Soccer predictions: actual data vs predicted data

Slate 20261102 · 2 match(es)

How to read this report. Each match shows the model's headline prediction, the stage-by-stage lambda (goal-rate) build, both teams' standing/form inputs, and every starter's projected line next to their real season baseline. A moderate / large deviation badge flags a projection that strays far from the player's season form — a quick way to spot when the inputs look right but a projection does not. When the real result is known an actual-vs-predicted banner appears.

All matches overview

Match	Score	Winner	H / D / A	O2.5	BTTS	Conf	λ H/A	Actual	Result
Newcastle vs Everton Premier League	1-1	Draw	36.0% / 24.7% / 39.3%	56.5%	59.5%	0.71	1.44 / 1.51	-	pending
Deportivo vs Osasuna LaLiga	2-1	Deportivo	44.5% / 24.2% / 31.3%	57.3%	59.7%	0.72	1.64 / 1.34	-	pending

Match detail

Newcastle vs Everton

Premier League · 20261102 · 4-2-3-1 vs 4-2-3-1

Predicted 1-1Winner: DrawWinning H 2-1 (8.2%)Winning A 1-2 (8.6%)Confidence 0.71λ home/away 1.44 / 1.51Most-likely P 11.5%

Actual vs predicted: result not available yet (upcoming fixture).

XGBoost Reasoning & FBref Dataset Tracing

Antagonist + XGBoost match analysis for Newcastle vs Everton. Player statistics regressed using position-specific XGBoost models (G, D, M, F) with 2026-2027 FBref priors. Micro-duels resolved across 4-2-3-1 vs 4-2-3-1 (duel lambdas: Home 1.557, Away 1.407). Final calibrated Poisson lambdas: Home 1.436, Away 1.511 (most likely score 1-1).

Home Starters 11/11 XGBoost (9 FBref) Away Starters 11/11 XGBoost (10 FBref) Bench Modeled H: 7 / A: 7 Duel λ (H/A) 1.56 / 1.41 Final λ (H/A) 1.44 / 1.51

FBref Team Priors: Newcastle United (Gls/90: 2.0, GA/90: 2.0 → E[λ]: 2.069) vs Everton (Gls/90: 1.5, GA/90: 1.5 → E[λ]: 2.069) | Blend Weight: 0.192

Modeling Pipeline Stages (5 layers)

1. XGBoost player regressions for volume & event rates with market-value quality scaling
2. Multi-source prior blending (FBref 2026-2027 tables + Sofascore season history)
3. Antagonist formation duel matrix resolving 1v1 and zonal matchups
4. Team-level prior calibration (FBref possession and attack/defense efficiencies)
5. Bivariate Poisson matrix with Dixon-Coles low-score correction

Standing & form context

Metric	Newcastle	Everton
League position	12.00	14.00
Points per game	1.37	1.30
Attack index	1.04	0.89
Defense (concede) index	0.93	1.11
Form multiplier	1.00	1.00
Standing λ multiplier	0.99	1.00

Lambda decomposition (how the goal rates were built)

Stage	Newcastle λ	Everton λ
FBref Squad Prior E[λ] (weight 0.192)	2.069	2.069
Pre-competitive-floor	1.612	1.855
Pre-calibration (Micro-duels)	1.557	1.407
Final λ	1.436	1.511

competition calibration scale 0.85competition observed total goals 2.781quality gap applied Falsemismatch score pressure applied Falsecompetitive underdog floor applied Falsestage discount 1.0

Scoreline distribution & markets

Top 5 scorelines	P
1-1	11.5%
1-2	8.6%
2-1	8.2%
0-1	7.8%
1-0	7.4%

Home win	36.0%
Draw	24.7%
Away win	39.3%
Over 2.5	56.5%
BTTS yes	59.5%

Pregame fair line — favorite Everton (39.3%), fair total 2.95, fair spread -0.07

Projected players vs season baseline

Proj = projected for this match · Season = this season's per-game average from match data · deviation badge compares projected xG to the player's season xG/game.

Newcastle — projected line vs season baseline (4-2-3-1)

Player Pos Min Proj G Proj xG Season xG/g Proj Sh Season Sh/g Proj Rtg Season Rtg Rec Model / FBref Prior

Lukáš Horníček G 90 0 0.00 - n/a 0.0 6.9 41.0 14 FBref Prior G_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost G_service raw output over 3 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0199, quality 0.677)	`market_value_eur=25000000` `quality_factor=0.6773` `boost_factor=1.0199` `reference_quality=0.505` `history_matches=8` `history_shrink=0.3846`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0333)	`blend_weight=0.0333` `history_matches=8` `season_overlap_discount=1.0` `per90_to_match_factor=1.0` `prior_reliability=0.1333` `competition=Premier League`	saves: 3.0697 -> 3.1174 fouls: 0.152 -> 0.1469
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Thierno Barry, Kiernan Dewsbury-Hall', 'matchup_zone': 'Goalkeeper shot-stopping context', 'opponent_lambda': 1.4069}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.44 (squad xG sum 1.1284, xG scale 1.2725, xA scale 1.2725, team xA 0.98 = 0.68 x lambda)	`scoreline_reset=true` `team_lambda=1.436` `squad_xg_sum=1.1284` `squad_xa_sum=0.7698` `team_expected_assists=0.9795` `team_xa_to_xg_ratio=0.6821`	shots_faced: 13.4 -> 14.4 expected_saves: 3.28 -> 3.53 high_pressure_saves: 1.13 -> 1.21
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 4 by projected rating, fifa_overall 77.0)	`match_squad_rank=4` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0` `scorer_weight=0.0` `squad_weight_total=1.5061`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0` `provider_weight=0.0` `squad_weight_total=1.0527`	-

Amar Dedić D 85 0 0.03 0.01 190% 1 0.3 6.7 28.3 14 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0280, quality 0.710)	`market_value_eur=29000000` `quality_factor=0.71` `boost_factor=1.028` `reference_quality=0.505` `history_matches=6` `history_shrink=0.4545`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0394)	`blend_weight=0.0394` `history_matches=6` `season_overlap_discount=1.0` `per90_to_match_factor=0.9397` `prior_reliability=0.1333` `competition=Premier League`	interceptionWon: 0.9217 -> 0.8854 wasFouled: 0.6599 -> 0.6894 wonTackle: 1.003 -> 0.982 fouls: 1.0349 -> 1.0497
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Tyrique George', 'matchup_zone': 'Shape fallback'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.44 (squad xG sum 1.1284, xG scale 1.2725, xA scale 1.2725, team xA 0.98 = 0.68 x lambda)	`scoreline_reset=true` `team_lambda=1.436` `squad_xg_sum=1.1284` `squad_xa_sum=0.7698` `team_expected_assists=0.9795` `team_xa_to_xg_ratio=0.6821`	expected_goals: 0.0265 -> 0.0337 expected_assists: 0.0424 -> 0.054
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 14 by projected rating, fifa_overall 76.0)	`match_squad_rank=14` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0331` `goal_rate=0.0` `scorer_weight=0.027`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0526` `assist_rate=0.4483` `provider_weight=0.0675`	-

Malick Thiaw D 90 0 0.07 0.10 37% 0 0.5 6.8 55.9 32 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0095, quality 0.809)	`market_value_eur=46000000` `quality_factor=0.8089` `boost_factor=1.0095` `reference_quality=0.505` `history_matches=43` `history_shrink=0.1042`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0090)	`blend_weight=0.009` `history_matches=43` `season_overlap_discount=1.0` `per90_to_match_factor=0.9983` `prior_reliability=0.1333` `competition=Premier League`	wasFouled: 0.3308 -> 0.3413 interceptionWon: 0.9739 -> 0.9651
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Thierno Barry', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.44 (squad xG sum 1.1284, xG scale 1.2725, xA scale 1.2725, team xA 0.98 = 0.68 x lambda)	`scoreline_reset=true` `team_lambda=1.436` `squad_xg_sum=1.1284` `squad_xa_sum=0.7698` `team_expected_assists=0.9795` `team_xa_to_xg_ratio=0.6821`	expected_goals: 0.0521 -> 0.0663 expected_assists: 0.0247 -> 0.0314
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 9 by projected rating, fifa_overall 81.0)	`match_squad_rank=9` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0641` `goal_rate=0.0131` `scorer_weight=0.053`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0309` `assist_rate=0.0` `provider_weight=0.0251`	-

Sven Botman D 89 0 0.06 0.06 ok 1 0.6 7.1 47.9 31 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0095, quality 0.739)	`market_value_eur=33000000` `quality_factor=0.7389` `boost_factor=1.0095` `reference_quality=0.505` `history_matches=32` `history_shrink=0.1351`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0117)	`blend_weight=0.0117` `history_matches=32` `season_overlap_discount=1.0` `per90_to_match_factor=0.9912` `prior_reliability=0.1333` `competition=Premier League`	interceptionWon: 1.1257 -> 1.1125 wonTackle: 0.6733 -> 0.6654
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Thierno Barry', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.44 (squad xG sum 1.1284, xG scale 1.2725, xA scale 1.2725, team xA 0.98 = 0.68 x lambda)	`scoreline_reset=true` `team_lambda=1.436` `squad_xg_sum=1.1284` `squad_xa_sum=0.7698` `team_expected_assists=0.9795` `team_xa_to_xg_ratio=0.6821`	expected_goals: 0.0495 -> 0.063 expected_assists: 0.0381 -> 0.0485
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 1 by projected rating, fifa_overall 81.0)	`match_squad_rank=1` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0611` `goal_rate=0.1396` `scorer_weight=0.0788`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0473` `assist_rate=0.0` `provider_weight=0.0388`	-

Yoane Wissa F 83 0 0.28 0.20 42% 2 0.9 6.7 43.8 35 FBref Prior F_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0072, quality 0.660)	`market_value_eur=23000000` `quality_factor=0.6595` `boost_factor=1.0072` `reference_quality=0.505` `history_matches=27` `history_shrink=0.1562`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0129)	`blend_weight=0.0129` `history_matches=27` `season_overlap_discount=1.0` `per90_to_match_factor=0.9244` `prior_reliability=0.1267` `competition=Premier League`	wasFouled: 1.0605 -> 1.0969 totalShots: 2.1406 -> 2.1509 totalOffside: 0.2146 -> 0.2244 goalAssist: 0.0684 -> 0.0738
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Vitaliy Mykolenko, Jarrad Branthwaite', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.18, 'pressing_faced': 'HIGH', 'numerical_situation': '1v2', 'xg_multiplier': 0.8377, 'skill_edge': -0.0644, 'height_edge': -0.046, 'attacker_skill': 0.6595, 'defender_skill': 0.8027, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Vitaliy Mykolenko', 'market_value': 26000000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.7498}, {'player': 'Jarrad Branthwaite', 'market_value': 46000000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.8555}], 'xg_override': 0.2178}	totalShots: 2.1509 -> 1.8018 shotOffTarget: 1.4406 -> 1.2068 onTargetScoringAttempt: 0.7035 -> 0.5893 bigChanceMissed: 0.509 -> 0.4264 goals: 0.2644 -> 0.2215 expectedGoals: 0.26 -> 0.2178 expectedGoalsOnTarget: 0.2416 -> 0.2024
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.44 (squad xG sum 1.1284, xG scale 1.2725, xA scale 1.2725, team xA 0.98 = 0.68 x lambda)	`scoreline_reset=true` `team_lambda=1.436` `squad_xg_sum=1.1284` `squad_xa_sum=0.7698` `team_expected_assists=0.9795` `team_xa_to_xg_ratio=0.6821`	expected_goals: 0.2178 -> 0.2772 expected_assists: 0.0457 -> 0.0582
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 15 by projected rating, fifa_overall 80.0)	`match_squad_rank=15` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.2421` `goal_rate=0.2215` `scorer_weight=0.281`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0565` `assist_rate=0.0738` `provider_weight=0.0728`	-

