ramseshk
6c1fb46e8a
- Realistic price model: scales by FVM (Lautaro FVM=370 → ~199 cr, QI×5.7) Formula: qi * max(1.2, fvm/65) with forward premium and small-sample penalty - 5 strategy engines: Balanced, Value, Stars, Safe, MILP-Lite Each with different score functions (value ratio, stability, risk adj) - Strategy comparison radar grid: total FV, efficiency, stars, avg games, budget% - Squad table with color-coded price bands (gold >100cr, sky >50cr, green <50cr) - Market distribution histogram by role - Top value picks section with cr/FV efficiency - Fixed role_chip HTML rendering in all pages (unsafe_allow_html=True) - Verified: 0 errors, 31 role chips, 5 KPIs, 8 charts
334 lines
13 KiB
Python
334 lines
13 KiB
Python
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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"""
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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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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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from dashboard.warehouse import load_players, load_predictions
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from dashboard.viz.components import inject_css, section, insight, role_chip, kpi_card
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from dashboard.viz.charts import budget_waterfall, value_scatter
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from dashboard.viz.template import (
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PITCH_GREEN, GOLD, RED, SKY, VIOLET, BG, CARD_BG, BORDER, TEXT_SECONDARY,
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WHITE, FANTABETO_TEMPLATE, HEATMAP_COLORS, ROLE_COLORS, ROLE_ICONS,
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)
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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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# ─── 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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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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))
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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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)
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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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fig.update_layout(
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template=FANTABETO_TEMPLATE, height=480,
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showlegend=False,
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)
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return fig, cdf
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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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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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# ── 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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# ── 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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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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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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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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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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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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# 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 '
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f'<span style="color:{TEXT_SECONDARY};font-size:11px;">({s["cr_per_fv"]:.1f} cr/FV)</span>',
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unsafe_allow_html=True,
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)
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st.divider()
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# ── Recommended Squad (rendered with chips) ──
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section("🎯 Selected Squad", f"{selected_label} — {len(selected)} players, {total_cost:.0f} cr")
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if selected:
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squad_df = pd.DataFrame(selected)
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for role in ["P", "D", "C", "A"]:
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rdf = squad_df[squad_df["role"] == role]
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if rdf.empty:
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continue
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role_name = {"P": "Goalkeepers", "D": "Defenders", "C": "Midfielders", "A": "Forwards"}[role]
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st.markdown(f"**{role_name} ({len(rdf)})**", unsafe_allow_html=False)
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for _, p in rdf.iterrows():
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chip_html = role_chip(p["role"])
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fvm_str = f" · FVM {p.get('fvm', 0):.0f}" if p.get("fvm", 0) > 0 else ""
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price_color = GOLD if p["price"] >= 100 else (SKY if p["price"] >= 50 else PITCH_GREEN)
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st.markdown(
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f'{chip_html} **{p["player"]}** ({p["team"]}) — '
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f'FV: {p["fv_avg"]:.2f} | '
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f'Max bid: <span style="color:{price_color};font-weight:600;">{p["price"]:.0f} cr</span> | '
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f'Games: {p["games"]:.0f}{fvm_str}',
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unsafe_allow_html=True,
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)
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else:
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st.warning("No valid squad found with these constraints. Try increasing budget or reducing quotas.")
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st.divider()
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# ── Market Analysis ──
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section("📈 Market Overview")
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c1, c2 = st.columns([1, 1])
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with c1:
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fig = _price_distribution_chart(players)
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st.plotly_chart(fig, width="stretch")
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insight("Estimated auction prices by role. Forwards and elite midfielders command a premium.")
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with c2:
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section("🔍 Value Scatter")
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fig = value_scatter(players)
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st.plotly_chart(fig, width="stretch")
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if __name__ == "__main__":
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run()
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