCE(wm.stl_percent, 0) >= 2.5 OR COALESCE(wm.blk_percent, 0) >= 3) + AND COALESCE(wm.d_rtg, 0) <= 110 AS is_defensive_specialist, + + wm.minutes_played >= t.min_q1 + AND COALESCE(wm.three_p_ar, 0) >= 0.4 + AND COALESCE(wm.d_rtg, 0) <= 110 AS is_three_and_d, + + COALESCE(wm.usg_percent, 0) >= t.usg_p90 AS is_extreme_usage, + COALESCE(wm.usg_percent, 0) >= t.usg_q3 AS is_high_usage, + COALESCE(wm.bpm, 0) >= t.bpm_p90 AS is_elite_impact, + COALESCE(wm.bpm, 0) >= t.bpm_q3 AS is_positive_impact, + + wm.created_at, + wm.updated_at, + CURRENT_TIMESTAMP AS dbt_loaded_at + + FROM with_minutes AS wm + LEFT JOIN season_thresholds AS t + ON wm.season_start_year = t.season_start_year + WHERE wm.game_id IS NOT NULL AND wm.player_id IS NOT NULL +) + +SELECT * FROM cleaned diff --git a/dbt_nba/models/staging/stg_player_game_basic_stats.sql b/dbt_nba/models/staging/stg_player_game_basic_stats.sql new file mode 100644 index 0000000..c7f51cc --- /dev/null +++ b/dbt_nba/models/staging/stg_player_game_basic_stats.sql @@ -0,0 +1,93 @@ +{{ + config( + materialized='view', + schema='staging' + ) +}} + +WITH source_data AS ( + SELECT * FROM {{ source('raw_nba', 'player_game_basic_stats') }} + WHERE deleted_at IS NULL +), + +cleaned AS ( + SELECT + game_id, + player_id, + team, + player_name, + COALESCE(status, 'Unknown') AS player_status, + CASE WHEN status = 'Played' THEN TRUE ELSE FALSE END AS did_play, + mp AS minutes_played_str, + CAST( + CASE + WHEN mp IS NOT NULL AND mp != '' AND mp LIKE '%:%' THEN + CAST(SPLIT_PART(mp, ':', 1) AS DECIMAL) + + (CAST(SPLIT_PART(mp, ':', 2) AS DECIMAL) / 60.0) + WHEN mp IS NOT NULL AND mp != '' THEN CAST(mp AS DECIMAL) + ELSE 0 + END AS DECIMAL(10, 2)) AS minutes_played, + COALESCE(fg, 0) AS field_goals_made, + COALESCE(fga, 0) AS field_goals_attempted, + COALESCE(fg_percent, 0) AS field_goal_pct, + COALESCE(three_p, 0) AS three_pointers_made, + COALESCE(three_pa, 0) AS three_pointers_attempted, + COALESCE(three_p_percent, 0) AS three_point_pct, + COALESCE(ft, 0) AS free_throws_made, + COALESCE(fta, 0) AS free_throws_attempted, + COALESCE(ft_percent, 0) AS free_throw_pct, + COALESCE(orb, 0) AS offensive_rebounds, + COALESCE(drb, 0) AS defensive_rebounds, + COALESCE(trb, 0) AS total_rebounds, + COALESCE(ast, 0) AS assists, + COALESCE(stl, 0) AS steals, + COALESCE(blk, 0) AS blocks, + COALESCE(tov, 0) AS turnovers, + COALESCE(pf, 0) AS personal_fouls, + COALESCE(pts, 0) AS points, + COALESCE(gm_sc, 0) AS game_score, + COALESCE(plus_minus, 0) AS plus_minus, + COALESCE(fg, 0) - COALESCE(three_p, 0) AS two_pointers_made, + COALESCE(fga, 0) - COALESCE(three_pa, 0) AS two_pointers_attempted, + CASE + WHEN (COALESCE(fga, 0) - COALESCE(three_pa, 0)) > 0 THEN + CAST(COALESCE(fg, 0) - COALESCE(three_p, 0) AS DECIMAL) / + CAST(COALESCE(fga, 0) - COALESCE(three_pa, 0) AS DECIMAL) + ELSE 0 + END AS two_point_pct, + CASE + WHEN COALESCE(fga, 0) > 0 THEN + CAST(COALESCE(pts, 0) AS DECIMAL) / CAST(COALESCE(fga, 0) AS DECIMAL) + ELSE 0 + END AS points_per_shot, + CASE + WHEN ( + (CASE WHEN COALESCE(pts, 0) >= 10 THEN 1 ELSE 0 END) + + (CASE WHEN COALESCE(trb, 0) >= 10 THEN 1 ELSE 0 END) + + (CASE WHEN COALESCE(ast, 0) >= 10 THEN 1 ELSE 0 END) + + (CASE WHEN COALESCE(stl, 0) >= 10 THEN 1 ELSE 0 END) + + (CASE WHEN COALESCE(blk, 0) >= 10 THEN 1 ELSE 0 END) + ) >= 2 THEN TRUE ELSE FALSE + END AS is_double_double, + CASE + WHEN ( + (CASE WHEN COALESCE(pts, 0) >= 10 THEN 1 ELSE 0 END) + + (CASE WHEN COALESCE(trb, 0) >= 10 THEN 1 ELSE 0 END) + + (CASE WHEN COALESCE(ast, 0) >= 10 THEN 1 ELSE 0 END) + + (CASE WHEN COALESCE(stl, 0) >= 10 THEN 1 ELSE 0 END) + + (CASE WHEN COALESCE(blk, 0) >= 10 THEN 1 ELSE 0 END) + ) >= 3 THEN TRUE ELSE FALSE + END AS is_triple_double, + CASE + WHEN mp IS NOT NULL AND mp LIKE '%:%' AND + CAST(SPLIT_PART(mp, ':', 1) AS DECIMAL) >= 20 THEN TRUE + ELSE FALSE + END AS likely_starter, + created_at, + updated_at, + CURRENT_TIMESTAMP AS dbt_loaded_at + FROM source_data + WHERE game_id IS NOT NULL AND player_id IS NOT NULL +) + +SELECT * FROM cleaned diff --git a/dbt_nba/models/staging/stg_player_shot_charts.sql b/dbt_nba/models/staging/stg_player_shot_charts.sql new file mode 100644 index 0000000..190049f --- /dev/null +++ b/dbt_nba/models/staging/stg_player_shot_charts.sql @@ -0,0 +1,105 @@ +{{ + config( + materialized='view', + schema='staging' + ) +}} + +WITH source_data AS ( + SELECT * + FROM {{ source('raw_nba', 'player_shot_charts') }} + WHERE deleted_at IS NULL +), + +cleaned AS ( + SELECT + id AS shot_id, + player_id, + season AS season_year, + CASE + WHEN date IS NOT NULL AND date != '' THEN + strptime(TRIM(date), '%b %d,%Y')::DATE + ELSE NULL + END AS game_date, + date AS game_date_raw, + CASE + WHEN date IS NOT NULL AND date != '' THEN + CASE + WHEN EXTRACT(MONTH FROM strptime(TRIM(date), '%b %d,%Y')::DATE) >= 10 + THEN EXTRACT(YEAR FROM strptime(TRIM(date), '%b %d,%Y')::DATE)::INT + ELSE EXTRACT(YEAR FROM strptime(TRIM(date), '%b %d,%Y')::DATE)::INT - 1 + END + ELSE NULL + END AS season_start_year, + qtr AS quarter_raw, + CASE + WHEN qtr LIKE '1st%' THEN 1 + WHEN qtr LIKE '2nd%' THEN 2 + WHEN qtr LIKE '3rd%' THEN 3 + WHEN qtr LIKE '4th%' THEN 4 + WHEN qtr ILIKE '%OT%' THEN 5 + ELSE NULL + END AS quarter_number, + CASE WHEN qtr ILIKE '%OT%' THEN TRUE ELSE FALSE END AS is_overtime_shot, + time_remaining AS time_remaining_raw, + CASE + WHEN time_remaining IS NOT NULL AND time_remaining LIKE '%:%' THEN + CAST(SPLIT_PART(time_remaining, ':', 1) AS INT) * 60 + + CAST(SPLIT_PART(time_remaining, ':', 2) AS INT) + ELSE NULL + END AS seconds_remaining_in_quarter, + top AS shot_y_coordinate, + "left" AS shot_x_coordinate, + COALESCE(result, FALSE) AS is_made, + CASE WHEN COALESCE(result, FALSE) = TRUE THEN 1 ELSE 0 END AS shot_made_flag, + CASE WHEN COALESCE(result, FALSE) = FALSE THEN 1 ELSE 0 END AS shot_missed_flag, + shot_type AS shot_type_raw, + CASE + WHEN shot_type = '3-pointer' THEN 3 + WHEN shot_type = '2-pointer' THEN 2 + ELSE NULL + END AS shot_point_value, + CASE WHEN shot_type = '3-pointer' THEN TRUE ELSE FALSE END AS is_three_pointer, + FALSE AS is_free_throw, + COALESCE(distance_ft, 0) AS distance_ft, + CASE + WHEN COALESCE(distance_ft, 0) <= 3 THEN 'At Rim (0-3 ft)' + WHEN COALESCE(distance_ft, 0) <= 10 THEN 'Short Range (4-10 ft)' + WHEN COALESCE(distance_ft, 0) <= 16 THEN 'Mid Range (11-16 ft)' + WHEN COALESCE(distance_ft, 0) <= 23 THEN 'Long Mid Range (17-23 ft)' + WHEN COALESCE(distance_ft, 0) <= 27 THEN 'Three Point (24-27 ft)' + ELSE 'Deep Three (28+ ft)' + END AS shot_distance_zone, + COALESCE(lead, FALSE) AS team_had_lead, + COALESCE(team_score, 0) AS team_score_at_shot, + COALESCE(opponent_team_score, 0) AS opponent_score_at_shot, + COALESCE(team_score, 0) - COALESCE(opponent_team_score, 0) AS score_margin_at_shot, + CASE + WHEN qtr LIKE '4th%' + AND time_remaining IS NOT NULL + AND time_remaining LIKE '%:%' + AND (CAST(SPLIT_PART(time_remaining, ':', 1) AS INT) * 60 + + CAST(SPLIT_PART(time_remaining, ':', 2) AS INT)) <= 300 + AND ABS(COALESCE(team_score, 0) - COALESCE(opponent_team_score, 0)) <= 5 + THEN TRUE + ELSE FALSE + END AS is_clutch_shot, + team AS team_abbr_raw, + opponent AS opponent_abbr_raw, + CASE + WHEN COALESCE(result, FALSE) = TRUE THEN + CASE + WHEN shot_type = '3-pointer' THEN 3 + WHEN shot_type = '2-pointer' THEN 2 + ELSE 0 + END + ELSE 0 + END AS points_generated, + created_at, + updated_at, + CURRENT_TIMESTAMP AS dbt_loaded_at + FROM source_data + WHERE id IS NOT NULL AND player_id IS NOT NULL AND date IS NOT NULL AND team IS NOT NULL +) + +SELECT * FROM cleaned diff --git a/dbt_nba/models/staging/stg_season_thresholds.sql b/dbt_nba/models/staging/stg_season_thresholds.sql new file mode 100644 index 0000000..32eb9a5 --- /dev/null +++ b/dbt_nba/models/staging/stg_season_thresholds.sql @@ -0,0 +1,59 @@ +{{ + config( + materialized='table', + schema='staging', + tags=["staging"] + ) +}} + +WITH player_stats AS ( + SELECT + g.season_start_year, + CASE + WHEN s.mp IS NOT NULL AND s.mp != '' AND s.mp LIKE '%:%' THEN + CAST(SPLIT_PART(s.mp, ':', 1) AS DECIMAL) + + (CAST(SPLIT_PART(s.mp, ':', 2) AS DECIMAL) / 60.0) + WHEN s.mp IS NOT NULL AND s.mp != '' AND regexp_matches(s.mp, '^[0-9\.]+$') THEN + CAST(s.mp AS DECIMAL) + ELSE 0 + END AS minutes_played, + COALESCE(s.usg_percent, 0) AS usage_pct, + COALESCE(s.bpm, 0) AS bpm, + COALESCE(s.ts_percent, 0) AS ts_pct + FROM {{ source('raw_nba', 'player_game_adv_stats') }} AS s + INNER JOIN {{ ref('stg_games') }} AS g + ON s.game_id = g.game_id + WHERE s.deleted_at IS NULL + AND s.game_id IS NOT NULL + AND s.player_id IS NOT NULL +) + +SELECT + season_start_year, + PERCENTILE_CONT(0.05) WITHIN GROUP (ORDER BY minutes_played) AS min_p5, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY minutes_played) AS min_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY minutes_played) AS min_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY minutes_played) AS min_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY minutes_played) AS min_p90, + PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY minutes_played) AS min_p95, + PERCENTILE_CONT(0.05) WITHIN GROUP (ORDER BY usage_pct) AS usg_p5, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY usage_pct) AS usg_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY usage_pct) AS usg_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY usage_pct) AS usg_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY usage_pct) AS usg_p90, + PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY usage_pct) AS usg_p95, + PERCENTILE_CONT(0.05) WITHIN GROUP (ORDER BY bpm) AS bpm_p5, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY bpm) AS bpm_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY bpm) AS bpm_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY bpm) AS bpm_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY bpm) AS bpm_p90, + PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY bpm) AS bpm_p95, + PERCENTILE_CONT(0.05) WITHIN GROUP (ORDER BY ts_pct) AS ts_p5, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY ts_pct) AS ts_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY ts_pct) AS ts_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY ts_pct) AS ts_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY ts_pct) AS ts_p90, + PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY ts_pct) AS ts_p95 +FROM player_stats +GROUP BY season_start_year +ORDER BY season_start_year diff --git a/dbt_nba/models/staging/stg_team_game_adv_stats.sql b/dbt_nba/models/staging/stg_team_game_adv_stats.sql new file mode 100644 index 0000000..33976ab --- /dev/null +++ b/dbt_nba/models/staging/stg_team_game_adv_stats.sql @@ -0,0 +1,102 @@ +{{ + config( + materialized='table', + schema='staging', + tags=["staging"] + ) +}} + +WITH source_data AS ( + SELECT * FROM {{ source('raw_nba', 'team_game_adv_stats') }} + WHERE deleted_at IS NULL +), + +games AS ( + SELECT game_id, season_start_year + FROM {{ ref('stg_games') }} +), + +team_season_thresholds AS ( + SELECT * FROM {{ ref('stg_team_season_thresholds') }} +), + +cleaned AS ( + SELECT + sd.game_id, + sd.team, + COALESCE(sd.mp, 240) AS minutes_played, + COALESCE(sd.ts_percent, 0) AS true_shooting_pct, + COALESCE(sd.efg_percent, 0) AS effective_fg_pct, + COALESCE(sd.three_p_ar, 0) AS three_point_attempt_rate, + COALESCE(sd.f_tr, 0) AS free_throw_rate, + COALESCE(sd.orb_percent, 0) AS offensive_rebound_pct, + COALESCE(sd.drb_percent, 0) AS defensive_rebound_pct, + COALESCE(sd.trb_percent, 0) AS total_rebound_pct, + COALESCE(sd.ast_percent, 0) AS assist_pct, + COALESCE(sd.stl_percent, 0) AS steal_pct, + COALESCE(sd.blk_percent, 0) AS block_pct, + COALESCE(sd.tov_percent, 0) AS turnover_pct, + COALESCE(sd.usg_percent, 100) AS usage_pct, + COALESCE(sd.o_rtg, 0) AS offensive_rating, + COALESCE(sd.d_rtg, 0) AS defensive_rating, + COALESCE(sd.o_rtg, 0) - COALESCE(sd.d_rtg, 0) AS net_rating, + + CASE + WHEN COALESCE(sd.o_rtg, 0) >= t.ortg_p90 THEN 'Elite Offense' + WHEN COALESCE(sd.o_rtg, 0) >= t.ortg_q3 THEN 'Above Average Offense' + WHEN COALESCE(sd.o_rtg, 0) >= t.ortg_q1 THEN 'Average Offense' + WHEN COALESCE(sd.o_rtg, 0) >= t.ortg_p10 THEN 'Below Average Offense' + ELSE 'Poor Offense' + END AS offensive_tier, + + CASE + WHEN COALESCE(sd.d_rtg, 0) <= t.drtg_p10 THEN 'Elite Defense' + WHEN COALESCE(sd.d_rtg, 0) <= t.drtg_q1 THEN 'Above Average Defense' + WHEN COALESCE(sd.d_rtg, 0) <= t.drtg_q3 THEN 'Average Defense' + WHEN COALESCE(sd.d_rtg, 0) <= t.drtg_p90 THEN 'Below Average Defense' + ELSE 'Poor Defense' + END AS defensive_tier, + + CASE + WHEN COALESCE(sd.three_p_ar, 0) >= t.tpar_p90 THEN 'Three Point Heavy' + WHEN COALESCE(sd.three_p_ar, 0) >= t.tpar_q3 THEN 'Three Point Leaning' + WHEN COALESCE(sd.three_p_ar, 0) >= t.tpar_q1 THEN 'Balanced' + WHEN COALESCE(sd.three_p_ar, 0) >= t.tpar_p10 THEN 'Inside Leaning' + ELSE 'Inside Focused' + END AS shot_selection_style, + + CASE + WHEN COALESCE(sd.ast_percent, 0) >= t.ast_p90 THEN 'Elite Ball Movement' + WHEN COALESCE(sd.ast_percent, 0) >= t.ast_q3 THEN 'High Ball Movement' + WHEN COALESCE(sd.ast_percent, 0) >= t.ast_q1 THEN 'Average Ball Movement' + WHEN COALESCE(sd.ast_percent, 0) >= t.ast_p10 THEN 'Low Ball Movement' + ELSE 'Isolation Heavy' + END AS ball_movement_style, + + CASE + WHEN COALESCE(sd.tov_percent, 0) <= t.tov_p10 THEN 'Elite Ball Security' + WHEN COALESCE(sd.tov_percent, 0) <= t.tov_q1 THEN 'Above Average Ball Security' + WHEN COALESCE(sd.tov_percent, 0) <= t.tov_q3 THEN 'Average Ball Security' + WHEN COALESCE(sd.tov_percent, 0) <= t.tov_p90 THEN 'Below Average Ball Security' + ELSE 'Poor Ball Security' + END AS ball_security_tier, + + CASE + WHEN (COALESCE(sd.stl_percent, 0) + COALESCE(sd.blk_percent, 0)) >= t.stl_blk_p90 THEN 'Elite Defensive Activity' + WHEN (COALESCE(sd.stl_percent, 0) + COALESCE(sd.blk_percent, 0)) >= t.stl_blk_q3 THEN 'High Defensive Activity' + WHEN (COALESCE(sd.stl_percent, 0) + COALESCE(sd.blk_percent, 0)) >= t.stl_blk_q1 THEN 'Average Defensive Activity' + WHEN (COALESCE(sd.stl_percent, 0) + COALESCE(sd.blk_percent, 0)) >= t.stl_blk_p10 THEN 'Low Defensive Activity' + ELSE 'Passive Defense' + END AS defensive_activity, + + sd.created_at, + sd.updated_at, + CURRENT_TIMESTAMP AS dbt_loaded_at + + FROM source_data AS sd + LEFT JOIN games AS g ON sd.game_id = g.game_id + LEFT JOIN team_season_thresholds AS t ON g.season_start_year = t.season_start_year + WHERE sd.game_id IS NOT NULL AND sd.team IS NOT NULL +) + +SELECT * FROM cleaned diff --git a/dbt_nba/models/staging/stg_team_game_basic_stats.sql b/dbt_nba/models/staging/stg_team_game_basic_stats.sql new file mode 100644 index 0000000..111df70 --- /dev/null +++ b/dbt_nba/models/staging/stg_team_game_basic_stats.sql @@ -0,0 +1,92 @@ +{{ + config( + materialized='view', + schema='staging' + ) +}} + +WITH source_data AS ( + SELECT * FROM {{ source('raw_nba', 'team_game_basic_stats') }} + WHERE deleted_at IS NULL +), + +cleaned AS ( + SELECT + game_id, + team, + COALESCE(mp, 240) AS minutes_played, + COALESCE(fg, 0) AS field_goals_made, + COALESCE(fga, 0) AS field_goals_attempted, + COALESCE(fg_percent, 0) AS field_goal_pct, + COALESCE(three_p, 0) AS three_pointers_made, + COALESCE(three_pa, 0) AS three_pointers_attempted, + COALESCE(three_p_percent, 0) AS three_point_pct, + COALESCE(ft, 0) AS free_throws_made, + COALESCE(fta, 0) AS free_throws_attempted, + COALESCE(ft_percent, 0) AS free_throw_pct, + COALESCE(orb, 0) AS offensive_rebounds, + COALESCE(drb, 0) AS defensive_rebounds, + COALESCE(trb, 0) AS total_rebounds, + COALESCE(ast, 0) AS assists, + COALESCE(stl, 0) AS steals, + COALESCE(blk, 0) AS blocks, + COALESCE(tov, 0) AS turnovers, + COALESCE(pf, 0) AS personal_fouls, + COALESCE(pts, 0) AS points, + COALESCE(fg, 0) - COALESCE(three_p, 0) AS two_pointers_made, + COALESCE(fga, 0) - COALESCE(three_pa, 0) AS two_pointers_attempted, + CASE + WHEN (COALESCE(fga, 0) - COALESCE(three_pa, 0)) > 0 THEN + CAST(COALESCE(fg, 0) - COALESCE(three_p, 0) AS DECIMAL) / + CAST(COALESCE(fga, 0) - COALESCE(three_pa, 0) AS DECIMAL) + ELSE 0 + END AS two_point_pct, + COALESCE(fga, 