Harvey Barnes F 85 1 0.30 0.15 103% 2 1.7 6.9 57.9 39 FBref Prior F_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0079, quality 0.770)	`market_value_eur=38000000` `quality_factor=0.7699` `boost_factor=1.0079` `reference_quality=0.505` `history_matches=45` `history_shrink=0.1`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0087)	`blend_weight=0.0087` `history_matches=45` `season_overlap_discount=1.0` `per90_to_match_factor=0.943` `prior_reliability=0.1333` `competition=Premier League`	totalCross: 3.6855 -> 3.6658 totalShots: 2.2026 -> 2.1918 onTargetScoringAttempt: 0.7315 -> 0.7252
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Vitaliy Mykolenko, Jarrad Branthwaite', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.18, 'pressing_faced': 'HIGH', 'numerical_situation': '1v2', 'xg_multiplier': 0.9401, 'skill_edge': -0.0147, 'height_edge': -0.026, 'attacker_skill': 0.7699, 'defender_skill': 0.8027, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Vitaliy Mykolenko', 'market_value': 26000000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.7498}, {'player': 'Jarrad Branthwaite', 'market_value': 46000000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.8555}], 'xg_override': 0.236}	totalShots: 2.1962 -> 2.0647 shotOffTarget: 1.471 -> 1.3829 onTargetScoringAttempt: 0.7252 -> 0.6818 goals: 0.2942 -> 0.2766 expectedGoals: 0.251 -> 0.236 expectedGoalsOnTarget: 0.2049 -> 0.1926 bigChanceMissed: 0.1296 -> 0.1218
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.44 (squad xG sum 1.1284, xG scale 1.2725, xA scale 1.2725, team xA 0.98 = 0.68 x lambda)	`scoreline_reset=true` `team_lambda=1.436` `squad_xg_sum=1.1284` `squad_xa_sum=0.7698` `team_expected_assists=0.9795` `team_xa_to_xg_ratio=0.6821`	expected_goals: 0.236 -> 0.3002 expected_assists: 0.1131 -> 0.1437
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 6 by projected rating, fifa_overall 80.0)	`match_squad_rank=6` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=1` `goal_probability=0.2593` `goal_rate=0.2766` `scorer_weight=0.3428`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.1339` `assist_rate=0.2277` `provider_weight=0.1796`	-

Joe Willock M 72 0 0.23 0.07 232% 2 0.9 6.9 44.2 33 FBref Prior M_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost M_service raw output over 8 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0046, quality 0.618)	`market_value_eur=18700000` `quality_factor=0.6181` `boost_factor=1.0046` `reference_quality=0.505` `history_matches=32` `history_shrink=0.1351`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0070)	`blend_weight=0.007` `history_matches=32` `season_overlap_discount=1.0` `per90_to_match_factor=0.8034` `prior_reliability=0.08` `competition=Premier League`	fouls: 0.7199 -> 0.7384 wasFouled: 1.1597 -> 1.1516 totalCross: 0.4003 -> 0.4069 totalShots: 1.888 -> 1.8941
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'James Tarkowski', 'matchup_zone': 'Left Wing vs Right Back — Flank Battle', 'duel_win_rate': 0.541, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.0894, 'skill_edge': 0.0568, 'height_edge': 0.004, 'attacker_skill': 0.6181, 'defender_skill': 0.4918, 'attacker_role': 'LW', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'James Tarkowski', 'market_value': 5200000, 'role': 'RB', 'price_index': 0.7, 'adjusted_quality': 0.4918}], 'xg_override': 0.1776}	aerialWon: 0.3971 -> 1.2 totalShots: 1.8941 -> 2.0634 shotOffTarget: 1.2799 -> 1.3943 onTargetScoringAttempt: 0.6087 -> 0.6631 expectedGoals: 0.163 -> 0.1776 goals: 0.157 -> 0.171 bigChanceMissed: 0.0919 -> 0.1001 expectedGoalsOnTarget: 0.0841 -> 0.0916
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.44 (squad xG sum 1.1284, xG scale 1.2725, xA scale 1.2725, team xA 0.98 = 0.68 x lambda)	`scoreline_reset=true` `team_lambda=1.436` `squad_xg_sum=1.1284` `squad_xa_sum=0.7698` `team_expected_assists=0.9795` `team_xa_to_xg_ratio=0.6821`	expected_goals: 0.1776 -> 0.226 expected_assists: 0.1083 -> 0.1378
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 7 by projected rating, fifa_overall 76.0)	`match_squad_rank=7` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.2023` `goal_rate=0.171` `scorer_weight=0.2194`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.1287` `assist_rate=0.1131` `provider_weight=0.142`	-

Jacob Murphy F 72 0 0.10 0.02 474% 1 0.4 6.8 54.1 37 FBref Prior F_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0003, quality 0.513)	`market_value_eur=9200000` `quality_factor=0.513` `boost_factor=1.0003` `reference_quality=0.505` `history_matches=40` `history_shrink=0.1111`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0014)	`blend_weight=0.0014` `history_matches=40` `season_overlap_discount=1.0` `per90_to_match_factor=0.7944` `prior_reliability=0.02` `competition=Premier League`	-
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Vitaliy Mykolenko, Jarrad Branthwaite', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.18, 'pressing_faced': 'HIGH', 'numerical_situation': '1v2', 'xg_multiplier': 0.7408, 'skill_edge': -0.1303, 'height_edge': -0.046, 'attacker_skill': 0.513, 'defender_skill': 0.8027, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Vitaliy Mykolenko', 'market_value': 26000000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.7498}, {'player': 'Jarrad Branthwaite', 'market_value': 46000000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.8555}], 'xg_override': 0.0814}	totalShots: 1.1778 -> 0.8725 shotOffTarget: 0.8554 -> 0.6337 onTargetScoringAttempt: 0.3224 -> 0.2388 bigChanceMissed: 0.1603 -> 0.1188 expectedGoals: 0.1099 -> 0.0814 goals: 0.1057 -> 0.0783
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.44 (squad xG sum 1.1284, xG scale 1.2725, xA scale 1.2725, team xA 0.98 = 0.68 x lambda)	`scoreline_reset=true` `team_lambda=1.436` `squad_xg_sum=1.1284` `squad_xa_sum=0.7698` `team_expected_assists=0.9795` `team_xa_to_xg_ratio=0.6821`	expected_goals: 0.0814 -> 0.1036 expected_assists: 0.0956 -> 0.1217
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 11 by projected rating, fifa_overall 79.0)	`match_squad_rank=11` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0984` `goal_rate=0.0783` `scorer_weight=0.1012`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=1` `assist_probability=0.1146` `assist_rate=0.2165` `provider_weight=0.1521`	-

Joelinton M 86 0 0.09 0.07 32% 1 1.0 6.8 48.1 21 XGBoost M_service

Step-by-step (9 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost M_service raw output over 8 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0058, quality 0.641)	`market_value_eur=21000000` `quality_factor=0.6408` `boost_factor=1.0058` `reference_quality=0.505` `history_matches=30` `history_shrink=0.1429`	-
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'James Garner', 'matchup_zone': 'CDM vs CDM', 'duel_win_rate': 0.354, 'pressing_faced': 'ULTRA_HIGH', 'numerical_situation': '1v1', 'xg_multiplier': 0.9026, 'skill_edge': -0.0823, 'height_edge': 0.016, 'attacker_skill': 0.6747, 'defender_skill': 0.8576, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'James Garner', 'market_value': 53000000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.8576}], 'xg_override': 0.0737}	aerialWon: 3.3331 -> 1.2 totalShots: 1.0305 -> 0.9301 shotOffTarget: 0.7464 -> 0.6737 onTargetScoringAttempt: 0.2841 -> 0.2564 goals: 0.0846 -> 0.0764 expectedGoals: 0.0817 -> 0.0737 expectedGoalsOnTarget: 0.0783 -> 0.0707
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.44 (squad xG sum 1.1284, xG scale 1.2725, xA scale 1.2725, team xA 0.98 = 0.68 x lambda)	`scoreline_reset=true` `team_lambda=1.436` `squad_xg_sum=1.1284` `squad_xa_sum=0.7698` `team_expected_assists=0.9795` `team_xa_to_xg_ratio=0.6821`	expected_goals: 0.0737 -> 0.0938 expected_assists: 0.0516 -> 0.0657
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 8 by projected rating, fifa_overall 80.0)	`match_squad_rank=8` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0895` `goal_rate=0.0764` `scorer_weight=0.0958`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0636` `assist_rate=0.0732` `provider_weight=0.0797`	-

Nico González M 77 0 0.05 0.01 569% 1 0.2 7.1 34.4 28 XGBoost M_service

Step-by-step (9 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost M_service raw output over 8 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0138, quality 0.837)	`market_value_eur=54000000` `quality_factor=0.837` `boost_factor=1.0138` `reference_quality=0.505` `history_matches=31` `history_shrink=0.1389`	-
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'James Garner', 'matchup_zone': 'CDM vs CDM', 'duel_win_rate': 0.437, 'pressing_faced': 'ULTRA_HIGH', 'numerical_situation': '1v1', 'xg_multiplier': 1.0251, 'skill_edge': 0.001, 'height_edge': 0.016, 'attacker_skill': 0.8599, 'defender_skill': 0.8576, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'James Garner', 'market_value': 53000000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.8576}], 'xg_override': 0.0423}	aerialWon: 1.1459 -> 1.2 totalShots: 0.5823 -> 0.597 shotOffTarget: 0.4404 -> 0.4515
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.44 (squad xG sum 1.1284, xG scale 1.2725, xA scale 1.2725, team xA 0.98 = 0.68 x lambda)	`scoreline_reset=true` `team_lambda=1.436` `squad_xg_sum=1.1284` `squad_xa_sum=0.7698` `team_expected_assists=0.9795` `team_xa_to_xg_ratio=0.6821`	expected_goals: 0.0423 -> 0.0538 expected_assists: 0.0741 -> 0.0943
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 2 by projected rating, fifa_overall 81.0)	`match_squad_rank=2` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0524` `goal_rate=0.0456` `scorer_weight=0.0557`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.09` `assist_rate=0.0665` `provider_weight=0.0885`	-

