Soccer predictions: actual data vs predicted data

Slate 20261021 · 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
Levante vs Athletic Club LaLiga	1-1	Draw	33.6% / 28.5% / 37.9%	40.7%	47.0%	0.7	1.11 / 1.20	-	pending

Match detail

Levante vs Athletic Club

LaLiga · 20261021 · 4-3-3 vs 4-2-3-1

Predicted 1-1Winner: DrawWinning H 1-0 (10.9%)Winning A 0-1 (11.8%)Confidence 0.7λ home/away 1.11 / 1.20Most-likely P 13.4%

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

XGBoost Reasoning & FBref Dataset Tracing

Antagonist + XGBoost match analysis for Levante vs Athletic Club. Player statistics regressed using position-specific XGBoost models (G, D, M, F) with 2026-2027 FBref priors. Micro-duels resolved across 4-3-3 vs 4-2-3-1 (duel lambdas: Home 1.171, Away 1.143). Final calibrated Poisson lambdas: Home 1.112, Away 1.200 (most likely score 1-1).

Home Starters 11/11 XGBoost (7 FBref) Away Starters 11/11 XGBoost (10 FBref) Bench Modeled H: 7 / A: 7 Duel λ (H/A) 1.17 / 1.14 Final λ (H/A) 1.11 / 1.20

FBref Team Priors: Levante UD (Gls/90: 1.6666666666666667, GA/90: 1.67 → E[λ]: 1.235) vs Athletic Club (Gls/90: 1.0, GA/90: 1.0 → E[λ]: 1.237) | 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	Levante	Athletic Club
League position	17.00	11.00
Points per game	1.08	1.32
Attack index	0.99	0.83
Defense (concede) index	0.80	1.12
Form multiplier	0.99	1.01
Standing λ multiplier	0.91	0.97

Lambda decomposition (how the goal rates were built)

Stage	Levante λ	Athletic Club λ
FBref Squad Prior E[λ] (weight 0.228)	1.235	1.237
Pre-competitive-floor	1.112	1.200
Pre-calibration (Micro-duels)	1.171	1.143
Final λ	1.112	1.200

competition calibration scale 1.0competition observed total goals 0.0quality gap applied Falsemismatch score pressure applied Falsecompetitive underdog floor applied Falsestage discount 1.0

Scoreline distribution & markets

Top 5 scorelines	P
1-1	13.4%
0-1	11.8%
1-0	10.9%
0-0	10.0%
1-2	7.9%

Home win	33.6%
Draw	28.5%
Away win	37.9%
Over 2.5	40.7%
BTTS yes	47.0%

Pregame fair line — favorite Athletic Club (37.9%), fair total 2.31, fair spread -0.09

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.

Levante — 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

Petar Ratkov F 73 0 0.21 0.03 533% 2 0.4 6.7 32.8 8 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 0.9997, quality 0.503)	`market_value_eur=8400000` `quality_factor=0.5035` `boost_factor=0.9997` `reference_quality=0.505` `history_matches=4` `history_shrink=0.5556`	-
`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': 'Aymeric Laporte, Yeray Álvarez', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.207, 'pressing_faced': 'LOW', 'numerical_situation': '1v2', 'xg_multiplier': 1.0109, 'skill_edge': 0.0074, 'height_edge': 0.0, 'attacker_skill': 0.5035, 'defender_skill': 0.4869, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Aymeric Laporte', 'market_value': 8400000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.5365}, {'player': 'Yeray Álvarez', 'market_value': 930000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4374}], 'xg_override': 0.2093}	totalShots: 1.5021 -> 1.5185 shotOffTarget: 0.9095 -> 0.9194 onTargetScoringAttempt: 0.5925 -> 0.599
`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 1.11 (squad xG sum 1.0988, xG scale 1.0120, xA scale 1.0460, team xA 0.72 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.112` `squad_xg_sum=1.0988` `squad_xa_sum=0.691` `team_expected_assists=0.7228` `team_xa_to_xg_ratio=0.65`	-
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 16 by projected rating, fifa_overall 68.0)	`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=0.1909` `goal_rate=0.1985` `scorer_weight=0.203`	-
`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=0.0353` `assist_rate=0.3137` `provider_weight=0.0449`	-