0) + 0.44 * COALESCE(fta, 0) - COALESCE(orb, 0) + COALESCE(tov, 0) AS possessions_estimate, + CASE + WHEN COALESCE(mp, 240) > 0 THEN + (COALESCE(fga, 0) + 0.44 * COALESCE(fta, 0) - COALESCE(orb, 0) + COALESCE(tov, 0)) * 48.0 / (COALESCE(mp, 240) / 5.0) + ELSE 0 + END AS pace, + CASE + WHEN COALESCE(fga, 0) > 0 THEN + CAST(COALESCE(fg, 0) + 0.5 * COALESCE(three_p, 0) AS DECIMAL) / CAST(COALESCE(fga, 0) AS DECIMAL) + ELSE 0 + END AS effective_fg_pct, + CASE + WHEN (COALESCE(fga, 0) + 0.44 * COALESCE(fta, 0) + COALESCE(tov, 0)) > 0 THEN + CAST(COALESCE(tov, 0) AS DECIMAL) / CAST(COALESCE(fga, 0) + 0.44 * COALESCE(fta, 0) + COALESCE(tov, 0) AS DECIMAL) + ELSE 0 + END AS turnover_rate, + CASE + WHEN (COALESCE(orb, 0) + COALESCE(drb, 0)) > 0 THEN + CAST(COALESCE(orb, 0) AS DECIMAL) / CAST(COALESCE(orb, 0) + COALESCE(drb, 0) AS DECIMAL) + ELSE 0 + END AS offensive_rebound_rate, + CASE + WHEN COALESCE(fga, 0) > 0 THEN + CAST(COALESCE(fta, 0) AS DECIMAL) / CAST(COALESCE(fga, 0) AS DECIMAL) + ELSE 0 + END AS free_throw_rate, + CASE + WHEN COALESCE(tov, 0) > 0 THEN + CAST(COALESCE(ast, 0) AS DECIMAL) / CAST(COALESCE(tov, 0) AS DECIMAL) + ELSE 0 + END AS ast_to_tov_ratio, + CASE + WHEN (COALESCE(fga, 0) + 0.44 * COALESCE(fta, 0) - COALESCE(orb, 0) + COALESCE(tov, 0)) > 0 THEN + CAST(COALESCE(pts, 0) AS DECIMAL) / CAST(COALESCE(fga, 0) + 0.44 * COALESCE(fta, 0) - COALESCE(orb, 0) + COALESCE(tov, 0) AS DECIMAL) + ELSE 0 + END AS points_per_possession, + CASE + WHEN COALESCE(fga, 0) > 0 THEN + CAST(COALESCE(three_pa, 0) AS DECIMAL) / CAST(COALESCE(fga, 0) AS DECIMAL) + ELSE 0 + END AS three_point_rate, + created_at, + updated_at, + CURRENT_TIMESTAMP AS dbt_loaded_at + FROM source_data + WHERE game_id IS NOT NULL AND team IS NOT NULL +) + +SELECT * FROM cleaned diff --git a/dbt_nba/models/staging/stg_team_season_thresholds.sql b/dbt_nba/models/staging/stg_team_season_thresholds.sql new file mode 100644 index 0000000..83d95aa --- /dev/null +++ b/dbt_nba/models/staging/stg_team_season_thresholds.sql @@ -0,0 +1,84 @@ +{{ + config( + materialized='table', + schema='staging', + tags=["staging"] + ) +}} + +WITH team_stats AS ( + SELECT + g.season_start_year, + COALESCE(s.o_rtg, 0) AS offensive_rating, + COALESCE(s.d_rtg, 0) AS defensive_rating, + COALESCE(s.three_p_ar, 0) AS three_point_attempt_rate, + COALESCE(s.ast_percent, 0) AS assist_pct, + COALESCE(s.orb_percent, 0) AS offensive_rebound_pct, + COALESCE(s.drb_percent, 0) AS defensive_rebound_pct, + COALESCE(s.tov_percent, 0) AS turnover_pct, + COALESCE(s.stl_percent, 0) + COALESCE(s.blk_percent, 0) AS stl_blk_combined, + COALESCE(s.ts_percent, 0) AS true_shooting_pct, + COALESCE(s.f_tr, 0) AS free_throw_rate + FROM {{ source('raw_nba', 'team_game_adv_stats') }} AS s + INNER JOIN {{ ref('stg_games') }} AS g + ON s.game_id = g.game_id + WHERE s.deleted_at IS NULL + AND s.game_id IS NOT NULL + AND s.team IS NOT NULL +) + +SELECT + season_start_year, + PERCENTILE_CONT(0.10) WITHIN GROUP (ORDER BY offensive_rating) AS ortg_p10, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY offensive_rating) AS ortg_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY offensive_rating) AS ortg_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY offensive_rating) AS ortg_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY offensive_rating) AS ortg_p90, + PERCENTILE_CONT(0.10) WITHIN GROUP (ORDER BY defensive_rating) AS drtg_p10, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY defensive_rating) AS drtg_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY defensive_rating) AS drtg_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY defensive_rating) AS drtg_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY defensive_rating) AS drtg_p90, + PERCENTILE_CONT(0.10) WITHIN GROUP (ORDER BY three_point_attempt_rate) AS tpar_p10, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY three_point_attempt_rate) AS tpar_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY three_point_attempt_rate) AS tpar_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY three_point_attempt_rate) AS tpar_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY three_point_attempt_rate) AS tpar_p90, + PERCENTILE_CONT(0.10) WITHIN GROUP (ORDER BY assist_pct) AS ast_p10, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY assist_pct) AS ast_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY assist_pct) AS ast_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY assist_pct) AS ast_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY assist_pct) AS ast_p90, + PERCENTILE_CONT(0.10) WITHIN GROUP (ORDER BY offensive_rebound_pct) AS orb_p10, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY offensive_rebound_pct) AS orb_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY offensive_rebound_pct) AS orb_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY offensive_rebound_pct) AS orb_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY offensive_rebound_pct) AS orb_p90, + PERCENTILE_CONT(0.10) WITHIN GROUP (ORDER BY defensive_rebound_pct) AS drb_p10, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY defensive_rebound_pct) AS drb_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY defensive_rebound_pct) AS drb_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY defensive_rebound_pct) AS drb_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY defensive_rebound_pct) AS drb_p90, + PERCENTILE_CONT(0.10) WITHIN GROUP (ORDER BY turnover_pct) AS tov_p10, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY turnover_pct) AS tov_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY turnover_pct) AS tov_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY turnover_pct) AS tov_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY turnover_pct) AS tov_p90, + PERCENTILE_CONT(0.10) WITHIN GROUP (ORDER BY stl_blk_combined) AS stl_blk_p10, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY stl_blk_combined) AS stl_blk_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY stl_blk_combined) AS stl_blk_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY stl_blk_combined) AS stl_blk_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY stl_blk_combined) AS stl_blk_p90, + PERCENTILE_CONT(0.10) WITHIN GROUP (ORDER BY true_shooting_pct) AS ts_p10, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY true_shooting_pct) AS ts_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY true_shooting_pct) AS ts_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY true_shooting_pct) AS ts_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY true_shooting_pct) AS ts_p90, + PERCENTILE_CONT(0.10) WITHIN GROUP (ORDER BY free_throw_rate) AS ftr_p10, + PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY free_throw_rate) AS ftr_q1, + PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY free_throw_rate) AS ftr_median, + PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY free_throw_rate) AS ftr_q3, + PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY free_throw_rate) AS ftr_p90 +FROM team_stats +GROUP BY season_start_year +ORDER BY season_start_year diff --git a/dbt_nba/package-lock.yml b/dbt_nba/package-lock.yml new file mode 100644 index 0000000..1a29e60 --- /dev/null +++ b/dbt_nba/package-lock.yml @@ -0,0 +1,5 @@ +packages: + - name: dbt_utils + package: dbt-labs/dbt_utils + version: 1.3.1 +sha1_hash: 1b75844a5c14558fe9ae17335b3632c080d6784c diff --git a/dbt_nba/packages.yml b/dbt_nba/packages.yml new file mode 100644 index 0000000..48c5d57 --- /dev/null +++ b/dbt_nba/packages.yml @@ -0,0 +1,3 @@ +packages: + - package: dbt-labs/dbt_utils + version: 1.3.1 diff --git a/dbt_nba/profiles.yml b/dbt_nba/profiles.yml new file mode 100644 index 0000000..95ce7fa --- /dev/null +++ b/dbt_nba/profiles.yml @@ -0,0 +1,6 @@ +dbt_nba: + target: dev + outputs: + dev: + type: duckdb + path: "{{ env_var('DBT_DUCKDB_PATH', 'data/DB/dbt_nba.duckdb') }}" diff --git a/dbt_nba/reports/.evidence/customization/.profile.json b/dbt_nba/reports/.evidence/customization/.profile.json new file mode 100644 index 0000000..53b28ad --- /dev/null +++ b/dbt_nba/reports/.evidence/customization/.profile.json @@ -0,0 +1 @@ +{"anonymousId":"8aa41dce-bd0b-40af-96a3-de949fffe712","traits":{"projectCreated":"2026-05-21T05:40:13.247Z"}} diff --git a/dbt_nba/reports/.evidence/customization/custom-formatting.json b/dbt_nba/reports/.evidence/customization/custom-formatting.json new file mode 100644 index 0000000..5bde568 --- /dev/null +++ b/dbt_nba/reports/.evidence/customization/custom-formatting.json @@ -0,0 +1,4 @@ +{ + "version": "1.0", + "customFormats": [] +} \ No newline at end of file diff --git a/dbt_nba/reports/.gitignore b/dbt_nba/reports/.gitignore new file mode 100644 index 0000000..59eb553 --- /dev/null +++ b/dbt_nba/reports/.gitignore @@ -0,0 +1,10 @@ +.evidence/template +.svelte-kit +build +node_modules +.DS_Store +static/data +*.options.yaml +.vscode/settings.json +.env +.evidence/meta diff --git a/dbt_nba/reports/.npmrc b/dbt_nba/reports/.npmrc new file mode 100644 index 0000000..4b92dc6 --- /dev/null +++ b/dbt_nba/reports/.npmrc @@ -0,0 +1,3 @@ +loglevel=error +audit=false +fund=false diff --git a/dbt_nba/reports/.vscode/extensions.json b/dbt_nba/reports/.vscode/extensions.json new file mode 100644 index 0000000..13e25e9 --- /dev/null +++ b/dbt_nba/reports/.vscode/extensions.json @@ -0,0 +1,5 @@ +{ + "recommendations": [ + "evidence.evidence-vscode" + ] +} \ No newline at end of file diff --git a/dbt_nba/reports/README.md b/dbt_nba/reports/README.md new file mode 100644 index 0000000..ab0ca44 --- /dev/null +++ b/dbt_nba/reports/README.md @@ -0,0 +1,48 @@ +# Evidence Template Project + +## Using Codespaces + +If you are using this template in Codespaces, click the `Start Evidence` button in the bottom status bar. This will install dependencies and open a preview of your project in your browser - you should get a popup prompting you to open in browser. + +Or you can use the following commands to get started: + +```bash +npm install +npm run sources +npm run dev -- --host 0.0.0.0 +``` + +See [the CLI docs](https://docs.evidence.dev/cli/) for more command information. + +**Note:** Codespaces is much faster on the Desktop app. After the Codespace has booted, select the hamburger menu → Open in VS Code Desktop. + +## Get Started from VS Code + +The easiest way to get started is using the [VS Code Extension](https://marketplace.visualstudio.com/items?itemName=Evidence.evidence-vscode): + + + +1. Install the extension from the VS Code Marketplace +2. Open the Command Palette (Ctrl/Cmd + Shift + P) and enter `Evidence: New Evidence Project` +3. Click `Start Evidence` in the bottom status bar + +## Get Started using the CLI + +```bash +npx degit evidence-dev/template my-project +cd my-project +npm install +npm run sources +npm run dev +``` + +Check out the docs for [alternative install methods](https://docs.evidence.dev/getting-started/install-evidence) including Docker, Github Codespaces, and alongside dbt. + + + +## Learning More + +- [Docs](https://docs.evidence.dev/) +- [Github](https://github.com/evidence-dev/evidence) +- [Slack Community](https://slack.evidence.dev/) +- [Evidence Home Page](https://www.evidence.dev) diff --git a/dbt_nba/reports/dev_err.txt b/dbt_nba/reports/dev_err.txt new file mode 100644 index 0000000..e69de29 diff --git a/dbt_nba/reports/dev_out.txt b/dbt_nba/reports/dev_out.txt new file mode 100644 index 0000000..e69de29 diff --git a/dbt_nba/reports/evidence.config.yaml b/dbt_nba/reports/evidence.config.yaml new file mode 100644 index 0000000..5c83d47 --- /dev/null +++ b/dbt_nba/reports/evidence.config.yaml @@ -0,0 +1,81 @@ +appearance: + default: system + switcher: true + +theme: + colorPalettes: + default: + light: + - "#236aa4" + - "#45a1bf" + - "#a5cdee" + - "#8dacbf" + - "#85c7c6" + - "#d2c6ac" + - "#f4b548" + - "#8f3d56" + - "#71b9f4" + - "#46a485" + dark: + - "#236aa4" + - "#45a1bf" + - "#a5cdee" + - "#8dacbf" + - "#85c7c6" + - "#d2c6ac" + - "#f4b548" + - "#8f3d56" + - "#71b9f4" + - "#46a485" + colorScales: + default: + light: + - "#ADD8E6" + - "#00008B" + dark: + - "#ADD8E6" + - "#00008B" + colors: + primary: + light: "#2563eb" + dark: "#3b82f6" + accent: + light: "#c2410c" + dark: "#fdba74" + base: + light: "#ffffff" + dark: "#09090b" + info: + light: "#0284c7" + dark: "#38bdf8" + positive: + light: "#16a34a" + dark: "#4ade80" + warning: + light: "#f8c900" + dark: "#fbbf24" + negative: + light: "#dc2626" + dark: "#f87171" + +plugins: + components: + # This loads all of evidence's core charts and UI components + # You probably don't want to edit this dependency unless you know what you are doing + "@evidence-dev/core-components": {} + + datasources: + # You can add additional datasources here by adding npm packages. + # Make to also add them to `package.json`. + "@evidence-dev/bigquery": { } + "@evidence-dev/csv": { } + "@evidence-dev/databricks": { } + "@evidence-dev/duckdb": { } + "@evidence-dev/mssql": { } + "@evidence-dev/mysql": { } + "@evidence-dev/postgres": { } + "@evidence-dev/source-javascript": { } + "@evidence-dev/snowflake": { } + "@evidence-dev/sqlite": { } + "@evidence-dev/trino": { } + "@evidence-dev/motherduck": { } diff --git a/dbt_nba/reports/package-lock.json b/dbt_nba/reports/package-lock.json new file mode 100644 index 0000000..c564d7b --- /dev/null +++ b/dbt_nba/reports/package-lock.json @@ -0,0 +1,17662 @@ +{ + "name": "my-evidence-project", + "version": "0.0.1", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "my-evidence-project", + "version": "0.0.1", + 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/dev/null +++ b/dbt_nba/reports/package.json @@ -0,0 +1,41 @@ +{ + "name": "my-evidence-project", + "version": "0.0.1", + "scripts": { + "build": "evidence build", + "build:strict": "evidence build:strict", + "dev": "evidence dev --open /", + "test": "evidence build", + "sources": "evidence sources", + "sources:strict": "evidence sources --strict", + "preview": "evidence preview" + }, + "engines": { + "npm": ">=7.0.0", + "node": ">=18.0.0" + }, + "type": "module", + "dependencies": { + "@evidence-dev/bigquery": "^2.0.12", + "@evidence-dev/core-components": "^5.4.2", + "@evidence-dev/csv": "^1.0.16", + "@evidence-dev/databricks": "^1.0.10", + "@evidence-dev/duckdb": "^2.0.1", + "@evidence-dev/evidence": "^40.1.8", + "@evidence-dev/motherduck": "^1.0.6", + "@evidence-dev/mssql": "^1.1.4", + "@evidence-dev/mysql": "^1.1.6", + "@evidence-dev/postgres": "^1.0.10", + "@evidence-dev/snowflake": "^1.2.4", + "@evidence-dev/source-javascript": "^0.0.3", + "@evidence-dev/sqlite": "^2.0.9", + "@evidence-dev/trino": "^1.0.11" + }, + "overrides": { + "jsonwebtoken": "9.0.0", + "trim@<0.0.3": ">0.0.3", + "sqlite3": "5.1.5", + "axios": "^1.7.4", + "vitest": "^3.2.6" + } +} diff --git a/dbt_nba/reports/pages/index.md b/dbt_nba/reports/pages/index.md new file mode 100644 index 0000000..2fbcd97 --- /dev/null +++ b/dbt_nba/reports/pages/index.md @@ -0,0 +1,182 @@ +--- +title: NBA Analytics Dashboard +--- + +```sql games +select * from nba.games +``` + +```sql season_summary +select * from nba.season_summary +``` + +```sql competitiveness +select * from nba.competitiveness +``` + +```sql home_advantage +select * from nba.home_advantage +``` + +```sql top_scorers +select * from nba.top_scorers +``` + +```sql pace_and_scoring +select * from nba.pace_and_scoring +``` + +```sql team_ratings +select * from nba.team_ratings +``` + +```sql play_styles +select * from nba.play_styles +``` + +```sql player_impact +select * from nba.player_impact +``` + +```sql game_drama +select * from nba.game_drama +``` + +```sql usage_efficiency +select * from nba.usage_efficiency +``` + +# 🏀 NBA Analytics + + + + + + +--- + +## League Evolution: Pace & Scoring + + + +## Game Drama Over Time + + + +--- + +## Current Season: Team Power Rankings + + + + + + + + + + + + + + +## Shot Selection Style & Winning + + + +--- + +## Player Impact: Usage vs Efficiency + + + +## Top 30 Players by Box Plus/Minus (Current Season) + + + + + + + + + + + + + + + + + +--- + +## Home Court Advantage Trend + + + +## Recent Games + + + + + + + + + + + + diff --git a/dbt_nba/reports/sources/nba/competitiveness.sql b/dbt_nba/reports/sources/nba/competitiveness.sql new file mode 100644 index 0000000..595c6d2 --- /dev/null +++ b/dbt_nba/reports/sources/nba/competitiveness.sql @@ -0,0 +1,6 @@ +select + game_competitiveness_tier, + count(*) as games +from main_marts.fct_game_results +group by game_competitiveness_tier +order by games desc diff --git a/dbt_nba/reports/sources/nba/connection.yaml b/dbt_nba/reports/sources/nba/connection.yaml new file mode 100644 index 0000000..7e5fdf6 --- /dev/null +++ b/dbt_nba/reports/sources/nba/connection.yaml @@ -0,0 +1,4 @@ +name: nba +type: duckdb +options: + filename: dbt_nba.duckdb diff --git a/dbt_nba/reports/sources/nba/game_drama.sql b/dbt_nba/reports/sources/nba/game_drama.sql new file mode 100644 index 0000000..109955b --- /dev/null +++ b/dbt_nba/reports/sources/nba/game_drama.sql @@ -0,0 +1,8 @@ +select + season_start_year || '-' || substr(cast(season_start_year + 1 as varchar), 3, 2) as season, + round(100.0 * sum(case when is_overtime then 1 else 0 end) / count(*), 1) as ot_pct, + round(100.0 * sum(case when point_differential <= 5 then 1 else 0 end) / count(*), 1) as clutch_pct, + round(100.0 * sum(case when point_differential >= 20 then 1 else 0 end) / count(*), 1) as blowout_pct +from main_intermediate.int_games_enriched +group by season_start_year +order by season_start_year diff --git a/dbt_nba/reports/sources/nba/games.sql b/dbt_nba/reports/sources/nba/games.sql new file mode 100644 index 0000000..d4eee9b --- /dev/null +++ b/dbt_nba/reports/sources/nba/games.sql @@ -0,0 +1,18 @@ +select + g.game_date, + g.home_points, + g.visitor_points, + g.total_points, + g.point_differential, + g.is_overtime, + g.is_playoff, + g.game_competitiveness_tier, + g.is_home_team_winner, + ht.team_abbr as home_team, + vt.team_abbr as visitor_team, + wt.team_abbr as winning_team +from main_marts.fct_game_results g +left join main_marts.dim_teams ht on g.home_team_key = ht.team_key +left join main_marts.dim_teams vt on g.visitor_team_key = vt.team_key +left join main_marts.dim_teams wt on g.winning_team_key = wt.team_key +order by g.game_date desc diff --git a/dbt_nba/reports/sources/nba/home_advantage.sql b/dbt_nba/reports/sources/nba/home_advantage.sql new file mode 100644 index 0000000..3f6926d --- /dev/null +++ b/dbt_nba/reports/sources/nba/home_advantage.sql @@ -0,0 +1,9 @@ +select + s.season_display, + count(*) as total_games, + sum(case when g.is_home_team_winner then 1 else 0 end) as home_wins, + round(100.0 * sum(case when g.is_home_team_winner then 1 else 0 end) / count(*), 1) as home_win_pct +from main_marts.fct_game_results g +left join main_marts.dim_seasons s on g.season_key = s.season_key +group by s.season_display +order by s.season_display diff --git a/dbt_nba/reports/sources/nba/pace_and_scoring.sql b/dbt_nba/reports/sources/nba/pace_and_scoring.sql new file mode 100644 index 0000000..b8b2248 --- /dev/null +++ b/dbt_nba/reports/sources/nba/pace_and_scoring.sql @@ -0,0 +1,10 @@ +select + season_start_year || '-' || substr(cast(season_start_year + 1 as varchar), 3, 2) as season, + round(avg(matchup_pace), 1) as avg_pace, + round(avg(total_points), 1) as avg_total_points, + round(avg(home_effective_fg_pct + visitor_effective_fg_pct) / 2 * 100, 1) as avg_efg_pct, + round(avg(point_differential), 1) as avg_margin, + count(*) as games +from main_intermediate.int_games_enriched +group by season_start_year +order by season_start_year diff --git a/dbt_nba/reports/sources/nba/play_styles.sql b/dbt_nba/reports/sources/nba/play_styles.sql new file mode 100644 index 0000000..e49c0d7 --- /dev/null +++ b/dbt_nba/reports/sources/nba/play_styles.sql @@ -0,0 +1,9 @@ +select + shot_selection_style, + count(*) as games, + round(avg(case when game_result = 'W' then 1.0 else 0.0 end) * 100, 1) as win_pct, + round(avg(offensive_rating), 1) as avg_off_rating +from main_intermediate.int_team_performance +where season_start_year = (select max(season_start_year) from main_intermediate.int_team_performance) +group by shot_selection_style +order by avg_off_rating desc diff --git a/dbt_nba/reports/sources/nba/player_impact.sql b/dbt_nba/reports/sources/nba/player_impact.sql new file mode 100644 index 0000000..4b85f3a --- /dev/null +++ b/dbt_nba/reports/sources/nba/player_impact.sql @@ -0,0 +1,20 @@ +select + player_name, + team, + count(*) as games, + round(avg(points), 1) as ppg, + round(avg(assists), 1) as apg, + round(avg(total_rebounds), 1) as rpg, + round(avg(box_plus_minus), 2) as avg_bpm, + round(avg(usage_pct), 1) as avg_usage, + round(avg(true_shooting_pct) * 100, 1) as ts_pct, + round(avg(net_rating), 1) as avg_net_rating, + sum(case when is_double_double then 1 else 0 end) as double_doubles, + sum(case when is_triple_double then 1 else 0 end) as triple_doubles +from main_intermediate.int_player_performance +where season_start_year = (select max(season_start_year) from main_intermediate.int_player_performance) + and minutes_played >= 20 +group by player_name, team +having count(*) >= 20 +order by avg_bpm desc +limit 30 diff --git a/dbt_nba/reports/sources/nba/season_summary.sql b/dbt_nba/reports/sources/nba/season_summary.sql new file mode 100644 index 0000000..480f8b7 --- /dev/null +++ b/dbt_nba/reports/sources/nba/season_summary.sql @@ -0,0 +1,11 @@ +select + s.season_display, + count(*) as total_games, + sum(case when g.is_overtime then 1 else 0 end) as overtime_games, + sum(case when g.is_playoff then 1 else 0 end) as playoff_games, + round(avg(g.total_points), 1) as avg_total_points, + round(avg(g.point_differential), 1) as avg_margin +from main_marts.fct_game_results g +left join main_marts.dim_seasons s on g.season_key = s.season_key +group by s.season_display +order by s.season_display desc diff --git a/dbt_nba/reports/sources/nba/team_ratings.sql b/dbt_nba/reports/sources/nba/team_ratings.sql new file mode 100644 index 0000000..c591dc2 --- /dev/null +++ b/dbt_nba/reports/sources/nba/team_ratings.sql @@ -0,0 +1,14 @@ +select + team, + count(*) as games, + sum(case when game_result = 'W' then 1 else 0 end) as wins, + round(100.0 * sum(case when game_result = 'W' then 1 else 0 end) / count(*), 1) as win_pct, + round(avg(offensive_rating), 1) as avg_off_rating, + round(avg(defensive_rating), 1) as avg_def_rating, + round(avg(net_rating), 1) as avg_net_rating, + round(avg(pace), 1) as avg_pace, + round(avg(effective_fg_pct) * 100, 1) as avg_efg_pct +from main_intermediate.int_team_performance +where season_start_year = (select max(season_start_year) from main_intermediate.int_team_performance) +group by team +order by avg_net_rating desc diff --git a/dbt_nba/reports/sources/nba/top_scorers.sql b/dbt_nba/reports/sources/nba/top_scorers.sql new file mode 100644 index 0000000..1fc3264 --- /dev/null +++ b/dbt_nba/reports/sources/nba/top_scorers.sql @@ -0,0 +1,13 @@ +select + p.player_name, + count(*) as games_played, + round(avg(ps.points), 1) as ppg, + round(avg(ps.total_rebounds), 1) as rpg, + round(avg(ps.assists), 1) as apg, + round(avg(ps.true_shooting_pct) * 100, 1) as ts_pct +from main_marts.fct_player_game_stats ps +left join main_marts.dim_players p on ps.player_key = p.player_key +group by p.player_name +having count(*) >= 50 +order by ppg desc +limit 20 diff --git a/dbt_nba/reports/sources/nba/usage_efficiency.sql b/dbt_nba/reports/sources/nba/usage_efficiency.sql new file mode 100644 index 0000000..44364ae --- /dev/null +++ b/dbt_nba/reports/sources/nba/usage_efficiency.sql @@ -0,0 +1,13 @@ +select + player_name, + team, + count(*) as games, + round(avg(usage_pct), 1) as usage, + round(avg(true_shooting_pct) * 100, 1) as efficiency, + round(avg(points), 1) as ppg +from main_intermediate.int_player_performance +where season_start_year = (select max(season_start_year) from main_intermediate.int_player_performance) + and minutes_played >= 20 +group by player_name, team +having count(*) >= 20 +order by usage desc diff --git a/dbt_nba/seeds/arena_mappings.csv b/dbt_nba/seeds/arena_mappings.csv new file mode 100644 index 0000000..71d34fc --- /dev/null +++ b/dbt_nba/seeds/arena_mappings.csv @@ -0,0 +1,97 @@ +arena_name,city,state_or_country,location +ARCO Arena (II),Sacramento,California,North Natomas +AT&T Center,San Antonio,Texas,East Side +AccorHotels Arena,Paris,France,Bercy +Air Canada Centre,Toronto,Ontario,Harbourfront +Alamodome,San Antonio,Texas,Downtown +Amalie Arena,Tampa,Florida,Channelside +America West Arena,Phoenix,Arizona,Downtown +American Airlines Center,Dallas,Texas,Victory Park +AmericanAirlines Arena,Miami,Florida,Waterfront +Amway Arena,Orlando,Florida,Downtown +Amway Center,Orlando,Florida,Parramore +BMO Harris Bradley Center,Milwaukee,Wisconsin,Downtown +Ball Arena,Denver,Colorado,Central Platte Valley +Bankers Life Fieldhouse,Indianapolis,Indiana,Downtown +Barclays Center,Brooklyn,New York,Atlantic Yards +Bradley Center,Milwaukee,Wisconsin,Downtown +Capital One Arena,Washington,D.C.,Penn Quarter +Charlotte Bobcats Arena,Charlotte,North Carolina,Uptown +Charlotte Coliseum,Charlotte,North Carolina,Southwest Charlotte +Chase Center,San Francisco,California,Mission Bay +Chesapeake Energy Arena,Oklahoma City,Oklahoma,Downtown +Compaq Center,Houston,Texas,Greenway Plaza +Conseco Fieldhouse,Indianapolis,Indiana,Downtown +Continental Airlines Arena,East Rutherford,New Jersey,Meadowlands +Crypto.com Arena,Los Angeles,California,South Park +Delta Center,Salt Lake City,Utah,Downtown +EnergySolutions Arena,Salt Lake City,Utah,Downtown +FTX Arena,Miami,Florida,Waterfront +FedEx Forum,Memphis,Tennessee,Downtown +FedExForum,Memphis,Tennessee,Downtown +First Union Center,Philadelphia,Pennsylvania,South Philadelphia +Fiserv Forum,Milwaukee,Wisconsin,Deer District +FleetCenter,Boston,Massachusetts,West End +Footprint Center,Phoenix,Arizona,Downtown +Ford Center,Oklahoma City,Oklahoma,Downtown +Frost Bank Center,San Antonio,Texas,East Side +Gainbridge Fieldhouse,Indianapolis,Indiana,Downtown +Golden 1 Center,Sacramento,California,Downtown Commons +Gund Arena,Cleveland,Ohio,Gateway District +Intuit Dome,Inglewood,California,Hollywood Park +Izod Center,East Rutherford,New Jersey,Meadowlands +Kaseya Center,Miami,Florida,Waterfront +KeyArena at Seattle Center,Seattle,Washington,Seattle Center +Kia Center,Orlando,Florida,Parramore +LLoyd Noble Center,Norman,Oklahoma,University of Oklahoma +Little Caesars Arena,Detroit,Michigan,Midtown +MCI Center,Washington,D.C.,Penn Quarter +Madison Square Garden (IV),New York,New York,Midtown Manhattan +Mexico City Arena,Mexico City,Mexico,Azcapotzalco +Moda Center,Portland,Oregon,Lloyd District +Moody Center,Austin,Texas,University of Texas +Mortgage Matchup Center,Cleveland,Ohio,Gateway District +New Orleans Arena,New Orleans,Louisiana,Central Business District +Oakland Arena,Oakland,California,Coliseum Complex +Oklahoma City Arena,Oklahoma City,Oklahoma,Downtown +Oracle Arena,Oakland,California,Coliseum Complex +Paycom Center,Oklahoma City,Oklahoma,Downtown +Pepsi Center,Denver,Colorado,Central Platte Valley +Pete Maravich Assembly Center,Baton Rouge,Louisiana,LSU Campus +Philips Arena,Atlanta,Georgia,Downtown +Phoenix Suns Arena,Phoenix,Arizona,Downtown +Power Balance Pavilion,Sacramento,California,North Natomas +Prudential Center,Newark,New Jersey,Downtown +Pyramid Arena,Memphis,Tennessee,Downtown +Quicken Loans Arena,Cleveland,Ohio,Gateway District +Rocket Arena,Houston,Texas,Greenway Plaza +Rocket Mortgage Fieldhouse,Cleveland,Ohio,Gateway District +Rose Garden Arena,Portland,Oregon,Lloyd District +SBC Center,San Antonio,Texas,East Side +STAPLES Center,Los Angeles,California,South Park +Saitama Super Arena,Saitama,Japan,Chūō-ku +Scotiabank Arena,Toronto,Ontario,Harbourfront +Sleep Train Arena,Sacramento,California,North Natomas +Smoothie King Center,New Orleans,Louisiana,Central Business District +Spectrum Center,Charlotte,North Carolina,Uptown +State Farm Arena,Atlanta,Georgia,Downtown +T-Mobile Arena,Las Vegas,Nevada,Paradise +TD Banknorth Garden,Boston,Massachusetts,West End +TD Garden,Boston,Massachusetts,West End +TD Waterhouse Centre,Orlando,Florida,Downtown +Talking Stick Resort Arena,Phoenix,Arizona,Downtown +Target Center,Minneapolis,Minnesota,Warehouse District +The Arena in Oakland,Oakland,California,Coliseum Complex +The O2 Arena,London,England,Greenwich Peninsula +The Palace of Auburn Hills,Auburn Hills,Michigan,Auburn Hills +Time Warner Cable Arena,Charlotte,North Carolina,Uptown +Toyota Center,Houston,Texas,Downtown +US Airways Center,Phoenix,Arizona,Downtown +UW–Milwaukee Panther Arena,Milwaukee,Wisconsin,Downtown +United Center,Chicago,Illinois,Near West Side +Verizon Center,Washington,D.C.,Penn Quarter +Vivint Arena,Salt Lake City,Utah,Downtown +Vivint Smart Home Arena,Salt Lake City,Utah,Downtown +Wachovia Center,Philadelphia,Pennsylvania,South Philadelphia +Wells Fargo Center,Philadelphia,Pennsylvania,South Philadelphia +Xfinity Mobile Arena,Manchester,New Hampshire,Downtown \ No newline at end of file diff --git a/dbt_nba/seeds/arena_maps.csv b/dbt_nba/seeds/arena_maps.csv new file mode 100644 index 0000000..7c0dbea --- /dev/null +++ b/dbt_nba/seeds/arena_maps.csv @@ -0,0 +1,97 @@ +arena_name,city,state_or_country,location,start_date,end_date +ARCO Arena (II),Sacramento,California,North Natomas,1988-11-08,2016-10-30 +AT&T Center,San Antonio,Texas,East Side,2006-01-01,2023-09-21 +AccorHotels Arena,Paris,France,Bercy (12th arrondissement),2015-10-01,2020-06-01 +Air Canada Centre,Toronto,Ontario,Downtown/Harbourfront,1999-02-20,2018-06-30 +Alamodome,San Antonio,Texas,Downtown,1993-05-15,2026-06-30 +Amalie Arena,Tampa,Florida,Channelside,1996-10-20,2026-06-30 +America West Arena,Phoenix,Arizona,Downtown,1992-06-06,2006-09-30 +American Airlines Center,Dallas,Texas,Victory Park,2001-07-17,2026-06-30 +AmericanAirlines Arena,Miami,Florida,Downtown/Biscayne Bay,1999-12-31,2021-06-01 +Amway Arena,Orlando,Florida,Downtown,1989-01-29,2010-10-01 +Amway Center,Orlando,Florida,Downtown,2010-10-01,2023-12-19 +BMO Harris Bradley Center,Milwaukee,Wisconsin,Downtown,2012-05-21,2018-09-17 +Ball Arena,Denver,Colorado,Central Platte Valley/LoDo,2020-10-22,2026-06-30 +Bankers Life Fieldhouse,Indianapolis,Indiana,Downtown,2011-12-01,2021-09-26 +Barclays Center,Brooklyn,New York,Downtown Brooklyn/Prospect Heights,2012-09-21,2026-06-30 +Bradley Center,Milwaukee,Wisconsin,Downtown,1988-10-01,2012-05-20 +Capital One Arena,Washington,District of Columbia,Chinatown/Penn Quarter,2017-08-09,2026-06-30 +Charlotte Bobcats Arena,Charlotte,North Carolina,Uptown (First Ward),2005-10-21,2008-08-01 +Charlotte Coliseum,Charlotte,North Carolina,Tyvola Road,1988-08-11,2005-10-26 +Chase Center,San Francisco,California,Mission Bay,2019-09-06,2026-06-30 +Chesapeake Energy Arena,Oklahoma City,Oklahoma,Downtown,2011-01-01,2021-04-01 +Compaq Center,Houston,Texas,Greenway Plaza,1998-01-01,2003-10-01 +Conseco Fieldhouse,Indianapolis,Indiana,Downtown,1999-11-06,2011-11-30 +Continental Airlines Arena,East Rutherford,New Jersey,Meadowlands Sports Complex,1996-01-04,2007-06-30 +Crypto.com Arena,Los Angeles,California,South Park/Downtown,2021-12-25,2026-06-30 +Delta Center,Salt Lake City,Utah,West Downtown,1991-10-04,2026-06-30 +EnergySolutions Arena,Salt Lake City,Utah,West Downtown,2006-11-20,2015-10-25 +FTX Arena,Miami,Florida,Downtown/Biscayne Bay,2021-06-01,2023-01-01 +FedEx Forum,Memphis,Tennessee,Downtown (near Beale Street),2004-09-06,2026-06-30 +FedExForum,Memphis,Tennessee,Downtown (near Beale Street),2004-09-06,2026-06-30 +First Union Center,Philadelphia,Pennsylvania,South Philadelphia Sports Complex,1998-09-01,2003-06-30 +Fiserv Forum,Milwaukee,Wisconsin,Deer District/Downtown,2018-08-26,2026-06-30 +FleetCenter,Boston,Massachusetts,West End,1995-09-30,2005-06-30 +Footprint Center,Phoenix,Arizona,Downtown,2021-07-16,2025-02-17 +Ford Center,Oklahoma City,Oklahoma,Downtown,2002-06-08,2010-12-31 +Frost Bank Center,San Antonio,Texas,East Side,2023-09-22,2026-06-30 +Gainbridge Fieldhouse,Indianapolis,Indiana,Downtown,2021-09-27,2026-06-30 +Golden 1 