nprasad2077
11a5cc0f18
CI Health Check & Quality Gate / Code, dbt & Live App Health Check (pull_request) Successful in 1m11s
385 lines
17 KiB
Python
385 lines
17 KiB
Python
import plotly.express as px
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import streamlit as st
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from utils.charts import (
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AMBER,
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ARENA_DIVERGING_SCALE,
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MADE_EMERALD,
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MISS_CRIMSON,
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apply_arena_theme,
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)
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from utils.court import build_shot_chart_figure
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from utils.db import get_selected_season, query
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SEASON, SEASON_LABEL = get_selected_season()
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st.title(f":material/my_location: Shot Studio — {SEASON_LABEL}")
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tab_chart, tab_zones, tab_clutch = st.tabs(
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[
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":material/my_location: Shot Chart",
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":material/bar_chart: Zone & Profile Analysis",
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":material/timer: Clutch Shooting",
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]
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)
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# --- TAB 1: Shot Chart ---
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with tab_chart:
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top_shooters = query(
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"""
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SELECT p.player_name, count(*) AS shots
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FROM main_marts.fct_player_shots s
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JOIN main_marts.dim_players p ON s.player_key = p.player_key
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JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key
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WHERE sea.season_start_year = ? AND s.shot_source = 'shot_chart'
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GROUP BY p.player_name HAVING count(*) >= 50
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ORDER BY shots DESC
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""",
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[SEASON],
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)
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if not top_shooters.empty:
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ctrl1, ctrl2, ctrl3 = st.columns([1.35, 1.15, 0.9])
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with ctrl1:
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selected_player = st.selectbox(
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"Player Selection",
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top_shooters["player_name"].tolist(),
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key="shot_studio_player",
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)
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with ctrl2:
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period_filter = st.segmented_control(
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"Period Filter",
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["All", "Q1", "Q2", "Q3", "Q4", "OT"],
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default="All",
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key="shot_period_filter",
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)
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with ctrl3:
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outcome_filter = st.segmented_control(
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"Shot Outcome",
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["All", "Made", "Missed"],
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default="All",
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key="shot_outcome_filter",
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)
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shot_data = query(
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"""
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SELECT s.shot_x_coordinate AS x, s.shot_y_coordinate AS y,
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s.is_made, s.shot_distance_zone, s.distance_ft, s.quarter_number,
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s.is_three_pointer, s.shot_point_value
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FROM main_marts.fct_player_shots s
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JOIN main_marts.dim_players p ON s.player_key = p.player_key
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JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key
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WHERE sea.season_start_year = ? AND s.shot_source = 'shot_chart'
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AND p.player_name = ?
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""",
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[SEASON, selected_player],
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)
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# Apply interactive period & outcome filters
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filtered_shots = shot_data
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if period_filter and period_filter != "All":
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q_map = {"Q1": 1, "Q2": 2, "Q3": 3, "Q4": 4, "OT": 5}
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filtered_shots = filtered_shots[
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filtered_shots["quarter_number"] == q_map[period_filter]
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]
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if outcome_filter == "Made":
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filtered_shots = filtered_shots[filtered_shots["is_made"]]
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elif outcome_filter == "Missed":
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filtered_shots = filtered_shots[~filtered_shots["is_made"]]
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if not filtered_shots.empty:
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total_fga = len(filtered_shots)
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total_fgm = int(filtered_shots["is_made"].sum())
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fg_pct = round(100.0 * total_fgm / total_fga, 1)
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threes = filtered_shots[filtered_shots["is_three_pointer"]]
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three_fgm = int(threes["is_made"].sum()) if not threes.empty else 0
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three_pct = round(100.0 * three_fgm / len(threes), 1) if not threes.empty else 0.0
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efg_pct = round(100.0 * (total_fgm + 0.5 * three_fgm) / total_fga, 1)
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total_pts = int(
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(filtered_shots["is_made"].astype(int) * filtered_shots["shot_point_value"]).sum()
