Project Al-Cihred: ML auction strategy with exact 500cr spend
- LightGBM trained on 11,300 per-matchday votes (R²=0.147, RMSE=1.19) - MILP with exact 499-500cr budget: 3GK+8DEF+8MID+6FWD - Practical constraints: 2 starting GKs, 21/25 reliable (>15g) - Realistic prices: Lautaro 229cr, Malen 220cr, Douvikas 56cr - Squad: Svilar(21)+Carnesecchi(18)+Christensen(1)=40GK · 8DEF 128cr · 8MID 152cr · 6FWD 180cr - Every player has 2-3 alternatives - Updated dashboard auction page to load the plan
This commit is contained in:
ramseshk
+157
-256
@@ -1,7 +1,7 @@
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import sys; from pathlib import Path; _p = Path(__file__).resolve().parent.parent.parent; str(_p) not in sys.path and sys.path.insert(0, str(_p))
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"""Page 3 — Auction War Room.
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Multiple squad-building strategies, realistic valuations, comparison dataviz.
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Loads the ML-optimized Project Al-Cihred auction plan with alternatives.
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"""
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import numpy as np
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@@ -18,316 +18,217 @@ from dashboard.viz.template import (
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WHITE, FANTABETO_TEMPLATE, HEATMAP_COLORS, ROLE_COLORS, ROLE_ICONS,
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)
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AUCTION_PLAN = Path(__file__).resolve().parent.parent.parent / "data" / "auction_plan_al_cihred.xlsx"
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PROJECTIONS = Path(__file__).resolve().parent.parent.parent / "data" / "player_projections_26_27.xlsx"
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@st.cache_data(ttl=3600)
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def _get_data():
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return load_players(), load_predictions()
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players = load_players()
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preds = load_predictions()
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# Load the precomputed auction plan
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plan = None
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if AUCTION_PLAN.exists():
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plan = pd.read_excel(AUCTION_PLAN)
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proj = None
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if PROJECTIONS.exists():
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proj = pd.read_excel(PROJECTIONS)
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return players, preds, plan, proj
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# ─── Realistic price model ─────────────────────────────────────────
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def _estimate_price(qi: float, fvm: float, games: float, role: str) -> float:
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"""Estimate final auction price based on market value and games played.
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Top players (FVM > 200) go for 5-8x QI. Budget players at ~1.2x QI minimum.
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"""
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if games < 5:
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fvm = fvm * 0.6 # small-sample penalty
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multiplier = max(1.2, fvm / 65.0)
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if role == "A":
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multiplier *= 1.15 # forwards carry a premium
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elif role == "C" and fvm > 150:
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multiplier *= 1.08
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return round(qi * multiplier, 0)
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# ─── Strategy engines ──────────────────────────────────────────────
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def _solve_strategy(players, budget, quotas, strategy, rng_seed=42):
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"""Generic knapsack solver with different scoring functions."""
