fahricansecer
988ee2f50d
betting_brain.py: - HARD_MIN_SAMPLES=50 floor for calibrator bypass - ev_edge < 0 + >= 0.20 hard vetoes - BTTS muted (grid search found no profitable config) - Per-market optimal envelopes (MS, OU25) - Score coherence filter: main_pick must agree with score prediction - HTFT reversal cross-check for MS picks feature_builder.py / data_loader.py: - Real home/away_position from data (was hardcoded 10) - Cup detection wired into UpsetEngine - _estimate_league_position with 300-day season filter New scripts: - diagnostic_backtest.py: per-bet diagnostic backtest with loss patterns - optimize_filters.py: grid search per-market optimal thresholds - analyze_backtest_csv.py: root-cause hypothesis testing on CSV - compare_backtests.py: side-by-side validation with verdict - test_score_coherence.py: smoke test for coherence filter (20/20 pass) Reports: - diagnostic_backtest_20260525_024437 (50-match smoke) - diagnostic_backtest_20260525_035649 (1000-match in-sample) - filter_optimization_patch.json (grid search winners per market) Social poster v3: - satori + resvg HTML/CSS rendering pipeline - Twemoji football/basketball + flag SVGs - caption SEO: 12 curated hashtags per post - image SEO: descriptive filenames + .json metadata sidecar - /health, /preview-png, /run-now endpoints Docs: - mds/SESSION_HANDOFF.md: full session state for cross-machine continuity - mds/SOCIAL_POSTER_SETUP.md: API keys + test commands Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
517 lines
27 KiB
Python
517 lines
27 KiB
Python
"""Feature Builder Mixin — V25/V28 feature vector assembly.
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Auto-extracted mixin module — split from services/single_match_orchestrator.py.
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All methods here are composed into SingleMatchOrchestrator via inheritance.
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`self` attributes (self.dsn, self.enrichment, self.v25_predictor, etc.) are
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initialised in the main __init__.
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"""
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from __future__ import annotations
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import json
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import re
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import time
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import math
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import os
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import pickle
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from collections import defaultdict
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from typing import Any, Dict, List, Optional, Set, Tuple, overload
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import pandas as pd
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import numpy as np
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import psycopg2
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from psycopg2.extras import RealDictCursor
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from data.db import get_clean_dsn
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from schemas.prediction import FullMatchPrediction
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from schemas.match_data import MatchData
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from models.v25_ensemble import V25Predictor, get_v25_predictor
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try:
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from models.v27_predictor import V27Predictor, compute_divergence, compute_value_edge
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except ImportError:
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class V27Predictor: # type: ignore[no-redef]
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def __init__(self): self.models = {}
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def load_models(self): return False
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def predict_all(self, features): return {}
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def compute_divergence(*args, **kwargs):
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return {}
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def compute_value_edge(*args, **kwargs):
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return {}
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from features.odds_band_analyzer import OddsBandAnalyzer
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try:
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from models.basketball_v25 import (
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BasketballMatchPrediction,
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get_basketball_v25_predictor,
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)
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except ImportError:
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BasketballMatchPrediction = Any # type: ignore[misc]
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def get_basketball_v25_predictor() -> Any:
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raise ImportError("Basketball predictor is not available")
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from core.engines.player_predictor import PlayerPrediction, get_player_predictor
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from features.upset_engine import get_upset_engine
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from services.feature_enrichment import FeatureEnrichmentService
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from services.betting_brain import BettingBrain
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from services.v26_shadow_engine import V26ShadowEngine, get_v26_shadow_engine
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from services.match_commentary import generate_match_commentary
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from utils.top_leagues import load_top_league_ids
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from utils.league_reliability import load_league_reliability
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from config.config_loader import build_threshold_dict, get_threshold_default
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from models.calibration import get_calibrator
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class FeatureBuilderMixin:
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def _build_v25_features(self, data: MatchData) -> Dict[str, float]:
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"""
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Build the single authoritative V25 pre-match feature vector.