Iván Romero F 77 1 0.26 0.24 ok 2 1.6 6.9 60.2 31 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 0.9995, quality 0.491)	`market_value_eur=7400000` `quality_factor=0.4915` `boost_factor=0.9995` `reference_quality=0.505` `history_matches=40` `history_shrink=0.1111`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0120)	`blend_weight=0.012` `history_matches=40` `season_overlap_discount=1.0` `per90_to_match_factor=0.8588` `prior_reliability=0.1667` `competition=La Liga`	totalShots: 1.9634 -> 1.9773 wasFouled: 2.0173 -> 2.0096 interceptionWon: 0.4209 -> 0.4158
`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': 'Jesús Areso', 'matchup_zone': 'Left Wing vs Right Back — Flank Battle', 'duel_win_rate': 0.434, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 0.9317, 'skill_edge': -0.0025, 'height_edge': -0.044, 'attacker_skill': 0.4915, 'defender_skill': 0.497, 'attacker_role': 'LW', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Jesús Areso', 'market_value': 5500000, 'role': 'RB', 'price_index': 0.7, 'adjusted_quality': 0.497}], 'xg_override': 0.2612}	totalShots: 1.9773 -> 1.8423 shotOffTarget: 1.1418 -> 1.0638 onTargetScoringAttempt: 0.8201 -> 0.7641 bigChanceMissed: 0.3636 -> 0.3388 goals: 0.3314 -> 0.3088 expectedGoals: 0.2803 -> 0.2612 expectedGoalsOnTarget: 0.2465 -> 0.2297
`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 1.11 (squad xG sum 1.0988, xG scale 1.0120, xA scale 1.0460, team xA 0.72 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.112` `squad_xg_sum=1.0988` `squad_xa_sum=0.691` `team_expected_assists=0.7228` `team_xa_to_xg_ratio=0.65`	-
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 3 by projected rating, fifa_overall 75.0)	`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=1` `goal_probability=0.2323` `goal_rate=0.3088` `scorer_weight=0.3047`	-
`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=0.0624` `assist_rate=0.0041` `provider_weight=0.0515`	-

Víctor García F 72 0 0.13 0.07 82% 1 0.6 6.8 46.3 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 0.9969, quality 0.437)	`market_value_eur=1500000` `quality_factor=0.4374` `boost_factor=0.9969` `reference_quality=0.505` `history_matches=28` `history_shrink=0.1515`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0079)	`blend_weight=0.0079` `history_matches=28` `season_overlap_discount=1.0` `per90_to_match_factor=0.8042` `prior_reliability=0.08` `competition=La Liga`	totalShots: 1.2384 -> 1.2286 totalCross: 4.0178 -> 4.0125
`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': 'Yuri Berchiche', 'matchup_zone': 'Right Wing vs Left Back — Flank Battle', 'duel_win_rate': 0.464, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 0.9765, 'skill_edge': 0.0, 'height_edge': -0.016, 'attacker_skill': 0.4374, 'defender_skill': 0.4374, 'attacker_role': 'RW', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Yuri Berchiche', 'market_value': 910000, 'role': 'LB', 'price_index': 0.7, 'adjusted_quality': 0.4374}], 'xg_override': 0.1259}	totalShots: 1.2353 -> 1.2063 shotOffTarget: 0.857 -> 0.8369 onTargetScoringAttempt: 0.3783 -> 0.3694 expectedGoalsOnTarget: 0.235 -> 0.2295 bigChanceMissed: 0.2219 -> 0.2167
`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 1.11 (squad xG sum 1.0988, xG scale 1.0120, xA scale 1.0460, team xA 0.72 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.112` `squad_xg_sum=1.0988` `squad_xa_sum=0.691` `team_expected_assists=0.7228` `team_xa_to_xg_ratio=0.65`	-
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 9 by projected rating, fifa_overall 72.0)	`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=0.1196` `goal_rate=0.1344` `scorer_weight=0.1336`	-
`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=0.0762` `assist_rate=0.0` `provider_weight=0.0634`	-