Lewis Hall D 89 0 0.05 0.03 76% 1 0.6 6.9 52.4 33 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0114, quality 0.831)	`market_value_eur=52000000` `quality_factor=0.8309` `boost_factor=1.0114` `reference_quality=0.505` `history_matches=38` `history_shrink=0.1163`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0101)	`blend_weight=0.0101` `history_matches=38` `season_overlap_discount=1.0` `per90_to_match_factor=0.9861` `prior_reliability=0.1333` `competition=Premier League`	fouls: 0.7338 -> 0.7413 wasFouled: 1.7102 -> 1.7029 wonTackle: 1.2852 -> 1.2921
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Kiernan Dewsbury-Hall', 'matchup_zone': 'Left Wing vs Right Back — Flank Battle'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.44 (squad xG sum 1.1284, xG scale 1.2725, xA scale 1.2725, team xA 0.98 = 0.68 x lambda)	`scoreline_reset=true` `team_lambda=1.436` `squad_xg_sum=1.1284` `squad_xa_sum=0.7698` `team_expected_assists=0.9795` `team_xa_to_xg_ratio=0.6821`	expected_goals: 0.0356 -> 0.0453 expected_assists: 0.0971 -> 0.1236
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 5 by projected rating, fifa_overall 83.0)	`match_squad_rank=5` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0443` `goal_rate=0.1032` `scorer_weight=0.0566`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.1163` `assist_rate=0.0684` `provider_weight=0.0995`	-

Team totals 1 1.44 14 6.9

Bench Candidates (7 modeled substitutes with empirical playing time)

Player	Pos	Exp Min	App Prob	Substitute Modeling Details
Ewen Jaouen	G	5.1m	14.2%	Market-value substitute prior
Tino Livramento	D	15.0m	36.4%	Market-value substitute prior
Dan Burn	D	13.6m	30.1%	Market-value substitute prior
Lewis Miley	M	23.1m	33.1%	Market-value substitute prior
Aladji Bamba	M	18.5m	71.0%	Market-value substitute prior
Matías Fernández-Pardo	F	26.9m	63.3%	Market-value substitute prior

Everton — projected line vs season baseline (4-2-3-1)

Player Pos Min Proj G Proj xG Season xG/g Proj Sh Season Sh/g Proj Rtg Season Rtg Rec Model / FBref Prior

Tyrique George F 78 0 0.11 0.03 256% 1 0.8 6.9 47.5 33 FBref Prior F_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0097, quality 0.686)	`market_value_eur=26000000` `quality_factor=0.6858` `boost_factor=1.0097` `reference_quality=0.505` `history_matches=23` `history_shrink=0.1786`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0124)	`blend_weight=0.0124` `history_matches=23` `season_overlap_discount=1.0` `per90_to_match_factor=0.8639` `prior_reliability=0.1067` `competition=Premier League`	totalShots: 1.4255 -> 1.4546 interceptionWon: 0.5375 -> 0.5442
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Malick Thiaw, Sven Botman', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.18, 'pressing_faced': 'HIGH', 'numerical_situation': '1v2', 'xg_multiplier': 0.8325, 'skill_edge': -0.0639, 'height_edge': -0.05, 'attacker_skill': 0.6858, 'defender_skill': 0.8279, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Malick Thiaw', 'market_value': 46000000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.8555}, {'player': 'Sven Botman', 'market_value': 33000000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.8002}], 'xg_override': 0.0839}	totalShots: 1.4546 -> 1.211 shotOffTarget: 1.0477 -> 0.8722 onTargetScoringAttempt: 0.3798 -> 0.3162 goals: 0.1065 -> 0.0887 expectedGoals: 0.1008 -> 0.0839 expectedGoalsOnTarget: 0.0927 -> 0.0772
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.51 (squad xG sum 1.1732, xG scale 1.2880, xA scale 1.2880, team xA 1.19 = 0.79 x lambda)	`scoreline_reset=true` `team_lambda=1.511` `squad_xg_sum=1.1732` `squad_xa_sum=0.9245` `team_expected_assists=1.1907` `team_xa_to_xg_ratio=0.788`	expected_goals: 0.0839 -> 0.1081 expected_assists: 0.1066 -> 0.1373
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 5 by projected rating, fifa_overall 72.0)	`match_squad_rank=5` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.1025` `goal_rate=0.0887` `scorer_weight=0.112`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.1283` `assist_rate=0.0` `provider_weight=0.1098`	-

Kiernan Dewsbury-Hall M 88 0 0.22 0.14 54% 2 1.4 7.2 57.2 30 FBref Prior M_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost M_service raw output over 8 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0101, quality 0.800)	`market_value_eur=44000000` `quality_factor=0.8002` `boost_factor=1.0101` `reference_quality=0.505` `history_matches=39` `history_shrink=0.1136`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0098)	`blend_weight=0.0098` `history_matches=39` `season_overlap_discount=1.0` `per90_to_match_factor=0.983` `prior_reliability=0.1333` `competition=Premier League`	wonTackle: 0.6698 -> 0.6777 totalCross: 3.6665 -> 3.674
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Lewis Hall', 'matchup_zone': 'Left Wing vs Right Back — Flank Battle', 'duel_win_rate': 0.442, 'pressing_faced': 'ULTRA_HIGH', 'numerical_situation': '1v1', 'xg_multiplier': 0.9439, 'skill_edge': -0.0342, 'height_edge': -0.004, 'attacker_skill': 0.8002, 'defender_skill': 0.8762, 'attacker_role': 'LW', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Lewis Hall', 'market_value': 52000000, 'role': 'RB', 'price_index': 0.7, 'adjusted_quality': 0.8762}], 'xg_override': 0.1675}	aerialWon: 0.4462 -> 1.0 totalShots: 1.9901 -> 1.8785 shotOffTarget: 1.3167 -> 1.2428 onTargetScoringAttempt: 0.6719 -> 0.6342 bigChanceMissed: 0.2224 -> 0.2099 goals: 0.1967 -> 0.1857 expectedGoals: 0.1775 -> 0.1675 expectedGoalsOnTarget: 0.1758 -> 0.1659
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.51 (squad xG sum 1.1732, xG scale 1.2880, xA scale 1.2880, team xA 1.19 = 0.79 x lambda)	`scoreline_reset=true` `team_lambda=1.511` `squad_xg_sum=1.1732` `squad_xa_sum=0.9245` `team_expected_assists=1.1907` `team_xa_to_xg_ratio=0.788`	expected_goals: 0.1675 -> 0.2157 expected_assists: 0.1612 -> 0.2076
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 1 by projected rating, fifa_overall 81.0)	`match_squad_rank=1` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.194` `goal_rate=0.1857` `scorer_weight=0.2338`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.1875` `assist_rate=0.1345` `provider_weight=0.1814`	-

Brennan Johnson F 73 0 0.19 0.10 100% 1 0.7 6.7 52.5 37 FBref Prior F_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0060, quality 0.694)	`market_value_eur=27000000` `quality_factor=0.6941` `boost_factor=1.006` `reference_quality=0.505` `history_matches=42` `history_shrink=0.1064`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0018)	`blend_weight=0.0018` `history_matches=42` `season_overlap_discount=1.0` `per90_to_match_factor=0.81` `prior_reliability=0.0267` `competition=Premier League`	totalCross: 2.712 -> 2.707
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Malick Thiaw, Sven Botman', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.18, 'pressing_faced': 'HIGH', 'numerical_situation': '1v2', 'xg_multiplier': 0.8674, 'skill_edge': -0.0602, 'height_edge': -0.03, 'attacker_skill': 0.6941, 'defender_skill': 0.8279, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Malick Thiaw', 'market_value': 46000000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.8555}, {'player': 'Sven Botman', 'market_value': 33000000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.8002}], 'xg_override': 0.1486}	totalShots: 1.3245 -> 1.1489 shotOffTarget: 0.8403 -> 0.7289 onTargetScoringAttempt: 0.4842 -> 0.42 goals: 0.1736 -> 0.1506 expectedGoals: 0.1713 -> 0.1486 bigChanceMissed: 0.1714 -> 0.1487
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.51 (squad xG sum 1.1732, xG scale 1.2880, xA scale 1.2880, team xA 1.19 = 0.79 x lambda)	`scoreline_reset=true` `team_lambda=1.511` `squad_xg_sum=1.1732` `squad_xa_sum=0.9245` `team_expected_assists=1.1907` `team_xa_to_xg_ratio=0.788`	expected_goals: 0.1486 -> 0.1914 expected_assists: 0.0422 -> 0.0544
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 16 by projected rating, fifa_overall 78.0)	`match_squad_rank=16` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.1742` `goal_rate=0.1506` `scorer_weight=0.1933`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0529` `assist_rate=0.0219` `provider_weight=0.0435`	-

James Garner M 87 0 0.09 0.03 212% 1 0.6 7.2 58.8 33 FBref Prior M_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost M_service raw output over 8 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0097, quality 0.834)	`market_value_eur=53000000` `quality_factor=0.834` `boost_factor=1.0097` `reference_quality=0.505` `history_matches=46` `history_shrink=0.098`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0034)	`blend_weight=0.0034` `history_matches=46` `season_overlap_discount=1.0` `per90_to_match_factor=0.972` `prior_reliability=0.0533` `competition=Premier League`	wonTackle: 1.4272 -> 1.4389
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Joelinton', 'matchup_zone': 'CDM vs CDM', 'duel_win_rate': 0.486, 'pressing_faced': 'MEDIUM', 'numerical_situation': '1v1', 'xg_multiplier': 1.0974, 'skill_edge': 0.0823, 'height_edge': -0.016, 'attacker_skill': 0.8576, 'defender_skill': 0.6747, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'Joelinton', 'market_value': 21000000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.6747}], 'xg_override': 0.0677}	aerialWon: 0.3361 -> 1.1 totalShots: 0.8689 -> 0.9536 shotOffTarget: 0.587 -> 0.6442 onTargetScoringAttempt: 0.2819 -> 0.3094 goals: 0.0637 -> 0.0699 expectedGoals: 0.0617 -> 0.0677
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.51 (squad xG sum 1.1732, xG scale 1.2880, xA scale 1.2880, team xA 1.19 = 0.79 x lambda)	`scoreline_reset=true` `team_lambda=1.511` `squad_xg_sum=1.1732` `squad_xa_sum=0.9245` `team_expected_assists=1.1907` `team_xa_to_xg_ratio=0.788`	expected_goals: 0.0677 -> 0.0872 expected_assists: 0.1222 -> 0.1574
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 2 by projected rating, fifa_overall 82.0)	`match_squad_rank=2` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0835` `goal_rate=0.0699` `scorer_weight=0.0888`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.1456` `assist_rate=0.0815` `provider_weight=0.1259`	-

