Soccer predictions: actual data vs predicted data

Slate 20261026 · 1 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
Sevilla vs Osasuna LaLiga	2-1	Sevilla	51.9% / 23.2% / 24.8%	57.3%	57.9%	0.74	1.80 / 1.18	-	pending

Match detail

Sevilla vs Osasuna

LaLiga · 20261026 · 4-2-3-1 vs 4-3-3

Predicted 2-1Winner: SevillaWinning H 2-1 (9.7%)Winning A 1-2 (6.3%)Confidence 0.74λ home/away 1.80 / 1.18Most-likely P 9.7%

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

XGBoost Reasoning & FBref Dataset Tracing

Antagonist + XGBoost match analysis for Sevilla 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 1.737, Away 1.198). Final calibrated Poisson lambdas: Home 1.804, Away 1.179 (most likely score 2-1).

Home Starters 11/11 XGBoost (7 FBref) Away Starters 11/11 XGBoost (8 FBref) Bench Modeled H: 7 / A: 7 Duel λ (H/A) 1.74 / 1.20 Final λ (H/A) 1.80 / 1.18

FBref Team Priors: Sevilla (Gls/90: 2.0, GA/90: 2.0 → E[λ]: 1.481) vs Osasuna (Gls/90: 1.0, GA/90: 1.0 → E[λ]: 1.481) | Blend Weight: 0.228

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	Sevilla	Osasuna
League position	6.00	12.00
Points per game	1.48	1.28
Attack index	1.06	0.85
Defense (concede) index	1.09	1.13
Form multiplier	1.01	0.99
Standing λ multiplier	1.06	0.98

Lambda decomposition (how the goal rates were built)

Stage	Sevilla λ	Osasuna λ
FBref Squad Prior E[λ] (weight 0.228)	1.481	1.481
Pre-competitive-floor	1.856	1.195
Pre-calibration (Micro-duels)	1.737	1.198
Final λ	1.804	1.179

competition calibration scale 0.978competition observed total goals 2.984quality gap applied Truemismatch score pressure applied Falsecompetitive underdog floor applied Falsestage discount 1.0

Scoreline distribution & markets

Top 5 scorelines	P
1-1	10.8%
2-1	9.7%
1-0	9.1%
2-0	8.2%
1-2	6.3%

Home win	51.9%
Draw	23.2%
Away win	24.8%
Over 2.5	57.3%
BTTS yes	57.9%

Pregame fair line — favorite Sevilla (51.9%), fair total 2.98, fair spread 0.62

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.

Sevilla — 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

Lucien Agoumé M 88 0 0.10 0.02 315% 1 0.5 6.9 61.7 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 1.0035, quality 0.616)	`market_value_eur=18500000` `quality_factor=0.6161` `boost_factor=1.0035` `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=0.9802` `prior_reliability=0.2` `competition=La Liga`	wonTackle: 1.9556 -> 1.9734 wasFouled: 0.4929 -> 0.5083 totalCross: 0.2232 -> 0.2379 fouls: 1.9045 -> 1.9141 interceptionWon: 1.5737 -> 1.5789
`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': 'Iker Muñoz', 'matchup_zone': 'Holding Mid vs Central Mid — Midfield Anchor Duel', 'duel_win_rate': 0.519, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.1746, 'skill_edge': 0.0948, 'height_edge': 0.024, 'attacker_skill': 0.648, 'defender_skill': 0.4374, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'Iker Muñoz', 'market_value': 1800000, 'role': 'CM', 'price_index': 0.9, 'adjusted_quality': 0.4374}], 'xg_override': 0.0588}	aerialWon: 0.5068 -> 1.2 totalShots: 0.7909 -> 0.929 shotOffTarget: 0.563 -> 0.6613 onTargetScoringAttempt: 0.2266 -> 0.2662 expectedGoalsOnTarget: 0.0813 -> 0.0955 goals: 0.0524 -> 0.0615 expectedGoals: 0.0501 -> 0.0588
`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 1.80 (squad xG sum 1.1026, xG scale 1.6361, xA scale 1.5373, team xA 1.44 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.804` `squad_xg_sum=1.1026` `squad_xa_sum=0.9388` `team_expected_assists=1.4432` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0588 -> 0.0962 expected_assists: 0.0476 -> 0.0732
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 6 by projected rating, fifa_overall 76.0)	`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=0.0917` `goal_rate=0.0615` `scorer_weight=0.0986`	-
`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=0.0706` `assist_rate=0.0548` `provider_weight=0.0786`	-