Daniel Requena M 88 0 0.09 0.03 221% 1 0.4 6.8 36.3 11 FBref Prior 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 0.9899, quality 0.437)	`market_value_eur=1500000` `quality_factor=0.4374` `boost_factor=0.9899` `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': 'Iñigo Ruiz de Galarreta', 'matchup_zone': 'Central Mid vs Defensive Mid — Pressing/Creation Duel', 'duel_win_rate': 0.498, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.0706, 'skill_edge': 0.0, 'height_edge': 0.048, 'attacker_skill': 0.4374, 'defender_skill': 0.4374, 'attacker_role': 'CM', 'attacker_mv_price_index': 0.9, 'defender_adjusted_parts': [{'player': 'Iñigo Ruiz de Galarreta', 'market_value': 1800000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.4374}], 'xg_override': 0.0885}	aerialWon: 0.4938 -> 1.2 totalShots: 1.0344 -> 1.1074 shotOffTarget: 0.6958 -> 0.7449 onTargetScoringAttempt: 0.3386 -> 0.3625 expectedGoals: 0.0827 -> 0.0885 goals: 0.0762 -> 0.0816
`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 1.11 (squad xG sum 1.0988, xG scale 1.0120, xA scale 1.0460, team xA 0.72 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.112` `squad_xg_sum=1.0988` `squad_xa_sum=0.691` `team_expected_assists=0.7228` `team_xa_to_xg_ratio=0.65`	-
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 8 by projected rating, fifa_overall 84.0)	`match_squad_rank=8` `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=0.0857` `goal_rate=0.0816` `scorer_weight=0.0851`	-
`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=0.0414` `assist_rate=0.0496` `provider_weight=0.0466`	-

Oriol Rey M 81 0 0.04 0.01 459% 1 0.2 6.8 35.6 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 0.9969, quality 0.437)	`market_value_eur=1700000` `quality_factor=0.4374` `boost_factor=0.9969` `reference_quality=0.505` `history_matches=28` `history_shrink=0.1515`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0197)	`blend_weight=0.0197` `history_matches=28` `season_overlap_discount=1.0` `per90_to_match_factor=0.9039` `prior_reliability=0.2` `competition=La Liga`	totalShots: 0.5439 -> 0.5332 totalCross: 0.4842 -> 0.4747 wasFouled: 0.6818 -> 0.6743 fouls: 1.1987 -> 1.2048
`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': 'Iñigo Ruiz de Galarreta', 'matchup_zone': 'Central Mid vs Defensive Mid — Pressing/Creation Duel', 'duel_win_rate': 0.454, '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': 'CM', 'attacker_mv_price_index': 0.9, 'defender_adjusted_parts': [{'player': 'Iñigo Ruiz de Galarreta', 'market_value': 1800000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.4374}], 'xg_override': 0.0356}	aerialWon: 0.8458 -> 0.9
`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 1.11 (squad xG sum 1.0988, xG scale 1.0120, xA scale 1.0460, team xA 0.72 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.112` `squad_xg_sum=1.0988` `squad_xa_sum=0.691` `team_expected_assists=0.7228` `team_xa_to_xg_ratio=0.65`	-
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 10 by projected rating, fifa_overall 73.0)	`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=0.0354` `goal_rate=0.0534` `scorer_weight=0.0435`	-
`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=0.0386` `assist_rate=0.0513` `provider_weight=0.0455`	-

Jon Ander Olasagasti M 84 0 0.09 0.09 ok 1 0.9 6.9 62.4 32 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 0.9978, quality 0.437)	`market_value_eur=2700000` `quality_factor=0.4374` `boost_factor=0.9978` `reference_quality=0.505` `history_matches=41` `history_shrink=0.1087`	-
`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': 'Iñigo Ruiz de Galarreta', 'matchup_zone': 'CDM vs CDM', 'duel_win_rate': 0.429, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.0132, 'skill_edge': 0.001, 'height_edge': 0.008, 'attacker_skill': 0.4396, 'defender_skill': 0.4374, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'Iñigo Ruiz de Galarreta', 'market_value': 1800000, 'role': 'CDM', 'price_index': 0.85, 'adjusted_quality': 0.4374}], 'xg_override': 0.0863}	aerialWon: 0.6155 -> 0.9 totalShots: 1.0516 -> 1.0655 shotOffTarget: 0.7215 -> 0.731
`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 1.11 (squad xG sum 1.0988, xG scale 1.0120, xA scale 1.0460, team xA 0.72 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.112` `squad_xg_sum=1.0988` `squad_xa_sum=0.691` `team_expected_assists=0.7228` `team_xa_to_xg_ratio=0.65`	expected_assists: 0.1278 -> 0.1337
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 6 by projected rating, fifa_overall 84.0)	`match_squad_rank=6` `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=0.0836` `goal_rate=0.0779` `scorer_weight=0.0819`	-
`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=0.1251` `assist_rate=0.1014` `provider_weight=0.1138`	-