Harrison Armstrong M 85 0 0.11 0.04 171% 1 0.4 6.8 41.2 31 FBref Prior M_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost M_service raw output over 8 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0127, quality 0.725)	`market_value_eur=31000000` `quality_factor=0.7249` `boost_factor=1.0127` `reference_quality=0.505` `history_matches=21` `history_shrink=0.1923`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0158)	`blend_weight=0.0158` `history_matches=21` `season_overlap_discount=1.0` `per90_to_match_factor=0.9396` `prior_reliability=0.1267` `competition=Premier League`	fouls: 0.9541 -> 0.9781 interceptionWon: 0.7179 -> 0.7379 totalShots: 0.9722 -> 0.9568 wonTackle: 0.9569 -> 0.9652 goalAssist: 0.0687 -> 0.0754
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Joelinton', 'matchup_zone': 'CDM vs CDM', 'duel_win_rate': 0.455, 'pressing_faced': 'MEDIUM', 'numerical_situation': '1v1', 'xg_multiplier': 1.0512, 'skill_edge': 0.0388, 'height_edge': -0.004, 'attacker_skill': 0.761, 'defender_skill': 0.6747, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'Joelinton', 'market_value': 21000000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.6747}], 'xg_override': 0.0845}	aerialWon: 0.5232 -> 1.2 totalShots: 0.9674 -> 1.0169 shotOffTarget: 0.6722 -> 0.7066 bigChanceMissed: 0.3388 -> 0.3561 onTargetScoringAttempt: 0.2952 -> 0.3103
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.51 (squad xG sum 1.1732, xG scale 1.2880, xA scale 1.2880, team xA 1.19 = 0.79 x lambda)	`scoreline_reset=true` `team_lambda=1.511` `squad_xg_sum=1.1732` `squad_xa_sum=0.9245` `team_expected_assists=1.1907` `team_xa_to_xg_ratio=0.788`	expected_goals: 0.0845 -> 0.1088 expected_assists: 0.0778 -> 0.1002
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 7 by projected rating, fifa_overall 73.0)	`match_squad_rank=7` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.1031` `goal_rate=0.0727` `scorer_weight=0.0993`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0953` `assist_rate=0.0754` `provider_weight=0.0983`	-

Vitaliy Mykolenko D 88 0 0.01 0.01 ok 0 0.2 6.7 52.1 31 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0060, quality 0.686)	`market_value_eur=26000000` `quality_factor=0.6858` `boost_factor=1.006` `reference_quality=0.505` `history_matches=40` `history_shrink=0.1111`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0096)	`blend_weight=0.0096` `history_matches=40` `season_overlap_discount=1.0` `per90_to_match_factor=0.9741` `prior_reliability=0.1333` `competition=Premier League`	wonTackle: 1.2722 -> 1.26 totalCross: 2.2029 -> 2.1958 interceptionWon: 1.0675 -> 1.0619
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Harvey Barnes', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.51 (squad xG sum 1.1732, xG scale 1.2880, xA scale 1.2880, team xA 1.19 = 0.79 x lambda)	`scoreline_reset=true` `team_lambda=1.511` `squad_xg_sum=1.1732` `squad_xa_sum=0.9245` `team_expected_assists=1.1907` `team_xa_to_xg_ratio=0.788`	expected_assists: 0.0564 -> 0.0726
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 11 by projected rating, fifa_overall 77.0)	`match_squad_rank=11` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0073` `goal_rate=0.0` `scorer_weight=0.0066`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.07` `assist_rate=0.0068` `provider_weight=0.0581`	-

Jarrad Branthwaite D 90 0 0.05 0.03 88% 1 0.5 6.8 54.0 22 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0207, quality 0.809)	`market_value_eur=46000000` `quality_factor=0.8089` `boost_factor=1.0207` `reference_quality=0.505` `history_matches=17` `history_shrink=0.2273`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0197)	`blend_weight=0.0197` `history_matches=17` `season_overlap_discount=1.0` `per90_to_match_factor=0.9956` `prior_reliability=0.1333` `competition=Premier League`	wasFouled: 0.8179 -> 0.8018 totalCross: 0.3679 -> 0.3607 interceptionWon: 1.2311 -> 1.2363
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Harvey Barnes', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.51 (squad xG sum 1.1732, xG scale 1.2880, xA scale 1.2880, team xA 1.19 = 0.79 x lambda)	`scoreline_reset=true` `team_lambda=1.511` `squad_xg_sum=1.1732` `squad_xa_sum=0.9245` `team_expected_assists=1.1907` `team_xa_to_xg_ratio=0.788`	expected_goals: 0.041 -> 0.0528
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 8 by projected rating, fifa_overall 79.0)	`match_squad_rank=8` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0514` `goal_rate=0.0368` `scorer_weight=0.0504`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0124` `assist_rate=0.0` `provider_weight=0.0105`	-

James Tarkowski D 90 0 0.04 0.06 32% 1 0.6 7.1 58.9 32 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9988, quality 0.465)	`market_value_eur=5200000` `quality_factor=0.4646` `boost_factor=0.9988` `reference_quality=0.505` `history_matches=45` `history_shrink=0.1`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0087)	`blend_weight=0.0087` `history_matches=45` `season_overlap_discount=1.0` `per90_to_match_factor=1.0` `prior_reliability=0.1333` `competition=Premier League`	interceptionWon: 1.369 -> 1.3831 wasFouled: 1.6721 -> 1.6793
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Joe Willock', 'matchup_zone': 'Left Wing vs Right Back — Flank Battle'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.51 (squad xG sum 1.1732, xG scale 1.2880, xA scale 1.2880, team xA 1.19 = 0.79 x lambda)	`scoreline_reset=true` `team_lambda=1.511` `squad_xg_sum=1.1732` `squad_xa_sum=0.9245` `team_expected_assists=1.1907` `team_xa_to_xg_ratio=0.788`	expected_goals: 0.0324 -> 0.0417 expected_assists: 0.0368 -> 0.0474
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 4 by projected rating, fifa_overall 80.0)	`match_squad_rank=4` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0408` `goal_rate=0.1091` `scorer_weight=0.0521`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0463` `assist_rate=0.1425` `provider_weight=0.0592`	-

Ainsley Maitland-Niles D 78 0 0.01 0.01 ok 0 0.2 6.7 32.5 13 XGBoost D_service

Step-by-step (9 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0042, quality 0.533)	`market_value_eur=10900000` `quality_factor=0.533` `boost_factor=1.0042` `reference_quality=0.505` `history_matches=5` `history_shrink=0.5`	-
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Jacob Murphy', 'matchup_zone': 'Shape fallback'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.51 (squad xG sum 1.1732, xG scale 1.2880, xA scale 1.2880, team xA 1.19 = 0.79 x lambda)	`scoreline_reset=true` `team_lambda=1.511` `squad_xg_sum=1.1732` `squad_xa_sum=0.9245` `team_expected_assists=1.1907` `team_xa_to_xg_ratio=0.788`	expected_assists: 0.1829 -> 0.2356
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 15 by projected rating, fifa_overall 77.0)	`match_squad_rank=15` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0068` `goal_rate=0.0` `scorer_weight=0.0061`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=1` `assist_probability=0.2099` `assist_rate=0.2678` `provider_weight=0.2945`	-

Jordan Pickford G 90 0 0.00 - n/a 0.0 7.1 57.1 33 FBref Prior G_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost G_service raw output over 3 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0018, quality 0.567)	`market_value_eur=13900000` `quality_factor=0.5671` `boost_factor=1.0018` `reference_quality=0.505` `history_matches=46` `history_shrink=0.098`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0085)	`blend_weight=0.0085` `history_matches=46` `season_overlap_discount=1.0` `per90_to_match_factor=1.0` `prior_reliability=0.1333` `competition=Premier League`	wasFouled: 0.2782 -> 0.2886 saves: 2.9521 -> 2.961
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Harvey Barnes, Yoane Wissa', 'matchup_zone': 'Goalkeeper shot-stopping context', 'opponent_lambda': 1.5568}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.51 (squad xG sum 1.1732, xG scale 1.2880, xA scale 1.2880, team xA 1.19 = 0.79 x lambda)	`scoreline_reset=true` `team_lambda=1.511` `squad_xg_sum=1.1732` `squad_xa_sum=0.9245` `team_expected_assists=1.1907` `team_xa_to_xg_ratio=0.788`	shots_faced: 14.8 -> 13.6 expected_saves: 3.62 -> 3.32 high_pressure_saves: 1.25 -> 1.15
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 3 by projected rating, fifa_overall 85.0)	`match_squad_rank=3` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0` `scorer_weight=0.0` `squad_weight_total=1.5809`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0` `provider_weight=0.0` `squad_weight_total=1.1299`	-

Thierno Barry F 85 1 0.47 0.30 58% 2 1.4 6.8 55.2 33 FBref Prior F_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0074, quality 0.758)	`market_value_eur=36000000` `quality_factor=0.7581` `boost_factor=1.0074` `reference_quality=0.505` `history_matches=46` `history_shrink=0.098`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0068)	`blend_weight=0.0068` `history_matches=46` `season_overlap_discount=1.0` `per90_to_match_factor=0.9466` `prior_reliability=0.1067` `competition=Premier League`	fouls: 0.8993 -> 0.8932
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Malick Thiaw, Sven Botman', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.18, 'pressing_faced': 'HIGH', 'numerical_situation': '1v2', 'xg_multiplier': 0.9627, 'skill_edge': -0.0314, 'height_edge': 0.006, 'attacker_skill': 0.7581, 'defender_skill': 0.8279, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Malick Thiaw', 'market_value': 46000000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.8555}, {'player': 'Sven Botman', 'market_value': 33000000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.8002}], 'xg_override': 0.3681}	totalShots: 2.3929 -> 2.3036 shotOffTarget: 1.3236 -> 1.2742 bigChanceMissed: 1.3147 -> 1.2657 onTargetScoringAttempt: 1.0693 -> 1.0294 expectedGoalsOnTarget: 0.4298 -> 0.4138 goals: 0.3944 -> 0.3797 expectedGoals: 0.3824 -> 0.3681
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.51 (squad xG sum 1.1732, xG scale 1.2880, xA scale 1.2880, team xA 1.19 = 0.79 x lambda)	`scoreline_reset=true` `team_lambda=1.511` `squad_xg_sum=1.1732` `squad_xa_sum=0.9245` `team_expected_assists=1.1907` `team_xa_to_xg_ratio=0.788`	expected_goals: 0.3681 -> 0.4742 expected_assists: 0.0303 -> 0.039
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 9 by projected rating, fifa_overall 76.0)	`match_squad_rank=9` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=1` `goal_probability=0.3776` `goal_rate=0.3797` `scorer_weight=0.4873`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0382` `assist_rate=0.0` `provider_weight=0.0312`	-

Team totals 1 1.51 13 6.9

Bench Candidates (7 modeled substitutes with empirical playing time)

Player	Pos	Exp Min	App Prob	Substitute Modeling Details
Mark Travers	G	4.4m	10.9%	Market-value substitute prior
Jake O'Brien	D	14.2m	32.7%	Market-value substitute prior
Nathan Patterson	D	13.9m	31.6%	Market-value substitute prior
Hayden Hackney	M	26.4m	52.5%	Market-value substitute prior
Merlin Röhl	M	22.7m	32.1%	Market-value substitute prior
Jack Grealish	F	26.1m	59.5%	Market-value substitute prior

Antagonist / tactical summary

Midfield is evenly contested — Nico González vs James Garner will be the pivotal duel. Total antagonist xG — Home: 0.83, Away: 0.92. Home danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle. Away danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle.

Midfield — possession 49.4% / 50.6%, dominance ratio 0.47, key duel: Nico González vs James Garner

Joe Willock xG 0.18Harvey Barnes xG 0.24Yoane Wissa xG 0.22Kiernan Dewsbury-Hall xG 0.17Thierno Barry xG 0.37Tyrique George xG 0.08

Summary

Antagonist matchup analysis for Newcastle vs Everton. Most likely scoreline: 1-1. Lambdas derived from 6 home and 6 away individual player duels. Head-to-head: Newcastle 3W-2D-5L vs Everton over 10 meetings.