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"""
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odds = self._sanitize_v25_odds(data.odds_data or {})
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ms_h = float(odds.get('ms_h') or 0)
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ms_d = float(odds.get('ms_d') or 0)
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ms_a = float(odds.get('ms_a') or 0)
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# Implied probabilities (vig-normalised)
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implied_home, implied_draw, implied_away = 0.33, 0.33, 0.33
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if ms_h > 0 and ms_d > 0 and ms_a > 0:
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raw_sum = 1 / ms_h + 1 / ms_d + 1 / ms_a
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implied_home = (1 / ms_h) / raw_sum
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implied_draw = (1 / ms_d) / raw_sum
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implied_away = (1 / ms_a) / raw_sum
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upset_potential = max(
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0.0,
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min(
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1.0,
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1.0 - abs(implied_home - implied_away) + (implied_draw * 0.35),
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),
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)
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# All enrichment queries in a single DB connection
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home_elo, away_elo = 1500.0, 1500.0
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home_venue_elo, away_venue_elo = 1500.0, 1500.0
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home_form_elo_val, away_form_elo_val = 1500.0, 1500.0
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enr = self.enrichment
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# Defaults — overridden by successful queries
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home_stats = dict(enr._DEFAULT_TEAM_STATS)
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away_stats = dict(enr._DEFAULT_TEAM_STATS)
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h2h = dict(enr._DEFAULT_H2H)
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home_form = dict(enr._DEFAULT_FORM)
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away_form = dict(enr._DEFAULT_FORM)
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ref = dict(enr._DEFAULT_REFEREE)
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league = dict(enr._DEFAULT_LEAGUE)
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home_momentum, away_momentum = 0.0, 0.0
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home_rolling = dict(enr._DEFAULT_ROLLING)
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away_rolling = dict(enr._DEFAULT_ROLLING)
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home_venue = dict(enr._DEFAULT_VENUE)
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away_venue = dict(enr._DEFAULT_VENUE)
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home_rest, away_rest = 7.0, 7.0
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odds_band_features = {}
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enrichment_failures = []
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try:
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with psycopg2.connect(self.dsn) as conn:
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with conn.cursor(cursor_factory=RealDictCursor) as cur:
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# ELO
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try:
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cur.execute(
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"SELECT home_elo, away_elo, "
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" home_home_elo, away_away_elo, "
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" home_form_elo, away_form_elo "
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"FROM football_ai_features "
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"WHERE match_id = %s LIMIT 1",
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(data.match_id,),
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)
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elo_row = cur.fetchone()
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if elo_row:
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home_elo = float(elo_row.get('home_elo') or 1500.0)
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away_elo = float(elo_row.get('away_elo') or 1500.0)
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home_venue_elo = float(elo_row.get('home_home_elo') or home_elo)
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away_venue_elo = float(elo_row.get('away_away_elo') or away_elo)
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home_form_elo_val = float(elo_row.get('home_form_elo') or home_elo)
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away_form_elo_val = float(elo_row.get('away_form_elo') or away_elo)
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else:
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cur.execute(
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"SELECT team_id, overall_elo, home_elo, away_elo, form_elo "
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"FROM team_elo_ratings WHERE team_id IN (%s, %s)",
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(data.home_team_id, data.away_team_id),
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)
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by_team = {str(r.get("team_id")): r for r in cur.fetchall()}
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home_row = by_team.get(str(data.home_team_id))
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away_row = by_team.get(str(data.away_team_id))
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if home_row:
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home_elo = float(home_row.get("overall_elo") or 1500.0)
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home_venue_elo = float(home_row.get("home_elo") or home_elo)
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home_form_elo_val = float(home_row.get("form_elo") or home_elo)
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if away_row:
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away_elo = float(away_row.get("overall_elo") or 1500.0)
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away_venue_elo = float(away_row.get("away_elo") or away_elo)
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away_form_elo_val = float(away_row.get("form_elo") or away_elo)
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setattr(data, "feature_source", "football_ai_features" if elo_row else "live_prematch_enrichment")
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# Staleness check: both teams at exact 1500 → ELO was never computed
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if home_elo == 1500.0 and away_elo == 1500.0:
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enrichment_failures.append("elo_stale:both_teams_at_default_1500")
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except Exception as e:
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enrichment_failures.append(f"elo:{e}")