Manuel Sánchez D 88 0 0.00 0.01 50% 0 0.2 6.8 54.0 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 0.9992, quality 0.479)	`market_value_eur=6400000` `quality_factor=0.4793` `boost_factor=0.9992` `reference_quality=0.505` `history_matches=41` `history_shrink=0.1087`	-
`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': 'Maroan Sannadi', '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 1.11 (squad xG sum 1.0988, xG scale 1.0120, xA scale 1.0460, team xA 0.72 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.112` `squad_xg_sum=1.0988` `squad_xa_sum=0.691` `team_expected_assists=0.7228` `team_xa_to_xg_ratio=0.65`	-
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 11 by projected rating, fifa_overall 78.0)	`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: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0036` `goal_rate=0.0019` `scorer_weight=0.0035`	-
`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=0.0602` `assist_rate=0.019` `provider_weight=0.0497`	-

Aïssa Mandi D 85 0 0.01 0.00 ok 0 0.1 6.8 37.7 11 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 0.9908, quality 0.437)	`market_value_eur=1100000` `quality_factor=0.4374` `boost_factor=0.9908` `reference_quality=0.505` `history_matches=6` `history_shrink=0.4545`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0591)	`blend_weight=0.0591` `history_matches=6` `season_overlap_discount=1.0` `per90_to_match_factor=0.9458` `prior_reliability=0.2` `competition=La Liga`	wonTackle: 0.7918 -> 0.8382 fouls: 0.4414 -> 0.4153 interceptionWon: 1.1447 -> 1.1329 wasFouled: 0.7703 -> 0.762
`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': 'Maroan Sannadi', '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 1.11 (squad xG sum 1.0988, xG scale 1.0120, xA scale 1.0460, team xA 0.72 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.112` `squad_xg_sum=1.0988` `squad_xa_sum=0.691` `team_expected_assists=0.7228` `team_xa_to_xg_ratio=0.65`	-
`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: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0051` `goal_rate=0.0` `scorer_weight=0.0046`	-
`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=0.0076` `assist_rate=0.0` `provider_weight=0.0066`	-

Adrián de la Fuente D 88 0 0.14 0.12 ok 2 1.5 7.0 64.9 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 0.9987, quality 0.465)	`market_value_eur=5200000` `quality_factor=0.4646` `boost_factor=0.9987` `reference_quality=0.505` `history_matches=41` `history_shrink=0.1087`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0141)	`blend_weight=0.0141` `history_matches=41` `season_overlap_discount=1.0` `per90_to_match_factor=0.9767` `prior_reliability=0.2` `competition=La Liga`	onTargetScoringAttempt: 0.8132 -> 0.8063 wonTackle: 0.7433 -> 0.7374
`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': 'Peio Canales', '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 1.11 (squad xG sum 1.0988, xG scale 1.0120, xA scale 1.0460, team xA 0.72 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.112` `squad_xg_sum=1.0988` `squad_xa_sum=0.691` `team_expected_assists=0.7228` `team_xa_to_xg_ratio=0.65`	expected_assists: 0.112 -> 0.1171
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 1 by projected rating, fifa_overall 86.0)	`match_squad_rank=1` `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=0.1337` `goal_rate=0.2727` `scorer_weight=0.1794`	-
`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=0.1105` `assist_rate=0.0022` `provider_weight=0.0937`	-