Tactical parity between 4-2-3-1 and 4-2-3-1. Match outcome driven primarily by individual player quality. Midfield is evenly contested — Nico González vs James Garner will be the pivotal duel. Total antagonist xG — Home: 0.83, Away: 0.92. Home danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle. Away danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle.

Deportivo vs Osasuna

LaLiga · 20261102 · 4-2-3-1 vs 4-3-3

Predicted 2-1Winner: DeportivoWinning H 2-1 (9.2%)Winning A 1-2 (7.5%)Confidence 0.72λ home/away 1.64 / 1.34Most-likely P 9.2%

Actual vs predicted: result not available yet (upcoming fixture).

XGBoost Reasoning & FBref Dataset Tracing

Antagonist + XGBoost match analysis for Deportivo vs Osasuna. Player statistics regressed using position-specific XGBoost models (G, D, M, F) with 2026-2027 FBref priors. Micro-duels resolved across 4-2-3-1 vs 4-3-3 (duel lambdas: Home 1.292, Away 1.167). Final calibrated Poisson lambdas: Home 1.642, Away 1.342 (most likely score 2-1).

Home Starters 11/11 XGBoost (10 FBref) Away Starters 11/11 XGBoost (8 FBref) Bench Modeled H: 7 / A: 7 Duel λ (H/A) 1.29 / 1.17 Final λ (H/A) 1.64 / 1.34

Modeling Pipeline Stages (5 layers)

1. XGBoost player regressions for volume & event rates with market-value quality scaling
2. Multi-source prior blending (FBref 2026-2027 tables + Sofascore season history)
3. Antagonist formation duel matrix resolving 1v1 and zonal matchups
4. Team-level prior calibration (FBref possession and attack/defense efficiencies)
5. Bivariate Poisson matrix with Dixon-Coles low-score correction

Standing & form context

Metric	Deportivo	Osasuna
League position	12.00	14.00
Points per game	1.20	1.20
Attack index	0.94	0.85
Defense (concede) index	1.15	0.94
Form multiplier	1.00	1.00
Standing λ multiplier	1.03	0.91

Lambda decomposition (how the goal rates were built)

Stage	Deportivo λ	Osasuna λ
Pre-competitive-floor	1.301	1.159
Pre-calibration (Micro-duels)	1.292	1.167
Final λ	1.642	1.342

competition calibration scale 1.213competition observed total goals 2.984quality gap applied Falsemismatch score pressure applied Falsecompetitive underdog floor applied Falsestage discount 1.0

Scoreline distribution & markets

Top 5 scorelines	P
1-1	11.2%
2-1	9.2%
1-0	8.2%
1-2	7.5%
2-0	6.8%

Home win	44.5%
Draw	24.2%
Away win	31.3%
Over 2.5	57.3%
BTTS yes	59.7%

Pregame fair line — favorite Deportivo (44.5%), fair total 2.98, fair spread 0.30

Projected players vs season baseline

Proj = projected for this match · Season = this season's per-game average from match data · deviation badge compares projected xG to the player's season xG/game.

Deportivo — projected line vs season baseline (4-2-3-1)

Player Pos Min Proj G Proj xG Season xG/g Proj Sh Season Sh/g Proj Rtg Season Rtg Rec Model / FBref Prior

Bil Nsongo F 71 1 0.40 0.11 269% 2 1.5 6.7 48.9 15 FBref Prior F_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9961, quality 0.466)	`market_value_eur=5300000` `quality_factor=0.4659` `boost_factor=0.9961` `reference_quality=0.505` `history_matches=10` `history_shrink=0.3333`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0231)	`blend_weight=0.0231` `history_matches=10` `season_overlap_discount=1.0` `per90_to_match_factor=0.7897` `prior_reliability=0.1067` `competition=La Liga`	wonTackle: 0.585 -> 0.6171 fouls: 0.8625 -> 0.8882 totalCross: 0.2558 -> 0.2727 wasFouled: 0.55 -> 0.5601 totalShots: 1.8388 -> 1.8299 goals: 0.2227 -> 0.2176
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Enzo Boyomo, Alejandro Catena', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.18, 'pressing_faced': 'LOW', 'numerical_situation': '1v2', 'xg_multiplier': 0.9217, 'skill_edge': -0.0313, 'height_edge': -0.022, 'attacker_skill': 0.4659, 'defender_skill': 0.5353, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Enzo Boyomo', 'market_value': 14600000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.6258}, {'player': 'Alejandro Catena', 'market_value': 2700000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4449}], 'xg_override': 0.2019}	totalShots: 1.8361 -> 1.6924 shotOffTarget: 1.2445 -> 1.1471 onTargetScoringAttempt: 0.5916 -> 0.5453 expectedGoals: 0.219 -> 0.2019 goals: 0.2176 -> 0.2006 bigChanceMissed: 0.1684 -> 0.1552
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 2 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=2`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.64 (squad xG sum 0.8207, xG scale 2.0006, xA scale 1.8887, team xA 1.31 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.642` `squad_xg_sum=0.8207` `squad_xa_sum=0.6955` `team_expected_assists=1.3136` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.2019 -> 0.4039 expected_assists: 0.0449 -> 0.0848
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 13 by projected rating, fifa_overall 69.0)	`match_squad_rank=13` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=1` `goal_probability=0.3323` `goal_rate=0.2006` `scorer_weight=0.4018`	-
`integer_assist_projection`	Team scoreline allocation: 2 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=2` `assists_allocated=0` `assist_probability=0.0813` `assist_rate=0.0` `provider_weight=0.0678`	-

Yeremay Hernández F 75 1 0.25 0.08 217% 1 0.5 6.9 48.9 14 FBref Prior F_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0166, quality 0.660)	`market_value_eur=23000000` `quality_factor=0.6595` `boost_factor=1.0166` `reference_quality=0.505` `history_matches=9` `history_shrink=0.3571`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0062)	`blend_weight=0.0062` `history_matches=9` `season_overlap_discount=1.0` `per90_to_match_factor=0.8309` `prior_reliability=0.0267` `competition=La Liga`	wonTackle: 0.8183 -> 0.839 totalOffside: 0.0 -> 0.0129 totalCross: 1.383 -> 1.3744 totalShots: 1.3108 -> 1.3027 interceptionWon: 0.9281 -> 0.9223
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Enzo Boyomo, Alejandro Catena', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.196, 'pressing_faced': 'LOW', 'numerical_situation': '1v2', 'xg_multiplier': 0.9939, 'skill_edge': 0.0559, 'height_edge': -0.06, 'attacker_skill': 0.6595, 'defender_skill': 0.5353, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Enzo Boyomo', 'market_value': 14600000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.6258}, {'player': 'Alejandro Catena', 'market_value': 2700000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4449}], 'xg_override': 0.1228}	totalShots: 1.3077 -> 1.2997 shotOffTarget: 0.8122 -> 0.8072
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 2 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=2`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.64 (squad xG sum 0.8207, xG scale 2.0006, xA scale 1.8887, team xA 1.31 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.642` `squad_xg_sum=0.8207` `squad_xa_sum=0.6955` `team_expected_assists=1.3136` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.1228 -> 0.2457 expected_assists: 0.1153 -> 0.2178
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 3 by projected rating, fifa_overall 78.0)	`match_squad_rank=3` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv_filled` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=1` `goal_probability=0.2178` `goal_rate=0.1298` `scorer_weight=0.2557`	-
`integer_assist_projection`	Team scoreline allocation: 2 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=2` `assists_allocated=0` `assist_probability=0.1957` `assist_rate=0.0` `provider_weight=0.1742`	-

Luismi Cruz F 66 0 0.23 0.05 402% 1 0.5 6.8 46.9 22 XGBoost F_service

Step-by-step (9 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9932, quality 0.437)	`market_value_eur=2100000` `quality_factor=0.4374` `boost_factor=0.9932` `reference_quality=0.505` `history_matches=10` `history_shrink=0.3333`	-
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Enzo Boyomo, Alejandro Catena', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.18, 'pressing_faced': 'LOW', 'numerical_situation': '1v2', 'xg_multiplier': 0.847, 'skill_edge': -0.0441, 'height_edge': -0.06, 'attacker_skill': 0.4374, 'defender_skill': 0.5353, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Enzo Boyomo', 'market_value': 14600000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.6258}, {'player': 'Alejandro Catena', 'market_value': 2700000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4449}], 'xg_override': 0.1162}	totalShots: 1.3222 -> 1.1199 shotOffTarget: 0.8986 -> 0.7611 onTargetScoringAttempt: 0.4236 -> 0.3588 goals: 0.1541 -> 0.1305 expectedGoals: 0.1372 -> 0.1162 expectedGoalsOnTarget: 0.1112 -> 0.0942
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 2 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=2`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.64 (squad xG sum 0.8207, xG scale 2.0006, xA scale 1.8887, team xA 1.31 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.642` `squad_xg_sum=0.8207` `squad_xa_sum=0.6955` `team_expected_assists=1.3136` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.1162 -> 0.2325 expected_assists: 0.1054 -> 0.1991
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 6 by projected rating, fifa_overall 74.0)	`match_squad_rank=6` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.2075` `goal_rate=0.1305` `scorer_weight=0.2525`	-
`integer_assist_projection`	Team scoreline allocation: 2 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=2` `assists_allocated=1` `assist_probability=0.1805` `assist_rate=0.1062` `provider_weight=0.2001`	-

David Mella F 67 0 0.19 0.04 389% 1 0.2 6.7 23.6 19 FBref Prior F_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9927, quality 0.456)	`market_value_eur=4500000` `quality_factor=0.456` `boost_factor=0.9927` `reference_quality=0.505` `history_matches=5` `history_shrink=0.5`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0108)	`blend_weight=0.0108` `history_matches=5` `season_overlap_discount=1.0` `per90_to_match_factor=0.7454` `prior_reliability=0.0333` `competition=La Liga`	totalCross: 1.0998 -> 1.1363 wonTackle: 0.0 -> 0.0161 totalOffside: 0.0 -> 0.0161 totalShots: 1.1605 -> 1.1479 fouls: 0.6068 -> 0.6164 wasFouled: 0.6068 -> 0.6164 interceptionWon: 0.7139 -> 0.7223
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Enzo Boyomo, Alejandro Catena', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.18, 'pressing_faced': 'LOW', 'numerical_situation': '1v2', 'xg_multiplier': 0.8593, 'skill_edge': -0.0357, 'height_edge': -0.06, 'attacker_skill': 0.456, 'defender_skill': 0.5353, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Enzo Boyomo', 'market_value': 14600000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.6258}, {'player': 'Alejandro Catena', 'market_value': 2700000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4449}], 'xg_override': 0.0926}	totalShots: 1.1564 -> 0.9937 shotOffTarget: 0.7815 -> 0.6715 onTargetScoringAttempt: 0.3749 -> 0.3222 bigChanceMissed: 0.2788 -> 0.2396 goals: 0.135 -> 0.116 expectedGoals: 0.1078 -> 0.0926 expectedGoalsOnTarget: 0.0656 -> 0.0564
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 2 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=2`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.64 (squad xG sum 0.8207, xG scale 2.0006, xA scale 1.8887, team xA 1.31 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.642` `squad_xg_sum=0.8207` `squad_xa_sum=0.6955` `team_expected_assists=1.3136` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0926 -> 0.1853 expected_assists: 0.04 -> 0.0755
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 17 by projected rating, fifa_overall 78.0)	`match_squad_rank=17` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv_filled` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.1691` `goal_rate=0.116` `scorer_weight=0.2157`	-
`integer_assist_projection`	Team scoreline allocation: 2 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=2` `assists_allocated=0` `assist_probability=0.0727` `assist_rate=0.0` `provider_weight=0.0604`	-