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df = players.copy()
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df["price"] = df.apply(lambda r: _estimate_price(
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r["qi"], r.get("fvm", r["fv_avg"] * 30), r.get("games_season", 30), r["role"]
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), axis=1)
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df["value_ratio"] = df["fv_avg"] / df["price"].clip(lower=1)
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df["stability"] = df["games_season"].clip(0, 38) / 38.0
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rng = np.random.RandomState(rng_seed + hash(strategy) % 1000)
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if strategy == "value":
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score = df["value_ratio"] * df["fv_avg"]
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elif strategy == "stars":
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score = df["fv_avg"] * df["fvm"] / 50.0 * df["stability"]
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elif strategy == "balanced":
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score = df["fv_avg"] * df["value_ratio"] * np.sqrt(df["stability"])
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elif strategy == "safe":
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df["risk_adj"] = np.where(df["games_season"] < 10, 0.4,
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np.where(df["games_season"] < 20, 0.7, 1.0))
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score = df["fv_avg"] * df["value_ratio"] * df["risk_adj"]
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elif strategy == "milp_lite":
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df["quality"] = df["fv_avg"] / df["fv_avg"].max()
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df["efficiency"] = df["fv_avg"] / df["price"].clip(lower=1)
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score = df["quality"] * 0.6 + df["efficiency"] * 0.4
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else:
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score = df["fv_avg"]
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scored = [(score.iloc[i], i) for i in range(len(df))
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if df.iloc[i]["role"] in quotas]
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scored.sort(key=lambda x: -x[0])
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filled = {k: 0 for k in quotas}
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remaining = budget
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selected = []
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for _, idx in scored:
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p = df.iloc[idx]
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role = p["role"]
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if filled[role] >= quotas[role]:
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continue
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price = p["price"]
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if price > remaining:
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continue
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selected.append({
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"player": p["player"], "role": role, "team": p["team"],
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"fv_avg": p["fv_avg"], "qi": p["qi"], "price": price,
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"games": p.get("games_season", 30),
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"fvm": p.get("fvm", 0),
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})
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remaining -= price
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filled[role] += 1
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total = budget - remaining
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total_fv = sum(s["fv_avg"] for s in selected)
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avg_games = np.mean([s["games"] for s in selected]) if selected else 0
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return selected, total, total_fv, remaining, avg_games
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# ─── Charts ─────────────────────────────────────────────────────────
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def _price_distribution_chart(players):
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"""Histogram of estimated prices by role."""
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df = players.copy()
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df["price"] = df.apply(lambda r: _estimate_price(
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r["qi"], r.get("fvm", 100), r.get("games_season", 30), r["role"]
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), axis=1)
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def _price_dist_chart(proj):
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if proj is None:
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return go.Figure()
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df = proj.copy()
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df["price"] = df["price"].fillna(0)
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fig = go.Figure()
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for role, color in ROLE_COLORS.items():
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rdf = df[df["role"] == role]["price"]
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if rdf.empty:
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continue
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fig.add_trace(go.Histogram(
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x=rdf, name=role, marker_color=color, opacity=0.7,
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nbinsx=40, hovertemplate=f"{role} price: %{{x:.0f}} cr<extra></extra>",
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x=rdf, name=role, marker_color=color, opacity=0.7, nbinsx=50,
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hovertemplate=f"{role} %{{x:.0f}} cr<extra></extra>",
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))
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fig.update_layout(
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template=FANTABETO_TEMPLATE, height=300, barmode="overlay",
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xaxis_title="Estimated Auction Price (cr)", yaxis_title="Players",
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bargap=0.05,
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xaxis_title="Estimated Auction Price (cr)", yaxis_title="Players", bargap=0.05,
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)
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return fig
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def _strategy_comparison_chart(results: dict):
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"""Radar-like comparison of strategies."""
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strategies = list(results.keys())
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metrics = ["Total FV", "Efficiency", "Stars (FVM>150)", "Avg Games", "Budget Used %"]
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rows = []
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for name, (selected, cost, fv, rem, avg_g) in results.items():
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stars = sum(1 for s in selected if s.get("fvm", 0) > 150)
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rows.append({
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"Strategy": name, "total_fv": fv,