Jeremy Toljan D 85 0 0.03 0.01 153% 0 0.2 6.6 59.4 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 0.9980, quality 0.437)	`market_value_eur=1900000` `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 0.0035)	`blend_weight=0.0035` `history_matches=45` `season_overlap_discount=1.0` `per90_to_match_factor=0.9412` `prior_reliability=0.0533` `competition=La Liga`	totalCross: 2.4902 -> 2.4979
`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': 'Maroan Sannadi', '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 1.11 (squad xG sum 1.0988, xG scale 1.0120, xA scale 1.0460, team xA 0.72 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.112` `squad_xg_sum=1.0988` `squad_xa_sum=0.691` `team_expected_assists=0.7228` `team_xa_to_xg_ratio=0.65`	-
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 18 by projected rating, fifa_overall 74.0)	`match_squad_rank=18` `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=0.031` `goal_rate=0.0003` `scorer_weight=0.0252`	-
`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=0.0738` `assist_rate=0.1999` `provider_weight=0.0959`	-

Mathew Ryan G 90 0 0.00 0.00 100% 0.1 7.0 65.8 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 0.437)	`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 0.0133)	`blend_weight=0.0133` `history_matches=44` `season_overlap_discount=1.0` `per90_to_match_factor=1.0` `prior_reliability=0.2` `competition=La Liga`	saves: 3.0283 -> 3.05
`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': 'Maroan Sannadi, Robert Navarro', 'matchup_zone': 'Goalkeeper shot-stopping context', 'opponent_lambda': 1.1434}`	-
`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 1.11 (squad xG sum 1.0988, xG scale 1.0120, xA scale 1.0460, team xA 0.72 = 0.65 x lambda)	`scoreline_reset=true` `team_lambda=1.112` `squad_xg_sum=1.0988` `squad_xa_sum=0.691` `team_expected_assists=0.7228` `team_xa_to_xg_ratio=0.65`	shots_faced: 10.9 -> 11.4 expected_saves: 2.67 -> 2.79 high_pressure_saves: 0.91 -> 0.96
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 2 by projected rating, fifa_overall 79.0)	`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=0.0` `scorer_weight=0.0` `squad_weight_total=1.193`	-
`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=0.6864`	-

Team totals 1 1.11 12 6.8

Bench Candidates (7 modeled substitutes with empirical playing time)

Player	Pos	Exp Min	App Prob	Substitute Modeling Details
Pablo Cunat Campos	G	4.4m	10.9%	Market-value substitute prior
Axel Tape	D	13.8m	31.1%	Market-value substitute prior
Matias Moreno	D	13.7m	30.5%	Market-value substitute prior
Carlos Álvarez	M	22.6m	31.9%	Market-value substitute prior
Enzo Bardeli	M	22.1m	50.5%	Market-value substitute prior
Etta Eyong	F	25.9m	58.5%	Market-value substitute prior

Athletic Club — 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

Maroan Sannadi F 68 0 0.28 0.02 1024% 2 0.2 6.6 25.7 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 0.9947, quality 0.437)	`market_value_eur=2800000` `quality_factor=0.4374` `boost_factor=0.9947` `reference_quality=0.505` `history_matches=14` `history_shrink=0.2632`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0046)	`blend_weight=0.0046` `history_matches=14` `season_overlap_discount=1.0` `per90_to_match_factor=0.7506` `prior_reliability=0.0267` `competition=La Liga`	totalShots: 1.5881 -> 1.5808
`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': 'Aïssa Mandi, Jeremy Toljan', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.232, 'pressing_faced': 'LOW', 'numerical_situation': '1v2', 'xg_multiplier': 1.047, 'skill_edge': 0.0, 'height_edge': 0.032, 'attacker_skill': 0.4374, 'defender_skill': 0.4374, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Aïssa Mandi', 'market_value': 1100000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4374}, {'player': 'Jeremy Toljan', 'market_value': 1900000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4374}], 'xg_override': 0.2144}	totalShots: 1.5853 -> 1.6598 shotOffTarget: 0.9903 -> 1.0368 onTargetScoringAttempt: 0.595 -> 0.623 expectedGoals: 0.2048 -> 0.2144 goals: 0.1988 -> 0.2081 bigChanceMissed: 0.1202 -> 0.1258
`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 1.20 (squad xG sum 0.9247, xG scale 1.2978, xA scale 1.0679, team xA 0.96 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.2` `squad_xg_sum=0.9247` `squad_xa_sum=0.8989` `team_expected_assists=0.96` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.2144 -> 0.2783
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 18 by projected rating, fifa_overall 77.0)	`match_squad_rank=18` `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=0.2429` `goal_rate=0.2081` `scorer_weight=0.2717`	-
`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=0.0295` `assist_rate=0.0` `provider_weight=0.0239`	-