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setattr(data, "feature_source", "fallback_defaults")
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# Team stats
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try:
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home_stats = enr.compute_team_stats(cur, data.home_team_id, data.match_date_ms)
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away_stats = enr.compute_team_stats(cur, data.away_team_id, data.match_date_ms)
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except Exception as e:
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enrichment_failures.append(f"team_stats:{e}")
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# H2H
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try:
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h2h = enr.compute_h2h(cur, data.home_team_id, data.away_team_id, data.match_date_ms)
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except Exception as e:
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enrichment_failures.append(f"h2h:{e}")
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# Form
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try:
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home_form = enr.compute_form_streaks(cur, data.home_team_id, data.match_date_ms)
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away_form = enr.compute_form_streaks(cur, data.away_team_id, data.match_date_ms)
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except Exception as e:
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enrichment_failures.append(f"form:{e}")
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# Referee
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try:
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ref = enr.compute_referee_stats(cur, data.referee_name, data.match_date_ms)
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except Exception as e:
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enrichment_failures.append(f"referee:{e}")
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# League
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try:
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league = enr.compute_league_averages(cur, data.league_id, data.match_date_ms)
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except Exception as e:
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enrichment_failures.append(f"league:{e}")
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# Momentum
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try:
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home_momentum = enr.compute_momentum(cur, data.home_team_id, data.match_date_ms)
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away_momentum = enr.compute_momentum(cur, data.away_team_id, data.match_date_ms)
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except Exception as e:
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enrichment_failures.append(f"momentum:{e}")
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# V27 Rolling + Venue + Rest
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try:
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home_rolling = enr.compute_rolling_stats(cur, data.home_team_id, data.match_date_ms)
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away_rolling = enr.compute_rolling_stats(cur, data.away_team_id, data.match_date_ms)
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home_venue = enr.compute_venue_stats(cur, data.home_team_id, data.match_date_ms, is_home=True)
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away_venue = enr.compute_venue_stats(cur, data.away_team_id, data.match_date_ms, is_home=False)
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home_rest = enr.compute_days_rest(cur, data.home_team_id, data.match_date_ms)
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away_rest = enr.compute_days_rest(cur, data.away_team_id, data.match_date_ms)
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except Exception as e:
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enrichment_failures.append(f"rolling/venue:{e}")
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# V28 Odds-Band
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try:
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odds_band_features = self.odds_band_analyzer.compute_all(
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cur=cur,
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home_team_id=data.home_team_id,
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away_team_id=data.away_team_id,
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league_id=data.league_id,
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odds=odds,
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before_ts=data.match_date_ms,
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referee_name=data.referee_name,
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)
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except Exception as e:
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enrichment_failures.append(f"odds_band:{e}")
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except Exception as e:
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enrichment_failures.append(f"db_connection:{e}")
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setattr(data, "feature_source", "fallback_defaults")
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setattr(data, "odds_band_features", odds_band_features)
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if enrichment_failures:
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print(f"⚠️ Enrichment partial failures for {data.match_id}: {', '.join(enrichment_failures)}")
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# ── Cup game detection (used by upset engine + elo dampening below) ──
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_league_name_lower = (getattr(data, 'league_name', '') or '').lower()
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_cup_keywords = ("kupa", "cup", "coupe", "copa", "coppa", "pokal",
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"trophy", "shield", "ziraat", "süper kupa", "super cup",
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"beker", "taça", "taca")
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_is_cup_match = any(kw in _league_name_lower for kw in _cup_keywords)
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# ── League size hint: top European leagues 18-20 teams, lower 16-24 ──
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# We don't have a per-league team count, so fall back to 20 (standard).
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# When standings infra lands this should pull from seasons table.
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_league_total_teams = 20
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# Upset engine features
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upset_atmosphere, upset_motivation, upset_fatigue = 0.0, 0.0, 0.0
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try:
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upset_engine = get_upset_engine()
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# Use the real position estimates from data_loader; fall back to mid-
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# table (10) only when the loader couldn't compute one. Hardcoding 10
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# for every team made motivation_score collapse to 0 for everyone.