Mario Soriano M 80 0 0.14 0.05 203% 1 0.9 7.2 50.6 14 FBref Prior M_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost M_service raw output over 8 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9965, quality 0.470)	`market_value_eur=5600000` `quality_factor=0.4695` `boost_factor=0.9965` `reference_quality=0.505` `history_matches=10` `history_shrink=0.3333`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0361)	`blend_weight=0.0361` `history_matches=10` `season_overlap_discount=1.0` `per90_to_match_factor=0.8848` `prior_reliability=0.1667` `competition=La Liga`	totalCross: 2.0746 -> 2.0252 interceptionWon: 1.5871 -> 1.5426 fouls: 0.744 -> 0.7299 wonTackle: 0.3481 -> 0.3611 onTargetScoringAttempt: 0.2563 -> 0.247 goalAssist: 0.1063 -> 0.1152 totalShots: 1.2861 -> 1.2777 wasFouled: 1.2543 -> 1.2473
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Moi Gómez', 'matchup_zone': 'Holding Mid vs Central Mid — Midfield Anchor Duel', 'duel_win_rate': 0.376, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 0.9645, 'skill_edge': 0.0199, 'height_edge': -0.044, 'attacker_skill': 0.4816, 'defender_skill': 0.4374, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'Moi Gómez', 'market_value': 1600000, 'role': 'CM', 'price_index': 0.9, 'adjusted_quality': 0.4374}], 'xg_override': 0.0694}	aerialWon: 0.2269 -> 0.6 totalShots: 1.2777 -> 1.2323 shotOffTarget: 1.0298 -> 0.9932 onTargetScoringAttempt: 0.247 -> 0.2382
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 2 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=2`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.64 (squad xG sum 0.8207, xG scale 2.0006, xA scale 1.8887, team xA 1.31 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.642` `squad_xg_sum=0.8207` `squad_xa_sum=0.6955` `team_expected_assists=1.3136` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0694 -> 0.1388 expected_assists: 0.1347 -> 0.2544
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 1 by projected rating, fifa_overall 75.0)	`match_squad_rank=1` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.1296` `goal_rate=0.0785` `scorer_weight=0.1494`	-
`integer_assist_projection`	Team scoreline allocation: 2 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=2` `assists_allocated=1` `assist_probability=0.2246` `assist_rate=0.1152` `provider_weight=0.2275`	-

Lorenzo Amatucci M 88 0 0.09 0.02 263% 1 0.1 6.9 46.2 12 FBref Prior M_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost M_service raw output over 8 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0024, quality 0.526)	`market_value_eur=10300000` `quality_factor=0.526` `boost_factor=1.0024` `reference_quality=0.505` `history_matches=8` `history_shrink=0.3846`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0500)	`blend_weight=0.05` `history_matches=8` `season_overlap_discount=1.0` `per90_to_match_factor=0.9787` `prior_reliability=0.2` `competition=La Liga`	wonTackle: 1.1958 -> 1.2339 wasFouled: 0.9958 -> 0.9623 interceptionWon: 1.6222 -> 1.59 totalShots: 0.5222 -> 0.4961 fouls: 1.9104 -> 1.8964 goalAssist: 0.0463 -> 0.0603 onTargetScoringAttempt: 0.1438 -> 0.1366
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Moi Gómez', 'matchup_zone': 'Holding Mid vs Central Mid — Midfield Anchor Duel', 'duel_win_rate': 0.441, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.0607, 'skill_edge': 0.0493, 'height_edge': -0.008, 'attacker_skill': 0.547, 'defender_skill': 0.4374, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'Moi Gómez', 'market_value': 1600000, 'role': 'CM', 'price_index': 0.9, 'adjusted_quality': 0.4374}], 'xg_override': 0.0447}	aerialWon: 1.2176 -> 0.8 totalShots: 0.5149 -> 0.5462 shotOffTarget: 0.3783 -> 0.4013 bigChanceMissed: 0.2213 -> 0.2347 onTargetScoringAttempt: 0.1366 -> 0.1449 expectedGoalsOnTarget: 0.1077 -> 0.1142
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 2 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=2`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.64 (squad xG sum 0.8207, xG scale 2.0006, xA scale 1.8887, team xA 1.31 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.642` `squad_xg_sum=0.8207` `squad_xa_sum=0.6955` `team_expected_assists=1.3136` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0447 -> 0.0894 expected_assists: 0.0344 -> 0.065
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 4 by projected rating, fifa_overall 73.0)	`match_squad_rank=4` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0855` `goal_rate=0.0496` `scorer_weight=0.094`	-
`integer_assist_projection`	Team scoreline allocation: 2 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=2` `assists_allocated=0` `assist_probability=0.0629` `assist_rate=0.0603` `provider_weight=0.0813`	-

Giacomo Quagliata D 81 0 0.05 0.02 198% 1 0.4 6.7 46.4 14 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9946, quality 0.451)	`market_value_eur=4099999` `quality_factor=0.4511` `boost_factor=0.9946` `reference_quality=0.505` `history_matches=10` `history_shrink=0.3333`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0433)	`blend_weight=0.0433` `history_matches=10` `season_overlap_discount=1.0` `per90_to_match_factor=0.9033` `prior_reliability=0.2` `competition=La Liga`	totalCross: 3.6398 -> 3.5734 totalShots: 0.526 -> 0.5032 wonTackle: 0.7778 -> 0.7571 fouls: 1.607 -> 1.5896 wasFouled: 1.8879 -> 1.8713 onTargetScoringAttempt: 0.2664 -> 0.2549 interceptionWon: 1.0879 -> 1.093
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Ante Budimir', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 2 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=2`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.64 (squad xG sum 0.8207, xG scale 2.0006, xA scale 1.8887, team xA 1.31 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.642` `squad_xg_sum=0.8207` `squad_xa_sum=0.6955` `team_expected_assists=1.3136` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.027 -> 0.054 expected_assists: 0.0863 -> 0.163
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 16 by projected rating, fifa_overall 73.0)	`match_squad_rank=16` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0526` `goal_rate=0.0` `scorer_weight=0.0432`	-
`integer_assist_projection`	Team scoreline allocation: 2 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=2` `assists_allocated=0` `assist_probability=0.1504` `assist_rate=0.0` `provider_weight=0.1304`	-

José María Giménez D 80 0 0.12 0.04 213% 1 0.4 6.9 45.7 27 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9983, quality 0.472)	`market_value_eur=5800000` `quality_factor=0.472` `boost_factor=0.9983` `reference_quality=0.505` `history_matches=25` `history_shrink=0.1667`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0029)	`blend_weight=0.0029` `history_matches=25` `season_overlap_discount=1.0` `per90_to_match_factor=0.887` `prior_reliability=0.0267` `competition=La Liga`	fouls: 0.9021 -> 0.9123
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Ante Budimir', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 2 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=2`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.64 (squad xG sum 0.8207, xG scale 2.0006, xA scale 1.8887, team xA 1.31 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.642` `squad_xg_sum=0.8207` `squad_xa_sum=0.6955` `team_expected_assists=1.3136` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0607 -> 0.1214 expected_assists: 0.0182 -> 0.0344
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 5 by projected rating, fifa_overall 82.0)	`match_squad_rank=5` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.1143` `goal_rate=0.0` `scorer_weight=0.0971`	-
`integer_assist_projection`	Team scoreline allocation: 2 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=2` `assists_allocated=0` `assist_probability=0.0338` `assist_rate=0.0415` `provider_weight=0.043`	-

Miguel Loureiro D 87 0 0.02 0.00 982% 0 0.1 6.8 23.7 14 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9899, quality 0.437)	`market_value_eur=1200000` `quality_factor=0.4374` `boost_factor=0.9899` `reference_quality=0.505` `history_matches=5` `history_shrink=0.5`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0195)	`blend_weight=0.0195` `history_matches=5` `season_overlap_discount=1.0` `per90_to_match_factor=0.97` `prior_reliability=0.06` `competition=La Liga`	interceptionWon: 1.1735 -> 1.2137 wonTackle: 1.0167 -> 1.0389 fouls: 1.317 -> 1.3334 totalShots: 0.4147 -> 0.4066 onTargetScoringAttempt: 0.3848 -> 0.3773 totalCross: 0.3271 -> 0.3207 wasFouled: 0.3271 -> 0.3207
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Jonathan Dubasin', 'matchup_zone': 'Right Wing vs Left Back — Flank Battle'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 2 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=2`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.64 (squad xG sum 0.8207, xG scale 2.0006, xA scale 1.8887, team xA 1.31 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.642` `squad_xg_sum=0.8207` `squad_xa_sum=0.6955` `team_expected_assists=1.3136` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0107 -> 0.0214 expected_assists: 0.0087 -> 0.0164
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 7 by projected rating, fifa_overall 77.0)	`match_squad_rank=7` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv_filled` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0212` `goal_rate=0.0` `scorer_weight=0.0176`	-
`integer_assist_projection`	Team scoreline allocation: 2 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=2` `assists_allocated=0` `assist_probability=0.0163` `assist_rate=0.0` `provider_weight=0.014`	-

Adrià Altimira D 83 0 0.00 0.00 100% 0 0.1 6.8 36.5 15 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9916, quality 0.437)	`market_value_eur=1700000` `quality_factor=0.4374` `boost_factor=0.9916` `reference_quality=0.505` `history_matches=7` `history_shrink=0.4167`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0289)	`blend_weight=0.0289` `history_matches=7` `season_overlap_discount=1.0` `per90_to_match_factor=0.9259` `prior_reliability=0.1067` `competition=La Liga`	fouls: 0.9052 -> 0.9459 totalShots: 0.4705 -> 0.4569 interceptionWon: 0.8126 -> 0.8226 wasFouled: 1.9251 -> 1.9196
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Rubén García', 'matchup_zone': 'Left Wing vs Right Back — Flank Battle'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 2 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=2`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.64 (squad xG sum 0.8207, xG scale 2.0006, xA scale 1.8887, team xA 1.31 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.642` `squad_xg_sum=0.8207` `squad_xa_sum=0.6955` `team_expected_assists=1.3136` `team_xa_to_xg_ratio=0.8`	expected_assists: 0.0351 -> 0.0663
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 11 by projected rating, fifa_overall 82.0)	`match_squad_rank=11` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv_filled` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0` `goal_rate=0.0` `scorer_weight=0.001`	-
`integer_assist_projection`	Team scoreline allocation: 2 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=2` `assists_allocated=0` `assist_probability=0.0641` `assist_rate=0.0` `provider_weight=0.053`	-

