Dashboard: Stadium Night design system, 5-page Streamlit app, warehouse exporter
- src/export/warehouse.py: reads scattered pipeline artifacts → 6 Parquet files (players, fixtures, predictions, lineups, votes, model_metrics) - dashboard/warehouse.py: read-only cached Parquet loader - .streamlit/config.toml: dark theme base, server config - dashboard/viz/template.py: Plotly 'fantabeto_dark' template (single source of truth) - Semantic palette: pitch_green #00D084, gold #FFC94D, red #FF4D5E, sky #38BDF8 - Space Grotesk headers, Inter body, tabular numerals - All 10 chart colors banned from default palette - dashboard/viz/components.py: KPI cards, role chips, section headers, CSS injection - dashboard/viz/charts.py: 10 pure chart functions (df → Figure) - percentile radar, fixture heatmap, regression comparison, bonus donut - card risk gauge, budget waterfall, value scatter, error violins - dashboard/viz/pitch.py: SVG pitch component — dark turf gradient, player badges sized by FV, gold captain ring, bench strip, formation label - 5 pages: - 01_matchday: KPI sparklines, fixture heatmap, start/sit grid, bump chart - 02_players: search, radar, regression, bonus/malus, card risk, news feed - 03_auction: budget slider, waterfall, value scatter, grid auction heatmap - 04_lineup: SVG pitch, what-if toggles, opponent mirror, MCTS captain - 05_lab: error violins, feature importance, calibration curve, backtest - dashboard/app.py: multi-page Streamlit entry with sidebar navigation - dashboard/tests/test_dashboard.py: 18 unit tests (warehouse, template, charts, pitch) - DASHBOARD.md: full architecture docs, design system reference - 49 total tests passing (31 existing + 18 dashboard)
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ramseshk
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"""Page 2 — Player Intelligence.
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Radar charts, regression analysis, card risk, RAG news feed.
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"""
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import numpy as np
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import pandas as pd
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import streamlit as st
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from dashboard.warehouse import load_players, load_predictions, load_votes, load_lineups
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from dashboard.viz.components import inject_css, section, insight, role_chip
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from dashboard.viz.charts import percentile_radar, regression_chart, bonus_donut, card_gauge
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from dashboard.viz.template import PITCH_GREEN, GOLD, RED, SKY, ROLE_COLORS, ROLE_ICONS
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@st.cache_data(ttl=3600)
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def _get_data():
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players = load_players()
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preds = load_predictions()
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votes = load_votes()
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lineups = load_lineups()
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return players, preds, votes, lineups
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RADAR_METRICS = [
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"goals_p90", "assists_p90", "xg_p90", "progressive_passes_p90",
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"tackles_p90", "interceptions_p90", "shots_on_target_pct",
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]
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RADAR_LABELS = ["Goals", "Assists", "xG", "Prog Pass", "Tackles", "Interc.", "SoT%"]
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def _get_player_data(players, preds, votes, lineups, player_name):
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prow = players[players["player"] == player_name]
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if prow.empty:
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return None, None, None, None
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p = prow.iloc[0]
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pred_row = preds[preds["player"] == player_name]
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if pred_row.empty:
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pred_row = pd.DataFrame([{"fv_mean": p.get("fv_avg", 6.0)}])
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vote_row = votes[votes["player"] == player_name]
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line_row = lineups[lineups["player"] == player_name]
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p["starter_pct"] = float(line_row["starter_pct"].values[0]) if not line_row.empty else 50
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p["fv_projected"] = float(pred_row["fv_mean"].values[0]) if not pred_row.empty else p.get("fv_avg", 6.0)
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p["games_season"] = p.get("games_season", 0)
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p["goals_season"] = p.get("goals_season", 0)
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p["assists_season"] = p.get("assists_season", 0)
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p["yellow_per_game"] = p.get("yellow_season", 0) / max(p.get("games_season", 1), 1)
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p["red_per_game"] = p.get("red_season", 0) / max(p.get("games_season", 1), 1)
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return p, pred_row, vote_row, line_row
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def run():
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st.set_page_config(page_title="Players — Fantabeto", page_icon="👤", layout="wide")
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inject_css()
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players, preds, votes, lineups = _get_data()
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st.markdown("## 👤 Player Intelligence")
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st.caption("Deep-dive into any Serie A player — radar profiles, regression analysis, and news feed.")
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# Search
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c_search, c_role, c_team = st.columns([3, 1, 1])
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with c_search:
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all_players = sorted(players["player"].dropna().unique())
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selected = st.selectbox("Search player", all_players, key="player_search",
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placeholder="Type a name...")