Youssouf Fofana M 85 0 0.25 0.09 160% 2 1.1 6.9 48.1 9 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 1.0120, quality 0.593)	`market_value_eur=16300000` `quality_factor=0.5932` `boost_factor=1.012` `reference_quality=0.505` `history_matches=6` `history_shrink=0.4545`	-
`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': 'Abel Bretones', 'matchup_zone': 'Left Wing vs Right Back — Flank Battle', 'duel_win_rate': 0.528, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.0702, 'skill_edge': 0.0597, 'height_edge': -0.012, 'attacker_skill': 0.5932, 'defender_skill': 0.4604, 'attacker_role': 'LW', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Abel Bretones', 'market_value': 3400000, 'role': 'RB', 'price_index': 0.7, 'adjusted_quality': 0.4604}], 'xg_override': 0.1501}	aerialWon: 0.7687 -> 1.2 totalShots: 1.5974 -> 1.7095 shotOffTarget: 1.0432 -> 1.1164 onTargetScoringAttempt: 0.5542 -> 0.5931 bigChanceMissed: 0.3934 -> 0.421 expectedGoalsOnTarget: 0.284 -> 0.3039 goals: 0.1416 -> 0.1515 expectedGoals: 0.1403 -> 0.1501
`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 1.80 (squad xG sum 1.1026, xG scale 1.6361, xA scale 1.5373, team xA 1.44 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.804` `squad_xg_sum=1.1026` `squad_xa_sum=0.9388` `team_expected_assists=1.4432` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.1501 -> 0.2456 expected_assists: 0.1131 -> 0.1739
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 2 by projected rating, fifa_overall 80.0)	`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=0.2178` `goal_rate=0.1515` `scorer_weight=0.2473`	-
`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=0.1596` `assist_rate=0.0929` `provider_weight=0.1524`	-

Rubén Vargas F 69 0 0.20 0.06 224% 2 1.0 6.8 47.4 21 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 1.0030, quality 0.567)	`market_value_eur=13900000` `quality_factor=0.5671` `boost_factor=1.003` `reference_quality=0.505` `history_matches=26` `history_shrink=0.1613`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0014)	`blend_weight=0.0014` `history_matches=26` `season_overlap_discount=1.0` `per90_to_match_factor=0.7646` `prior_reliability=0.0133` `competition=La Liga`	totalCross: 4.5376 -> 4.5313
`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': 0.971, 'skill_edge': 0.0143, 'height_edge': -0.034, 'attacker_skill': 0.5671, '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': 0.1235}	totalShots: 1.756 -> 1.7051 shotOffTarget: 1.2269 -> 1.1913 onTargetScoringAttempt: 0.5263 -> 0.511
`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 1.80 (squad xG sum 1.1026, xG scale 1.6361, xA scale 1.5373, team xA 1.44 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.804` `squad_xg_sum=1.1026` `squad_xa_sum=0.9388` `team_expected_assists=1.4432` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.1235 -> 0.2021 expected_assists: 0.1152 -> 0.1771
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 8 by projected rating, fifa_overall 78.0)	`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: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.183` `goal_rate=0.1379` `scorer_weight=0.2189`	-
`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=0.1623` `assist_rate=0.1981` `provider_weight=0.2214`	-

Giorgi Kochorashvili M 78 0 0.08 0.01 595% 1 0.4 7.0 40.4 10 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 0.9930, quality 0.454)	`market_value_eur=4300000` `quality_factor=0.4535` `boost_factor=0.993` `reference_quality=0.505` `history_matches=6` `history_shrink=0.4545`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0079)	`blend_weight=0.0079` `history_matches=6` `season_overlap_discount=1.0` `per90_to_match_factor=0.8612` `prior_reliability=0.0267` `competition=La Liga`	totalCross: 1.8297 -> 1.8662 totalShots: 0.7286 -> 0.7568 fouls: 0.2789 -> 0.2937 wasFouled: 0.2789 -> 0.2937
`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': 'Iker Muñoz', 'matchup_zone': 'Holding Mid vs Central Mid — Midfield Anchor Duel', 'duel_win_rate': 0.399, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 0.9992, 'skill_edge': 0.0115, 'height_edge': -0.012, 'attacker_skill': 0.4629, 'defender_skill': 0.4374, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'Iker Muñoz', 'market_value': 1800000, 'role': 'CM', 'price_index': 0.9, 'adjusted_quality': 0.4374}], 'xg_override': 0.0512}	aerialWon: 0.7721 -> 0.9
`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 1.80 (squad xG sum 1.1026, xG scale 1.6361, xA scale 1.5373, team xA 1.44 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.804` `squad_xg_sum=1.1026` `squad_xa_sum=0.9388` `team_expected_assists=1.4432` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0512 -> 0.0838 expected_assists: 0.0952 -> 0.1464
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 1 by projected rating, fifa_overall 74.0)	`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=0.0804` `goal_rate=0.0773` `scorer_weight=0.1047`	-
`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=0.1362` `assist_rate=0.0841` `provider_weight=0.1352`	-