Center,Sacramento,California,Downtown Commons (DoCo),2016-10-04,2026-06-30 +Gund Arena,Cleveland,Ohio,Gateway District/Downtown,1994-10-01,2005-08-01 +Intuit Dome,Inglewood,California,Hollywood Park,2024-08-15,2026-06-30 +Izod Center,East Rutherford,New Jersey,Meadowlands Sports Complex,2007-07-01,2015-01-15 +Kaseya Center,Miami,Florida,Downtown/Biscayne Bay,2023-04-01,2026-06-30 +KeyArena at Seattle Center,Seattle,Washington,Seattle Center/Lower Queen Anne,1995-10-26,2008-04-13 +Kia Center,Orlando,Florida,Downtown,2023-12-20,2026-06-30 +LLoyd Noble Center,Norman,Oklahoma,University of Oklahoma Campus,1975-11-01,2026-06-30 +Little Caesars Arena,Detroit,Michigan,Midtown/District Detroit,2017-09-05,2026-06-30 +MCI Center,Washington,District of Columbia,Chinatown/Penn Quarter,1997-12-02,2006-02-28 +Madison Square Garden (IV),New York,New York,Midtown Manhattan (Penn Station),1968-02-11,2026-06-30 +Mexico City Arena,Mexico City,Mexico,Azcapotzalco,2012-02-25,2026-06-30 +Moda Center,Portland,Oregon,Rose Quarter/Lloyd District,2013-08-13,2026-06-30 +Moody Center,Austin,Texas,UT Campus,2022-04-20,2026-06-30 +Mortgage Matchup Center,Phoenix,Arizona,Downtown,2025-10-02,2026-06-30 +New Orleans Arena,New Orleans,Louisiana,Central Business District,1999-10-01,2014-02-04 +Oakland Arena,Oakland,California,Coliseum Industrial,2019-06-14,2026-06-30 +Oklahoma City Arena,Oklahoma City,Oklahoma,Downtown,2010-01-01,2010-12-31 +Oracle Arena,Oakland,California,Coliseum Industrial,2006-10-20,2019-06-13 +Paycom Center,Oklahoma City,Oklahoma,Downtown,2021-07-27,2026-06-30 +Pepsi Center,Denver,Colorado,Central Platte Valley/LoDo,1999-10-01,2020-10-21 +Pete Maravich Assembly Center,Baton Rouge,Louisiana,LSU Campus,1972-01-15,2026-06-30 +Philips Arena,Atlanta,Georgia,Centennial Olympic Park District,1999-09-01,2018-08-28 +Phoenix Suns Arena,Phoenix,Arizona,Downtown,2020-11-01,2021-07-15 +Power Balance Pavilion,Sacramento,California,North Natomas,2011-03-01,2012-12-31 +Prudential Center,Newark,New Jersey,Downtown Newark,2007-10-25,2026-06-30 +Pyramid Arena,Memphis,Tennessee,Downtown (Riverfront),1991-11-09,2004-12-31 +Quicken Loans Arena,Cleveland,Ohio,Gateway District/Downtown,2005-08-01,2019-03-31 +Rocket Arena,Cleveland,Ohio,Gateway District/Downtown,2025-02-18,2026-06-30 +Rocket Mortgage Fieldhouse,Cleveland,Ohio,Gateway District/Downtown,2019-04-01,2025-02-17 +Rose Garden Arena,Portland,Oregon,Rose Quarter/Lloyd District,1995-10-12,2013-08-12 +SBC Center,San Antonio,Texas,East Side,2002-10-18,2005-12-31 +STAPLES Center,Los Angeles,California,South Park/Downtown,1999-10-17,2021-12-24 +Saitama Super Arena,Saitama,Japan,Chuo-ku/Shin-Toshin,2000-09-01,2026-06-30 +Scotiabank Arena,Toronto,Ontario,Downtown/Harbourfront,2018-07-01,2026-06-30 +Sleep Train Arena,Sacramento,California,North Natomas,2013-01-01,2016-10-30 +Smoothie King Center,New Orleans,Louisiana,Central Business District,2014-02-05,2026-06-30 +Spectrum Center,Charlotte,North Carolina,Uptown (First Ward),2016-08-17,2026-06-30 +State Farm Arena,Atlanta,Georgia,Centennial Olympic Park District,2018-08-29,2026-06-30 +T-Mobile Arena,Las Vegas,Nevada,Las Vegas Strip,2016-04-06,2026-06-30 +TD Banknorth Garden,Boston,Massachusetts,West End,2005-07-01,2009-07-15 +TD Garden,Boston,Massachusetts,West End,2009-07-16,2026-06-30 +TD Waterhouse Centre,Orlando,Florida,Downtown,1999-10-01,2002-12-31 +Talking Stick Resort Arena,Phoenix,Arizona,Downtown,2015-09-01,2020-10-31 +Target Center,Minneapolis,Minnesota,Warehouse District/North Loop,1990-10-16,2026-06-30 +The Arena in Oakland,Oakland,California,Coliseum Industrial,1997-10-01,2004-12-31 +The O2 Arena,London,United Kingdom,Greenwich Peninsula,2007-06-24,2026-06-30 +The Palace of Auburn Hills,Auburn Hills,Michigan,Auburn Hills (I-75 corridor),1988-10-01,2017-10-12 +Time Warner Cable Arena,Charlotte,North Carolina,Uptown (First Ward),2008-08-01,2016-08-16 +Toyota Center,Houston,Texas,Downtown/East Downtown,2003-10-06,2026-06-30 +US Airways Center,Phoenix,Arizona,Downtown,2006-10-01,2015-08-31 +UW–Milwaukee Panther Arena,Milwaukee,Wisconsin,Downtown,1950-08-04,2026-06-30 +United Center,Chicago,Illinois,Near West Side,1994-08-18,2026-06-30 +Verizon Center,Washington,District of Columbia,Chinatown/Penn Quarter,2006-03-01,2017-08-08 +Vivint Arena,Salt Lake City,Utah,West Downtown,2020-01-01,2023-06-30 +Vivint Smart Home Arena,Salt Lake City,Utah,West Downtown,2015-10-26,2019-12-31 +Wachovia Center,Philadelphia,Pennsylvania,South Philadelphia Sports Complex,2003-07-01,2010-06-30 +Wells Fargo Center,Philadelphia,Pennsylvania,South Philadelphia Sports Complex,2010-07-01,2025-08-31 +Xfinity Mobile Arena,Philadelphia,Pennsylvania,South Philadelphia Sports Complex,2025-09-01,2026-06-30 \ No newline at end of file diff --git a/dbt_nba/seeds/team_abbreviation_mappings.csv b/dbt_nba/seeds/team_abbreviation_mappings.csv new file mode 100644 index 0000000..28cec7b --- /dev/null +++ b/dbt_nba/seeds/team_abbreviation_mappings.csv @@ -0,0 +1,41 @@ +source_abbr,team_abbr +ATL,ATL +BOS,BOS +BKN,BKN +NJN,BKN +BRK,BKN +CHA,CHA +CHH,CHA +CHO,CHA +CHI,CHI +CLE,CLE +DAL,DAL +DEN,DEN +DET,DET +GSW,GSW +HOU,HOU +IND,IND +LAC,LAC +LAL,LAL +MEM,MEM +MIA,MIA +MIL,MIL +MIN,MIN +NOP,NOP +NOH,NOP +NOK,NOP +NYK,NYK +OKC,OKC +SEA,OKC +ORL,ORL +PHI,PHI +PHX,PHX +PHO,PHX +POR,POR +SAC,SAC +SAS,SAS +TOR,TOR +UTA,UTA +WAS,WAS +VAN,MEM +SDC,LAC \ No newline at end of file diff --git a/dbt_nba/seeds/team_mappings.csv b/dbt_nba/seeds/team_mappings.csv new file mode 100644 index 0000000..b477f75 --- /dev/null +++ b/dbt_nba/seeds/team_mappings.csv @@ -0,0 +1,36 @@ +team_abbr,full_name +ATL,Atlanta Hawks +BOS,Boston Celtics +BKN,Brooklyn Nets +BKN,New Jersey Nets +CHA,Charlotte Hornets +CHA,Charlotte Bobcats +CHI,Chicago Bulls +CLE,Cleveland Cavaliers +DAL,Dallas Mavericks +DEN,Denver Nuggets +DET,Detroit Pistons +GSW,Golden State Warriors +HOU,Houston Rockets +IND,Indiana Pacers +LAC,Los Angeles Clippers +LAL,Los Angeles Lakers +MEM,Memphis Grizzlies +MIA,Miami Heat +MIL,Milwaukee Bucks +MIN,Minnesota Timberwolves +NOP,New Orleans Pelicans +NOP,New Orleans Hornets +NOP,New Orleans/Oklahoma City Hornets +NYK,New York Knicks +OKC,Oklahoma City Thunder +OKC,Seattle SuperSonics +ORL,Orlando Magic +PHI,Philadelphia 76ers +PHX,Phoenix Suns +POR,Portland Trail Blazers +SAC,Sacramento Kings +SAS,San Antonio Spurs +TOR,Toronto Raptors +UTA,Utah Jazz +WAS,Washington Wizards \ No newline at end of file diff --git a/dbt_nba/seeds/team_maps.csv b/dbt_nba/seeds/team_maps.csv new file mode 100644 index 0000000..f68edc6 --- /dev/null +++ b/dbt_nba/seeds/team_maps.csv @@ -0,0 +1,39 @@ +team_abbr,full_name,start_year,end_year,is_current,source,notes +ATL,Atlanta Hawks,1968,2026,TRUE,all,No changes since 1968 +BOS,Boston Celtics,1946,2026,TRUE,all,No changes since 1946 +BRK,Brooklyn Nets,2012,2026,TRUE,bref,Basketball-Reference abbreviation for Brooklyn +CHA,Charlotte Bobcats,2004,2014,FALSE,bref,Renamed to Hornets in 2014; ESPN/NBA.com also use CHA for current Hornets +CHH,Charlotte Hornets,1988,2002,FALSE,bref,Original Hornets; relocated to New Orleans after 2001-02 +CHI,Chicago Bulls,1966,2026,TRUE,all,No changes since 1966 +CHO,Charlotte Hornets,2014,2026,TRUE,bref,Basketball-Reference abbreviation; reclaimed original Hornets history +CLE,Cleveland Cavaliers,1970,2026,TRUE,all,No changes since 1970 +DAL,Dallas Mavericks,1980,2026,TRUE,all,Expansion team 1980-81 season +DEN,Denver Nuggets,1976,2026,TRUE,all,Joined NBA in 1976 from ABA +DET,Detroit Pistons,1957,2026,TRUE,all,In Detroit since 1957 +GSW,Golden State Warriors,1971,2026,TRUE,all,Name changed from San Francisco Warriors in 1971 +HOU,Houston Rockets,1971,2026,TRUE,all,Moved from San Diego in 1971 +IND,Indiana Pacers,1976,2026,TRUE,all,Joined NBA in 1976 from ABA +LAC,Los Angeles Clippers,1984,2026,TRUE,all,Relocated from San Diego +LAL,Los Angeles Lakers,1960,2026,TRUE,all,Moved from Minneapolis in 1960 +MEM,Memphis Grizzlies,2001,2026,TRUE,all,Relocated from Vancouver +MIA,Miami Heat,1988,2026,TRUE,all,Expansion team 1988-89 season +MIL,Milwaukee Bucks,1968,2026,TRUE,all,No changes since 1968 +MIN,Minnesota Timberwolves,1989,2026,TRUE,all,Expansion team 1989-90 season +NJN,New Jersey Nets,1977,2012,FALSE,all,Moved to Brooklyn after 2011-12 season +NOH,New Orleans Hornets,2002,2005,FALSE,bref,Relocated from Charlotte; excludes NOK years; renamed Pelicans in 2013 +NOK,New Orleans/Oklahoma City Hornets,2005,2007,FALSE,bref,Hurricane Katrina temporary relocation; split home games +NOP,New Orleans Pelicans,2013,2026,TRUE,all,Renamed from Hornets +NYK,New York Knicks,1946,2026,TRUE,all,No changes since 1946 +OKC,Oklahoma City Thunder,2008,2026,TRUE,all,Relocated from Seattle +ORL,Orlando Magic,1989,2026,TRUE,all,Expansion team 1989-90 season +PHI,Philadelphia 76ers,1963,2026,TRUE,all,Moved from Syracuse in 1963 +PHO,Phoenix Suns,1968,2026,TRUE,bref,Basketball-Reference abbreviation +POR,Portland Trail Blazers,1970,2026,TRUE,all,No changes since 1970 +SAC,Sacramento Kings,1985,2026,TRUE,all,Relocated from Kansas City +SAS,San Antonio Spurs,1976,2026,TRUE,all,Joined NBA in 1976 from ABA +SDC,San Diego Clippers,1978,1984,FALSE,bref,Moved to Los Angeles after 1983-84 season +SEA,Seattle SuperSonics,1967,2008,FALSE,all,Relocated to Oklahoma City after 2007-08 season +TOR,Toronto Raptors,1995,2026,TRUE,all,Expansion team 1995-96 season +UTA,Utah Jazz,1979,2026,TRUE,all,Moved from New Orleans in 1979 +VAN,Vancouver Grizzlies,1995,2001,FALSE,all,Expansion team 1995-96; relocated to Memphis after 2000-01 +WAS,Washington Wizards,1997,2026,TRUE,all,Renamed from Bullets \ No newline at end of file diff --git a/dbt_nba/selectors.yml b/dbt_nba/selectors.yml new file mode 100644 index 0000000..8f2a31b --- /dev/null +++ b/dbt_nba/selectors.yml @@ -0,0 +1,16 @@ +selectors: + - name: nba_pipeline + description: "Executes the full pipeline: Staging -> Intermediate -> Dimensions -> Facts" + default: false + definition: + union: + - method: tag + value: staging + - method: tag + value: intermediate + - method: tag + value: dimension + - method: tag + value: fact + - method: tag + value: marts diff --git a/dbt_nba/snapshots/.gitkeep b/dbt_nba/snapshots/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/dbt_nba/tests/.gitkeep b/dbt_nba/tests/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..ee65c14 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,13 @@ +[project] +name = "dbt-nba" +version = "1.0.0" +requires-python = ">=3.11,<3.13" +dependencies = [ + "dbt-duckdb", + "streamlit>=1.45.1", + "duckdb>=1.3.0", + "plotly>=6.1.2", + "streamlit-echarts>=0.4.0", + "boto3>=1.34.0", + "python-dotenv>=1.0.0", +] diff --git a/scripts/db_storage.py b/scripts/db_storage.py new file mode 100755 index 0000000..a0e2402 --- /dev/null +++ b/scripts/db_storage.py @@ -0,0 +1,302 @@ +#!/usr/bin/env python3 +""" +S3/Cloudflare R2 Object Storage utility for DuckDB database management. + +Supports: +- Cloudflare R2 (recommended: zero egress fees) +- Hetzner S3 Object Storage +- SeaweedFS / MinIO / AWS S3 + +Commands: + python scripts/db_storage.py status + python scripts/db_storage.py upload [--file PATH] [--key KEY] + python scripts/db_storage.py download [--force] [--key KEY] +""" + +import argparse +import hashlib +import os +import shutil +import sys +import time +from pathlib import Path + +try: + from dotenv import load_dotenv + # Load .env file from repo root + repo_root = Path(__file__).resolve().parent.parent + load_dotenv(repo_root / ".env") +except ImportError: + repo_root = Path(__file__).resolve().parent.parent + +try: + import boto3 + from boto3.s3.transfer import TransferConfig + from botocore.client import Config + from botocore.exceptions import ClientError +except ImportError: + print("ERROR: boto3 is not installed. Please run `uv sync` to install dependencies.", file=sys.stderr) + sys.exit(1) + +# Default Paths +DEFAULT_LOCAL_DB = repo_root / "data" / "DB" / "dbt_nba.duckdb" +DEFAULT_REPORTS_DB = repo_root / "dbt_nba" / "reports" / "sources" / "nba" / "dbt_nba.duckdb" + +# Tuned TransferConfig for resilient large multipart transfers over HTTPS +TRANSFER_CONFIG = TransferConfig( + multipart_threshold=32 * 1024 * 1024, # 32 MB + max_concurrency=4, # 4 concurrent threads + multipart_chunksize=32 * 1024 * 1024, # 32 MB per chunk + use_threads=True, + num_download_attempts=5, +) + + +def get_s3_config(): + """Retrieve S3/R2 configuration from environment variables.""" + endpoint_url = os.getenv("S3_ENDPOINT_URL") or os.getenv("R2_ENDPOINT_URL") + bucket_name = os.getenv("S3_BUCKET_NAME") or os.getenv("R2_BUCKET_NAME") + access_key = os.getenv("S3_ACCESS_KEY_ID") or os.getenv("AWS_ACCESS_KEY_ID") or os.getenv("R2_ACCESS_KEY_ID") + secret_key = os.getenv("S3_SECRET_ACCESS_KEY") or os.getenv("AWS_SECRET_ACCESS_KEY") or os.getenv("R2_SECRET_ACCESS_KEY") + region_name = os.getenv("S3_REGION", "auto") + db_key = os.getenv("S3_DB_KEY", "dbt_nba.duckdb") + + missing = [] + if not endpoint_url: + missing.append("S3_ENDPOINT_URL") + if not bucket_name: + missing.append("S3_BUCKET_NAME") + if not access_key: + missing.append("S3_ACCESS_KEY_ID") + if not secret_key: + missing.append("S3_SECRET_ACCESS_KEY") + + if missing: + print(f"ERROR: Missing required environment variables in .env: {', '.join(missing)}", file=sys.stderr) + print("Please check .env.example for guidance.", file=sys.stderr) + sys.exit(1) + + return { + "endpoint_url": endpoint_url, + "bucket_name": bucket_name, + "access_key": access_key, + "secret_key": secret_key, + "region_name": region_name, + "db_key": db_key, + } + + +def get_s3_client(config): + """Create a boto3 S3 client configured for custom S3/R2 endpoints.""" + return boto3.client( + "s3", + endpoint_url=config["endpoint_url"], + aws_access_key_id=config["access_key"], + aws_secret_access_key=config["secret_key"], + region_name=config["region_name"], + config=Config( + signature_version="s3v4", + s3={"addressing_style": "path"}, + retries={"max_attempts": 5, "mode": "adaptive"}, + ), + ) + + +class ProgressPercentage: + """Console progress tracker for S3 file transfer.""" + + def __init__(self, filename, total_size, action="Transferring"): + self._filename = filename + self._total_size = float(total_size) if total_size > 0 else 1.0 + self._seen_so_far = 0 + self._start_time = time.time() + self._action = action + + def __call__(self, bytes_amount): + self._seen_so_far += bytes_amount + percentage = (self._seen_so_far / self._total_size) * 100 + elapsed = max(time.time() - self._start_time, 0.001) + speed_mb = (self._seen_so_far / (1024 * 1024)) / elapsed + mb_seen = self._seen_so_far / (1024 * 1024) + mb_total = self._total_size / (1024 * 1024) + + sys.stdout.write( + f"\r{self._action} {Path(self._filename).name}: {mb_seen:.1f}/{mb_total:.1f} MB " + f"({percentage:5.1f}%) @ {speed_mb:5.1f} MB/s" + ) + sys.stdout.flush() + if self._seen_so_far >= self._total_size: + sys.stdout.write("\n") + sys.stdout.flush() + + +def compute_file_md5(filepath: Path) -> str: + """Compute hex MD5 hash of a local file.""" + hash_md5 = hashlib.md5() + with open(filepath, "rb") as f: + for chunk in iter(lambda: f.read(1024 * 1024), b""): + hash_md5.update(chunk) + return hash_md5.hexdigest() + + +def upload_db(file_path: Path = None, key: str = None): + """Upload DuckDB file to S3/R2 with auto-retry.""" + config = get_s3_config() + target_file = Path(file_path) if file_path else DEFAULT_LOCAL_DB + object_key = key or config["db_key"] + + if not target_file.exists(): + print(f"ERROR: Local database file not found at {target_file}", file=sys.stderr) + sys.exit(1) + + file_size = target_file.stat().st_size + print(f"==> Uploading {target_file} ({file_size / (1024*1024):.2f} MB) to s3://{config['bucket_name']}/{object_key}...") + + s3 = get_s3_client(config) + max_retries = 3 + + for attempt in range(1, max_retries + 1): + try: + progress = ProgressPercentage(str(target_file), file_size, action="Uploading") + s3.upload_file( + Filename=str(target_file), + Bucket=config["bucket_name"], + Key=object_key, + Config=TRANSFER_CONFIG, + Callback=progress, + ) + print(f"✓ Successfully