Robert Navarro F 76 1 0.27 0.12 129% 2 0.8 7.1 49.8 40 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 0.9996, quality 0.493)	`market_value_eur=7500000` `quality_factor=0.4927` `boost_factor=0.9996` `reference_quality=0.505` `history_matches=38` `history_shrink=0.1163`	-
`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': 'Aïssa Mandi, Jeremy Toljan', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.201, 'pressing_faced': 'LOW', 'numerical_situation': '1v2', 'xg_multiplier': 1.0013, 'skill_edge': 0.0249, 'height_edge': -0.024, 'attacker_skill': 0.4927, 'defender_skill': 0.4374, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Aïssa Mandi', 'market_value': 1100000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4374}, {'player': 'Jeremy Toljan', 'market_value': 1900000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4374}], 'xg_override': 0.2093}	-
`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 1.20 (squad xG sum 0.9247, xG scale 1.2978, xA scale 1.0679, team xA 0.96 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.2` `squad_xg_sum=0.9247` `squad_xa_sum=0.8989` `team_expected_assists=0.96` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.2093 -> 0.2716 expected_assists: 0.1536 -> 0.164
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 5 by projected rating, fifa_overall 76.0)	`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=0.2378` `goal_rate=0.273` `scorer_weight=0.3383`	-
`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=0.1513` `assist_rate=0.0` `provider_weight=0.1312`	-

Peio Canales M 88 0 0.12 0.05 159% 1 0.6 6.9 36.1 9 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 1.0021, 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`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0498)	`blend_weight=0.0498` `history_matches=5` `season_overlap_discount=1.0` `per90_to_match_factor=0.9822` `prior_reliability=0.1533` `competition=La Liga`	onTargetScoringAttempt: 0.3642 -> 0.3886 wasFouled: 1.2339 -> 1.2575 totalCross: 2.0042 -> 1.9895 interceptionWon: 0.7206 -> 0.706 totalShots: 1.0396 -> 1.0519 fouls: 0.671 -> 0.6588 wonTackle: 1.0518 -> 1.042 goalAssist: 0.1231 -> 0.117
`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': 'Adrián de la Fuente', 'matchup_zone': 'Left Wing vs Right Back — Flank Battle', 'duel_win_rate': 0.48, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.0003, 'skill_edge': 0.0122, 'height_edge': -0.012, 'attacker_skill': 0.5189, 'defender_skill': 0.4918, 'attacker_role': 'LW', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Adrián de la Fuente', 'market_value': 5200000, 'role': 'RB', 'price_index': 0.7, 'adjusted_quality': 0.4918}], 'xg_override': 0.0945}	aerialWon: 0.0 -> 0.9
`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 1.20 (squad xG sum 0.9247, xG scale 1.2978, xA scale 1.0679, team xA 0.96 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.2` `squad_xg_sum=0.9247` `squad_xa_sum=0.8989` `team_expected_assists=0.96` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0945 -> 0.1226 expected_assists: 0.1102 -> 0.1177
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 8 by projected rating, fifa_overall 73.0)	`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=0.1154` `goal_rate=0.1004` `scorer_weight=0.1276`	-
`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=0.111` `assist_rate=0.117` `provider_weight=0.1227`	-