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_home_pos = getattr(data, 'home_position', None)
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_away_pos = getattr(data, 'away_position', None)
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if _home_pos is None or _home_pos <= 0:
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_home_pos = 10
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if _away_pos is None or _away_pos <= 0:
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_away_pos = 10
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upset_feats = upset_engine.get_features(
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home_team_name=getattr(data, 'home_team_name', '') or '',
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home_team_id=data.home_team_id,
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away_team_name=getattr(data, 'away_team_name', '') or '',
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league_name=getattr(data, 'league_name', '') or '',
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home_position=_home_pos,
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away_position=_away_pos,
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match_date_ms=data.match_date_ms,
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is_cup_match=_is_cup_match,
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home_days_rest=int(home_rest),
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away_days_rest=int(away_rest),
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total_teams=_league_total_teams,
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)
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upset_atmosphere = upset_feats.get('upset_atmosphere', 0.0)
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upset_motivation = upset_feats.get('upset_motivation', 0.0)
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upset_fatigue = upset_feats.get('upset_fatigue', 0.0)
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except Exception as e:
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print(f"⚠️ Upset engine failed: {e}")
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odds_presence = {
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'odds_ms_h_present': 1.0 if ms_h > 1.01 else 0.0,
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'odds_ms_d_present': 1.0 if ms_d > 1.01 else 0.0,
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'odds_ms_a_present': 1.0 if ms_a > 1.01 else 0.0,
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'odds_ht_ms_h_present': 1.0 if float(odds.get('ht_h') or 0) > 1.01 else 0.0,
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'odds_ht_ms_d_present': 1.0 if float(odds.get('ht_d') or 0) > 1.01 else 0.0,
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'odds_ht_ms_a_present': 1.0 if float(odds.get('ht_a') or 0) > 1.01 else 0.0,
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'odds_ou05_o_present': 1.0 if float(odds.get('ou05_o') or 0) > 1.01 else 0.0,
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'odds_ou05_u_present': 1.0 if float(odds.get('ou05_u') or 0) > 1.01 else 0.0,
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'odds_ou15_o_present': 1.0 if float(odds.get('ou15_o') or 0) > 1.01 else 0.0,
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'odds_ou15_u_present': 1.0 if float(odds.get('ou15_u') or 0) > 1.01 else 0.0,
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'odds_ou25_o_present': 1.0 if float(odds.get('ou25_o') or 0) > 1.01 else 0.0,
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'odds_ou25_u_present': 1.0 if float(odds.get('ou25_u') or 0) > 1.01 else 0.0,
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'odds_ou35_o_present': 1.0 if float(odds.get('ou35_o') or 0) > 1.01 else 0.0,
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'odds_ou35_u_present': 1.0 if float(odds.get('ou35_u') or 0) > 1.01 else 0.0,
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'odds_ht_ou05_o_present': 1.0 if float(odds.get('ht_ou05_o') or 0) > 1.01 else 0.0,
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'odds_ht_ou05_u_present': 1.0 if float(odds.get('ht_ou05_u') or 0) > 1.01 else 0.0,
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'odds_ht_ou15_o_present': 1.0 if float(odds.get('ht_ou15_o') or 0) > 1.01 else 0.0,
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'odds_ht_ou15_u_present': 1.0 if float(odds.get('ht_ou15_u') or 0) > 1.01 else 0.0,
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'odds_btts_y_present': 1.0 if float(odds.get('btts_y') or 0) > 1.01 else 0.0,
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'odds_btts_n_present': 1.0 if float(odds.get('btts_n') or 0) > 1.01 else 0.0,
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}
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# ── Calendar features (V27) ──
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import datetime
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match_dt = datetime.datetime.utcfromtimestamp(data.match_date_ms / 1000)
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match_month = match_dt.month
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is_season_start = 1.0 if match_month in (7, 8, 9) else 0.0
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is_season_end = 1.0 if match_month in (5, 6) else 0.0
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# ── Derived / Interaction features (V27) ──
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# Cup games: home ELO advantage is ~30% weaker (rotation, lower motivation)
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# Uses _is_cup_match computed earlier (before upset engine call).