Leo Román G 85 0 0.00 - n/a 0.0 7.1 62.3 35 FBref Prior G_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost G_service raw output over 3 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0001, quality 0.507)	`market_value_eur=8700000` `quality_factor=0.5071` `boost_factor=1.0001` `reference_quality=0.505` `history_matches=43` `history_shrink=0.1042`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0135)	`blend_weight=0.0135` `history_matches=43` `season_overlap_discount=1.0` `per90_to_match_factor=0.9418` `prior_reliability=0.2` `competition=La Liga`	-
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Ante Budimir, Jonathan Dubasin', 'matchup_zone': 'Goalkeeper shot-stopping context', 'opponent_lambda': 1.1665}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 2 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=2`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.64 (squad xG sum 0.8207, xG scale 2.0006, xA scale 1.8887, team xA 1.31 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.642` `squad_xg_sum=0.8207` `squad_xa_sum=0.6955` `team_expected_assists=1.3136` `team_xa_to_xg_ratio=0.8`	shots_faced: 11.1 -> 12.7 expected_saves: 2.72 -> 3.1 high_pressure_saves: 0.93 -> 1.07
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 2 by projected rating, fifa_overall 77.0)	`match_squad_rank=2` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0` `scorer_weight=0.0` `squad_weight_total=1.6989`	-
`integer_assist_projection`	Team scoreline allocation: 2 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=2` `assists_allocated=0` `assist_probability=0.0` `provider_weight=0.0` `squad_weight_total=1.1613`	-

Team totals 2 1.64 11 6.9

Bench Candidates (7 modeled substitutes with empirical playing time)

Player	Pos	Exp Min	App Prob	Substitute Modeling Details
Álvaro Ferllo	G	4.4m	10.9%	Market-value substitute prior
Bright Ede	D	13.7m	30.6%	Market-value substitute prior
Lucas Noubi	D	13.6m	29.9%	Market-value substitute prior
Marc Casadó	M	22.6m	52.9%	Market-value substitute prior
Angeliño	M	22.0m	50.1%	Market-value substitute prior
Jonathan Asp Jensen	F	25.4m	56.5%	Market-value substitute prior

Osasuna — projected line vs season baseline (4-3-3)

Player Pos Min Proj G Proj xG Season xG/g Proj Sh Season Sh/g Proj Rtg Season Rtg Rec Model / FBref Prior

Jonathan Dubasin F 70 0 0.21 0.13 56% 2 0.6 6.8 45.3 17 FBref Prior F_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9949, quality 0.454)	`market_value_eur=4300000` `quality_factor=0.4535` `boost_factor=0.9949` `reference_quality=0.505` `history_matches=10` `history_shrink=0.3333`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0188)	`blend_weight=0.0188` `history_matches=10` `season_overlap_discount=1.0` `per90_to_match_factor=0.7748` `prior_reliability=0.0867` `competition=La Liga`	wonTackle: 0.9366 -> 0.919 interceptionWon: 0.8744 -> 0.8916 totalOffside: 0.5308 -> 0.5208 fouls: 0.7694 -> 0.7773 wasFouled: 0.2255 -> 0.2325
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Miguel Loureiro', 'matchup_zone': 'Right Wing vs Left Back — Flank Battle', 'duel_win_rate': 0.487, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.0107, 'skill_edge': 0.0073, 'height_edge': 0.0, 'attacker_skill': 0.4535, 'defender_skill': 0.4374, 'attacker_role': 'RW', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Miguel Loureiro', 'market_value': 1200000, 'role': 'LB', 'price_index': 0.7, 'adjusted_quality': 0.4374}], 'xg_override': 0.182}	totalShots: 1.4945 -> 1.5105 shotOffTarget: 0.9585 -> 0.9688 onTargetScoringAttempt: 0.536 -> 0.5417
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.34 (squad xG sum 1.1611, xG scale 1.1558, xA scale 1.1792, team xA 0.87 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.342` `squad_xg_sum=1.1611` `squad_xa_sum=0.7397` `team_expected_assists=0.8723` `team_xa_to_xg_ratio=0.65`	expected_goals: 0.182 -> 0.2104 expected_assists: 0.0804 -> 0.0948
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 9 by projected rating, fifa_overall 77.0)	`match_squad_rank=9` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv_filled` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.1897` `goal_rate=0.2315` `scorer_weight=0.2553`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0904` `assist_rate=0.0` `provider_weight=0.0758`	-

Ante Budimir F 87 1 0.48 0.48 ok 3 2.6 6.8 63.9 34 FBref Prior F_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9981, quality 0.437)	`market_value_eur=2600000` `quality_factor=0.4374` `boost_factor=0.9981` `reference_quality=0.505` `history_matches=47` `history_shrink=0.0962`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0092)	`blend_weight=0.0092` `history_matches=47` `season_overlap_discount=1.0` `per90_to_match_factor=0.9712` `prior_reliability=0.1467` `competition=La Liga`	totalShots: 2.4772 -> 2.4902 wasFouled: 1.2591 -> 1.2718 onTargetScoringAttempt: 0.9927 -> 1.0035 wonTackle: 0.5852 -> 0.592
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Giacomo Quagliata, José María Giménez', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.216, 'pressing_faced': 'LOW', 'numerical_situation': '1v2', 'xg_multiplier': 1.0236, 'skill_edge': -0.0199, 'height_edge': 0.036, 'attacker_skill': 0.4374, 'defender_skill': 0.4817, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Giacomo Quagliata', 'market_value': 4099999, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4679}, {'player': 'José María Giménez', 'market_value': 5800000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4955}], 'xg_override': 0.4125}	totalShots: 2.4902 -> 2.549 shotOffTarget: 1.4845 -> 1.5195 onTargetScoringAttempt: 1.0035 -> 1.0272 expectedGoalsOnTarget: 0.5953 -> 0.6093 bigChanceMissed: 0.528 -> 0.5405 goals: 0.4355 -> 0.4458 expectedGoals: 0.403 -> 0.4125
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.34 (squad xG sum 1.1611, xG scale 1.1558, xA scale 1.1792, team xA 0.87 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.342` `squad_xg_sum=1.1611` `squad_xa_sum=0.7397` `team_expected_assists=0.8723` `team_xa_to_xg_ratio=0.65`	expected_goals: 0.4125 -> 0.4767 expected_assists: 0.0323 -> 0.0381
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 8 by projected rating, fifa_overall 82.0)	`match_squad_rank=8` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=1` `goal_probability=0.3792` `goal_rate=0.4458` `scorer_weight=0.511`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0374` `assist_rate=0.0` `provider_weight=0.0305`	-

Rubén García F 71 0 0.14 0.06 113% 1 0.9 6.9 58.1 42 FBref Prior F_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost F_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9980, quality 0.437)	`market_value_eur=1700000` `quality_factor=0.4374` `boost_factor=0.998` `reference_quality=0.505` `history_matches=45` `history_shrink=0.1`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0074)	`blend_weight=0.0074` `history_matches=45` `season_overlap_discount=1.0` `per90_to_match_factor=0.7902` `prior_reliability=0.1133` `competition=La Liga`	totalCross: 4.4939 -> 4.5019 totalShots: 1.1581 -> 1.1637
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Adrià Altimira', 'matchup_zone': 'Left Wing vs Right Back — Flank Battle', 'duel_win_rate': 0.484, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.0059, 'skill_edge': 0.0, 'height_edge': 0.004, 'attacker_skill': 0.4374, 'defender_skill': 0.4374, 'attacker_role': 'LW', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Adrià Altimira', 'market_value': 1700000, 'role': 'RB', 'price_index': 0.7, 'adjusted_quality': 0.4374}], 'xg_override': 0.1181}	totalShots: 1.1637 -> 1.1706
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.34 (squad xG sum 1.1611, xG scale 1.1558, xA scale 1.1792, team xA 0.87 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.342` `squad_xg_sum=1.1611` `squad_xa_sum=0.7397` `team_expected_assists=0.8723` `team_xa_to_xg_ratio=0.65`	expected_goals: 0.1181 -> 0.1365 expected_assists: 0.0981 -> 0.1157
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 5 by projected rating, fifa_overall 77.0)	`match_squad_rank=5` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.1276` `goal_rate=0.1379` `scorer_weight=0.1526`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=1` `assist_probability=0.1093` `assist_rate=0.1156` `provider_weight=0.1294`	-

Iker Muñoz M 81 0 0.06 0.01 314% 1 0.4 6.9 42.9 33 FBref Prior M_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost M_service raw output over 8 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9968, quality 0.437)	`market_value_eur=1800000` `quality_factor=0.4374` `boost_factor=0.9968` `reference_quality=0.505` `history_matches=27` `history_shrink=0.1562`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0156)	`blend_weight=0.0156` `history_matches=27` `season_overlap_discount=1.0` `per90_to_match_factor=0.8978` `prior_reliability=0.1533` `competition=La Liga`	wasFouled: 1.7395 -> 1.7246 totalCross: 0.5181 -> 0.5283
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Mario Soriano', 'matchup_zone': 'CDM vs CDM', 'duel_win_rate': 0.46, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.059, 'skill_edge': -0.0199, 'height_edge': 0.06, 'attacker_skill': 0.4374, 'defender_skill': 0.4816, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'Mario Soriano', 'market_value': 5600000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.4816}], 'xg_override': 0.0496}	aerialWon: 1.643 -> 1.0 totalShots: 0.611 -> 0.647 shotOffTarget: 0.453 -> 0.4797 onTargetScoringAttempt: 0.158 -> 0.1673
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.34 (squad xG sum 1.1611, xG scale 1.1558, xA scale 1.1792, team xA 0.87 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.342` `squad_xg_sum=1.1611` `squad_xa_sum=0.7397` `team_expected_assists=0.8723` `team_xa_to_xg_ratio=0.65`	expected_goals: 0.0496 -> 0.0573 expected_assists: 0.0402 -> 0.0474
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 6 by projected rating, fifa_overall 73.0)	`match_squad_rank=6` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0557` `goal_rate=0.0518` `scorer_weight=0.0586`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0463` `assist_rate=0.0556` `provider_weight=0.0555`	-

Moi Gómez M 77 0 0.05 0.01 560% 1 0.2 7.1 45.6 32 FBref Prior M_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost M_service raw output over 8 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9973, quality 0.437)	`market_value_eur=1600000` `quality_factor=0.4374` `boost_factor=0.9973` `reference_quality=0.505` `history_matches=33` `history_shrink=0.1316`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0114)	`blend_weight=0.0114` `history_matches=33` `season_overlap_discount=1.0` `per90_to_match_factor=0.8591` `prior_reliability=0.1333` `competition=La Liga`	totalCross: 2.1195 -> 2.1002 interceptionWon: 0.648 -> 0.6553 wonTackle: 0.5733 -> 0.5668 totalShots: 0.5036 -> 0.4979
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Mario Soriano', 'matchup_zone': 'Central Mid vs Defensive Mid — Pressing/Creation Duel', 'duel_win_rate': 0.474, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.0355, 'skill_edge': -0.0199, 'height_edge': 0.044, 'attacker_skill': 0.4374, 'defender_skill': 0.4816, 'attacker_role': 'CM', 'attacker_mv_price_index': 0.9, 'defender_adjusted_parts': [{'player': 'Mario Soriano', 'market_value': 5600000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.4816}], 'xg_override': 0.0412}	aerialWon: 0.4176 -> 0.9 totalShots: 0.502 -> 0.5198 shotOffTarget: 0.3657 -> 0.3787
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.34 (squad xG sum 1.1611, xG scale 1.1558, xA scale 1.1792, team xA 0.87 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.342` `squad_xg_sum=1.1611` `squad_xa_sum=0.7397` `team_expected_assists=0.8723` `team_xa_to_xg_ratio=0.65`	expected_goals: 0.0412 -> 0.0476 expected_assists: 0.1159 -> 0.1366
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 1 by projected rating, fifa_overall 76.0)	`match_squad_rank=1` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0465` `goal_rate=0.0638` `scorer_weight=0.0594`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.1277` `assist_rate=0.1022` `provider_weight=0.1253`	-