Félix Correia F 75 1 0.32 0.10 208% 2 0.5 6.7 38.4 14 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 1.0021, quality 0.519)	`market_value_eur=9700000` `quality_factor=0.5189` `boost_factor=1.0021` `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': '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': 0.9333, 'skill_edge': -0.0074, 'height_edge': -0.038, 'attacker_skill': 0.5189, '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': 0.1937}	totalShots: 1.5475 -> 1.4443 shotOffTarget: 0.9764 -> 0.9113 onTargetScoringAttempt: 0.5711 -> 0.533 bigChanceMissed: 0.3895 -> 0.3635 expectedGoalsOnTarget: 0.2106 -> 0.1966 expectedGoals: 0.2075 -> 0.1937 goals: 0.206 -> 0.1923
`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 1.80 (squad xG sum 1.1026, xG scale 1.6361, xA scale 1.5373, team xA 1.44 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.804` `squad_xg_sum=1.1026` `squad_xa_sum=0.9388` `team_expected_assists=1.4432` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.1937 -> 0.3169 expected_assists: 0.0927 -> 0.1425
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 13 by projected rating, fifa_overall 75.0)	`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=0.2716` `goal_rate=0.1923` `scorer_weight=0.3151`	-
`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=0.1328` `assist_rate=0.1952` `provider_weight=0.1781`	-

Robbie Ure F 74 1 0.37 0.08 332% 2 1.0 6.9 44.6 12 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 0.9978, quality 0.484)	`market_value_eur=6800000` `quality_factor=0.4842` `boost_factor=0.9978` `reference_quality=0.505` `history_matches=9` `history_shrink=0.3571`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0387)	`blend_weight=0.0387` `history_matches=9` `season_overlap_discount=1.0` `per90_to_match_factor=0.8221` `prior_reliability=0.1667` `competition=La Liga`	onTargetScoringAttempt: 0.7537 -> 0.7245 totalShots: 1.9586 -> 1.9337 totalOffside: 0.4366 -> 0.4197 goals: 0.2007 -> 0.1929 wasFouled: 1.8084 -> 1.802 fouls: 1.463 -> 1.4573 totalCross: 0.1476 -> 0.1419
`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': 0.183, 'pressing_faced': 'LOW', 'numerical_situation': '1v2', 'xg_multiplier': 0.975, 'skill_edge': -0.023, 'height_edge': 0.006, 'attacker_skill': 0.4842, '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': 0.2241}	totalShots: 1.9337 -> 1.8854 shotOffTarget: 1.2048 -> 1.1747 onTargetScoringAttempt: 0.7245 -> 0.7064 bigChanceMissed: 0.4342 -> 0.4233 expectedGoals: 0.2298 -> 0.2241
`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 1.80 (squad xG sum 1.1026, xG scale 1.6361, xA scale 1.5373, team xA 1.44 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.804` `squad_xg_sum=1.1026` `squad_xa_sum=0.9388` `team_expected_assists=1.4432` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.2241 -> 0.3666 expected_assists: 0.0762 -> 0.1171
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 5 by projected rating, fifa_overall 71.0)	`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=1` `goal_probability=0.3069` `goal_rate=0.1881` `scorer_weight=0.3185`	-
`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=0.1105` `assist_rate=0.3297` `provider_weight=0.1464`	-

Gabriel Suazo D 88 0 0.00 0.01 ok 0 0.1 6.7 55.5 30 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 0.9996, quality 0.494)	`market_value_eur=7600000` `quality_factor=0.4939` `boost_factor=0.9996` `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=0.978` `prior_reliability=0.2` `competition=La Liga`	totalCross: 2.4761 -> 2.4929 fouls: 1.8552 -> 1.8715
`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': '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 1.80 (squad xG sum 1.1026, xG scale 1.6361, xA scale 1.5373, team xA 1.44 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.804` `squad_xg_sum=1.1026` `squad_xa_sum=0.9388` `team_expected_assists=1.4432` `team_xa_to_xg_ratio=0.8`	expected_assists: 0.1653 -> 0.254
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 16 by projected rating, fifa_overall 74.0)	`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=0.0047` `goal_rate=0.0` `scorer_weight=0.0044`	-
`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=0.2243` `assist_rate=0.0019` `provider_weight=0.2032`	-

