fantabeto/DASHBOARD.md

Fantabeto Dashboard

Project Al-Cihred — Production-grade Streamlit dashboard for the 2026/27 Fantacalcio season.

streamlit run dashboard/app.py

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Architecture

data/warehouse/          ← Versioned Parquet (read by dashboard)
  players.parquet          505 rows — roster + 25/26 stats + per-90 metrics
  fixtures.parquet         10 rows — matchday 1 home/away + formations
  predictions.parquet      505 rows — FV projections with std + starter prob
  lineups.parquet          466 rows — probable lineups with starter %
  votes.parquet            12,049 rows — historical per-matchday votes (25/26)
  model_metrics.parquet    1 row — RMSE, R², sample count

dashboard/
  app.py                   ← Entry point: multi-page routing via sidebar
  warehouse.py             ← Read-only Parquet loader (@st.cache_data)
  viz/
    template.py            ← Plotly template "fantabeto_dark" + color palette
    charts.py              ← Pure functions: df → Figure (heatmap, radar, etc.)
    pitch.py               ← SVG pitch component with player badges
    components.py          ← KPI cards, role chips, section headers, CSS
  pages/
    01_matchday.py         ← Control Room: KPIs, fixture heatmap, start/sit
    02_players.py          ← Intelligence: radar, regression, card risk, news
    03_auction.py          ← War Room: waterfall, value scatter, grid heatmap
    04_lineup.py           ← Optimizer: SVG pitch, what-if toggles, opponent
    05_lab.py              ← Model Lab: error violins, calibration, backtest
  tests/
    test_dashboard.py      18 unit tests (warehouse, template, charts, pitch)

src/export/warehouse.py    ← Exporter: reads pipeline artifacts → Parquet
.streamlit/config.toml     ← Dark theme base, server config

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Design System — Stadium Night

Property	Value
Background	#0B0F17
Card bg	#111827 at 70% opacity, 14px radius, blur 8px
Border	#1F2937
Pitch Green	#00D084 — positive, bonus
Gold	#FFC94D — captain, highlight
Red	#FF4D5E — malus, risk
Sky	#38BDF8 — neutral data
Violet	#A78BFA — uncertainty
Type	Space Grotesk (headers), Inter (body), tabular numerals
Charts	All use fantabeto_dark template. No default Plotly palette anywhere.

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Pages

1. Matchday Control Room

• KPI row: projected points, players at risk (P(start) < 70%), matchday count, FV trend sparkline
• Fixture difficulty heatmap: teams × gameweeks, colored by opponent xGA/FV strength
• Start/Sit grid: top 20 players with role chips, H/A indicator, projected FV ± std, risk status (🟢 START / 🟡 DOUBT / 🔴 RISK)
• Bump chart: projected rank trajectory across first 10 GWs

2. Player Intelligence

• Search: autocomplete on all 505 players, filtered by role/team
• Player header: FV avg, games played, GW1 start %, role chip, FVM, QI
• Percentile radar: 7 metrics normalized vs role average (goals, assists, xG, prog passes, tackles, interceptions, SoT%)
• Goals vs Expected: bar chart with divergence annotation
• Bonus/malus donut: goal bonus + assist bonus − card malus breakdown
• Card risk gauge: yellow/red card per-game risk indicators
• News feed: placeholder for RAG pipeline output (injuries, suspensions, tactical shifts)
• Historical votes: last 10 matchday votes table

3. Auction War Room

• Budget controls: sliders for budget (300–700 cr) and role quotas
• KPI row: players drafted, total spent, projected FV, avg price per player
• Budget waterfall: Sankey-style allocation per role (GK/DEF/MID/FWD)
• Value scatter: FV avg vs QI, bubble size = games played, top steals labeled, cost-per-FV isolines
• Grid auction heatmap: top players × bid multipliers, colored by value surplus
• Recommended squad: per-role player list with max bid, FV, and games played

4. Lineup Optimizer

• What-if controls: force IN/OUT a player, select formation (4-4-2, 4-3-3, 3-5-2, etc.)
• KPI row: expected points (with captain), captain name, avg start %, win probability
• SVG pitch: dark turf gradient, player badges sized by FV, gold captain ring, risk markers, bench strip, formation label
• Player list: role-chipped players with FV and risk flags
• Opponent mirror: per-duel edge arrows (🟢 advantage / 🔴 disadvantage / ⚪ neutral)

5. Model Lab

• KPI row: RMSE, R², training samples, feature count
• Error violins: prediction error distribution by role
• Feature importance: Pearson correlation with FV avg
• Calibration curve: predicted vs observed uncertainty
• Backtest: league average FV across 38 matchdays
• Architecture notes: model overview, key insights, limitations

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Running

# First time: export warehouse
python -m src.export.warehouse

# Install (if not already)
pip install streamlit

# Launch dashboard
streamlit run dashboard/app.py

# Run tests
python -m pytest dashboard/tests/ -v

Data Refresh

The warehouse reads pre-computed Parquet files. To refresh after running the pipeline:

python -m src.export.warehouse

Then reload the dashboard — @st.cache_data(ttl=3600) will pick up new files after 1 hour or on manual cache clear.

Dependencies

Added to requirements.txt:

• streamlit>=1.35

Already present:

• plotly>=5.18, pandas>=2.1, numpy>=1.26, openpyxl>=3.1