shot chart and navbar

streamlit_app/app_pages/quarter_analysis.py

@@ -0,0 +1,155 @@
+import plotly.express as px
+import streamlit as st
+
+from utils.charts import AMBER, MADE_EMERALD, MISS_CRIMSON, apply_arena_theme
+from utils.db import get_selected_season, query
+
+SEASON, SEASON_LABEL = get_selected_season()
+
+st.title(f":material/timer: Momentum — {SEASON_LABEL}")
+
+tab_overview, tab_teams, tab_comebacks = st.tabs(
+    [
+        ":material/bar_chart: Quarter Scoring",
+        ":material/emoji_events: Team by Quarter",
+        ":material/replay: Comebacks",
+    ]
+)
+
+QUARTER_COLORS = {
+    "Q1": "#38BDF8",
+    "Q2": MADE_EMERALD,
+    "Q3": AMBER,
+    "Q4": MISS_CRIMSON,
+}
+
+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=QUARTER_COLORS,
+            labels={"period": "Quarter", "avg_pts": "Avg Points"},
+        )
+        fig.update_layout(showlegend=False)
+        apply_arena_theme(fig)
+        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=QUARTER_COLORS,
+            labels={"period": "Quarter", "avg_abs_diff": "Avg |Differential|"},
+        )
+        fig2.update_layout(showlegend=False)
+        apply_arena_theme(fig2)
+        st.plotly_chart(fig2, width="stretch")
+
+    st.caption("Q4 has lower scoring and tighter margins — games tighten up in crunch time.")
+
+with tab_teams:
+    quarter_sel = (
+        st.segmented_control(
+            "Quarter",
+            ["Q1", "Q2", "Q3", "Q4"],
+            default="Q1",
+        )
+        or "Q1"
+    )
+
+    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"))
+    apply_arena_theme(fig, height=600)
+    st.plotly_chart(fig, width="stretch")
+
+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 %")
+        apply_arena_theme(fig)
+        st.plotly_chart(fig, width="stretch")
+        st.dataframe(comebacks, hide_index=True, width="stretch")
+    else:
+        st.info("No comeback data available for this season.")