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"efficiency": fv / max(cost, 1),
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"stars": stars, "avg_games": avg_g,
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"budget_pct": cost / max(cost + rem, 1) * 100,
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})
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cdf = pd.DataFrame(rows)
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fig = make_subplots(rows=2, cols=3, subplot_titles=metrics,
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specs=[[{"type": "bar"}, {"type": "bar"}, {"type": "bar"}],
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[{"type": "bar"}, {"type": "indicator"}, {"type": "bar"}]])
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colors = [SKY, PITCH_GREEN, GOLD, VIOLET, "#FB923C"]
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for i, metric in enumerate(["total_fv", "efficiency", "stars"]):
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row, col = 1, i + 1
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fig.add_trace(go.Bar(
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x=cdf["Strategy"], y=cdf[metric],
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marker_color=colors[:len(cdf)], text=[f"{v:.1f}" for v in cdf[metric]],
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textposition="outside", showlegend=False,
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), row=row, col=col)
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fig.add_trace(go.Bar(
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x=cdf["Strategy"], y=cdf["avg_games"],
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marker_color=colors[:len(cdf)], text=[f"{v:.1f}" for v in cdf["avg_games"]],
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textposition="outside", showlegend=False,
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), row=2, col=1)
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best = cdf.nlargest(1, "efficiency").iloc[0]
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fig.add_trace(go.Indicator(
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mode="number+delta", value=best["efficiency"],
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title={"text": f"Best: {best['Strategy']}"},
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delta={"reference": cdf["efficiency"].mean()},
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number={"font": {"color": PITCH_GREEN, "size": 32}},
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), row=2, col=2)
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fig.add_trace(go.Bar(
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x=cdf["Strategy"], y=cdf["budget_pct"],
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marker_color=colors[:len(cdf)], text=[f"{v:.0f}%" for v in cdf["budget_pct"]],
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textposition="outside", showlegend=False,
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), row=2, col=3)
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def _role_radar(squad):
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"""Radar showing squad balance: avg FV, games, stability per role."""
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roles = ["P", "D", "C", "A"]
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avg_fv, avg_g, avg_s = [], [], []
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for r in roles:
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rs = [s for s in squad if s.get("role") == r]
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avg_fv.append(np.mean([s.get("fv_proj", 0) for s in rs]) if rs else 0)
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avg_g.append(np.mean([s.get("games", 0) for s in rs]) if rs else 0)
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avg_s.append(np.mean([s.get("stability", 0) for s in rs]) if rs else 0)
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fig = go.Figure()
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fig.add_trace(go.Scatterpolar(
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r=avg_fv + [avg_fv[0]], theta=roles + [roles[0]],
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fill="toself", fillcolor=f"rgba(56,189,248,0.2)",
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line=dict(color=SKY, width=2), name="Avg FV",
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))
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fig.add_trace(go.Scatterpolar(
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r=avg_g + [avg_g[0]], theta=roles + [roles[0]],
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fill="toself", fillcolor=f"rgba(0,208,132,0.15)",
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line=dict(color=PITCH_GREEN, width=2), name="Avg Games",
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))
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fig.update_layout(
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template=FANTABETO_TEMPLATE, height=480,
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showlegend=False,
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template=FANTABETO_TEMPLATE, height=280,
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polar=dict(
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radialaxis=dict(showticklabels=False, gridcolor=GRIDLINE),
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angularaxis=dict(gridcolor=GRIDLINE),
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bgcolor=BG,
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),
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showlegend=True, legend=dict(orientation="h", yanchor="bottom", y=-0.2),
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margin=dict(l=30, r=30, t=30, b=60),
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)
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return fig, cdf
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return fig
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# ─── Main ──────────────────────────────────────────────────────────
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def run():
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inject_css()
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players, preds = _get_data()
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players, preds, plan, proj = _get_data()
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st.markdown("## 💰 Auction War Room")
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st.caption("Project Al-Cihred — Multi-Strategy Draft for 2026/27 Classic Auction")
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st.caption("Project Al-Cihred — ML-Optimized Auction Strategy for 2026/27")
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# ── Controls ──
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c_budget, c_gk, c_def, c_mid, c_fwd = st.columns(5)
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with c_budget:
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budget = st.slider("Budget (cr)", 300, 700, 500, 10)
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with c_gk:
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n_gk = st.number_input("GK", 1, 5, 3)
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with c_def:
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n_def = st.number_input("DEF", 3, 12, 8)
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with c_mid:
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n_mid = st.number_input("MID", 3, 12, 8)
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with c_fwd:
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n_fwd = st.number_input("FWD", 1, 8, 6)
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quotas = {"P": n_gk, "D": n_def, "C": n_mid, "A": n_fwd}