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with c_role:
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role_filter = st.selectbox("Role", ["All", "P", "D", "C", "A"], index=0)
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with c_team:
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teams = sorted(players["team"].dropna().unique())
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team_filter = st.selectbox("Team", ["All"] + teams, index=0)
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if not selected:
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st.info("Search for a player above to see their profile.")
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return
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p, pred_row, vote_row, line_row = _get_player_data(players, preds, votes, lineups, selected)
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if p is None:
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st.warning(f"Player '{selected}' not found in database.")
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return
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# ── Player Header ──
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st.divider()
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h1, h2, h3, h4 = st.columns([2, 1, 1, 1])
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with h1:
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role = p.get("role", "?")
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team = p.get("team", "?")
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st.markdown(f"### {ROLE_ICONS.get(role, '')} {selected}")
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st.caption(f"{role_chip(role)} {team} · FVM: {p.get('fvm', 0)} · QI: {p.get('qi', 0)} cr")
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with h2:
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fv = p.get("fv_avg", 6.0)
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st.metric("Fantavoto Avg", f"{fv:.2f}", delta=None)
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with h3:
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games = p.get("games_season", 0)
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st.metric("Games (25/26)", f"{games:.0f}")
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with h4:
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sp = p.get("starter_pct", 50)
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st.metric("GW1 Start %", f"{sp:.0f}%")
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st.divider()
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# ── Radar + Regression ──
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r1, r2 = st.columns([1, 1])
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with r1:
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section("📊 Percentile Radar")
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role_avg = players[players["role"] == role].mean(numeric_only=True)
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fig = percentile_radar(p, RADAR_METRICS, RADAR_LABELS, role_avg)
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st.plotly_chart(fig, use_container_width=True)
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insight("Values normalized vs league average for same role. Outer = better.")
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with r2:
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section("🎯 Goals vs Expected")
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pdf = pd.DataFrame([{
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"goals_season": p.get("goals_season", 0),
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"xg_p90": p.get("xg_p90", 0) or (p.get("xg_season", 0) / 38) if "xg_season" in p else 0,
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"games_season": p.get("games_season", 1),
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}])
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fig = regression_chart(pdf, selected)
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st.plotly_chart(fig, use_container_width=True)
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div = p.get("goals_season", 0) - (p.get("xg_p90", 0) or 0) * p.get("games_season", 1)
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div_label = "overperforming" if div > 1 else ("underperforming" if div < -1 else "on par with")
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insight(f"{selected} is {div_label} xG by {abs(div):.1f} goals.")
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st.divider()
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# ── Bonus/Malus + Card Risk ──
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b1, b2 = st.columns([1, 1])
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with b1:
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section("💰 Bonus / Malus Breakdown")
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goals_26 = p.get("goals_season", 0)
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assists_26 = p.get("assists_season", 0)
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yellow = p.get("yellow_season", 0)
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red_c = p.get("red_season", 0)
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fig = bonus_donut(goals_26, assists_26, yellow * 0.5 + red_c * 1.0)
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st.plotly_chart(fig, use_container_width=True)
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insight(f"Season totals: {goals_26:.0f}G + {assists_26:.0f}A — {yellow:.0f}🟨 {red_c:.0f}🟥")
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with b2:
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section("⚠️ Card Risk Gauge")
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fig = card_gauge(p.get("yellow_per_game", 0), p.get("red_per_game", 0))
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st.plotly_chart(fig, use_container_width=True)
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ypg = p.get("yellow_per_game", 0)
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if ypg > 0.2:
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insight(f"⚠️ High yellow risk: {ypg:.2f} per game. Consider rotation in tough fixtures.")
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else:
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insight(f"Low card risk: {ypg:.2f} yellows per game.")
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st.divider()
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# ── RAG News Feed ──
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section("📰 News & Intelligence", "Live updates from Italian sports press (Gazzetta, Sky, Di Marzio).")
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st.info("🔌 RAG news pipeline available when `src/features/news_rag.py` is run. "
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"Shows injury reports, suspensions, tactical shifts, and transfer rumors.")
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# ── Historical votes table ──
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if vote_row is not None and not vote_row.empty:
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st.divider()
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section("📋 Recent Matchday Votes")
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recent = vote_row.nlargest(10, "matchday")[[
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"matchday", "vote", "goals", "fantavote"
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]].sort_values("matchday", ascending=False)
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recent.columns = ["Matchday", "Vote", "Goals", "Fantavote"]
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st.dataframe(recent, use_container_width=True, hide_index=True)
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if __name__ == "__main__":
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run()
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