Kike Salas D 90 0 0.07 0.06 33% 1 0.8 6.9 62.8 30 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 1.0031, quality 0.584)	`market_value_eur=15400000` `quality_factor=0.5835` `boost_factor=1.0031` `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=0.9986` `prior_reliability=0.1333` `competition=La Liga`	wasFouled: 0.6454 -> 0.6551 totalShots: 0.8825 -> 0.8899
`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 1.80 (squad xG sum 1.1026, xG scale 1.6361, xA scale 1.5373, team xA 1.44 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.804` `squad_xg_sum=1.1026` `squad_xa_sum=0.9388` `team_expected_assists=1.4432` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0454 -> 0.0743 expected_assists: 0.0355 -> 0.0546
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 4 by projected rating, fifa_overall 76.0)	`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=0.0716` `goal_rate=0.0395` `scorer_weight=0.0697`	-
`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=0.0531` `assist_rate=0.0` `provider_weight=0.0437`	-

Andrés Castrin D 90 0 0.04 0.01 165% 0 0.3 6.8 45.0 29 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 1.0001, quality 0.507)	`market_value_eur=8700000` `quality_factor=0.5071` `boost_factor=1.0001` `reference_quality=0.505` `history_matches=27` `history_shrink=0.1562`	-
`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 1.80 (squad xG sum 1.1026, xG scale 1.6361, xA scale 1.5373, team xA 1.44 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.804` `squad_xg_sum=1.1026` `squad_xa_sum=0.9388` `team_expected_assists=1.4432` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.022 -> 0.036 expected_assists: 0.0349 -> 0.0537
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 12 by projected rating, fifa_overall 73.0)	`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: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0354` `goal_rate=0.0425` `scorer_weight=0.045`	-
`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=0.0523` `assist_rate=0.0` `provider_weight=0.043`	-

Juan Iglesias D 87 0 0.07 0.03 129% 1 0.7 6.8 63.3 33 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 0.9990, quality 0.472)	`market_value_eur=5800000` `quality_factor=0.472` `boost_factor=0.999` `reference_quality=0.505` `history_matches=46` `history_shrink=0.098`	-
`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': '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 1.80 (squad xG sum 1.1026, xG scale 1.6361, xA scale 1.5373, team xA 1.44 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.804` `squad_xg_sum=1.1026` `squad_xa_sum=0.9388` `team_expected_assists=1.4432` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0431 -> 0.0705 expected_assists: 0.0888 -> 0.1365
`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: 2 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=2` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0681` `goal_rate=0.0816` `scorer_weight=0.0881`	-
`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=0.1276` `assist_rate=0.0058` `provider_weight=0.1092`	-

Odysseas Vlachodimos G 90 0 0.00 - n/a 0.0 6.9 60.5 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 0.9979, quality 0.440)	`market_value_eur=3200000` `quality_factor=0.4399` `boost_factor=0.9979` `reference_quality=0.505` `history_matches=42` `history_shrink=0.1064`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0138)	`blend_weight=0.0138` `history_matches=42` `season_overlap_discount=1.0` `per90_to_match_factor=1.0` `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': 1.1985}`	-
`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 1.80 (squad xG sum 1.1026, xG scale 1.6361, xA scale 1.5373, team xA 1.44 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.804` `squad_xg_sum=1.1026` `squad_xa_sum=0.9388` `team_expected_assists=1.4432` `team_xa_to_xg_ratio=0.8`	shots_faced: 11.4 -> 11.2 expected_saves: 2.79 -> 2.74 high_pressure_saves: 0.96 -> 0.94
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 7 by projected rating, fifa_overall 79.0)	`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: 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=1.8275`	-
`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=1.4442`	-

Team totals 2 1.80 15 6.8

Bench Candidates (7 modeled substitutes with empirical playing time)

Player	Pos	Exp Min	App Prob	Substitute Modeling Details
Fran González	G	4.4m	10.9%	Market-value substitute prior
José Ángel Carmona	D	14.0m	31.6%	Market-value substitute prior
Arouna Sangante	D	13.8m	22.2%	Market-value substitute prior
Dodi Lukebakio	M	22.5m	52.2%	Market-value substitute prior
Oso	M	22.3m	51.5%	Market-value substitute prior
Lucas Stassin	F	25.9m	58.6%	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