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# ── Run all strategies ──
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strategies = {
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"⚖️ Balanced": "balanced",
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"💎 Value": "value",
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"⭐ Stars": "stars",
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"🛡 Safe": "safe",
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"🧮 MILP-Lite": "milp_lite",
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}
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all_results = {}
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for label, key in strategies.items():
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sel, cost, fv, rem, avg_g = _solve_strategy(players, budget, quotas, key)
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all_results[label] = (sel, cost, fv, rem, avg_g)
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# Default display = Balanced
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selected_label = st.selectbox("Active strategy", list(strategies.keys()), index=0,
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help="Switch between squad-building philosophies")
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selected, total_cost, total_fv, remaining, avg_games = all_results[selected_label]
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# Load plan data into squad list
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squad = []
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total_cost = 0
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total_fv = 0
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if plan is not None and len(plan) > 0:
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for _, p in plan.iterrows():
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s = {
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"player": p.get("player", ""), "role": p.get("role", ""),
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"team": p.get("team", ""), "price": p.get("bid_cap", 0),
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"qi": p.get("qi", 0), "fvm": p.get("fvm", 0),
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"fv_proj": p.get("fv_proj", 0), "mv_proj": p.get("mv_proj", 0),
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"goals": p.get("goals", 0), "assists": p.get("assists", 0),
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"games": p.get("games", 0), "starter_pct": p.get("starter%", 0),
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"stability": p.get("stability", 0),
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}
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s["alts"] = []
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for j in range(1, 3):
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if f"alt{j}" in plan.columns and pd.notna(p.get(f"alt{j}")):
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s["alts"].append({
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"player": p.get(f"alt{j}"), "price": p.get(f"alt{j}_price"),
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"fv": p.get(f"alt{j}_fv"),
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})
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squad.append(s)
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total_cost += s["price"]
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total_fv += s["fv_proj"]
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else:
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st.warning("⚠️ No auction plan found. Run `python -c '...'` to generate one.")
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return
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# ── KPI Row ──
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k1, k2, k3, k4, k5 = st.columns(5)
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with k1:
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st.markdown(kpi_card("DRAFTED", str(len(selected)),
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f"{sum(quotas.values())} target", SKY), unsafe_allow_html=True)
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st.markdown(kpi_card("BUDGET USED", f"{total_cost} cr",
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"500 cr ceiling", PITCH_GREEN if total_cost >= 499 else GOLD),
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unsafe_allow_html=True)
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with k2:
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st.markdown(kpi_card("SPENT", f"{total_cost:.0f} cr",
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f"{remaining:.0f} cr left", PITCH_GREEN), unsafe_allow_html=True)
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with k3:
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st.markdown(kpi_card("PROJ FV", f"{total_fv:.1f}",
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f"{total_fv/max(total_cost,1):.2f} cr/FV", GOLD), unsafe_allow_html=True)
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f"{total_fv/total_cost:.3f} cr/FV", GOLD), unsafe_allow_html=True)
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with k3:
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stable = sum(1 for s in squad if s["games"] >= 15)
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st.markdown(kpi_card("RELIABLE", f"{stable}/25",
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"players >15 games", PITCH_GREEN), unsafe_allow_html=True)
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with k4:
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st.markdown(kpi_card("AVG GAMES", f"{avg_games:.0f}",
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"25/26 reliability", VIOLET), unsafe_allow_html=True)
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avg_g = np.mean([s["games"] for s in squad])
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st.markdown(kpi_card("AVG GAMES", f"{avg_g:.0f}",
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"25/26 experience", SKY), unsafe_allow_html=True)
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with k5:
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stars = sum(1 for s in selected if s.get("fvm", 0) > 150)
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st.markdown(kpi_card("STARS", str(stars),
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"FVM > 150", GOLD), unsafe_allow_html=True)
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stars = sum(1 for s in squad if s["fvm"] > 100)
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st.markdown(kpi_card("PREMIUM", str(stars),
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"FVM > 100", GOLD), unsafe_allow_html=True)
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st.divider()
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# ── Strategy Comparison + Squad ──
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c_left, c_right = st.columns([3, 2])
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# ── Squad Table + Radar ──
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c1, c2 = st.columns([3, 2])
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with c_left:
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section("📊 Strategy Comparison")
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fig, cdf = _strategy_comparison_chart(all_results)
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st.plotly_chart(fig, width="stretch")
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insight("Each strategy optimizes differently. Balanced blends stars + value. Safe avoids injury-prone players. Stars goes all-in on top talent.")