Jon Moncayola M 82 0 0.05 0.05 ok 1 0.5 6.8 60.8 32 FBref Prior M_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost M_service raw output over 8 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9996, quality 0.490)	`market_value_eur=7300000` `quality_factor=0.4903` `boost_factor=0.9996` `reference_quality=0.505` `history_matches=46` `history_shrink=0.098`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0106)	`blend_weight=0.0106` `history_matches=46` `season_overlap_discount=1.0` `per90_to_match_factor=0.9056` `prior_reliability=0.1667` `competition=La Liga`	totalCross: 2.467 -> 2.4523 interceptionWon: 0.5678 -> 0.581 wonTackle: 0.8813 -> 0.8912
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` duel_context={'opponent_faced': 'Mario Soriano', 'matchup_zone': 'Central Mid vs Defensive Mid — Pressing/Creation Duel', 'duel_win_rate': 0.518, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.1004, 'skill_edge': 0.0083, 'height_edge': 0.06, 'attacker_skill': 0.5, 'defender_skill': 0.4816, 'attacker_role': 'CM', 'attacker_mv_price_index': 0.9, 'defender_adjusted_parts': [{'player': 'Mario Soriano', 'market_value': 5600000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.4816}], 'xg_override': 0.0417}	aerialWon: 0.4309 -> 1.1 totalShots: 0.5455 -> 0.6003 shotOffTarget: 0.3683 -> 0.4053 onTargetScoringAttempt: 0.1772 -> 0.195 goals: 0.0626 -> 0.0689
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.34 (squad xG sum 1.1611, xG scale 1.1558, xA scale 1.1792, team xA 0.87 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.342` `squad_xg_sum=1.1611` `squad_xa_sum=0.7397` `team_expected_assists=0.8723` `team_xa_to_xg_ratio=0.65`	expected_goals: 0.0417 -> 0.0482 expected_assists: 0.0713 -> 0.0841
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 7 by projected rating, fifa_overall 81.0)	`match_squad_rank=7` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv_filled` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0471` `goal_rate=0.0689` `scorer_weight=0.0602`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0807` `assist_rate=0.0685` `provider_weight=0.0822`	-

Abel Bretones D 89 0 0.01 0.00 1206% 0 0.1 6.7 45.3 33 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9978, quality 0.442)	`market_value_eur=3400000` `quality_factor=0.4424` `boost_factor=0.9978` `reference_quality=0.505` `history_matches=38` `history_shrink=0.1163`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0151)	`blend_weight=0.0151` `history_matches=38` `season_overlap_discount=1.0` `per90_to_match_factor=0.9874` `prior_reliability=0.2` `competition=La Liga`	wonTackle: 1.3267 -> 1.3365 fouls: 1.0963 -> 1.1046 wasFouled: 0.7503 -> 0.7439
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'David Mella', 'matchup_zone': 'Shape fallback'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.34 (squad xG sum 1.1611, xG scale 1.1558, xA scale 1.1792, team xA 0.87 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.342` `squad_xg_sum=1.1611` `squad_xa_sum=0.7397` `team_expected_assists=0.8723` `team_xa_to_xg_ratio=0.65`	expected_assists: 0.0593 -> 0.0699
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 15 by projected rating, fifa_overall 76.0)	`match_squad_rank=15` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0132` `goal_rate=0.0073` `scorer_weight=0.0124`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0675` `assist_rate=0.0348` `provider_weight=0.0559`	-

Enzo Boyomo D 86 0 0.05 0.03 78% 0 0.4 6.8 43.6 29 FBref Prior D_service

Step-by-step (10 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 1.0026, quality 0.575)	`market_value_eur=14600000` `quality_factor=0.5748` `boost_factor=1.0026` `reference_quality=0.505` `history_matches=35` `history_shrink=0.125`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0033)	`blend_weight=0.0033` `history_matches=35` `season_overlap_discount=1.0` `per90_to_match_factor=0.9601` `prior_reliability=0.04` `competition=La Liga`	wonTackle: 0.823 -> 0.8307 fouls: 0.8518 -> 0.8594
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=true`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Yeremay Hernández', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.34 (squad xG sum 1.1611, xG scale 1.1558, xA scale 1.1792, team xA 0.87 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.342` `squad_xg_sum=1.1611` `squad_xa_sum=0.7397` `team_expected_assists=0.8723` `team_xa_to_xg_ratio=0.65`	expected_goals: 0.0428 -> 0.0495
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 10 by projected rating, fifa_overall 79.0)	`match_squad_rank=10` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0483` `goal_rate=0.0069` `scorer_weight=0.0396`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0102` `assist_rate=0.0669` `provider_weight=0.0129`	-

Alejandro Catena D 90 0 0.12 0.05 127% 1 0.3 6.9 60.2 32 XGBoost D_service

Step-by-step (9 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9979, quality 0.437)	`market_value_eur=2700000` `quality_factor=0.4374` `boost_factor=0.9979` `reference_quality=0.505` `history_matches=44` `history_shrink=0.102`	-
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Yeremay Hernández', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.34 (squad xG sum 1.1611, xG scale 1.1558, xA scale 1.1792, team xA 0.87 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.342` `squad_xg_sum=1.1611` `squad_xa_sum=0.7397` `team_expected_assists=0.8723` `team_xa_to_xg_ratio=0.65`	expected_goals: 0.1015 -> 0.1173 expected_assists: 0.0959 -> 0.1131
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 4 by projected rating, fifa_overall 85.0)	`match_squad_rank=4` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv_filled` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.1107` `goal_rate=0.0176` `scorer_weight=0.0938`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.1069` `assist_rate=0.1121` `provider_weight=0.1257`	-

Valentin Rosier D 85 0 0.03 0.02 53% 1 0.6 6.7 59.3 27 XGBoost D_service

Step-by-step (9 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost D_service raw output over 7 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9974, quality 0.439)	`market_value_eur=3100000` `quality_factor=0.4387` `boost_factor=0.9974` `reference_quality=0.505` `history_matches=34` `history_shrink=0.1282`	-
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Luismi Cruz', 'matchup_zone': 'Shape fallback'}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.34 (squad xG sum 1.1611, xG scale 1.1558, xA scale 1.1792, team xA 0.87 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.342` `squad_xg_sum=1.1611` `squad_xa_sum=0.7397` `team_expected_assists=0.8723` `team_xa_to_xg_ratio=0.65`	expected_assists: 0.0611 -> 0.072
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 12 by projected rating, fifa_overall 78.0)	`match_squad_rank=12` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0333` `goal_rate=0.0299` `scorer_weight=0.0342`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0695` `assist_rate=0.0012` `provider_weight=0.0576`	-

Aitor Fernández G 90 0 0.00 - n/a 0.0 7.0 14.3 34 FBref Prior G_service

Step-by-step (9 steps)

Step	Description	Key params	Changes
`xgboost_raw`	XGBoost G_service raw output over 3 target models	-	-
`market_value_boost`	Market-value quality scaling (boost 0.9916, quality 0.437)	`market_value_eur=375000` `quality_factor=0.4374` `boost_factor=0.9916` `reference_quality=0.505` `history_matches=7` `history_shrink=0.4167`	-
`final_cap`	Stats clamped to historical constraints and shot-component identity	`stats_clamped=0` `shot_identity_applied=false`	-
`enriched_model_output`	Model output after duel-context enrichment (pre router post-processing)	`duel_context_applied=true` `duel_context={'opponent_faced': 'Bil Nsongo, Yeremay Hernández', 'matchup_zone': 'Goalkeeper shot-stopping context', 'opponent_lambda': 1.2922}`	-
`scoreline_reset`	Team scoreline allocation: goal/assist counters reset to 0; the team's 1 goals are handed out as whole numbers by integer_goal_projection / integer_assist_projection	`scoreline_reset=true` `team_goals=1`	-
`team_lambda_rescale`	Player expected_goals/expected_assists rescaled to unconditional contributions so squad sum equals team lambda 1.34 (squad xG sum 1.1611, xG scale 1.1558, xA scale 1.1792, team xA 0.87 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.342` `squad_xg_sum=1.1611` `squad_xa_sum=0.7397` `team_expected_assists=0.8723` `team_xa_to_xg_ratio=0.65`	shots_faced: 12.3 -> 15.6 expected_saves: 3.01 -> 3.82 high_pressure_saves: 1.03 -> 1.31
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 3 by projected rating, fifa_overall 77.0)	`match_squad_rank=3` `squad_role=starter` `squad_rank_basis=projected_rating` `affects_projection=false` `ranking_source=sofascore_plus_fifa_csv` `ranking_status=combined`	-
`integer_goal_projection`	Team scoreline allocation: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0` `scorer_weight=0.0` `squad_weight_total=1.4159`	-
`integer_assist_projection`	Team scoreline allocation: 1 projected assists distributed by provider weight (xA x creation x appearance; nobody assists his own goal)	`allocation_applied=true` `team_assists=1` `assists_allocated=0` `assist_probability=0.0` `provider_weight=0.0` `squad_weight_total=0.8339`	-

Team totals 1 1.34 13 6.8

Bench Candidates (7 modeled substitutes with empirical playing time)

Player	Pos	Exp Min	App Prob	Substitute Modeling Details
Sergio Herrera	G	4.4m	13.1%	Market-value substitute prior
Jorge Herrando	D	13.6m	21.7%	Market-value substitute prior
Rockson Yeboah	D	13.6m	29.9%	Market-value substitute prior
Aimar Oroz	M	14.9m	43.9%	Market-value substitute prior
Lucas Torró	M	18.1m	56.4%	Market-value substitute prior
Romain Del Castillo	F	25.5m	56.6%	Market-value substitute prior

Antagonist / tactical summary

Midfield is evenly contested — Lorenzo Amatucci vs Jon Moncayola will be the pivotal duel. Total antagonist xG — Home: 0.65, Away: 0.85. Home danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle. Away danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle.

Midfield — possession 51.5% / 48.5%, dominance ratio 0.51, key duel: Lorenzo Amatucci vs Jon Moncayola

Yeremay Hernández xG 0.12Bil Nsongo xG 0.20Luismi Cruz xG 0.12Rubén García xG 0.12Jonathan Dubasin xG 0.18Ante Budimir xG 0.41

Summary

Antagonist matchup analysis for Deportivo vs Osasuna. Most likely scoreline: 2-1. Lambdas derived from 6 home and 6 away individual player duels. Head-to-head: Deportivo 4W-3D-3L vs Osasuna over 10 meetings.

Tactical parity between 4-2-3-1 and 4-3-3. Match outcome driven primarily by individual player quality. Midfield is evenly contested — Lorenzo Amatucci vs Jon Moncayola will be the pivotal duel. Total antagonist xG — Home: 0.65, Away: 0.85. Home danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle. Away danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle.

Generated 2026-11-02 14:40:10 UTC · Soccer prediction reporting