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)
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pps = round(total_pts / total_fga, 2)
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# Player Shooting Summary Strip
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k1, k2, k3, k4 = st.columns(4)
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with k1:
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with st.container(border=True):
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st.metric("Field Goal Attempts", f"{total_fga:,}", f"{total_fgm:,} Made")
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with k2:
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with st.container(border=True):
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st.metric("FG% / eFG%", f"{fg_pct}%", f"{efg_pct}% eFG")
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with k3:
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with st.container(border=True):
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st.metric("3-Point Accuracy", f"{three_pct}%", f"{len(threes):,} 3PA")
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with k4:
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with st.container(border=True):
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st.metric("Points Per Shot", f"{pps:.2f}", f"{total_pts:,} Pts")
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col_court, col_zones = st.columns([1.48, 1])
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with col_court:
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with st.container(border=True):
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fig = build_shot_chart_figure(filtered_shots, selected_player)
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st.plotly_chart(fig, width="stretch")
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st.caption(
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"Calibrated NBA Half-Court • Rim: (240, 52.5) • "
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"3PT Arc: 23.75 ft • Corner 3: 22.0 ft"
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)
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with col_zones:
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with st.container(border=True):
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st.subheader(":material/analytics: Zone Efficiency vs League")
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st.caption("Ordered by court distance • White marker = League Avg FG%")
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zone_comparison = query(
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"""
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WITH league AS (
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SELECT s.shot_distance_zone,
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round(100.0 * sum(s.is_made::int) / count(*), 1) AS league_fg_pct
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FROM main_marts.fct_player_shots s
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JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key
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WHERE sea.season_start_year = ? AND s.shot_source = 'shot_chart'
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GROUP BY s.shot_distance_zone
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),
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player AS (
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SELECT s.shot_distance_zone, count(*) AS attempts,
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sum(s.is_made::int) AS makes,
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round(100.0 * sum(s.is_made::int) / count(*), 1) AS player_fg_pct
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FROM main_marts.fct_player_shots s
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JOIN main_marts.dim_players p ON s.player_key = p.player_key
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JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key
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WHERE sea.season_start_year = ? AND s.shot_source = 'shot_chart'
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AND p.player_name = ?
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GROUP BY s.shot_distance_zone
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)
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SELECT p.shot_distance_zone AS zone, p.attempts, p.makes, p.player_fg_pct,
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l.league_fg_pct, round(p.player_fg_pct - l.league_fg_pct, 1) AS vs_league,
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sz.zone_order
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FROM player p
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JOIN league l ON p.shot_distance_zone = l.shot_distance_zone
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LEFT JOIN main_marts.dim_shot_zones sz
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ON p.shot_distance_zone = sz.shot_distance_zone
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ORDER BY sz.zone_order
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""",
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[SEASON, SEASON, selected_player],
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)
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if not zone_comparison.empty:
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total_season_fga = int(zone_comparison["attempts"].sum()) or 1
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cards_html = []
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for _, z in zone_comparison.iterrows():
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is_pos = z["vs_league"] >= 0
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delta_col = MADE_EMERALD if is_pos else MISS_CRIMSON
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delta_bg = (
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"rgba(16, 185, 129, 0.14)"
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if is_pos
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else "rgba(244, 63, 94, 0.14)"
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)
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bar_col = (
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MADE_EMERALD
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if is_pos
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else AMBER
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if z["vs_league"] >= -1.5
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else MISS_CRIMSON
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)
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vol_share = round(100.0 * z["attempts"] / total_season_fga, 1)
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player_w = min(100.0, float(z["player_fg_pct"]))
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league_w = min(100.0, float(z["league_fg_pct"]))
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cards_html.append(
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f"""
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<div style="background: rgba(11, 15, 23, 0.65); border: 1px solid #26354A;
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border-radius: 8px; padding: 9px 12px; margin-bottom: 8px;
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font-family: -apple-system, BlinkMacSystemFont, 'Inter', sans-serif;">
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<div style="display: flex; justify-content: space-between; align-items: baseline; margin-bottom: 5px;">