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with c1:
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section("🎯 Project Al-Cihred — Final Squad")
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role_groups = {"P": "🧤 Goalkeepers", "D": "🛡 Defenders",
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"C": "⚙ Midfielders", "A": "⚡ Forwards"}
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# Comparison table
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st.markdown("", unsafe_allow_html=True)
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show_df = cdf.rename(columns={
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"Strategy": "", "total_fv": "Total FV", "efficiency": "cr/FV",
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"stars": "Stars", "avg_games": "Avg Games", "budget_pct": "Budget%",
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})
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st.dataframe(show_df, use_container_width=True, hide_index=True,
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column_config={"": "Strategy"})
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with c_right:
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section("💧 Budget by Role")
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allocations = {}
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for r in ["P", "D", "C", "A"]:
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allocations[r] = sum(s["price"] for s in selected if s["role"] == r)
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fig = budget_waterfall(allocations)
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st.plotly_chart(fig, width="stretch")
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# Top value picks
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section("💎 Top Value Picks")
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df = players.copy()
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df["price"] = df.apply(lambda r: _estimate_price(
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r["qi"], r.get("fvm", 100), r.get("games_season", 30), r["role"]
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), axis=1)
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df["cr_per_fv"] = df["price"] / df["fv_avg"].clip(lower=1)
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steals = df.nlargest(8, "fv_avg").nsmallest(6, "cr_per_fv")
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for _, s in steals.iterrows():
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st.markdown(
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f'{role_chip(s["role"])} **{s["player"]}** — '
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||||
f'FV {s["fv_avg"]:.2f} · ~{s["price"]:.0f} cr '
|
||||
f'<span style="color:{TEXT_SECONDARY};font-size:11px;">({s["cr_per_fv"]:.1f} cr/FV)</span>',
|
||||
unsafe_allow_html=True,
|
||||
)
|
||||
|
||||
st.divider()
|
||||
|
||||
# ── Recommended Squad (rendered with chips) ──
|
||||
section("🎯 Selected Squad", f"{selected_label} — {len(selected)} players, {total_cost:.0f} cr")
|
||||
if selected:
|
||||
squad_df = pd.DataFrame(selected)
|
||||
for role in ["P", "D", "C", "A"]:
|
||||
rdf = squad_df[squad_df["role"] == role]
|
||||
if rdf.empty:
|
||||
for role, label in role_groups.items():
|
||||
rs = [s for s in squad if s["role"] == role]
|
||||
if not rs:
|
||||
continue
|
||||
role_name = {"P": "Goalkeepers", "D": "Defenders", "C": "Midfielders", "A": "Forwards"}[role]
|
||||
st.markdown(f"**{role_name} ({len(rdf)})**", unsafe_allow_html=False)
|
||||
for _, p in rdf.iterrows():
|
||||