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elo_diff = (home_elo - away_elo) * (0.70 if _is_cup_match else 1.0)
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form_elo_diff = home_form_elo_val - away_form_elo_val
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attack_vs_defense_home = data.home_goals_avg - data.away_conceded_avg
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attack_vs_defense_away = data.away_goals_avg - data.home_conceded_avg
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xga_home = data.home_conceded_avg
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xga_away = data.away_conceded_avg
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xg_diff = xga_home - xga_away
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mom_diff = home_momentum - away_momentum
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form_momentum_interaction = mom_diff * form_elo_diff / 1000.0
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elo_form_consistency = 1.0 - abs(elo_diff - form_elo_diff) / max(abs(elo_diff), 100.0)
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upset_x_elo_gap = upset_potential * abs(elo_diff) / 500.0
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return {
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# META (1)
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'mst_utc': float(data.match_date_ms),
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# ELO (8)
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'home_overall_elo': home_elo,
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'away_overall_elo': away_elo,
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'elo_diff': elo_diff,
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'home_home_elo': home_venue_elo,
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'away_away_elo': away_venue_elo,
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'home_form_elo': home_form_elo_val,
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'away_form_elo': away_form_elo_val,
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'form_elo_diff': form_elo_diff,
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# Form (12)
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'home_goals_avg': data.home_goals_avg,
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'home_conceded_avg': data.home_conceded_avg,
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'away_goals_avg': data.away_goals_avg,
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'away_conceded_avg': data.away_conceded_avg,
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'home_clean_sheet_rate': home_form['clean_sheet_rate'],
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'away_clean_sheet_rate': away_form['clean_sheet_rate'],
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'home_scoring_rate': home_form['scoring_rate'],
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'away_scoring_rate': away_form['scoring_rate'],
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'home_winning_streak': home_form['winning_streak'],
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'away_winning_streak': away_form['winning_streak'],
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'home_unbeaten_streak': home_form['unbeaten_streak'],
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'away_unbeaten_streak': away_form['unbeaten_streak'],
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# H2H (10 — original 6 + V27 expanded 4)
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'h2h_total_matches': h2h['total_matches'],
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'h2h_home_win_rate': h2h['home_win_rate'],
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'h2h_draw_rate': h2h['draw_rate'],
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'h2h_avg_goals': h2h['avg_goals'],
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'h2h_btts_rate': h2h['btts_rate'],
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'h2h_over25_rate': h2h['over25_rate'],
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'h2h_home_goals_avg': h2h['home_goals_avg'],
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'h2h_away_goals_avg': h2h['away_goals_avg'],
|
|
'h2h_recent_trend': h2h['recent_trend'],
|
|
'h2h_venue_advantage': h2h['venue_advantage'],
|
|
# Stats (8)
|
|
'home_avg_possession': home_stats['avg_possession'],
|
|
'away_avg_possession': away_stats['avg_possession'],
|
|