uploaded {object_key} to {config['bucket_name']}") + return + except Exception as e: + if attempt < max_retries: + print(f"\n[Attempt {attempt}/{max_retries}] Upload encountered error ({e}). Retrying in 3s...", file=sys.stderr) + time.sleep(3) + else: + print(f"\nERROR: Failed to upload to S3 after {max_retries} attempts: {e}", file=sys.stderr) + sys.exit(1) + + +def download_db(force: bool = False, key: str = None): + """Download DuckDB file from S3/R2 to local destinations with auto-retry.""" + config = get_s3_config() + object_key = key or config["db_key"] + s3 = get_s3_client(config) + + print(f"==> Checking remote object s3://{config['bucket_name']}/{object_key}...") + try: + head = s3.head_object(Bucket=config["bucket_name"], Key=object_key) + except ClientError as e: + print(f"ERROR: Could not find or access remote object {object_key} in {config['bucket_name']}: {e}", file=sys.stderr) + sys.exit(1) + + remote_size = head.get("ContentLength", 0) + last_modified = head.get("LastModified") + + print(f" Remote size: {remote_size / (1024*1024):.2f} MB | Last modified: {last_modified}") + + # Check if local file is already identical + if not force and DEFAULT_LOCAL_DB.exists() and DEFAULT_LOCAL_DB.stat().st_size == remote_size: + # Check if reports db also exists + if DEFAULT_REPORTS_DB.exists() and DEFAULT_REPORTS_DB.stat().st_size == remote_size: + print("✓ Local database is already up-to-date. Skipping download (use --force to re-download).") + return + + # Ensure parent directories exist + DEFAULT_LOCAL_DB.parent.mkdir(parents=True, exist_ok=True) + DEFAULT_REPORTS_DB.parent.mkdir(parents=True, exist_ok=True) + + temp_file = DEFAULT_LOCAL_DB.with_suffix(".duckdb.tmp") + max_retries = 3 + + for attempt in range(1, max_retries + 1): + try: + progress = ProgressPercentage(str(DEFAULT_LOCAL_DB), remote_size, action="Downloading") + s3.download_file( + Bucket=config["bucket_name"], + Key=object_key, + Filename=str(temp_file), + Config=TRANSFER_CONFIG, + Callback=progress, + ) + # Atomically move temp file to main db path + shutil.move(str(temp_file), str(DEFAULT_LOCAL_DB)) + # Copy to reports location as well + shutil.copy2(str(DEFAULT_LOCAL_DB), str(DEFAULT_REPORTS_DB)) + print(f"✓ Downloaded database to:\n - {DEFAULT_LOCAL_DB}\n - {DEFAULT_REPORTS_DB}") + return + except Exception as e: + if temp_file.exists(): + temp_file.unlink() + if attempt < max_retries: + print(f"\n[Attempt {attempt}/{max_retries}] Download encountered error ({e}). Retrying in 3s...", file=sys.stderr) + time.sleep(3) + else: + print(f"\nERROR: Failed to download from S3 after {max_retries} attempts: {e}", file=sys.stderr) + sys.exit(1) + + +def status_db(): + """Show status of remote and local databases.""" + config = get_s3_config() + object_key = config["db_key"] + s3 = get_s3_client(config) + + print("=" * 60) + print("Database Storage Status (Cloudflare R2 / S3)") + print("=" * 60) + print(f"Endpoint: {config['endpoint_url']}") + print(f"Bucket: {config['bucket_name']}") + print(f"DB Key: {object_key}") + print("-" * 60) + + try: + head = s3.head_object(Bucket=config["bucket_name"], Key=object_key) + remote_size = head.get("ContentLength", 0) + last_modified = head.get("LastModified") + print(f"Remote DB: Exists ({remote_size / (1024*1024):.2f} MB)") + print(f" Last Modified: {last_modified}") + except ClientError as e: + print(f"Remote DB: NOT FOUND ({e})") + + print("-" * 60) + if DEFAULT_LOCAL_DB.exists(): + print(f"Local DB (Data): Exists ({DEFAULT_LOCAL_DB.stat().st_size / (1024*1024):.2f} MB) -> {DEFAULT_LOCAL_DB}") + else: + print(f"Local DB (Data): Missing -> {DEFAULT_LOCAL_DB}") + + if DEFAULT_REPORTS_DB.exists(): + print(f"Local DB (Reports): Exists ({DEFAULT_REPORTS_DB.stat().st_size / (1024*1024):.2f} MB) -> {DEFAULT_REPORTS_DB}") + else: + print(f"Local DB (Reports): Missing -> {DEFAULT_REPORTS_DB}") + print("=" * 60) + + +def main(): + parser = argparse.ArgumentParser(description="Manage NBA DuckDB sync with Cloudflare R2 / S3.") + subparsers = parser.add_subparsers(dest="command", required=True) + + # status + subparsers.add_parser("status", help="Check remote and local database status") + + # upload + upload_parser = subparsers.add_parser("upload", help="Upload local DuckDB to S3/R2") + upload_parser.add_argument("--file", help="Path to local .duckdb file (default: data/DB/dbt_nba.duckdb)") + upload_parser.add_argument("--key", help="Remote object key (default: from env or dbt_nba.duckdb)") + + # download + download_parser = subparsers.add_parser("download", help="Download DuckDB from S3/R2") + download_parser.add_argument("--force", action="store_true", help="Force download even if local file matches") + download_parser.add_argument("--key", help="Remote object key (default: from env or dbt_nba.duckdb)") + + args = parser.parse_args() + + if args.command == "status": + status_db() + elif args.command == "upload": + upload_db(file_path=args.file, key=args.key) + elif args.command == "download": + download_db(force=args.force, key=args.key) + + +if __name__ == "__main__": + main() diff --git a/scripts/extract.sql b/scripts/extract.sql new file mode 100644 index 0000000..d40895b --- /dev/null +++ b/scripts/extract.sql @@ -0,0 +1,28 @@ +-- Extract source tables from Postgres into DuckDB +-- Requires: POSTGRES_URL environment variable +-- Usage: Run via scripts/pipeline.sh (handles variable substitution) + +INSTALL postgres; +LOAD postgres; + +ATTACH '${POSTGRES_URL}' AS pg (TYPE POSTGRES, READ_ONLY); + +DROP TABLE IF EXISTS main.games; +DROP TABLE IF EXISTS main.line_scores; +DROP TABLE IF EXISTS main.player_game_basic_stats; +DROP TABLE IF EXISTS main.player_game_adv_stats; +DROP TABLE IF EXISTS main.player_shot_charts; +DROP TABLE IF EXISTS main.team_game_basic_stats; +DROP TABLE IF EXISTS main.team_game_adv_stats; + +CREATE TABLE main.games AS SELECT * FROM pg.public.games; +CREATE TABLE main.line_scores AS SELECT * FROM pg.public.line_scores; +CREATE TABLE main.player_game_basic_stats AS SELECT * FROM pg.public.player_game_basic_stats; +CREATE TABLE main.player_game_adv_stats AS SELECT * FROM pg.public.player_game_adv_stats; +CREATE TABLE main.player_shot_charts AS SELECT * FROM pg.public.player_shot_charts; +CREATE TABLE main.team_game_basic_stats AS SELECT * FROM pg.public.team_game_basic_stats; +CREATE TABLE main.team_game_adv_stats AS SELECT * FROM pg.public.team_game_adv_stats; + +DETACH pg; + +SELECT 'Extraction complete' AS status; diff --git a/scripts/pipeline.sh b/scripts/pipeline.sh new file mode 100755 index 0000000..214fb87 --- /dev/null +++ b/scripts/pipeline.sh @@ -0,0 +1,47 @@ +#!/usr/bin/env bash +set -euo pipefail + +REPO_ROOT="$(cd "$(dirname "$0")/.." && pwd)" +DB_PATH="$REPO_ROOT/data/DB/dbt_nba.duckdb" +REPORTS_DB="$REPO_ROOT/dbt_nba/reports/sources/nba/dbt_nba.duckdb" + +# Load .env if it exists +if [ -f "$REPO_ROOT/.env" ]; then + set -a + source "$REPO_ROOT/.env" + set +a +fi + +# Check prerequisites +if [ -z "${POSTGRES_URL:-}" ]; then + echo "ERROR: POSTGRES_URL environment variable is not set" >&2 + echo "Please export POSTGRES_URL or configure it in $REPO_ROOT/.env" >&2 + exit 1 +fi + +echo "==> [1/4] Extracting from Postgres into DuckDB..." +mkdir -p "$(dirname "$DB_PATH")" +mkdir -p "$(dirname "$REPORTS_DB")" +rm -f "$DB_PATH" +envsubst < "$REPO_ROOT/scripts/extract.sql" | duckdb "$DB_PATH" + +echo "==> [2/4] Running dbt build --full-refresh..." +export DBT_PROJECT_DIR="$REPO_ROOT/dbt_nba" +export DBT_PROFILES_DIR="$REPO_ROOT/dbt_nba" +export DBT_DUCKDB_PATH="$DB_PATH" + +uv run --project "$REPO_ROOT" dbt deps --project-dir "$DBT_PROJECT_DIR" --profiles-dir "$DBT_PROFILES_DIR" +uv run --project "$REPO_ROOT" dbt build --full-refresh --project-dir "$DBT_PROJECT_DIR" --profiles-dir "$DBT_PROFILES_DIR" + +echo "==> [3/4] Copying database to reports directory..." +cp "$DB_PATH" "$REPORTS_DB" + +echo "==> [4/4] Syncing database artifact to Cloudflare R2 / S3..." +if [ -n "${S3_ENDPOINT_URL:-}" ] || [ -n "${R2_ENDPOINT_URL:-}" ]; then + uv run --project "$REPO_ROOT" python "$REPO_ROOT/scripts/db_storage.py" upload + echo "==> Cloudflare R2 upload complete!" +else + echo "==> S3_ENDPOINT_URL not set; skipping remote upload (local DB ready)." +fi + +echo "==> Pipeline complete successfully!" diff --git a/scripts/run_app.sh b/scripts/run_app.sh new file mode 100755 index 0000000..053c3f2 --- /dev/null +++ b/scripts/run_app.sh @@ -0,0 +1,41 @@ +#!/usr/bin/env bash +set -euo pipefail + +REPO_ROOT="$(cd "$(dirname "$0")/.." && pwd)" +REPORTS_DB="$REPO_ROOT/dbt_nba/reports/sources/nba/dbt_nba.duckdb" +DATA_DB="$REPO_ROOT/data/DB/dbt_nba.duckdb" + +# Load .env if present +if [ -f "$REPO_ROOT/.env" ]; then + set -a + source "$REPO_ROOT/.env" + set +a +fi + +FORCE_SYNC=0 +for arg in "$@"; do + if [ "$arg" == "--sync" ] || [ "$arg" == "-s" ]; then + FORCE_SYNC=1 + fi +done + +# Check if database is missing or sync requested +if [ ! -f "$REPORTS_DB" ] || [ "$FORCE_SYNC" -eq 1 ]; then + if [ -f "$DATA_DB" ] && [ "$FORCE_SYNC" -eq 0 ]; then + echo "==> Copying local database to reports path..." + mkdir -p "$(dirname "$REPORTS_DB")" + cp "$DATA_DB" "$REPORTS_DB" + elif [ -n "${S3_ENDPOINT_URL:-}" ] || [ -n "${R2_ENDPOINT_URL:-}" ]; then + echo "==> Local database missing or --sync requested; downloading latest from Cloudflare R2 / S3..." + uv run --project "$REPO_ROOT" python "$REPO_ROOT/scripts/db_storage.py" download + else + echo "ERROR: Local database file not found at $REPORTS_DB." >&2 + echo "Please either:" >&2 + echo " 1. Run ./scripts/pipeline.sh to extract and build from Postgres, or" >&2 + echo " 2. Configure R2 credentials in .env and run: uv run python scripts/db_storage.py download" >&2 + exit 1 + fi +fi + +echo "==> Starting Streamlit NBA Analytics Dashboard..." +uv run --project "$REPO_ROOT" streamlit run "$REPO_ROOT/streamlit_app/app.py" diff --git a/streamlit_app/.streamlit/config.toml b/streamlit_app/.streamlit/config.toml new file mode 100644 index 0000000..4c4d4e4 --- /dev/null +++ b/streamlit_app/.streamlit/config.toml @@ -0,0 +1,8 @@ +[theme] +primaryColor = "#3b82f6" +backgroundColor = "#0e1117" +secondaryBackgroundColor = "#1e293b" +textColor = "#f8fafc" + +[server] +maxUploadSize = 5 diff --git a/streamlit_app/app.py b/streamlit_app/app.py new file mode 100644 index 0000000..137552c --- /dev/null +++ b/streamlit_app/app.py @@ -0,0 +1,1391 @@ +import streamlit as st +import duckdb +import pandas as pd +import plotly.express as px +import plotly.graph_objects as go +from streamlit_echarts import st_echarts +from pathlib import Path + +st.set_page_config(page_title="NBA Analytics", page_icon="🏀", layout="wide") + +# ============================================================ +# CONNECTION & QUERY INFRASTRUCTURE +# ============================================================ + +DB_PATH = str(Path(__file__).resolve().parent.parent / "dbt_nba" / "reports" / "sources" / "nba" / "dbt_nba.duckdb") + +if not Path(DB_PATH).exists(): + DB_PATH = str(Path("dbt_nba") / "reports" / "sources" / "nba" / "dbt_nba.duckdb") + + +@st.cache_resource +def get_connection(): + """Persistent read-only DuckDB connection.""" + return duckdb.connect(DB_PATH, read_only=True) + + +@st.cache_data(ttl=3600) +def query(sql: str, params: list = None) -> pd.DataFrame: + """Execute SQL (optionally parameterized) and return cached DataFrame.""" + conn = get_connection() + if params: + return conn.execute(sql, params).df() + return conn.execute(sql).df() + + +@st.cache_data(ttl=3600) +def get_seasons() -> pd.DataFrame: + return query("SELECT season_start_year, season_display FROM main_marts.dim_seasons ORDER BY season_start_year DESC") + + +# ============================================================ +# GLOBAL STATE & SIDEBAR +# ============================================================ + +seasons = get_seasons() +season_options = seasons["season_start_year"].tolist() +season_labels = dict(zip(seasons["season_start_year"], seasons["season_display"])) + +if "season" not in st.session_state: + st.session_state["season"] = season_options[0] + +st.sidebar.title("🏀 NBA Analytics") + +st.session_state["season"] = st.sidebar.selectbox( + "Season", + season_options, + index=season_options.index(st.session_state["season"]), + format_func=lambda x: season_labels[x], +) + +st.sidebar.divider() + +page = st.sidebar.radio( + "Navigate", + ["Overview", "Teams", "Player Lab", "Shot Charts", "Head to Head", "Game Trends", "Quarter Analysis"], +) + +# Convenience accessors +SEASON = st.session_state["season"] +SEASON_LABEL = season_labels[SEASON] + + + + +# ============================================================ +# PAGE: League Pulse (Overview) +# ============================================================ +if page == "Overview": + st.title(f"📊 League Pulse — {SEASON_LABEL}") + + # --- KPIs with delta vs prior season --- + summary = query(""" + SELECT count(*) AS total_games, + sum(CASE WHEN is_overtime THEN 1 ELSE 0 END) AS ot_games, + round(avg(total_points), 1) AS avg_pts, + round(avg(point_differential), 1) AS avg_margin, + round(100.0 * sum(CASE WHEN is_home_team_winner THEN 1 ELSE 0 END) / count(*), 1) AS home_win_pct, + min(game_date) AS first_game, + max(game_date) AS last_game + FROM main_marts.fct_game_results + WHERE season_key IN (SELECT season_key FROM main_marts.dim_seasons WHERE season_start_year = ?) + """, [SEASON]) + + prior = query(""" + SELECT round(avg(total_points), 1) AS avg_pts, + round(avg(point_differential), 1) AS avg_margin, + round(100.0 * sum(CASE WHEN is_home_team_winner THEN 1 ELSE 0 END) / count(*), 1) AS home_win_pct + FROM main_marts.fct_game_results + WHERE season_key IN (SELECT season_key FROM main_marts.dim_seasons WHERE season_start_year = ?) + """, [SEASON - 1]) + + has_prior = not prior.empty and prior["avg_pts"][0] is not None + c1, c2, c3, c4, c5 = st.columns(5) + c1.metric("Total Games", f"{summary['total_games'][0]:,}") + c2.metric("Avg Points/Game", summary["avg_pts"][0], + delta=f"{summary['avg_pts'][0] - prior['avg_pts'][0]:+.1f}" if has_prior else None) + c3.metric("Avg Margin", summary["avg_margin"][0], + delta=f"{summary['avg_margin'][0] - prior['avg_margin'][0]:+.1f}" if has_prior else None) + c4.metric("Home Win %", f"{summary['home_win_pct'][0]}%", + delta=f"{summary['home_win_pct'][0] - prior['home_win_pct'][0]:+.1f}%" if has_prior else None) + c5.metric("OT Games", f"{summary['ot_games'][0]:,}") + + st.caption(f"Season: {summary['first_game'][0]} → {summary['last_game'][0]} | Deltas vs prior season") + + # --- Row 2: Competitiveness donut + Top 5 standings --- + col1, col2 = st.columns(2) + + with col1: + comp = query(""" + SELECT game_competitiveness_tier AS tier, count(*) AS games + FROM main_marts.fct_game_results + WHERE season_key IN (SELECT season_key FROM main_marts.dim_seasons WHERE season_start_year = ?) + GROUP BY tier ORDER BY games DESC + """, [SEASON]) + fig = px.pie(comp, names="tier", values="games", title="Game Competitiveness", + hole=0.4, color_discrete_sequence=px.colors.qualitative.Set2) + fig.update_traces(textinfo="percent+label") + st.plotly_chart(fig, width="stretch") + + with col2: + standings = query(""" + SELECT team, count(*) AS games, + sum(CASE WHEN game_result = 'W' THEN 1 ELSE 0 END) AS wins, + sum(CASE WHEN game_result = 'L' THEN 1 ELSE 0 END) AS losses, + round(100.0 * sum(CASE WHEN game_result = 'W' THEN 1 ELSE 0 END) / count(*), 1) AS win_pct, + round(avg(net_rating), 1) AS net_rtg + FROM main_intermediate.int_team_performance + WHERE season_start_year = ? + GROUP BY team ORDER BY win_pct DESC LIMIT 8 + """, [SEASON]) + st.subheader("🏆 Top Teams") + st.dataframe( + standings, + column_config={ + "win_pct": st.column_config.ProgressColumn("Win%", min_value=0, max_value=100, format="%.1f%%"), + }, + hide_index=True, width="stretch", + ) + + # --- Row 3: Weekly scoring trend --- + weekly = query(""" + SELECT date_trunc('week', game_date)::date::varchar AS week, + count(*) AS games, + round(avg(total_points), 1) AS avg_pts, + round(avg(point_differential), 1) AS avg_margin + FROM main_marts.fct_game_results + WHERE season_key