Fix Players page KeyError + use_container_width deprecation

- Players page: fix regression_chart xg_col default (goals_p90 -> xg_p90)
- charts.py: make regression_chart defensive against missing columns
- All pages: replace use_container_width=True with width='stretch'
- Verified: all 5 pages render with zero errors via Playwright browser test

dashboard/pages/01_matchday.py

@@ -11,7 +11,7 @@ import streamlit as st
 from dashboard.warehouse import load_predictions, load_fixtures, load_players, load_lineups, load_votes
 from dashboard.viz.components import inject_css, kpi_card, section, insight, role_chip
 from dashboard.viz.charts import fixture_heatmap, kpi_sparkline, error_violins
-from dashboard.viz.template import PITCH_GREEN, GOLD, RED, SKY, VIOLET, WHITE, TEXT_SECONDARY, ROLE_COLORS, ROLE_ICONS
+from dashboard.viz.template import PITCH_GREEN, GOLD, RED, SKY, VIOLET, WHITE, TEXT_SECONDARY, ROLE_COLORS, ROLE_ICONS, FANTABETO_TEMPLATE
 
 
 @st.cache_data(ttl=3600)
@@ -87,7 +87,7 @@ def run():
         ), unsafe_allow_html=True)
     with k4:
         st.plotly_chart(kpi_sparkline(trend, "FV Trend", PITCH_GREEN),
-                        use_container_width=True, config={"displayModeBar": False})
+                        width="stretch", config={"displayModeBar": False})
         st.caption("Last 5 GW trend")
 
     st.divider()
@@ -98,7 +98,7 @@ def run():
         section("📅 Fixture Difficulty")
         heatmap_df = _build_fixture_heatmap(players, fixtures)
         fig = fixture_heatmap(heatmap_df)
-        st.plotly_chart(fig, use_container_width=True)
+        st.plotly_chart(fig, width="stretch")
         insight("Warmer colors = tougher opponent. Based on opponent avg FV from 25/26.")
 
     with c2:
@@ -132,7 +132,7 @@ def run():
     ]
     show.columns = ["Player", "Role", "Team", "H/A", "Opponent", "Projected FV", "Status"]
     st.dataframe(
-        show, use_container_width=True, hide_index=True,
+        show, width="stretch", hide_index=True,
         column_config={
             "Player": st.column_config.TextColumn(width="medium"),
             "Projected FV": st.column_config.TextColumn(width="small"),
@@ -151,7 +151,6 @@ def run():
     ranks = list(np.cumsum(np.diff([8] + ranks, prepend=8).clip(-2, 2)))
 
     import plotly.graph_objects as go
-    from dashboard.viz.template import FANTABETO_TEMPLATE, PITCH_GREEN, SKY, TEXT_SECONDARY
 
     fig = go.Figure()
     fig.add_trace(go.Scatter(
@@ -167,7 +166,7 @@ def run():
         xaxis=dict(title=""),
         margin=dict(l=10, r=10, t=10, b=10),
     )
-    st.plotly_chart(fig, use_container_width=True)
+    st.plotly_chart(fig, width="stretch")
 
 
 if __name__ == "__main__":

dashboard/pages/02_players.py

@@ -108,7 +108,7 @@ def run():
         section("📊 Percentile Radar")
         role_avg = players[players["role"] == role].mean(numeric_only=True)
         fig = percentile_radar(p, RADAR_METRICS, RADAR_LABELS, role_avg)
-        st.plotly_chart(fig, use_container_width=True)
+        st.plotly_chart(fig, width="stretch")
         insight("Values normalized vs league average for same role. Outer = better.")
 
     with r2:
@@ -119,7 +119,7 @@ def run():
             "games_season": p.get("games_season", 1),
         }])
         fig = regression_chart(pdf, selected)
-        st.plotly_chart(fig, use_container_width=True)
+        st.plotly_chart(fig, width="stretch")
         div = p.get("goals_season", 0) - (p.get("xg_p90", 0) or 0) * p.get("games_season", 1)
         div_label = "overperforming" if div > 1 else ("underperforming" if div < -1 else "on par with")
         insight(f"{selected} is {div_label} xG by {abs(div):.1f} goals.")
@@ -135,13 +135,13 @@ def run():
         yellow = p.get("yellow_season", 0)
         red_c = p.get("red_season", 0)
         fig = bonus_donut(goals_26, assists_26, yellow * 0.5 + red_c * 1.0)
-        st.plotly_chart(fig, use_container_width=True)
+        st.plotly_chart(fig, width="stretch")
         insight(f"Season totals: {goals_26:.0f}G + {assists_26:.0f}A — {yellow:.0f}🟨 {red_c:.0f}🟥")
 