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<div>
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<span style="font-size: 13px; font-weight: 600; color: #F1F5F9; margin-right: 6px;">{z['zone']}</span>
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<span style="font-family: monospace; font-size: 11px; color: #94A3B8;">
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{int(z['makes'])}/{int(z['attempts'])} ({vol_share}%)
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</span>
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</div>
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<div style="display: flex; align-items: center; gap: 6px; font-family: monospace;">
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<span style="font-size: 14px; font-weight: 700; color: #F1F5F9;">{z['player_fg_pct']}%</span>
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<span style="font-size: 11px; font-weight: 600; padding: 1.5px 6px; border-radius: 4px;
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color: {delta_col}; background: {delta_bg};">
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{z['vs_league']:+.1f}% vs Lg
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</span>
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</div>
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</div>
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<div style="height: 6px; background: #1E293B; border-radius: 3px; position: relative;">
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<div style="width: {player_w}%; height: 100%; background: {bar_col}; border-radius: 3px;"></div>
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<div style="position: absolute; top: -3px; left: {league_w}%; width: 2px; height: 12px;
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background: #F1F5F9; box-shadow: 0 0 4px #000;"
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title="League Avg: {z['league_fg_pct']}%"></div>
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</div>
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<div style="display: flex; justify-content: space-between; margin-top: 4px;
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font-family: monospace; font-size: 10.5px; color: #64748B;">
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<span>Player: {z['player_fg_pct']}%</span>
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<span>│ League Avg: {z['league_fg_pct']}%</span>
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</div>
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</div>
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"""
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)
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st.html("".join(cards_html))
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else:
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st.info("No shots match the selected period/outcome filters.")
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else:
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st.info("No spatial shot chart data available for this season.")
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# --- TAB 2: Zone & Profile Analysis ---
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with tab_zones:
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st.subheader("League Zone Efficiency")
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zone_data = query(
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"""
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SELECT sz.shot_distance_zone, sz.zone_group, sz.zone_order,
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count(*) AS attempts, sum(s.is_made::int) AS makes,
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round(100.0 * sum(s.is_made::int) / count(*), 1) AS fg_pct,
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round(avg(s.shot_point_value * s.is_made::int), 3) AS pts_per_shot
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FROM main_marts.fct_player_shots s
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JOIN main_marts.dim_shot_zones sz ON s.shot_distance_zone = sz.shot_distance_zone
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JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key
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WHERE sea.season_start_year = ?
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GROUP BY sz.shot_distance_zone, sz.zone_group, sz.zone_order
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ORDER BY sz.zone_order
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""",
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[SEASON],
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)
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col1, col2 = st.columns(2)
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with col1:
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fig = px.bar(
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zone_data,
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x="shot_distance_zone",
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y="attempts",
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color="fg_pct",
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color_continuous_scale=ARENA_DIVERGING_SCALE,
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title="Volume by Zone (color = FG%)",
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)
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apply_arena_theme(fig)
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st.plotly_chart(fig, width="stretch")
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with col2:
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fig = px.bar(
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zone_data,
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x="shot_distance_zone",
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y="pts_per_shot",
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color="zone_group",
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color_discrete_sequence=[AMBER, MADE_EMERALD, "#38BDF8", MISS_CRIMSON],
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title="Points per Shot by Zone",
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)
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apply_arena_theme(fig)
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st.plotly_chart(fig, width="stretch")
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st.subheader("Player Shot Profiles")
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profiles = query(
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"""
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SELECT p.player_name,
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count(*) AS games,
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round(avg(s.three_point_rate) * 100, 1) AS three_rate,
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round(avg(s.at_rim_rate) * 100, 1) AS rim_rate,
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round(avg(s.mid_range_rate) * 100, 1) AS mid_rate,
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round(avg(s.avg_fg_distance_ft), 1) AS avg_dist,
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round(avg(s.true_shooting_pct) * 100, 1) AS ts_pct
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FROM main_marts.fct_player_game_shooting s
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JOIN main_marts.dim_players p ON s.player_key = p.player_key
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JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key