IN (SELECT season_key FROM main_marts.dim_seasons WHERE season_start_year = ?) + GROUP BY week ORDER BY week + """, [SEASON]) + fig_weekly = px.area(weekly, x="week", y="avg_pts", + title="Weekly Scoring Trend", color_discrete_sequence=["#3b82f6"]) + fig_weekly.update_layout(xaxis_title="Week", yaxis_title="Avg Total Points", height=350) + st.plotly_chart(fig_weekly, width="stretch") + + # --- Row 4: Calendar heatmap + Season highlights --- + col1, col2 = st.columns([2, 1]) + + with col1: + cal_data = query(""" + SELECT game_date::varchar AS day, count(*) AS games + FROM main_marts.fct_game_results + WHERE season_key IN (SELECT season_key FROM main_marts.dim_seasons WHERE season_start_year = ?) + GROUP BY game_date ORDER BY game_date + """, [SEASON]) + if not cal_data.empty: + cal_list = cal_data.values.tolist() + cal_option = { + "tooltip": {"position": "top", "formatter": "{c0} games on {b0}"}, + "visualMap": { + "min": 0, "max": int(cal_data["games"].max()), + "calculable": True, "orient": "horizontal", "left": "center", "top": "top", + "inRange": {"color": ["#ebedf0", "#9be9a8", "#40c463", "#30a14e", "#216e39"]}, + }, + "calendar": {"range": [cal_list[0][0][:7], cal_list[-1][0][:7]], + "cellSize": ["auto", 15]}, + "series": [{"type": "heatmap", "coordinateSystem": "calendar", "data": cal_list}], + } + st_echarts(options=cal_option, height="200px") + + with col2: + st.subheader("🔥 Top Performances") + highlights = query(""" + SELECT player_name AS player, team, + game_date::date::varchar AS date, points AS pts, + assists AS ast, total_rebounds AS reb + FROM main_intermediate.int_player_performance + WHERE season_start_year = ? + ORDER BY points DESC LIMIT 5 + """, [SEASON]) + st.dataframe(highlights, hide_index=True, width="stretch") + + # --- Row 5: Recent games --- + st.subheader("Recent Games") + games = query(""" + SELECT g.game_date, ht.team_abbr AS home, vt.team_abbr AS away, + g.home_points, g.visitor_points, wt.team_abbr AS winner, + g.point_differential AS margin, g.game_competitiveness_tier AS type + FROM main_marts.fct_game_results g + LEFT JOIN main_marts.dim_teams ht ON g.home_team_key = ht.team_key + LEFT JOIN main_marts.dim_teams vt ON g.visitor_team_key = vt.team_key + LEFT JOIN main_marts.dim_teams wt ON g.winning_team_key = wt.team_key + WHERE g.season_key IN (SELECT season_key FROM main_marts.dim_seasons WHERE season_start_year = ?) + ORDER BY g.game_date DESC LIMIT 25 + """, [SEASON]) + st.dataframe(games, hide_index=True, width="stretch") + + + +# ============================================================ +# PAGE: Teams +# ============================================================ +# PAGE: Teams +# ============================================================ +elif page == "Teams": + st.title(f"🏆 Team Intelligence — {SEASON_LABEL}") + + # --- Core ratings data (parameterized, all four factors) --- + ratings = query(""" + SELECT team, count(*) AS games, + sum(CASE WHEN game_result = 'W' THEN 1 ELSE 0 END) AS wins, + sum(CASE WHEN game_result = 'L' THEN 1 ELSE 0 END) AS losses, + round(100.0 * sum(CASE WHEN game_result = 'W' THEN 1 ELSE 0 END) / count(*), 1) AS win_pct, + round(avg(offensive_rating), 1) AS off_rtg, + round(avg(defensive_rating), 1) AS def_rtg, + round(avg(net_rating), 1) AS net_rtg, + round(avg(pace), 1) AS pace, + round(avg(effective_fg_pct) * 100, 1) AS efg_pct, + round(avg(turnover_rate) * 100, 1) AS tov_rate, + round(avg(offensive_rebound_rate) * 100, 1) AS orb_rate, + round(avg(free_throw_rate) * 100, 1) AS ft_rate + FROM main_intermediate.int_team_performance + WHERE season_start_year = ? + GROUP BY team ORDER BY net_rtg DESC + """, [SEASON]) + + tab_ratings, tab_factors, tab_drilldown = st.tabs( + ["📊 Ratings & Standings", "📐 Four Factors & Styles", "🔍 Team Drill-Down"] + ) + + # --- TAB: Ratings & Standings --- + with tab_ratings: + # Offense vs Defense quadrant scatter — the key chart + fig = px.scatter( + ratings, x="off_rtg", y="def_rtg", text="team", + size="win_pct", color="net_rtg", color_continuous_scale="RdYlGn", + title="Offensive vs Defensive Rating (size = Win%)", + ) + fig.update_traces(textposition="top center", marker=dict(sizemin=8)) + avg_off = ratings["off_rtg"].mean() + avg_def = ratings["def_rtg"].mean() + fig.add_hline(y=avg_def, line_dash="dash", line_color="gray", opacity=0.4) + fig.add_vline(x=avg_off, line_dash="dash", line_color="gray", opacity=0.4) + fig.update_layout( + xaxis_title="Offensive Rating → (higher = better)", + yaxis_title="← Defensive Rating (lower = better)", + yaxis=dict(autorange="reversed"), height=550, + ) + st.plotly_chart(fig, width="stretch") + + # Win% bar for all teams + win_sorted = ratings.sort_values("win_pct", ascending=True) + fig_win = px.bar(win_sorted, x="win_pct", y="team", orientation="h", + color="net_rtg", color_continuous_scale="RdYlGn", + title="Win % (color = Net Rating)") + fig_win.update_layout(height=600, yaxis_title="", xaxis_title="Win %") + fig_win.add_vline(x=50, line_dash="dash", line_color="gray", opacity=0.5) + st.plotly_chart(fig_win, width="stretch") + + # Standings table with progress columns + st.dataframe( + ratings, + column_config={ + "win_pct": st.column_config.ProgressColumn("Win%", min_value=0, max_value=100, format="%.1f%%"), + "efg_pct": st.column_config.ProgressColumn("eFG%", min_value=45, max_value=60, format="%.1f%%"), + }, + hide_index=True, width="stretch", + ) + + # --- TAB: Four Factors & Styles --- + with tab_factors: + st.subheader("Four Factors") + st.caption("The four key drivers of team success: eFG%, Turnover Rate, Offensive Rebound Rate, Free Throw Rate") + + # 2x2 grid for all four factors + col1, col2 = st.columns(2) + with col1: + top10 = ratings.nlargest(10, "efg_pct") + fig = px.bar(top10, x="efg_pct", y="team", orientation="h", + color="efg_pct", color_continuous_scale="Greens", + title="eFG% (Top 10)") + fig.update_layout(yaxis=dict(autorange="reversed"), height=350, showlegend=False) + st.plotly_chart(fig, width="stretch") + with col2: + top10 = ratings.nsmallest(10, "tov_rate") + fig = px.bar(top10, x="tov_rate", y="team", orientation="h", + color="tov_rate", color_continuous_scale="Reds_r", + title="Turnover Rate (Top 10 — lowest)") + fig.update_layout(yaxis=dict(autorange="reversed"), height=350, showlegend=False) + st.plotly_chart(fig, width="stretch") + + col3, col4 = st.columns(2) + with col3: + top10 = ratings.nlargest(10, "orb_rate") + fig = px.bar(top10, x="orb_rate", y="team", orientation="h", + color="orb_rate", color_continuous_scale="Purples", + title="Offensive Rebound Rate (Top 10)") + fig.update_layout(yaxis=dict(autorange="reversed"), height=350, showlegend=False) + st.plotly_chart(fig, width="stretch") + with col4: + top10 = ratings.nlargest(10, "ft_rate") + fig = px.bar(top10, x="ft_rate", y="team", orientation="h", + color="ft_rate", color_continuous_scale="Oranges", + title="Free Throw Rate (Top 10)") + fig.update_layout(yaxis=dict(autorange="reversed"), height=350, showlegend=False) + st.plotly_chart(fig, width="stretch") + + # Play style sunbursts + st.subheader("Team Styles") + col1, col2 = st.columns(2) + with col1: + styles = query(""" + SELECT shot_selection_style, ball_movement_style, count(*) AS games, + round(avg(CASE WHEN game_result = 'W' THEN 1.0 ELSE 0.0 END) * 100, 1) AS win_pct + FROM main_intermediate.int_team_performance + WHERE season_start_year = ? + GROUP BY shot_selection_style, ball_movement_style + ORDER BY win_pct DESC + """, [SEASON]) + fig = px.sunburst(styles, path=["shot_selection_style", "ball_movement_style"], + values="games", color="win_pct", color_continuous_scale="RdYlGn", + title="Offense: Shot Selection × Ball Movement") + st.plotly_chart(fig, width="stretch") + + with col2: + defense = query(""" + SELECT defensive_activity, ball_movement_style, count(*) AS games, + round(avg(CASE WHEN game_result = 'W' THEN 1.0 ELSE 0.0 END) * 100, 1) AS win_pct + FROM main_intermediate.int_team_performance + WHERE season_start_year = ? + GROUP BY defensive_activity, ball_movement_style + ORDER BY win_pct DESC + """, [SEASON]) + fig = px.sunburst(defense, path=["defensive_activity", "ball_movement_style"], + values="games", color="win_pct", color_continuous_scale="RdYlGn", + title="Defense: Activity × Ball Movement") + st.plotly_chart(fig, width="stretch") + + # --- TAB: Team Drill-Down --- + with tab_drilldown: + team_list = ratings["team"].tolist() + selected_team = st.selectbox("Select Team", team_list) + + # Rolling 10-game trajectory + trajectory = query(""" + SELECT game_date::date::varchar AS game_date, + round(net_rating, 1) AS net_rtg, + round(avg(net_rating) OVER ( + ORDER BY game_date ROWS BETWEEN 9 PRECEDING AND CURRENT ROW + ), 1) AS rolling_net_rtg, + round(avg(offensive_rating) OVER ( + ORDER BY game_date ROWS BETWEEN 9 PRECEDING AND CURRENT ROW + ), 1) AS rolling_off, + round(avg(defensive_rating) OVER ( + ORDER BY game_date ROWS BETWEEN 9 PRECEDING AND CURRENT ROW + ), 1) AS rolling_def + FROM main_intermediate.int_team_performance + WHERE season_start_year = ? AND team = ? + ORDER BY game_date + """, [SEASON, selected_team]) + + if not trajectory.empty: + fig = go.Figure() + fig.add_trace(go.Scatter( + x=trajectory["game_date"], y=trajectory["rolling_net_rtg"], + name="Net Rating", mode="lines", line=dict(color="#3b82f6", width=3), + )) + fig.add_trace(go.Scatter( + x=trajectory["game_date"], y=trajectory["rolling_off"], + name="Offense", mode="lines", line=dict(color="#22c55e", width=1.5, dash="dot"), + )) + fig.add_trace(go.Scatter( + x=trajectory["game_date"], y=trajectory["rolling_def"], + name="Defense", mode="lines", line=dict(color="#ef4444", width=1.5, dash="dot"), + )) + fig.add_hline(y=0, line_dash="dash", line_color="gray", opacity=0.4) + fig.update_layout( + title=f"{selected_team} — 10-Game Rolling Ratings", + xaxis_title="Date", yaxis_title="Rating", height=400, + ) + st.plotly_chart(fig, width="stretch") + + # Game log + st.subheader(f"{selected_team} Game Log") + game_log = query(""" + SELECT game_date::date::varchar AS date, opponent_team AS opp, game_result AS result, + points AS pts, round(offensive_rating, 1) AS off_rtg, + round(defensive_rating, 1) AS def_rtg, round(net_rating, 1) AS net_rtg, + round(pace, 1) AS pace + FROM main_intermediate.int_team_performance + WHERE season_start_year = ? AND team = ? + ORDER BY game_date DESC + """, [SEASON, selected_team]) + st.dataframe(game_log, hide_index=True, width="stretch") + + + +# ============================================================ +# PAGE: Player Lab +# ============================================================ +elif page == "Player Lab": + st.title(f"👤 Player Lab — {SEASON_LABEL}") + + min_games = st.sidebar.slider("Min Games", 5, 60, 20, key="player_min_games") + + # --- Core player data --- + players = query(""" + SELECT player_name, team, count(*) AS games, + round(avg(points), 1) AS ppg, + round(avg(assists), 1) AS apg, + round(avg(total_rebounds), 1) AS rpg, + round(avg(steals), 1) AS spg, + round(avg(blocks), 1) AS bpg, + round(avg(box_plus_minus), 2) AS bpm, + round(avg(usage_pct), 1) AS usage, + round(avg(true_shooting_pct) * 100, 1) AS ts_pct, + round(avg(net_rating), 1) AS net_rtg, + sum(CASE WHEN is_double_double THEN 1 ELSE 0 END) AS double_doubles, + sum(CASE WHEN is_triple_double THEN 1 ELSE 0 END) AS triple_doubles + FROM main_intermediate.int_player_performance + WHERE season_start_year = ? AND minutes_played >= 20 + GROUP BY player_name, team + HAVING count(*) >= ? + ORDER BY bpm DESC + """, [SEASON, min_games]) + + tab_board, tab_compare, tab_archetypes = st.tabs( + ["📊 Leaderboard", "🔀 Comparison", "🧬 Archetypes"] + ) + + # --- TAB: Leaderboard --- + with tab_board: + # Usage vs Efficiency quadrant scatter + fig = px.scatter( + players, x="usage", y="ts_pct", size="ppg", + hover_name="player_name", color="bpm", + color_continuous_scale="RdYlGn", + title="Usage vs True Shooting % (size = PPG, color = BPM)", + ) + fig.update_layout(height=500, xaxis_title="Usage %", yaxis_title="True Shooting %") + # Quadrant lines at median + fig.add_hline(y=players["ts_pct"].median(), line_dash="dot", line_color="gray", opacity=0.4) + fig.add_vline(x=players["usage"].median(), line_dash="dot", line_color="gray", opacity=0.4) + st.plotly_chart(fig, width="stretch") + + # Scoring vs Playmaking + Top Scorers side by side + col1, col2 = st.columns(2) + with col1: + fig2 = px.scatter(players, x="ppg", y="apg", hover_name="player_name", + color="bpm", color_continuous_scale="RdYlGn", + size="usage", title="Scoring vs Playmaking") + st.plotly_chart(fig2, width="stretch") + with col2: + top_scorers = players.nlargest(15, "ppg") + fig3 = px.bar(top_scorers, x="ppg", y="player_name", orientation="h", + color="ts_pct", color_continuous_scale="RdYlGn", + title="Top 15 Scorers (color = TS%)") + fig3.update_layout(yaxis=dict(autorange="reversed"), height=450) + st.plotly_chart(fig3, width="stretch") + + # Enhanced leaderboard table + st.dataframe( + players, + column_config={ + "ts_pct": st.column_config.ProgressColumn( + "TS%", min_value=40, max_value=75, format="%.1f%%" + ), + "usage": st.column_config.ProgressColumn( + "USG%", min_value=5, max_value=40, format="%.1f%%" + ), + "bpm": st.column_config.NumberColumn("BPM", format="%.2f"), + }, + width="stretch", + hide_index=True, + ) + + # --- TAB: Comparison --- + with tab_compare: + player_list = players["player_name"].tolist() + selected_players = st.multiselect( + "Select players to compare", player_list, default=player_list[:3] + ) + if selected_players: + comp = players[players["player_name"].isin(selected_players)] + categories = ["ppg", "apg", "rpg", "spg", "bpg", "usage", "ts_pct"] + labels = ["PPG", "APG", "RPG", "SPG", "BPG", "USG%", "TS%"] + + # ECharts radar — actual values with per-stat max + # Distinct color palette to avoid similar blues + RADAR_COLORS = [ + "#e6194b", "#3cb44b", "#4363d8", "#f58231", "#911eb4", + "#42d4f4", "#f032e6", "#bfef45", "#fabed4", "#469990", + ] + radar_indicators = [ + {"name": labels[i], "max": float(players[c].max() * 1.15)} + for i, c in enumerate(categories) + ] + radar_data = [ + { + "value": [float(row[c]) for c in categories], + "name": row["player_name"], + "lineStyle": {"color": RADAR_COLORS[i % len(RADAR_COLORS)], "width": 2.5}, + "areaStyle": {"color": RADAR_COLORS[i % len(RADAR_COLORS)], "opacity": 0.3}, + "itemStyle": {"color": RADAR_COLORS[i % len(RADAR_COLORS)]}, + } + for i, (_, row) in enumerate(comp.iterrows()) + ] + echarts_option = { + "legend": {"data": [d["name"] for d in radar_data], "top": "bottom"}, + "color": RADAR_COLORS[:len(radar_data)], + "radar": { + "indicator": radar_indicators, + "shape": "polygon", + "splitArea": {"areaStyle": {"color": ["#1a1a2e", "#16213e", "#0f3460", "#1a1a2e", "#16213e"]}}, + "axisLine": {"lineStyle": {"color": "rgba(200, 200, 200, 0.3)"}}, + "splitLine": {"lineStyle": {"color": "rgba(200, 200, 200, 0.2)"}}, + }, + "series": [{"type": "radar", "data": radar_data}], + } + st_echarts(options=echarts_option, height="500px") + + # Side-by-side stat table + st.dataframe( + comp.set_index("player_name")[categories + ["bpm", "net_rtg", "games"]].rename( + columns=dict(zip(categories, labels)) + ), + width="stretch", + ) + + # Parallel coordinates for top 15 by BPM + st.subheader("Parallel Coordinates (Top 15 BPM)") + top_parallel = players.nlargest(15, "bpm") + parallel_dims = ["ppg", "apg", "rpg", "usage", "ts_pct", "bpm", "net_rtg"] + parallel_labels = ["PPG", "APG", "RPG", "USG%", "TS%", "BPM", "Net Rtg"] + parallel_option = { + "parallelAxis": [ + {"dim": i, "name": parallel_labels[i], + "min": float(top_parallel[parallel_dims[i]].min()), + "max": float(top_parallel[parallel_dims[i]].max())} + for i in range(len(parallel_dims)) + ], + "tooltip": {"trigger": "item"}, + "series": [{ + "type": "parallel", + "lineStyle": {"width": 2, "opacity": 0.6}, + "data": [ + [float(row[c]) for c in parallel_dims] + for _, row in top_parallel.iterrows() + ], + }], + } + st_echarts(options=parallel_option, height="400px") + + # --- TAB: Archetypes --- + with tab_archetypes: + # Modal archetype