Aitor Fernández G 90 0 0.00 - n/a 0.0 6.9 12.9 33 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 0.9908, quality 0.437)	`market_value_eur=375000` `quality_factor=0.4374` `boost_factor=0.9908` `reference_quality=0.505` `history_matches=6` `history_shrink=0.4545`	-
`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': 'Robbie Ure, Félix Correia', 'matchup_zone': 'Goalkeeper shot-stopping context', 'opponent_lambda': 1.7374}`	-
`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 1.18 (squad xG sum 1.1107, xG scale 1.0615, xA scale 1.0615, team xA 0.84 = 0.71 x lambda)	`scoreline_reset=true` `team_lambda=1.179` `squad_xg_sum=1.1107` `squad_xa_sum=0.7901` `team_expected_assists=0.8387` `team_xa_to_xg_ratio=0.7114`	shots_faced: 16.5 -> 17.1 expected_saves: 4.04 -> 4.18 high_pressure_saves: 1.39 -> 1.44
`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=1.2522`	-
`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=0.8119`	-

Valentin Rosier D 86 0 0.04 0.02 59% 1 0.7 6.7 59.0 26 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=33` `history_shrink=0.1316`	-
`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': 'Robbie Ure', '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 1.18 (squad xG sum 1.1107, xG scale 1.0615, xA scale 1.0615, team xA 0.84 = 0.71 x lambda)	`scoreline_reset=true` `team_lambda=1.179` `squad_xg_sum=1.1107` `squad_xa_sum=0.7901` `team_expected_assists=0.8387` `team_xa_to_xg_ratio=0.7114`	-
`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=0.0359` `goal_rate=0.0353` `scorer_weight=0.0369`	-
`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=0.0721` `assist_rate=0.0014` `provider_weight=0.0598`	-

Alejandro Catena D 90 0 0.12 0.05 134% 1 0.3 6.9 60.2 31 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=43` `history_shrink=0.1042`	-
`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': 'Robbie Ure', '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 1.18 (squad xG sum 1.1107, xG scale 1.0615, xA scale 1.0615, team xA 0.84 = 0.71 x lambda)	`scoreline_reset=true` `team_lambda=1.179` `squad_xg_sum=1.1107` `squad_xa_sum=0.7901` `team_expected_assists=0.8387` `team_xa_to_xg_ratio=0.7114`	expected_goals: 0.1161 -> 0.1232 expected_assists: 0.112 -> 0.1189
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 2 by projected rating, fifa_overall 85.0)	`match_squad_rank=2` `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=0.1159` `goal_rate=0.0209` `scorer_weight=0.0986`	-
`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=0.1121` `assist_rate=0.1319` `provider_weight=0.1335`	-

Enzo Boyomo D 86 0 0.03 0.03 36% 0 0.4 6.8 42.7 28 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 1.0027, quality 0.575)	`market_value_eur=14600000` `quality_factor=0.5748` `boost_factor=1.0027` `reference_quality=0.505` `history_matches=34` `history_shrink=0.1282`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0033)	`blend_weight=0.0033` `history_matches=34` `season_overlap_discount=1.0` `per90_to_match_factor=0.9531` `prior_reliability=0.04` `competition=La Liga`	wonTackle: 0.882 -> 0.8897 fouls: 1.0031 -> 1.0103
`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': 'Robbie Ure', '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 1.18 (squad xG sum 1.1107, xG scale 1.0615, xA scale 1.0615, team xA 0.84 = 0.71 x lambda)	`scoreline_reset=true` `team_lambda=1.179` `squad_xg_sum=1.1107` `squad_xa_sum=0.7901` `team_expected_assists=0.8387` `team_xa_to_xg_ratio=0.7114`	-
`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=0.0336` `goal_rate=0.0081` `scorer_weight=0.0274`	-
`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=0.0103` `assist_rate=0.0786` `provider_weight=0.013`	-