Álex Berenguer F 84 0 0.16 0.07 132% 1 0.9 6.9 49.8 32 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 0.451)	`market_value_eur=4099999` `quality_factor=0.4511` `boost_factor=0.9981` `reference_quality=0.505` `history_matches=38` `history_shrink=0.1163`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0116)	`blend_weight=0.0116` `history_matches=38` `season_overlap_discount=1.0` `per90_to_match_factor=0.9373` `prior_reliability=0.1533` `competition=La Liga`	totalShots: 1.2965 -> 1.3046 wasFouled: 0.9958 -> 0.989
`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': 'Aïssa Mandi, Jeremy Toljan', 'matchup_zone': 'Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle', 'duel_win_rate': 0.18, 'pressing_faced': 'LOW', 'numerical_situation': '1v2', 'xg_multiplier': 0.9561, 'skill_edge': 0.0061, 'height_edge': -0.036, 'attacker_skill': 0.4511, 'defender_skill': 0.4374, 'attacker_role': 'ST', 'attacker_mv_price_index': 1.0, 'defender_adjusted_parts': [{'player': 'Aïssa Mandi', 'market_value': 1100000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4374}, {'player': 'Jeremy Toljan', 'market_value': 1900000, 'role': 'CB', 'price_index': 0.75, 'adjusted_quality': 0.4374}], 'xg_override': 0.125}	totalShots: 1.3046 -> 1.2473 shotOffTarget: 0.9033 -> 0.8636 onTargetScoringAttempt: 0.3979 -> 0.3804 expectedGoalsOnTarget: 0.1886 -> 0.1803 goals: 0.1432 -> 0.1369 expectedGoals: 0.1307 -> 0.125 bigChanceMissed: 0.1263 -> 0.1208
`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 1.20 (squad xG sum 0.9247, xG scale 1.2978, xA scale 1.0679, team xA 0.96 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.2` `squad_xg_sum=0.9247` `squad_xa_sum=0.8989` `team_expected_assists=0.96` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.125 -> 0.1622 expected_assists: 0.1263 -> 0.1349
`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=0.1497` `goal_rate=0.1369` `scorer_weight=0.1732`	-
`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=0.1262` `assist_rate=0.0189` `provider_weight=0.1079`	-

Iñigo Ruiz de Galarreta M 70 0 0.06 0.02 215% 1 0.6 7.0 55.9 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 0.9977, quality 0.437)	`market_value_eur=1800000` `quality_factor=0.4374` `boost_factor=0.9977` `reference_quality=0.505` `history_matches=40` `history_shrink=0.1111`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0038)	`blend_weight=0.0038` `history_matches=40` `season_overlap_discount=1.0` `per90_to_match_factor=0.7826` `prior_reliability=0.0533` `competition=La Liga`	fouls: 1.9279 -> 1.9355
`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': 'Oriol Rey', 'matchup_zone': 'Holding Mid vs Central Mid — Midfield Anchor Duel', 'duel_win_rate': 0.396, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 0.9941, 'skill_edge': 0.0, 'height_edge': -0.004, 'attacker_skill': 0.4374, 'defender_skill': 0.4374, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'Oriol Rey', 'market_value': 1700000, 'role': 'CM', 'price_index': 0.9, 'adjusted_quality': 0.4374}], 'xg_override': 0.0469}	aerialWon: 1.1363 -> 0.9 totalShots: 0.9331 -> 0.9276
`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 1.20 (squad xG sum 0.9247, xG scale 1.2978, xA scale 1.0679, team xA 0.96 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.2` `squad_xg_sum=0.9247` `squad_xa_sum=0.8989` `team_expected_assists=0.96` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0469 -> 0.0609 expected_assists: 0.1381 -> 0.1475
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 6 by projected rating, fifa_overall 82.0)	`match_squad_rank=6` `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=0.0591` `goal_rate=0.0472` `scorer_weight=0.0611`	-
`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=0.1371` `assist_rate=0.0986` `provider_weight=0.118`	-