per player (most frequent tier combo) — with player detail + archetype_players = query(""" + WITH player_archetype_counts AS ( + SELECT player_name, team, usage_tier, impact_tier, shooting_efficiency_tier, + count(*) AS games_in_archetype + FROM main_intermediate.int_player_performance + WHERE season_start_year = ? AND minutes_played >= 20 + AND usage_tier != 'Insufficient Minutes' + GROUP BY player_name, team, usage_tier, impact_tier, shooting_efficiency_tier + ), + player_modal AS ( + SELECT *, ROW_NUMBER() OVER ( + PARTITION BY player_name, team ORDER BY games_in_archetype DESC + ) AS rn + FROM player_archetype_counts + ) + SELECT player_name, team, usage_tier, impact_tier, shooting_efficiency_tier, + games_in_archetype + FROM player_modal + WHERE rn = 1 + """, [SEASON]) + + if not archetype_players.empty: + # Aggregate for sunburst + archetypes = ( + archetype_players.groupby(["usage_tier", "impact_tier", "shooting_efficiency_tier"]) + .agg(players=("player_name", "count")) + .reset_index() + .query("players >= 2") + .sort_values("players", ascending=False) + ) + + fig_arch = px.sunburst( + archetypes, + path=["usage_tier", "impact_tier", "shooting_efficiency_tier"], + values="players", + title="Player Archetype Hierarchy (click segments, then filter below)", + color="players", color_continuous_scale="Blues", + ) + fig_arch.update_layout(height=600) + st.plotly_chart(fig_arch, width="stretch") + + # Drill-down selectors + st.subheader("🔍 Drill Into Archetype") + usage_tiers = ["All"] + sorted(archetype_players["usage_tier"].unique().tolist()) + sel_usage = st.selectbox("Usage Tier", usage_tiers, key="arch_usage") + + filtered = archetype_players + if sel_usage != "All": + filtered = filtered[filtered["usage_tier"] == sel_usage] + + impact_tiers = ["All"] + sorted(filtered["impact_tier"].unique().tolist()) + sel_impact = st.selectbox("Impact Tier", impact_tiers, key="arch_impact") + + if sel_impact != "All": + filtered = filtered[filtered["impact_tier"] == sel_impact] + + eff_tiers = ["All"] + sorted(filtered["shooting_efficiency_tier"].unique().tolist()) + sel_eff = st.selectbox("Shooting Efficiency Tier", eff_tiers, key="arch_eff") + + if sel_eff != "All": + filtered = filtered[filtered["shooting_efficiency_tier"] == sel_eff] + + # Show filtered player table with stats + st.caption(f"Showing {len(filtered)} players matching selection") + st.dataframe( + filtered[["player_name", "team", "usage_tier", "impact_tier", + "shooting_efficiency_tier", "games_in_archetype"]] + .sort_values("games_in_archetype", ascending=False) + .reset_index(drop=True), + width="stretch", hide_index=True, + ) + + # Specialist flags breakdown — percentage-based threshold (25%) + st.subheader("Player Specialists") + specialists = query(""" + SELECT player_name, team, count(*) AS games, + round(avg(points), 1) AS ppg, + round(avg(box_plus_minus), 2) AS bpm, + round(100.0 * sum(CASE WHEN is_versatile THEN 1 ELSE 0 END) / count(*), 1) AS pct_versatile, + round(100.0 * sum(CASE WHEN is_defensive_specialist THEN 1 ELSE 0 END) / count(*), 1) AS pct_def_spec, + round(100.0 * sum(CASE WHEN is_three_and_d THEN 1 ELSE 0 END) / count(*), 1) AS pct_three_and_d + FROM main_intermediate.int_player_performance + WHERE season_start_year = ? AND minutes_played >= 20 + GROUP BY player_name, team + HAVING count(*) >= ? + """, [SEASON, min_games]) + + col1, col2, col3 = st.columns(3) + with col1: + versatile = specialists[specialists["pct_versatile"] >= 25].sort_values("pct_versatile", ascending=False) + st.metric("🎯 Versatile", len(versatile)) + st.caption("≥25% of games with double-double stats") + st.dataframe( + versatile[["player_name", "team", "ppg", "pct_versatile"]], + hide_index=True, width="stretch", + ) + with col2: + def_spec = specialists[specialists["pct_def_spec"] >= 25].sort_values("pct_def_spec", ascending=False) + st.metric("🛡️ Defensive Specialists", len(def_spec)) + st.caption("≥25% of games as defensive specialist") + st.dataframe( + def_spec[["player_name", "team", "ppg", "pct_def_spec"]], + hide_index=True, width="stretch", + ) + with col3: + three_d = specialists[specialists["pct_three_and_d"] >= 25].sort_values("pct_three_and_d", ascending=False) + st.metric("🏹 Three-and-D", len(three_d)) + st.caption("≥25% of games as 3-and-D player") + st.dataframe( + three_d[["player_name", "team", "ppg", "pct_three_and_d"]], + hide_index=True, width="stretch", + ) + + + +# ============================================================ +# PAGE: Shot Charts +# ============================================================ +# PAGE: Shot Studio +# ============================================================ +elif page == "Shot Charts": + st.title(f"🎯 Shot Studio — {SEASON_LABEL}") + + tab_chart, tab_zones, tab_clutch = st.tabs( + ["🏀 Shot Chart", "📊 Zone & Profile Analysis", "⏱️ Clutch Shooting"] + ) + + # --- TAB: Shot Chart --- + with tab_chart: + top_shooters = query(""" + SELECT p.player_name, count(*) AS shots + FROM main_marts.fct_player_shots s + JOIN main_marts.dim_players p ON s.player_key = p.player_key + JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key + WHERE sea.season_start_year = ? AND s.shot_source = 'shot_chart' + GROUP BY p.player_name HAVING count(*) >= 50 + ORDER BY shots DESC + """, [SEASON]) + + if not top_shooters.empty: + selected_player = st.selectbox("Player", top_shooters["player_name"].tolist()) + + shot_data = query(""" + SELECT s.shot_x_coordinate AS x, s.shot_y_coordinate AS y, + s.is_made, s.shot_distance_zone, s.distance_ft, s.quarter_number + FROM main_marts.fct_player_shots s + JOIN main_marts.dim_players p ON s.player_key = p.player_key + JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key + WHERE sea.season_start_year = ? AND s.shot_source = 'shot_chart' + AND p.player_name = ? + """, [SEASON, selected_player]) + + if not shot_data.empty: + shot_data["result"] = shot_data["is_made"].map({True: "Made", False: "Missed"}) + + col1, col2 = st.columns([2, 1]) + with col1: + fig = px.scatter( + shot_data, x="x", y="y", color="result", + color_discrete_map={"Made": "#22c55e", "Missed": "#ef4444"}, + opacity=0.7, title=f"{selected_player} — Shot Chart ({len(shot_data)} shots)", + hover_data=["shot_distance_zone", "distance_ft", "quarter_number"], + ) + fig.update_traces(marker=dict(size=6, line=dict(width=0.5, color="rgba(0,0,0,0.3)"))) + + # --- Draw NBA half-court lines --- + # Coordinate system: basket at (240, 55), court width ~480, baseline at y~0 + import numpy as np + court_color = "rgba(255,255,255,0.45)" + paint_color = "rgba(255,200,100,0.12)" + + # Paint / key (16ft wide = ~160 data units, 19ft deep = ~190 units) + fig.add_shape(type="rect", x0=160, x1=320, y0=0, y1=190, + line=dict(color=court_color, width=1.5), + fillcolor=paint_color) + + # Backboard + fig.add_shape(type="line", x0=210, x1=270, y0=40, y1=40, + line=dict(color=court_color, width=2)) + + # Basket (rim) + fig.add_shape(type="circle", x0=232, x1=248, y0=47, y1=63, + line=dict(color="rgba(255,120,0,0.7)", width=2)) + + # Free throw circle (radius ~60 data units at y=190) + theta_ft = np.linspace(0, np.pi, 50) + ft_x = 240 + 60 * np.cos(theta_ft) + ft_y = 190 + 60 * np.sin(theta_ft) + fig.add_trace(go.Scatter(x=ft_x, y=ft_y, mode="lines", + line=dict(color=court_color, width=1.5), showlegend=False, hoverinfo="skip")) + # Dashed bottom half of FT circle + ft_x_b = 240 + 60 * np.cos(-theta_ft) + ft_y_b = 190 + 60 * np.sin(-theta_ft) + fig.add_trace(go.Scatter(x=ft_x_b, y=ft_y_b, mode="lines", + line=dict(color=court_color, width=1, dash="dash"), showlegend=False, hoverinfo="skip")) + + # Restricted area arc (4ft radius = ~40 data units) + theta_ra = np.linspace(0, np.pi, 30) + ra_x = 240 + 40 * np.cos(theta_ra) + ra_y = 55 + 40 * np.sin(theta_ra) + fig.add_trace(go.Scatter(x=ra_x, y=ra_y, mode="lines", + line=dict(color=court_color, width=1.5), showlegend=False, hoverinfo="skip")) + + # Three-point arc (23.75ft = ~238 data units radius from basket) + # Corner threes are straight lines from baseline to where the arc begins + theta_3_start = np.arcsin(35/238) + corner_x_left = 240 - 238 * np.cos(theta_3_start) + corner_x_right = 240 + 238 * np.cos(theta_3_start) + corner_y = 55 + 35 # y where arc begins + fig.add_shape(type="line", x0=corner_x_left, x1=corner_x_left, y0=0, y1=corner_y, + line=dict(color=court_color, width=1.5)) + fig.add_shape(type="line", x0=corner_x_right, x1=corner_x_right, y0=0, y1=corner_y, + line=dict(color=court_color, width=1.5)) + # Arc portion + theta_3 = np.linspace(theta_3_start, np.pi - theta_3_start, 80) + arc_x = 240 + 238 * np.cos(theta_3) + arc_y = 55 + 238 * np.sin(theta_3) + fig.add_trace(go.Scatter(x=arc_x, y=arc_y, mode="lines", + line=dict(color=court_color, width=2), showlegend=False, hoverinfo="skip")) + + # Half-court line + fig.add_shape(type="line", x0=-10, x1=490, y0=470, y1=470, + line=dict(color=court_color, width=1.5)) + # Center circle (top arc only) + theta_cc = np.linspace(np.pi, 2*np.pi, 40) + cc_x = 240 + 60 * np.cos(theta_cc) + cc_y = 470 + 60 * np.sin(theta_cc) + fig.add_trace(go.Scatter(x=cc_x, y=cc_y, mode="lines", + line=dict(color=court_color, width=1.5), showlegend=False, hoverinfo="skip")) + + fig.update_layout( + height=600, + xaxis=dict(showgrid=False, zeroline=False, showticklabels=False, range=[-20, 500]), + yaxis=dict(showgrid=False, zeroline=False, showticklabels=False, + range=[-20, 480], scaleanchor="x"), + plot_bgcolor="#1a1a2e", + ) + st.plotly_chart(fig, width="stretch") + + with col2: + # Per-player zone efficiency vs league + st.subheader("Zone Efficiency vs League") + zone_comparison = query(""" + WITH league AS ( + SELECT s.shot_distance_zone, + round(100.0 * sum(s.is_made::int) / count(*), 1) AS league_fg_pct + FROM main_marts.fct_player_shots s + JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key + WHERE sea.season_start_year = ? AND s.shot_source = 'shot_chart' + GROUP BY s.shot_distance_zone + ), + player AS ( + SELECT s.shot_distance_zone, count(*) AS attempts, + round(100.0 * sum(s.is_made::int) / count(*), 1) AS player_fg_pct + FROM main_marts.fct_player_shots s + JOIN main_marts.dim_players p ON s.player_key = p.player_key + JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key + WHERE sea.season_start_year = ? AND s.shot_source = 'shot_chart' + AND p.player_name = ? + GROUP BY s.shot_distance_zone + ) + SELECT p.shot_distance_zone AS zone, p.attempts, p.player_fg_pct, + l.league_fg_pct, round(p.player_fg_pct - l.league_fg_pct, 1) AS vs_league + FROM player p JOIN league l ON p.shot_distance_zone = l.shot_distance_zone + ORDER BY p.attempts DESC + """, [SEASON, SEASON, selected_player]) + + if not zone_comparison.empty: + for _, z in zone_comparison.iterrows(): + delta_color = "normal" if z["vs_league"] >= 0 else "inverse" + st.metric( + z["zone"], + f"{z['player_fg_pct']}%", + delta=f"{z['vs_league']:+.1f}% vs league", + delta_color=delta_color, + ) + else: + st.info("No spatial shot chart data available for this season.") + + # --- TAB: Zone & Profile Analysis --- + with tab_zones: + # League zone breakdown + st.subheader("League Zone Efficiency") + zone_data = query(""" + SELECT sz.shot_distance_zone, sz.zone_group, sz.zone_order, + count(*) AS attempts, sum(s.is_made::int) AS makes, + round(100.0 * sum(s.is_made::int) / count(*), 1) AS fg_pct, + round(avg(s.shot_point_value * s.is_made::int), 3) AS pts_per_shot + FROM main_marts.fct_player_shots s + JOIN main_marts.dim_shot_zones sz ON s.shot_distance_zone = sz.shot_distance_zone + JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key + WHERE sea.season_start_year = ? + GROUP BY sz.shot_distance_zone, sz.zone_group, sz.zone_order + ORDER BY sz.zone_order + """, [SEASON]) + + col1, col2 = st.columns(2) + with col1: + fig = px.bar(zone_data, x="shot_distance_zone", y="attempts", + color="fg_pct", color_continuous_scale="RdYlGn", + title="Volume by Zone (color = FG%)") + st.plotly_chart(fig, width="stretch") + with col2: + fig = px.bar(zone_data, x="shot_distance_zone", y="pts_per_shot", + color="zone_group", title="Points per Shot by Zone") + st.plotly_chart(fig, width="stretch") + + # Shot Profile Scatter — player archetypes by shooting style + st.subheader("Player Shot Profiles") + profiles = query(""" + SELECT p.player_name, + count(*) AS games, + round(avg(s.three_point_rate) * 100, 1) AS three_rate, + round(avg(s.at_rim_rate) * 100, 1) AS rim_rate, + round(avg(s.mid_range_rate) * 100, 1) AS mid_rate, + round(avg(s.avg_fg_distance_ft), 1) AS avg_dist, + round(avg(s.true_shooting_pct) * 100, 1) AS ts_pct + FROM main_marts.fct_player_game_shooting s + JOIN main_marts.dim_players p ON s.player_key = p.player_key + JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key + WHERE sea.season_start_year = ? AND s.fg_attempts >= 10 + GROUP BY p.player_name HAVING count(*) >= 20 + ORDER BY ts_pct DESC + """, [SEASON]) + + if not profiles.empty: + fig = px.scatter( + profiles, x="three_rate", y="rim_rate", size="ts_pct", + hover_name="player_name", color="ts_pct", + color_continuous_scale="RdYlGn", + title="Shot Profile Clustering: Three-Point Rate vs Rim Rate (size/color = TS%)", + ) + fig.update_layout( + xaxis_title="Three-Point Rate %", yaxis_title="At-Rim Rate %", height=500, + ) + st.plotly_chart(fig, width="stretch") + + # Shot profile type breakdown (treemap) + profile_types = query(""" + SELECT shot_profile_type, count(*) AS player_games, + round(avg(true_shooting_pct) * 100, 1) AS avg_ts, + round(avg(total_points), 1) AS avg_pts + FROM main_marts.fct_player_game_shooting s + JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key + WHERE sea.season_start_year = ? + GROUP BY shot_profile_type ORDER BY player_games DESC + """, [SEASON]) + fig = px.treemap(profile_types, path=["shot_profile_type"], values="player_games", + color="avg_ts", color_continuous_scale="RdYlGn", + title="Shot Profile Types (size = frequency, color = TS%)") + st.plotly_chart(fig, width="stretch") + + # --- TAB: Clutch Shooting --- + with tab_clutch: + clutch = query(""" + SELECT p.player_name, + count(*) AS clutch_attempts, + sum(s.is_made::int) AS clutch_makes, + round(100.0 * sum(s.is_made::int) / count(*), 1) AS clutch_pct, + round(avg(s.score_margin_at_shot), 1) AS avg_margin + FROM main_marts.fct_player_shots s + JOIN main_marts.dim_players p ON s.player_key = p.player_key + JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key + WHERE s.is_clutch_shot AND sea.season_start_year = ? + GROUP BY p.player_name HAVING count(*) >= 15 + ORDER BY clutch_makes DESC LIMIT 30 + """, [SEASON]) + + if not clutch.empty: + fig = px.scatter( + clutch, x="clutch_attempts", y="clutch_pct", + size="clutch_makes", hover_name="player_name", + color="clutch_pct", color_continuous_scale="RdYlGn", + title="Clutch Shooting: Volume vs Accuracy", + ) + fig.add_hline(y=clutch["clutch_pct"].median(), line_dash="dash", + line_color="gray", annotation_text="Median") + fig.update_layout( + xaxis_title="Clutch Attempts", yaxis_title="Clutch FG%", height=500, + ) + st.plotly_chart(fig, width="stretch") + + # Clutch leaderboard table + st.subheader("Clutch Leaders") + st.dataframe( + clutch[["player_name", "clutch_attempts", "clutch_makes", "clutch_pct", "avg_margin"]], + hide_index=True, width="stretch", + column_config={ + "clutch_pct": st.column_config.ProgressColumn( + "FG%", min_value=25, max_value=65, format="%.1f%%" + ), + }, + ) + else: + st.info("No clutch shooting data available for this season.") + + + +# ============================================================ +# PAGE: Head to Head +# ============================================================ +# PAGE: Matchups +# ============================================================ +elif page == "Head to Head": + st.title(f"⚔️ Matchup Center — {SEASON_LABEL}") + + teams = query("SELECT DISTINCT team FROM main_intermediate.int_team_performance ORDER BY team") + team_list = teams["team"].tolist() + + col1, col2, col3 = st.columns([2, 2, 1]) + with col1: + team_a = st.selectbox("Team A", team_list, index=0) + with col2: + team_b = st.selectbox("Team