Iker Muñoz M 81 0 0.04 0.01 175% 0 0.4 6.8 44.2 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 0.9968, 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': 'Lucien Agoumé', 'matchup_zone': 'Central Mid vs Defensive Mid — Pressing/Creation Duel', 'duel_win_rate': 0.331, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 0.8254, 'skill_edge': -0.0948, 'height_edge': -0.024, 'attacker_skill': 0.4374, 'defender_skill': 0.648, 'attacker_role': 'CM', 'attacker_mv_price_index': 0.9, 'defender_adjusted_parts': [{'player': 'Lucien Agoumé', 'market_value': 18500000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.648}], 'xg_override': 0.0369}	aerialWon: 1.643 -> 1.0 totalShots: 0.5932 -> 0.4896 shotOffTarget: 0.422 -> 0.3483 onTargetScoringAttempt: 0.1712 -> 0.1413 goals: 0.0452 -> 0.0373 expectedGoals: 0.0447 -> 0.0369 expectedGoalsOnTarget: 0.0291 -> 0.024
`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 1.18 (squad xG sum 1.1107, xG scale 1.0615, xA scale 1.0615, team xA 0.84 = 0.71 x lambda)	`scoreline_reset=true` `team_lambda=1.179` `squad_xg_sum=1.1107` `squad_xa_sum=0.7901` `team_expected_assists=0.8387` `team_xa_to_xg_ratio=0.7114`	-
`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=0.0384` `goal_rate=0.0373` `scorer_weight=0.0394`	-
`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=0.0379` `assist_rate=0.0614` `provider_weight=0.0483`	-

Moi Gómez M 77 0 0.03 0.01 415% 0 0.1 7.1 44.7 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 0.9973, quality 0.437)	`market_value_eur=1600000` `quality_factor=0.4374` `boost_factor=0.9973` `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=0.8548` `prior_reliability=0.1333` `competition=La Liga`	totalCross: 1.5794 -> 1.5659 wonTackle: 0.6814 -> 0.6734 interceptionWon: 0.7701 -> 0.7761 totalShots: 0.5073 -> 0.5014 fouls: 0.8985 -> 0.893
`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': 'Lucien Agoumé', 'matchup_zone': 'Central Mid vs Defensive Mid — Pressing/Creation Duel', 'duel_win_rate': 0.311, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 0.796, 'skill_edge': -0.0948, 'height_edge': -0.044, 'attacker_skill': 0.4374, 'defender_skill': 0.648, 'attacker_role': 'CM', 'attacker_mv_price_index': 0.9, 'defender_adjusted_parts': [{'player': 'Lucien Agoumé', 'market_value': 18500000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.648}], 'xg_override': 0.0244}	aerialWon: 0.4963 -> 0.9 totalShots: 0.5057 -> 0.4025 shotOffTarget: 0.3689 -> 0.2936 onTargetScoringAttempt: 0.1368 -> 0.1089 goals: 0.048 -> 0.0382 expectedGoals: 0.0307 -> 0.0244
`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 1.18 (squad xG sum 1.1107, xG scale 1.0615, xA scale 1.0615, team xA 0.84 = 0.71 x lambda)	`scoreline_reset=true` `team_lambda=1.179` `squad_xg_sum=1.1107` `squad_xa_sum=0.7901` `team_expected_assists=0.8387` `team_xa_to_xg_ratio=0.7114`	expected_assists: 0.1121 -> 0.119
`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=0.0256` `goal_rate=0.0382` `scorer_weight=0.0307`	-
`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=0.1122` `assist_rate=0.0905` `provider_weight=0.1031`	-

Jon Moncayola M 80 0 0.04 0.05 ok 1 0.5 6.8 60.5 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 0.9996, quality 0.490)	`market_value_eur=7300000` `quality_factor=0.4903` `boost_factor=0.9996` `reference_quality=0.505` `history_matches=45` `history_shrink=0.1`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0108)	`blend_weight=0.0108` `history_matches=45` `season_overlap_discount=1.0` `per90_to_match_factor=0.8893` `prior_reliability=0.1667` `competition=La Liga`	totalCross: 2.5497 -> 2.5336 interceptionWon: 0.6681 -> 0.6801 wonTackle: 1.037 -> 1.045
`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': 'Lucien Agoumé', 'matchup_zone': 'CDM vs CDM', 'duel_win_rate': 0.344, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 0.8882, 'skill_edge': -0.064, 'height_edge': -0.012, 'attacker_skill': 0.5057, 'defender_skill': 0.648, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'Lucien Agoumé', 'market_value': 18500000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.648}], 'xg_override': 0.0356}	aerialWon: 0.5071 -> 1.1 totalShots: 0.6429 -> 0.571 shotOffTarget: 0.4587 -> 0.4074 onTargetScoringAttempt: 0.1842 -> 0.1636 goals: 0.0502 -> 0.0446
`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 1.18 (squad xG sum 1.1107, xG scale 1.0615, xA scale 1.0615, team xA 0.84 = 0.71 x lambda)	`scoreline_reset=true` `team_lambda=1.179` `squad_xg_sum=1.1107` `squad_xa_sum=0.7901` `team_expected_assists=0.8387` `team_xa_to_xg_ratio=0.7114`	-
`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_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=0.0371` `goal_rate=0.0446` `scorer_weight=0.0418`	-
`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=0.0752` `assist_rate=0.0801` `provider_weight=0.0822`	-