'home_avg_shots_on_target': home_stats['avg_shots_on_target'],
|
|
'away_avg_shots_on_target': away_stats['avg_shots_on_target'],
|
|
'home_shot_conversion': home_stats['shot_conversion'],
|
|
'away_shot_conversion': away_stats['shot_conversion'],
|
|
'home_avg_corners': home_stats['avg_corners'],
|
|
'away_avg_corners': away_stats['avg_corners'],
|
|
# Odds (24)
|
|
'odds_ms_h': ms_h,
|
|
'odds_ms_d': ms_d,
|
|
'odds_ms_a': ms_a,
|
|
'implied_home': implied_home,
|
|
'implied_draw': implied_draw,
|
|
'implied_away': implied_away,
|
|
'odds_ht_ms_h': float(odds.get('ht_h') or 0),
|
|
'odds_ht_ms_d': float(odds.get('ht_d') or 0),
|
|
'odds_ht_ms_a': float(odds.get('ht_a') or 0),
|
|
'odds_ou05_o': float(odds.get('ou05_o') or 0),
|
|
'odds_ou05_u': float(odds.get('ou05_u') or 0),
|
|
'odds_ou15_o': float(odds.get('ou15_o') or 0),
|
|
'odds_ou15_u': float(odds.get('ou15_u') or 0),
|
|
'odds_ou25_o': float(odds.get('ou25_o') or 0),
|
|
'odds_ou25_u': float(odds.get('ou25_u') or 0),
|
|
'odds_ou35_o': float(odds.get('ou35_o') or 0),
|
|
'odds_ou35_u': float(odds.get('ou35_u') or 0),
|
|
'odds_ht_ou05_o': float(odds.get('ht_ou05_o') or 0),
|
|
'odds_ht_ou05_u': float(odds.get('ht_ou05_u') or 0),
|
|
'odds_ht_ou15_o': float(odds.get('ht_ou15_o') or 0),
|
|
'odds_ht_ou15_u': float(odds.get('ht_ou15_u') or 0),
|
|
'odds_btts_y': float(odds.get('btts_y') or 0),
|
|
'odds_btts_n': float(odds.get('btts_n') or 0),
|
|
**odds_presence,
|
|
# League (9 — original 2 + V27 expanded 5 + xga 2)
|
|
'home_xga': xga_home,
|
|
'away_xga': xga_away,
|
|
'league_avg_goals': league['avg_goals'],
|
|
'league_zero_goal_rate': league['zero_goal_rate'],
|
|
'league_home_win_rate': league['home_win_rate'],
|
|
'league_draw_rate': league['draw_rate'],
|
|
'league_btts_rate': league['btts_rate'],
|
|
'league_ou25_rate': league['ou25_rate'],
|
|
'league_reliability_score': league['reliability_score'],
|
|
# Upset (4)
|
|
'upset_atmosphere': upset_atmosphere,
|
|
'upset_motivation': upset_motivation,
|
|
'upset_fatigue': upset_fatigue,
|
|
'upset_potential': upset_potential,
|
|
# Referee (5)
|
|
'referee_home_bias': ref['home_bias'],
|
|
'referee_avg_goals': ref['avg_goals'],
|
|
'referee_cards_total': ref['cards_total'],
|
|
'referee_avg_yellow': ref['avg_yellow'],
|
|
'referee_experience': ref['experience'],
|
|
# Momentum (3)
|
|
'home_momentum_score': home_momentum,
|
|
'away_momentum_score': away_momentum,
|
|
'momentum_diff': mom_diff,
|
|
# ── V27 Rolling Stats (13) ──
|
|
'home_rolling5_goals': home_rolling['rolling5_goals'],
|
|
'home_rolling5_conceded': home_rolling['rolling5_conceded'],
|
|
'home_rolling10_goals': home_rolling['rolling10_goals'],
|
|
'home_rolling10_conceded': home_rolling['rolling10_conceded'],
|
|
'home_rolling20_goals': home_rolling['rolling20_goals'],
|
|
'home_rolling20_conceded': home_rolling['rolling20_conceded'],
|
|
'away_rolling5_goals': away_rolling['rolling5_goals'],
|
|
'away_rolling5_conceded': away_rolling['rolling5_conceded'],
|
|
'away_rolling10_goals': away_rolling['rolling10_goals'],
|
|
'away_rolling10_conceded': away_rolling['rolling10_conceded'],
|
|
'home_rolling5_cs': home_rolling['rolling5_cs'],
|
|
'away_rolling5_cs': away_rolling['rolling5_cs'],
|
|
# ── V27 Venue Stats (4) ──
|
|
'home_venue_goals': home_venue['venue_goals'],
|
|
'home_venue_conceded': home_venue['venue_conceded'],
|
|
'away_venue_goals': away_venue['venue_goals'],
|
|
'away_venue_conceded': away_venue['venue_conceded'],
|
|
# ── V27 Goal Trend (2) ──
|
|
'home_goal_trend': home_rolling['rolling5_goals'] - home_rolling['rolling10_goals'],
|
|
'away_goal_trend': away_rolling['rolling5_goals'] - away_rolling['rolling10_goals'],
|
|
# ── V27 Calendar (4) ──
|
|
'home_days_rest': home_rest,
|
|
'away_days_rest': away_rest,
|
|
'match_month': float(match_month),
|
|
'is_season_start': is_season_start,
|
|
'is_season_end': is_season_end,
|
|
# ── V27 Interaction (6) ──
|
|
'attack_vs_defense_home': attack_vs_defense_home,
|
|
'attack_vs_defense_away': attack_vs_defense_away,
|
|
'xg_diff': xg_diff,
|
|
'form_momentum_interaction': form_momentum_interaction,
|
|
'elo_form_consistency': elo_form_consistency,
|
|
'upset_x_elo_gap': upset_x_elo_gap,
|
|
# Squad Features (9) — PlayerPredictorEngine
|
|
**self._get_squad_features(data),
|
|
# V28 Odds-Band Historical Performance Features
|
|
**odds_band_features,
|
|
}
|
|
|
|
def _get_squad_features(self, data: MatchData) -> Dict[str, float]:
|
|
"""Non-fatal squad analysis with 12 player-level features."""
