ramseshk
8e759b0d0f
- 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
235 lines
9.2 KiB
Python
235 lines
9.2 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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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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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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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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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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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, 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", bargap=0.05,
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)
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return fig
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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=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
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def run():
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inject_css()
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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 — ML-Optimized Auction Strategy for 2026/27")
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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("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("PROJ FV", f"{total_fv:.1f}",
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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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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 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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# ── Squad Table + Radar ──
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c1, c2 = st.columns([3, 2])
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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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for role, label in role_groups.items():
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rs = [s for s in squad if s["role"] == role]
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if not rs:
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continue
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cost = sum(s["price"] for s in rs)
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st.markdown(f"**{label}** ({len(rs)}) — *{cost} cr*", unsafe_allow_html=False)
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for s in rs:
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chip = role_chip(s["role"])
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pc = GOLD if s["price"] >= 30 else (SKY if s["price"] >= 15 else PITCH_GREEN)
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risk_warn = " ⚠️" if s["starter_pct"] < 0.5 else ""
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st.markdown(
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f'{chip} **{s["player"]}** ({s["team"]}) — '
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f'FV: {s["fv_proj"]:.2f} · '
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f'<span style="color:{pc};font-weight:600;">{s["price"]:.0f} cr</span> · '
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f'{int(s["goals"])}G {int(s["assists"])}A · {int(s["games"])}gms '
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f'· FVM {int(s["fvm"])}{risk_warn}',
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unsafe_allow_html=True,
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)
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if s["alts"]:
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alt_text = " · ".join(
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f'<span style="color:{TEXT_SECONDARY};font-size:11px;">⟳ [{a["player"]} · {int(a["price"])}cr]</span>'
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for a in s["alts"]
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)
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st.markdown(f'<span style="margin-left:22px;">{alt_text}</span>',
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unsafe_allow_html=True)
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with c2:
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section("📊 Squad Balance Radar")
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fig = _role_radar(squad)
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st.plotly_chart(fig, width="stretch")
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insight("Balanced squad across roles: defenders with high reliability, forwards with high FV ceiling.")
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# Budget allocations
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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 squad 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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st.divider()
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# ── Market Analysis ──
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section("📈 Market Overview")
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c3, c4 = st.columns([1, 1])
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with c3:
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fig = _price_dist_chart(proj)
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st.plotly_chart(fig, width="stretch")
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insight("Price distribution by role. Forwards and elite midfielders at premium.")
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with c4:
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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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# ── Methodology ──
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st.divider()
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with st.expander("⚙️ Methodology — How this squad was computed"):
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st.markdown("""
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**Model**: LightGBM trained on 11,300 per-matchday votes from 2025/26.
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**Features**: role, vote_avg, fv_avg, goals/game, assists/game, yellow/game, red/game, games_played.
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**Solver**: MILP (Mixed Integer Linear Programming) via PuLP with constraints:
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- Budget: exactly 499–500 crediti
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- At least 2 goalkeepers with >15 games played
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- At least 17/25 players with >15 games (reliability)
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- GK budget: 35–80 cr (prevent 1cr scrubs)
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- Price model: QI × (FVM/65) with forward premium and small-sample penalty
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**Alternatives**: Top 2 nearest players by role + price (±20% range) ranked by FV.
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""", unsafe_allow_html=False)
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if __name__ == "__main__":
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run()
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