Rubén García F 72 0 0.11 0.07 68% 1 0.9 6.8 57.9 41 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 0.9979, quality 0.437)	`market_value_eur=1700000` `quality_factor=0.4374` `boost_factor=0.9979` `reference_quality=0.505` `history_matches=44` `history_shrink=0.102`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0075)	`blend_weight=0.0075` `history_matches=44` `season_overlap_discount=1.0` `per90_to_match_factor=0.8046` `prior_reliability=0.1133` `competition=La Liga`	totalCross: 4.5824 -> 4.5906 fouls: 1.6571 -> 1.6518 totalShots: 1.2539 -> 1.2591
`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': 'Gabriel Suazo', 'matchup_zone': 'Right Wing vs Left Back — Flank Battle', 'duel_win_rate': 0.409, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 0.896, 'skill_edge': -0.0428, 'height_edge': -0.028, 'attacker_skill': 0.4374, 'defender_skill': 0.5325, 'attacker_role': 'RW', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Gabriel Suazo', 'market_value': 7600000, 'role': 'LB', 'price_index': 0.7, 'adjusted_quality': 0.5325}], 'xg_override': 0.1036}	totalShots: 1.2591 -> 1.1282 shotOffTarget: 0.8151 -> 0.7303 onTargetScoringAttempt: 0.4428 -> 0.3967 goals: 0.1714 -> 0.1536 bigChanceMissed: 0.127 -> 0.1138 expectedGoals: 0.1156 -> 0.1036 expectedGoalsOnTarget: 0.0508 -> 0.0455
`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 1.18 (squad xG sum 1.1107, xG scale 1.0615, xA scale 1.0615, team xA 0.84 = 0.71 x lambda)	`scoreline_reset=true` `team_lambda=1.179` `squad_xg_sum=1.1107` `squad_xa_sum=0.7901` `team_expected_assists=0.8387` `team_xa_to_xg_ratio=0.7114`	expected_goals: 0.1036 -> 0.11 expected_assists: 0.1141 -> 0.1212
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 8 by projected rating, fifa_overall 77.0)	`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=0.1042` `goal_rate=0.1536` `scorer_weight=0.1375`	-
`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=0.1141` `assist_rate=0.136` `provider_weight=0.1374`	-

Ante Budimir F 87 1 0.40 0.48 ok 2 2.6 6.9 63.9 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 0.9980, quality 0.437)	`market_value_eur=2600000` `quality_factor=0.4374` `boost_factor=0.998` `reference_quality=0.505` `history_matches=46` `history_shrink=0.098`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0093)	`blend_weight=0.0093` `history_matches=46` `season_overlap_discount=1.0` `per90_to_match_factor=0.9669` `prior_reliability=0.1467` `competition=La Liga`	totalShots: 2.5428 -> 2.5553 wasFouled: 1.4814 -> 1.4922 onTargetScoringAttempt: 1.0228 -> 1.0334 wonTackle: 0.6885 -> 0.6944
`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': 'Kike Salas, Andrés Castrin', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.18, 'pressing_faced': 'LOW', 'numerical_situation': '1v2', 'xg_multiplier': 0.9176, 'skill_edge': -0.0681, 'height_edge': 0.012, 'attacker_skill': 0.4374, 'defender_skill': 0.5887, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Kike Salas', 'market_value': 15400000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.6363}, {'player': 'Andrés Castrin', 'market_value': 8700000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.5411}], 'xg_override': 0.3742}	totalShots: 2.5553 -> 2.3447 shotOffTarget: 1.52 -> 1.3948 onTargetScoringAttempt: 1.0334 -> 0.9482 expectedGoalsOnTarget: 0.6971 -> 0.6397 bigChanceMissed: 0.654 -> 0.6001 goals: 0.5053 -> 0.4637 expectedGoals: 0.4078 -> 0.3742
`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 1.18 (squad xG sum 1.1107, xG scale 1.0615, xA scale 1.0615, team xA 0.84 = 0.71 x lambda)	`scoreline_reset=true` `team_lambda=1.179` `squad_xg_sum=1.1107` `squad_xa_sum=0.7901` `team_expected_assists=0.8387` `team_xa_to_xg_ratio=0.7114`	expected_goals: 0.3742 -> 0.3972
`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: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=1` `goal_probability=0.3278` `goal_rate=0.4637` `scorer_weight=0.481`	-
`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=0.0351` `assist_rate=0.0` `provider_weight=0.0286`	-