|
|
defaults = {
|
|
'home_squad_quality': 12.0, 'away_squad_quality': 12.0, 'squad_diff': 0.0,
|
|
'home_key_players': 3.0, 'away_key_players': 3.0,
|
|
'home_missing_impact': 0.0, 'away_missing_impact': 0.0,
|
|
'home_goals_form': 1.3, 'away_goals_form': 1.3,
|
|
'home_lineup_goals_per90': 0.0, 'away_lineup_goals_per90': 0.0,
|
|
'home_lineup_assists_per90': 0.0, 'away_lineup_assists_per90': 0.0,
|
|
'home_squad_continuity': 0.5, 'away_squad_continuity': 0.5,
|
|
'home_top_scorer_form': 0.0, 'away_top_scorer_form': 0.0,
|
|
'home_avg_player_exp': 0.0, 'away_avg_player_exp': 0.0,
|
|
'home_goals_diversity': 0.0, 'away_goals_diversity': 0.0,
|
|
}
|
|
try:
|
|
engine = get_player_predictor()
|
|
pred = engine.predict(
|
|
match_id=data.match_id,
|
|
home_team_id=data.home_team_id,
|
|
away_team_id=data.away_team_id,
|
|
home_lineup=data.home_lineup,
|
|
away_lineup=data.away_lineup,
|
|
sidelined_data=data.sidelined_data,
|
|
)
|
|
result = {
|
|
'home_squad_quality': float(pred.home_squad_quality or 0.0),
|
|
'away_squad_quality': float(pred.away_squad_quality or 0.0),
|
|
'squad_diff': float(pred.squad_diff or 0.0),
|
|
'home_key_players': float(pred.home_key_players or 0),
|
|
'away_key_players': float(pred.away_key_players or 0),
|
|
'home_missing_impact': float(pred.home_missing_impact or 0.0),
|
|
'away_missing_impact': float(pred.away_missing_impact or 0.0),
|
|
'home_goals_form': float(pred.home_goals_form or 0.0),
|
|
'away_goals_form': float(pred.away_goals_form or 0.0),
|
|
'home_lineup_goals_per90': float(pred.home_lineup_goals_per90 or 0.0),
|
|
'away_lineup_goals_per90': float(pred.away_lineup_goals_per90 or 0.0),
|
|
'home_lineup_assists_per90': float(pred.home_lineup_assists_per90 or 0.0),
|
|
'away_lineup_assists_per90': float(pred.away_lineup_assists_per90 or 0.0),
|
|
'home_squad_continuity': float(pred.home_squad_continuity or 0.5),
|
|
'away_squad_continuity': float(pred.away_squad_continuity or 0.5),
|
|
'home_top_scorer_form': float(pred.home_top_scorer_form or 0),
|
|
'away_top_scorer_form': float(pred.away_top_scorer_form or 0),
|
|
'home_avg_player_exp': float(pred.home_avg_player_exp or 0.0),
|
|
'away_avg_player_exp': float(pred.away_avg_player_exp or 0.0),
|
|
'home_goals_diversity': float(pred.home_goals_diversity or 0.0),
|
|
'away_goals_diversity': float(pred.away_goals_diversity or 0.0),
|
|
}
|
|
for side in ('home', 'away'):
|
|
sq = result[f'{side}_squad_quality']
|
|
if sq > 50 or sq < 0:
|
|
print(f"🚨 SCALE MISMATCH: {side}_squad_quality={sq:.1f} "
|
|
f"(expected 3-36). Check player_predictor formula!")
|
|
return result
|
|
except Exception as e:
|
|
print(f"⚠️ Squad features failed: {e}")
|
|
return defaults
|
|
|
|
def _sanitize_v25_odds(self, odds_data: Dict[str, Any]) -> Dict[str, float]:
|
|
sanitized: Dict[str, float] = {}
|
|
for key in self.V25_ODDS_FEATURE_KEYS:
|
|
sanitized[key] = self._real_market_odds(odds_data, key)
|
|
for key in ("dc_1x", "dc_x2", "dc_12", "oe_odd", "oe_even", "cards_o", "cards_u", "hcap_h", "hcap_d", "hcap_a"):
|
|
if key in odds_data:
|
|
sanitized[key] = self._real_market_odds(odds_data, key)
|
|
return sanitized
|