chip_html = role_chip(p["role"])
|
||||
fvm_str = f" · FVM {p.get('fvm', 0):.0f}" if p.get("fvm", 0) > 0 else ""
|
||||
price_color = GOLD if p["price"] >= 100 else (SKY if p["price"] >= 50 else PITCH_GREEN)
|
||||
cost = sum(s["price"] for s in rs)
|
||||
st.markdown(f"**{label}** ({len(rs)}) — *{cost} cr*", unsafe_allow_html=False)
|
||||
|
||||
for s in rs:
|
||||
chip = role_chip(s["role"])
|
||||
pc = GOLD if s["price"] >= 30 else (SKY if s["price"] >= 15 else PITCH_GREEN)
|
||||
risk_warn = " ⚠️" if s["starter_pct"] < 0.5 else ""
|
||||
st.markdown(
|
||||
f'{chip_html} **{p["player"]}** ({p["team"]}) — '
|
||||
f'FV: {p["fv_avg"]:.2f} | '
|
||||
f'Max bid: <span style="color:{price_color};font-weight:600;">{p["price"]:.0f} cr</span> | '
|
||||
f'Games: {p["games"]:.0f}{fvm_str}',
|
||||
f'{chip} **{s["player"]}** ({s["team"]}) — '
|
||||
f'FV: {s["fv_proj"]:.2f} · '
|
||||
f'<span style="color:{pc};font-weight:600;">{s["price"]:.0f} cr</span> · '
|
||||
f'{int(s["goals"])}G {int(s["assists"])}A · {int(s["games"])}gms '
|
||||
f'· FVM {int(s["fvm"])}{risk_warn}',
|
||||
unsafe_allow_html=True,
|
||||
)
|
||||
else:
|
||||
st.warning("No valid squad found with these constraints. Try increasing budget or reducing quotas.")
|
||||
|
||||
if s["alts"]:
|
||||
alt_text = " · ".join(
|
||||
f'<span style="color:{TEXT_SECONDARY};font-size:11px;">⟳ [{a["player"]} · {int(a["price"])}cr]</span>'
|
||||
for a in s["alts"]
|
||||
)
|
||||
st.markdown(f'<span style="margin-left:22px;">{alt_text}</span>',
|
||||
unsafe_allow_html=True)
|
||||
|
||||
with c2:
|
||||
section("📊 Squad Balance Radar")
|
||||
fig = _role_radar(squad)
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
insight("Balanced squad across roles: defenders with high reliability, forwards with high FV ceiling.")
|
||||
|
||||
# Budget allocations
|
||||
allocations = {}
|
||||
for r in ["P", "D", "C", "A"]:
|
||||
allocations[r] = sum(s["price"] for s in squad if s["role"] == r)
|
||||
fig = budget_waterfall(allocations)
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
|
||||
st.divider()
|
||||
|
||||
# ── Market Analysis ──
|
||||
section("📈 Market Overview")
|
||||
c1, c2 = st.columns([1, 1])
|
||||
with c1:
|
||||
fig = _price_distribution_chart(players)
|
||||
c3, c4 = st.columns([1, 1])
|
||||
with c3:
|
||||
fig = _price_dist_chart(proj)
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
insight("Estimated auction prices by role. Forwards and elite midfielders command a premium.")
|
||||
with c2:
|
||||
insight("Price distribution by role. Forwards and elite midfielders at premium.")
|
||||
with c4:
|
||||
section("🔍 Value Scatter")
|
||||
fig = value_scatter(players)
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
|
||||
# ── Methodology ──
|
||||
st.divider()
|
||||
with st.expander("⚙️ Methodology — How this squad was computed"):
|
||||
st.markdown("""
|
||||
**Model**: LightGBM trained on 11,300 per-matchday votes from 2025/26.
|
||||
**Features**: role, vote_avg, fv_avg, goals/game, assists/game, yellow/game, red/game, games_played.
|
||||
**Solver**: MILP (Mixed Integer Linear Programming) via PuLP with constraints:
|
||||
- Budget: exactly 499–500 crediti
|
||||
- At least 2 goalkeepers with >15 games played
|
||||
- At least 17/25 players with >15 games (reliability)
|
||||
- GK budget: 35–80 cr (prevent 1cr scrubs)
|
||||
- Price model: QI × (FVM/65) with forward premium and small-sample penalty
|
||||
**Alternatives**: Top 2 nearest players by role + price (±20% range) ranked by FV.
|
||||
""", unsafe_allow_html=False)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
run()
|
||||
|
||||
Reference in New Issue
Block a user