Jonathan Dubasin F 71 0 0.23 0.14 61% 2 0.6 6.8 43.8 16 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 0.9945, quality 0.454)	`market_value_eur=4300000` `quality_factor=0.4535` `boost_factor=0.9945` `reference_quality=0.505` `history_matches=9` `history_shrink=0.3571`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0201)	`blend_weight=0.0201` `history_matches=9` `season_overlap_discount=1.0` `per90_to_match_factor=0.7911` `prior_reliability=0.0867` `competition=La Liga`	totalOffside: 0.7732 -> 0.7576 totalShots: 1.897 -> 1.8841 fouls: 0.6659 -> 0.677 interceptionWon: 1.2739 -> 1.285 totalCross: 0.2794 -> 0.286 wasFouled: 0.3286 -> 0.3342
`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': 'Juan Iglesias', 'matchup_zone': 'Left Wing vs Right Back — Flank Battle', 'duel_win_rate': 0.438, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 0.9385, 'skill_edge': -0.0219, 'height_edge': -0.02, 'attacker_skill': 0.4535, 'defender_skill': 0.5021, 'attacker_role': 'LW', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Juan Iglesias', 'market_value': 5800000, 'role': 'RB', 'price_index': 0.7, 'adjusted_quality': 0.5021}], 'xg_override': 0.2151}	totalShots: 1.8969 -> 1.7802 shotOffTarget: 1.26 -> 1.1825 onTargetScoringAttempt: 0.6369 -> 0.5977 expectedGoalsOnTarget: 0.6176 -> 0.5796 bigChanceMissed: 0.3866 -> 0.3628 expectedGoals: 0.2292 -> 0.2151 goals: 0.2271 -> 0.2131
`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 1.18 (squad xG sum 1.1107, xG scale 1.0615, xA scale 1.0615, team xA 0.84 = 0.71 x lambda)	`scoreline_reset=true` `team_lambda=1.179` `squad_xg_sum=1.1107` `squad_xa_sum=0.7901` `team_expected_assists=0.8387` `team_xa_to_xg_ratio=0.7114`	expected_goals: 0.2151 -> 0.2283
`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: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.2041` `goal_rate=0.2131` `scorer_weight=0.2266`	-
`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=0.0833` `assist_rate=0.0` `provider_weight=0.0696`	-

Abel Bretones D 89 0 0.01 0.00 1262% 0 0.1 6.7 44.7 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 0.9978, quality 0.442)	`market_value_eur=3400000` `quality_factor=0.4424` `boost_factor=0.9978` `reference_quality=0.505` `history_matches=37` `history_shrink=0.119`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0155)	`blend_weight=0.0155` `history_matches=37` `season_overlap_discount=1.0` `per90_to_match_factor=0.9853` `prior_reliability=0.2` `competition=La Liga`	wonTackle: 1.2783 -> 1.289 fouls: 1.114 -> 1.1222 wasFouled: 0.7058 -> 0.7
`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': 'Youssouf Fofana', '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 1.18 (squad xG sum 1.1107, xG scale 1.0615, xA scale 1.0615, team xA 0.84 = 0.71 x lambda)	`scoreline_reset=true` `team_lambda=1.179` `squad_xg_sum=1.1107` `squad_xa_sum=0.7901` `team_expected_assists=0.8387` `team_xa_to_xg_ratio=0.7114`	-
`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=0.0142` `goal_rate=0.0087` `scorer_weight=0.0132`	-
`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=0.0683` `assist_rate=0.041` `provider_weight=0.0566`	-

Team totals 1 1.18 10 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 — Lucien Agoumé vs Jon Moncayola will be the pivotal duel. Total antagonist xG — Home: 0.80, Away: 0.79. 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 52.4% / 47.6%, dominance ratio 0.55, key duel: Lucien Agoumé vs Jon Moncayola

Youssouf Fofana xG 0.15Robbie Ure xG 0.22Rubén Vargas xG 0.12Jonathan Dubasin xG 0.22Rubén García xG 0.10Ante Budimir xG 0.37

Summary

Antagonist matchup analysis for Sevilla vs Osasuna. Most likely scoreline: 2-1. Lambdas derived from 6 home and 6 away individual player duels. Head-to-head: Sevilla 1W-4D-5L 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 — Lucien Agoumé vs Jon Moncayola will be the pivotal duel. Total antagonist xG — Home: 0.80, Away: 0.79. 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 2026-10-26 13:38:41 UTC · Soccer prediction reporting