B", team_list, index=min(1, len(team_list) - 1)) + with col3: + scope = st.radio("Scope", ["This Season", "All-Time"], horizontal=False) + + if team_a == team_b: + st.warning("Select two different teams.") + else: + season_filter = "AND season_start_year = ?" if scope == "This Season" else "" + season_filter_tp = "AND tp.season_start_year = ?" if scope == "This Season" else "" + season_params = [SEASON] if scope == "This Season" else [] + + # --- Aggregate stats for both teams --- + h2h_agg = query(f""" + SELECT team, count(*) AS games, + sum(CASE WHEN game_result = 'W' THEN 1 ELSE 0 END) AS wins, + round(avg(points), 1) AS avg_pts, + round(avg(offensive_rating), 1) AS off_rtg, + round(avg(defensive_rating), 1) AS def_rtg, + round(avg(net_rating), 1) AS net_rtg, + round(avg(pace), 1) AS pace, + round(avg(effective_fg_pct) * 100, 1) AS efg_pct, + round(avg(turnover_rate) * 100, 1) AS tov_rate + FROM main_intermediate.int_team_performance + WHERE ((team = ? AND opponent_team = ?) OR (team = ? AND opponent_team = ?)) + {season_filter} + GROUP BY team + """, [team_a, team_b, team_b, team_a] + season_params) + + if h2h_agg.empty or len(h2h_agg) < 2: + st.info("No matchup history found between these teams for this scope.") + else: + row_a = h2h_agg[h2h_agg["team"] == team_a].iloc[0] + row_b = h2h_agg[h2h_agg["team"] == team_b].iloc[0] + + # KPIs + c1, c2, c3, c4, c5 = st.columns(5) + c1.metric(f"{team_a} Wins", int(row_a["wins"])) + c2.metric(f"{team_a} PPG", row_a["avg_pts"]) + c3.metric("Games", int(row_a["games"])) + c4.metric(f"{team_b} PPG", row_b["avg_pts"]) + c5.metric(f"{team_b} Wins", int(row_b["wins"])) + + tab_compare, tab_games, tab_players = st.tabs( + ["📊 Comparison", "📋 Game Log", "👤 Key Players"] + ) + + # --- TAB: Comparison radar --- + with tab_compare: + categories = ["avg_pts", "off_rtg", "pace", "efg_pct"] + # For def_rtg and tov_rate, lower is better — invert for radar + cat_labels = ["Points", "Off Rating", "Pace", "eFG%", "Def Rating (inv)", "Low TOV (inv)"] + vals_a = [float(row_a["avg_pts"]), float(row_a["off_rtg"]), float(row_a["pace"]), + float(row_a["efg_pct"]), 130 - float(row_a["def_rtg"]), 20 - float(row_a["tov_rate"])] + vals_b = [float(row_b["avg_pts"]), float(row_b["off_rtg"]), float(row_b["pace"]), + float(row_b["efg_pct"]), 130 - float(row_b["def_rtg"]), 20 - float(row_b["tov_rate"])] + + radar_option = { + "legend": {"data": [team_a, team_b], "top": "bottom"}, + "color": ["#3b82f6", "#ef4444"], + "radar": { + "indicator": [ + {"name": "Points", "max": max(vals_a[0], vals_b[0]) * 1.15}, + {"name": "Off Rating", "max": max(vals_a[1], vals_b[1]) * 1.1}, + {"name": "Pace", "max": max(vals_a[2], vals_b[2]) * 1.1}, + {"name": "eFG%", "max": max(vals_a[3], vals_b[3]) * 1.15}, + {"name": "Defense (inv)", "max": max(vals_a[4], vals_b[4]) * 1.15}, + {"name": "Ball Security (inv)", "max": max(vals_a[5], vals_b[5]) * 1.15}, + ], + "shape": "polygon", + "splitArea": {"areaStyle": {"color": ["#1a1a2e", "#16213e", "#0f3460", "#1a1a2e"]}}, + "axisLine": {"lineStyle": {"color": "rgba(200,200,200,0.3)"}}, + "splitLine": {"lineStyle": {"color": "rgba(200,200,200,0.2)"}}, + }, + "series": [{"type": "radar", "data": [ + {"value": vals_a, "name": team_a, + "lineStyle": {"color": "#3b82f6", "width": 2.5}, + "areaStyle": {"color": "#3b82f6", "opacity": 0.25}, + "itemStyle": {"color": "#3b82f6"}}, + {"value": vals_b, "name": team_b, + "lineStyle": {"color": "#ef4444", "width": 2.5}, + "areaStyle": {"color": "#ef4444", "opacity": 0.25}, + "itemStyle": {"color": "#ef4444"}}, + ]}], + } + st_echarts(options=radar_option, height="450px") + + # Side-by-side stat comparison + st.dataframe( + h2h_agg.set_index("team")[["games", "wins", "avg_pts", "off_rtg", "def_rtg", "net_rtg", "pace", "efg_pct", "tov_rate"]], + width="stretch", + ) + + # --- TAB: Game Log --- + with tab_games: + game_log = query(f""" + SELECT g.game_date::date::varchar AS date, + ht.team_abbr AS home, vt.team_abbr AS away, + g.home_points, g.visitor_points, wt.team_abbr AS winner, + g.point_differential AS margin, + g.game_competitiveness_tier AS type + FROM main_marts.fct_game_results g + JOIN main_marts.dim_teams ht ON g.home_team_key = ht.team_key + JOIN main_marts.dim_teams vt ON g.visitor_team_key = vt.team_key + JOIN main_marts.dim_teams wt ON g.winning_team_key = wt.team_key + {"JOIN main_marts.dim_seasons s ON g.season_key = s.season_key" if scope == "This Season" else ""} + WHERE ((ht.team_abbr = ? AND vt.team_abbr = ?) OR (ht.team_abbr = ? AND vt.team_abbr = ?)) + {"AND s.season_start_year = ?" if scope == "This Season" else ""} + ORDER BY g.game_date DESC + """, [team_a, team_b, team_b, team_a] + season_params) + + if not game_log.empty: + # Margin chart + game_log["winner_color"] = game_log["winner"].apply( + lambda w: team_a if w == team_a else team_b + ) + fig = px.bar(game_log, x="date", y="margin", color="winner_color", + color_discrete_map={team_a: "#3b82f6", team_b: "#ef4444"}, + title="Game Margins (color = winner)") + fig.update_layout(xaxis_title="", yaxis_title="Point Margin", height=350) + st.plotly_chart(fig, width="stretch") + + st.dataframe(game_log.drop(columns=["winner_color"]), hide_index=True, width="stretch") + + # --- TAB: Key Players --- + with tab_players: + players_in_matchup = query(f""" + SELECT pp.player_name, pp.team, count(*) AS games, + round(avg(pp.points), 1) AS ppg, + round(avg(pp.assists), 1) AS apg, + round(avg(pp.total_rebounds), 1) AS rpg, + round(avg(pp.box_plus_minus), 2) AS bpm + FROM main_intermediate.int_player_performance pp + JOIN main_intermediate.int_team_performance tp + ON pp.game_id = tp.game_id AND pp.team = tp.team + WHERE ((tp.team = ? AND tp.opponent_team = ?) + OR (tp.team = ? AND tp.opponent_team = ?)) + {season_filter_tp} + AND pp.minutes_played >= 15 + GROUP BY pp.player_name, pp.team + HAVING count(*) >= 2 + ORDER BY ppg DESC LIMIT 15 + """, [team_a, team_b, team_b, team_a] + season_params) + + if not players_in_matchup.empty: + col1, col2 = st.columns(2) + with col1: + st.subheader(f"{team_a} Performers") + team_a_players = players_in_matchup[players_in_matchup["team"] == team_a] + st.dataframe( + team_a_players[["player_name", "games", "ppg", "apg", "rpg", "bpm"]], + hide_index=True, width="stretch", + ) + with col2: + st.subheader(f"{team_b} Performers") + team_b_players = players_in_matchup[players_in_matchup["team"] == team_b] + st.dataframe( + team_b_players[["player_name", "games", "ppg", "apg", "rpg", "bpm"]], + hide_index=True, width="stretch", + ) + else: + st.info("No player data available for this matchup scope.") + + + +# ============================================================ +# PAGE: Game Trends +# ============================================================ +# PAGE: Era Trends +# ============================================================ +elif page == "Game Trends": + st.title("📈 Era Trends") + + tab_scoring, tab_drama, tab_three = st.tabs( + ["🏀 Scoring & Pace", "🎭 Game Character", "🎯 Three-Point Era"] + ) + + # --- TAB: Scoring & Pace --- + with tab_scoring: + pace = query(""" + SELECT s.season_display AS season, s.season_start_year, + round(avg(g.matchup_pace), 1) AS avg_pace, + round(avg(g.total_points), 1) AS avg_points, + round(avg(g.home_effective_fg_pct + g.visitor_effective_fg_pct) / 2 * 100, 1) AS avg_efg_pct + FROM main_intermediate.int_games_enriched g + JOIN main_marts.dim_seasons s ON g.season_start_year = s.season_start_year + GROUP BY s.season_display, s.season_start_year + ORDER BY s.season_start_year + """) + + # Dual-axis: points + pace + fig = go.Figure() + fig.add_trace(go.Scatter( + x=pace["season"], y=pace["avg_points"], + name="Avg Points/Game", mode="lines+markers", + line=dict(color="#3b82f6", width=3), + )) + fig.add_trace(go.Scatter( + x=pace["season"], y=pace["avg_pace"], + name="Pace", mode="lines+markers", yaxis="y2", + line=dict(color="#f59e0b", width=2, dash="dot"), + )) + fig.update_layout( + title="Scoring & Pace Evolution", + yaxis=dict(title="Avg Points/Game"), + yaxis2=dict(title="Pace (possessions/48min)", overlaying="y", side="right"), + height=450, + ) + st.plotly_chart(fig, width="stretch") + + # eFG% trend + pace_clean = pace.dropna(subset=["avg_efg_pct"]) + fig_eff = px.area(pace_clean, x="season", y="avg_efg_pct", + title="League eFG% Trend", color_discrete_sequence=["#10b981"]) + fig_eff.update_layout(yaxis_title="eFG%", height=350) + st.plotly_chart(fig_eff, width="stretch") + + # --- TAB: Game Character --- + with tab_drama: + drama = query(""" + SELECT s.season_display AS season, + round(100.0 * sum(CASE WHEN g.is_overtime THEN 1 ELSE 0 END) / count(*), 1) AS ot_pct, + round(100.0 * sum(CASE WHEN g.point_differential <= 5 THEN 1 ELSE 0 END) / count(*), 1) AS clutch_pct, + round(100.0 * sum(CASE WHEN g.point_differential >= 20 THEN 1 ELSE 0 END) / count(*), 1) AS blowout_pct + FROM main_intermediate.int_games_enriched g + JOIN main_marts.dim_seasons s ON g.season_start_year = s.season_start_year + GROUP BY s.season_display, s.season_start_year + ORDER BY s.season_start_year + """) + fig = px.area( + drama, x="season", y=["clutch_pct", "blowout_pct", "ot_pct"], + title="Game Drama: Clutch (≤5pt margin) vs Blowouts (20+) vs Overtime", + color_discrete_sequence=["#22c55e", "#ef4444", "#a855f7"], + ) + fig.update_layout(yaxis_title="% of Games", legend_title="Category", height=450) + st.plotly_chart(fig, width="stretch") + + # Home court advantage + home = query(""" + SELECT s.season_display AS season, + round(100.0 * sum(CASE WHEN g.is_home_team_winner THEN 1 ELSE 0 END) / count(*), 1) AS home_win_pct + FROM main_marts.fct_game_results g + JOIN main_marts.dim_seasons s ON g.season_key = s.season_key + GROUP BY s.season_display, s.season_start_year + ORDER BY s.season_start_year + """) + fig2 = px.line(home, x="season", y="home_win_pct", + title="Home Court Advantage Over Time", markers=True) + fig2.update_layout(yaxis_range=[40, 70], height=400) + fig2.add_hline(y=50, line_dash="dash", line_color="gray", annotation_text="50%") + st.plotly_chart(fig2, width="stretch") + + # --- TAB: Three-Point Era --- + with tab_three: + three_pt = query(""" + SELECT s.season_display AS season, s.season_start_year, + round(avg(three_point_rate) * 100, 1) AS avg_three_rate, + round(avg(CASE WHEN three_point_fg_pct > 0 THEN three_point_fg_pct ELSE NULL END) * 100, 1) AS avg_three_pct, + round(avg(three_point_attempts), 1) AS avg_3pa + FROM main_marts.fct_player_game_shooting sh + JOIN main_marts.dim_seasons s ON sh.season_key = s.season_key + WHERE sh.fg_attempts >= 5 + GROUP BY s.season_display, s.season_start_year + ORDER BY s.season_start_year + """) + + if not three_pt.empty: + # Dual-axis: three-point rate + accuracy + fig = go.Figure() + fig.add_trace(go.Bar( + x=three_pt["season"], y=three_pt["avg_three_rate"], + name="3PA Rate (%)", marker_color="#3b82f6", opacity=0.7, + )) + fig.add_trace(go.Scatter( + x=three_pt["season"], y=three_pt["avg_three_pct"], + name="3P Accuracy (%)", mode="lines+markers", yaxis="y2", + line=dict(color="#22c55e", width=3), + )) + fig.update_layout( + title="The Three-Point Revolution: Volume vs Accuracy", + yaxis=dict(title="3PA Rate (% of all FGA)"), + yaxis2=dict(title="3P FG%", overlaying="y", side="right", + range=[30, 50]), + height=450, barmode="overlay", + ) + st.plotly_chart(fig, width="stretch") + + # Average 3PA per player-game trend + fig2 = px.area(three_pt, x="season", y="avg_3pa", + title="Avg Three-Point Attempts per Player-Game", + color_discrete_sequence=["#8b5cf6"]) + fig2.update_layout(yaxis_title="3PA per Game", height=350) + st.plotly_chart(fig2, width="stretch") + else: + st.info("Three-point shooting data not available.") + + + +# ============================================================ +# PAGE: Quarter Analysis +# ============================================================ +# PAGE: Momentum (Quarter Analysis) +# ============================================================ +elif page == "Quarter Analysis": + st.title(f"⏱️ Momentum — {SEASON_LABEL}") + + tab_overview, tab_teams, tab_comebacks = st.tabs( + ["📊 Quarter Scoring", "🏆 Team by Quarter", "🔄 Comebacks"] + ) + + # --- TAB: Quarter Scoring --- + with tab_overview: + quarter_avg = query(""" + SELECT q.period, + round(avg(q.points_scored), 1) AS avg_pts, + round(avg(abs(q.period_point_differential)), 1) AS avg_abs_diff, + round(stddev(q.points_scored), 1) AS scoring_volatility + FROM main_marts.fct_quarter_scoring q + JOIN main_marts.dim_seasons s ON q.season_key = s.season_key + WHERE s.season_start_year = ? AND q.period IN ('Q1','Q2','Q3','Q4') + GROUP BY q.period ORDER BY q.period + """, [SEASON]) + + col1, col2 = st.columns(2) + with col1: + fig = px.bar(quarter_avg, x="period", y="avg_pts", + title="Avg Points per Quarter", + color="period", + color_discrete_map={"Q1": "#3b82f6", "Q2": "#8b5cf6", "Q3": "#f59e0b", "Q4": "#ef4444"}, + labels={"period": "Quarter", "avg_pts": "Avg Points"}) + fig.update_layout(showlegend=False) + st.plotly_chart(fig, width="stretch") + with col2: + fig2 = px.bar(quarter_avg, x="period", y="avg_abs_diff", + title="Avg Quarter Lopsidedness (|differential|)", + color="period", + color_discrete_map={"Q1": "#3b82f6", "Q2": "#8b5cf6", "Q3": "#f59e0b", "Q4": "#ef4444"}, + labels={"period": "Quarter", "avg_abs_diff": "Avg |Differential|"}) + fig2.update_layout(showlegend=False) + st.plotly_chart(fig2, width="stretch") + + st.caption("Q4 has lower scoring and tighter margins — games tighten up in crunch time.") + + # --- TAB: Team by Quarter --- + with tab_teams: + quarter_sel = st.radio("Quarter", ["Q1", "Q2", "Q3", "Q4"], horizontal=True) + + team_quarter = query(""" + SELECT t.team_abbr AS team, + round(avg(q.points_scored), 1) AS avg_pts, + round(avg(q.period_point_differential), 2) AS avg_diff, + count(*) AS games + FROM main_marts.fct_quarter_scoring q + JOIN main_marts.dim_teams t ON q.team_key = t.team_key + JOIN main_marts.dim_seasons s ON q.season_key = s.season_key + WHERE s.season_start_year = ? AND q.period = ? + GROUP BY t.team_abbr HAVING count(*) >= 10 + ORDER BY avg_diff DESC + """, [SEASON, quarter_sel]) + + fig = px.bar(team_quarter, x="avg_pts", y="team", orientation="h", + color="avg_diff", color_continuous_scale="RdYlGn", + title=f"{quarter_sel} Leaders — sorted by differential (color = +/-)") + fig.update_layout(yaxis=dict(autorange="reversed"), height=600) + st.plotly_chart(fig, width="stretch") + + # --- TAB: Comebacks --- + with tab_comebacks: + comebacks = query(""" + WITH q3_trailing AS ( + SELECT q.game_id, q.team_key, + sum(q.period_point_differential) AS through_q3_diff + FROM main_marts.fct_quarter_scoring q + JOIN main_marts.dim_seasons s ON q.season_key = s.season_key + WHERE s.season_start_year = ? AND q.period IN ('Q1','Q2','Q3') + GROUP BY q.game_id, q.team_key + HAVING sum(q.period_point_differential) < -10 + ), + q4_results AS ( + SELECT q.game_id, q.team_key, q.period_point_differential AS q4_diff + FROM main_marts.fct_quarter_scoring q + JOIN main_marts.dim_seasons s ON q.season_key = s.season_key + WHERE s.season_start_year = ? AND q.period = 'Q4' + ) + SELECT t.team_abbr AS team, + count(*) AS trailing_10_after_q3, + sum(CASE WHEN c.through_q3_diff + r.q4_diff > 0 THEN 1 ELSE 0 END) AS comebacks, + round(100.0 * sum(CASE WHEN c.through_q3_diff + r.q4_diff > 0 THEN 1 ELSE 0 END) / count(*), 1) AS comeback_pct + FROM q3_trailing c + JOIN q4_results r ON c.game_id = r.game_id AND c.team_key = r.team_key + JOIN main_marts.dim_teams t ON c.team_key = t.team_key + GROUP BY t.team_abbr HAVING count(*) >= 3 + ORDER BY comeback_pct DESC + """, [SEASON, SEASON]) + + if not comebacks.empty: + fig = px.bar(comebacks, x="team", y="comeback_pct", + color="comebacks", color_continuous_scale="Greens", + title="Comeback Rate (Down 10+ After Q3)") + fig.update_layout(xaxis_title="", yaxis_title="Comeback %") + st.plotly_chart(fig, width="stretch") + st.dataframe(comebacks, hide_index=True, width="stretch") + else: + st.info("No comeback data available for this season.") diff --git a/streamlit_app/requirements.txt 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