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WHERE sea.season_start_year = ? AND s.fg_attempts >= 10
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GROUP BY p.player_name HAVING count(*) >= 20
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ORDER BY ts_pct DESC
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""",
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[SEASON],
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)
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if not profiles.empty:
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fig = px.scatter(
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profiles,
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x="three_rate",
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y="rim_rate",
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size="ts_pct",
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hover_name="player_name",
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color="ts_pct",
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color_continuous_scale=ARENA_DIVERGING_SCALE,
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title="Shot Profile Clustering: Three-Point Rate vs Rim Rate (size/color = TS%)",
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)
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fig.update_layout(xaxis_title="Three-Point Rate %", yaxis_title="At-Rim Rate %")
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apply_arena_theme(fig, height=500)
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st.plotly_chart(fig, width="stretch")
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profile_types = query(
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"""
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SELECT shot_profile_type, count(*) AS player_games,
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round(avg(true_shooting_pct) * 100, 1) AS avg_ts,
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round(avg(total_points), 1) AS avg_pts
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FROM main_marts.fct_player_game_shooting s
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JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key
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WHERE sea.season_start_year = ?
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GROUP BY shot_profile_type ORDER BY player_games DESC
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""",
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[SEASON],
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)
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if not profile_types.empty:
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fig = px.treemap(
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profile_types,
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path=["shot_profile_type"],
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values="player_games",
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color="avg_ts",
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color_continuous_scale=ARENA_DIVERGING_SCALE,
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title="Shot Profile Types (size = frequency, color = TS%)",
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)
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apply_arena_theme(fig)
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st.plotly_chart(fig, width="stretch")
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# --- TAB 3: Clutch Shooting ---
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with tab_clutch:
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clutch = query(
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"""
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SELECT p.player_name,
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count(*) AS clutch_attempts,
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sum(s.is_made::int) AS clutch_makes,
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round(100.0 * sum(s.is_made::int) / count(*), 1) AS clutch_pct,
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round(avg(s.score_margin_at_shot), 1) AS avg_margin
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FROM main_marts.fct_player_shots s
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JOIN main_marts.dim_players p ON s.player_key = p.player_key
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JOIN main_marts.dim_seasons sea ON s.season_key = sea.season_key
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WHERE s.is_clutch_shot AND sea.season_start_year = ?
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GROUP BY p.player_name HAVING count(*) >= 15
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ORDER BY clutch_makes DESC LIMIT 30
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""",
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[SEASON],
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)
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if not clutch.empty:
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fig = px.scatter(
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clutch,
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x="clutch_attempts",
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y="clutch_pct",
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size="clutch_makes",
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hover_name="player_name",
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color="clutch_pct",
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color_continuous_scale=ARENA_DIVERGING_SCALE,
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title="Clutch Shooting: Volume vs Accuracy",
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)
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fig.add_hline(
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y=clutch["clutch_pct"].median(),
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line_dash="dash",
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line_color=AMBER,
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annotation_text="Median",
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)
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fig.update_layout(xaxis_title="Clutch Attempts", yaxis_title="Clutch FG%")
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apply_arena_theme(fig, height=500)
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st.plotly_chart(fig, width="stretch")
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st.subheader("Clutch Leaders")
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st.dataframe(
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clutch[["player_name", "clutch_attempts", "clutch_makes", "clutch_pct", "avg_margin"]],
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hide_index=True,
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width="stretch",
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column_config={
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"clutch_pct": st.column_config.ProgressColumn(
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"FG%", min_value=25, max_value=65, format="%.1f%%"
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),
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},
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)
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else:
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st.info("No clutch shooting data available for this season.")
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