     with b2:
         section("⚠️ Card Risk Gauge")
         fig = card_gauge(p.get("yellow_per_game", 0), p.get("red_per_game", 0))
-        st.plotly_chart(fig, use_container_width=True)
+        st.plotly_chart(fig, width="stretch")
         ypg = p.get("yellow_per_game", 0)
         if ypg > 0.2:
             insight(f"⚠️ High yellow risk: {ypg:.2f} per game. Consider rotation in tough fixtures.")
@@ -163,7 +163,7 @@ def run():
             "matchday", "vote", "goals", "fantavote"
         ]].sort_values("matchday", ascending=False)
         recent.columns = ["Matchday", "Vote", "Goals", "Fantavote"]
-        st.dataframe(recent, use_container_width=True, hide_index=True)
+        st.dataframe(recent, width="stretch", hide_index=True)
 
 
 if __name__ == "__main__":

dashboard/pages/03_auction.py

@@ -151,13 +151,13 @@ def run():
         for r in ["P", "D", "C", "A"]:
             allocations[r] = sum(s["estimated_price"] for s in selected if s["role"] == r)
         fig = budget_waterfall(allocations)
-        st.plotly_chart(fig, use_container_width=True)
+        st.plotly_chart(fig, width="stretch")
         insight("How your budget maps across roles. Aim for ~15% GK, ~35% DEF, ~30% MID, ~20% FWD.")
 
     with c2:
         section("📈 Value Scatter")
         fig = value_scatter(players)
-        st.plotly_chart(fig, use_container_width=True)
+        st.plotly_chart(fig, width="stretch")
         insight("Top-right: high FV, high price. Bottom-right: value steals. "
                 "Bubble size = games played. Dashed lines = cost-per-FV-point isolines.")
 
@@ -186,7 +186,7 @@ def run():
     # ── Grid Auction Heatmap ──
     section("🔢 Grid Auction Simulator")
     fig = _grid_heatmap(players)
-    st.plotly_chart(fig, use_container_width=True)
+    st.plotly_chart(fig, width="stretch")
     insight("Green = good value at that bid multiplier. Red = overpaying. "
             "Bid at the 'green' multiplier for each player.")
 

dashboard/pages/05_lab.py

@@ -147,7 +147,7 @@ def run():
         error_df = _build_error_data(preds, votes)
         if not error_df.empty:
             fig = error_violins(error_df)
-            st.plotly_chart(fig, use_container_width=True)
+            st.plotly_chart(fig, width="stretch")
             insight("How prediction errors distribute across roles. Wider = more uncertainty.")
         else:
             st.info("No actual vote data available to compute errors.")
@@ -156,7 +156,7 @@ def run():
         section("🔬 Feature Importance")
         fig = _feature_importance_plot(players)
         if fig.data:
-            st.plotly_chart(fig, use_container_width=True)
+            st.plotly_chart(fig, width="stretch")
             insight("Pearson correlation of each feature with season Fantavoto average.")
         else:
             st.info("Insufficient numeric features for correlation analysis.")
@@ -169,7 +169,7 @@ def run():
         section("📐 Calibration Curve")
         fig = _calibration_curve(preds)
         if fig.data:
-            st.plotly_chart(fig, use_container_width=True)
+            st.plotly_chart(fig, width="stretch")
             insight("Ideal: points on diagonal → predicted uncertainty matches actual variance.")
         else:
             st.info("Bootstrap std not available for calibration.")
@@ -178,7 +178,7 @@ def run():
         section("📈 Backtest: League Avg per GW")
         fig = _backtest_chart(votes)
         if fig.data:
-            st.plotly_chart(fig, use_container_width=True)
+            st.plotly_chart(fig, width="stretch")
             insight("Average Fantavoto across the 2025/26 season. Dashed line = 6.0 baseline.")
         else:
             st.info("No vote data available.")

dashboard/viz/charts.py

@@ -112,15 +112,18 @@ def percentile_radar(player_row: pd.Series, metrics: list, labels: list,
 # ─────────────────────────────────────────────────────────────────
 
 def regression_chart(df: pd.DataFrame, player: str,
-                     xg_col="goals_p90", goal_col="goals_season",
+                     xg_col="xg_p90", goal_col="goals_season",
                      games_col="games_season") -> go.Figure:
-    """Rolling actual goals vs expected with divergence shading.
+    """Rolling actual goals vs expected with divergence shading."""
-    Uses season-level totals as static chart.
+    actual = df[goal_col].iloc[0] if goal_col in df.columns else 0
-    """
+    xg_val = df[xg_col].iloc[0] if xg_col in df.columns else 0
+    games = df[games_col].iloc[0] if games_col in df.columns else 1
+    expected = xg_val * 1.2 * games
+
     fig = go.Figure()
     fig.add_trace(go.Bar(
         x=["Actual Goals", "Expected (xG * 1.2)"],
-        y=[df[goal_col].iloc[0], df[xg_col].iloc[0] * 1.2 * df[games_col].iloc[0]],
+        y=[actual, expected],
         marker_color=[PITCH_GREEN, SKY], texttemplate="%{y:.1f}",
         textposition="outside", textfont=dict(color=TEXT, size=13),
         showlegend=False,