Beñat Gerenabarrena M 79 0 0.04 0.00 1240% 1 0.1 6.8 36.2 11 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 0.9945, quality 0.465)	`market_value_eur=5200000` `quality_factor=0.4646` `boost_factor=0.9945` `reference_quality=0.505` `history_matches=6` `history_shrink=0.4545`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0414)	`blend_weight=0.0414` `history_matches=6` `season_overlap_discount=1.0` `per90_to_match_factor=0.8832` `prior_reliability=0.14` `competition=La Liga`	totalCross: 0.9183 -> 0.9847 interceptionWon: 1.8922 -> 1.9357 fouls: 2.2092 -> 2.2396 wonTackle: 1.6329 -> 1.6523 wasFouled: 1.4026 -> 1.4142 onTargetScoringAttempt: 0.1566 -> 0.1673
`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': 'Oriol Rey', 'matchup_zone': 'Holding Mid vs Central Mid — Midfield Anchor Duel', 'duel_win_rate': 0.409, 'pressing_faced': 'LOW', 'numerical_situation': '1v1', 'xg_multiplier': 1.0137, 'skill_edge': 0.0173, 'height_edge': -0.008, 'attacker_skill': 0.4759, 'defender_skill': 0.4374, 'attacker_role': 'CDM', 'attacker_mv_price_index': 0.85, 'defender_adjusted_parts': [{'player': 'Oriol Rey', 'market_value': 1700000, 'role': 'CM', 'price_index': 0.9, 'adjusted_quality': 0.4374}], 'xg_override': 0.0293}	aerialWon: 0.6972 -> 0.9 totalShots: 0.5063 -> 0.5132
`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 1.20 (squad xG sum 0.9247, xG scale 1.2978, xA scale 1.0679, team xA 0.96 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.2` `squad_xg_sum=0.9247` `squad_xa_sum=0.8989` `team_expected_assists=0.96` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0293 -> 0.038
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 11 by projected rating, fifa_overall 80.0)	`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: 1 projected goals distributed by scorer weight (xG x finishing x appearance)	`team_goals=1` `allocation_applied=true` `goals_allocated=0` `goal_probability=0.0373` `goal_rate=0.0364` `scorer_weight=0.0414`	-
`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=0.0455` `assist_rate=0.06` `provider_weight=0.0548`	-

Yuri Berchiche D 82 0 0.05 0.02 164% 1 0.9 7.1 53.8 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 0.9976, quality 0.437)	`market_value_eur=910000` `quality_factor=0.4374` `boost_factor=0.9976` `reference_quality=0.505` `history_matches=37` `history_shrink=0.119`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0057)	`blend_weight=0.0057` `history_matches=37` `season_overlap_discount=1.0` `per90_to_match_factor=0.9063` `prior_reliability=0.0733` `competition=La Liga`	totalShots: 1.1492 -> 1.1566 wonTackle: 0.9959 -> 0.9903
`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': 'Víctor García', 'matchup_zone': 'Right Wing vs Left 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 1.20 (squad xG sum 0.9247, xG scale 1.2978, xA scale 1.0679, team xA 0.96 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.2` `squad_xg_sum=0.9247` `squad_xa_sum=0.8989` `team_expected_assists=0.96` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0419 -> 0.0544 expected_assists: 0.1701 -> 0.1816
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 2 by projected rating, fifa_overall 79.0)	`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=0.0529` `goal_rate=0.0` `scorer_weight=0.0435`	-
`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=0.1661` `assist_rate=0.1756` `provider_weight=0.1861`	-

Aymeric Laporte D 88 0 0.08 0.07 ok 1 0.5 7.1 53.4 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 0.9999, quality 0.503)	`market_value_eur=8400000` `quality_factor=0.5035` `boost_factor=0.9999` `reference_quality=0.505` `history_matches=32` `history_shrink=0.1351`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0141)	`blend_weight=0.0141` `history_matches=32` `season_overlap_discount=1.0` `per90_to_match_factor=0.9723` `prior_reliability=0.16` `competition=La Liga`	fouls: 1.0853 -> 1.0757 wonTackle: 0.783 -> 0.7891
`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': 'Petar Ratkov', '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 1.20 (squad xG sum 0.9247, xG scale 1.2978, xA scale 1.0679, team xA 0.96 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.2` `squad_xg_sum=0.9247` `squad_xa_sum=0.8989` `team_expected_assists=0.96` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0622 -> 0.0807
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 3 by projected rating, fifa_overall 84.0)	`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=0.0775` `goal_rate=0.0` `scorer_weight=0.0646`	-
`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=0.0158` `assist_rate=0.0035` `provider_weight=0.0131`	-

Yeray Álvarez D 79 0 0.04 0.01 166% 1 0.2 6.7 36.5 20 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 0.9937, quality 0.437)	`market_value_eur=930000` `quality_factor=0.4374` `boost_factor=0.9937` `reference_quality=0.505` `history_matches=11` `history_shrink=0.3125`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0271)	`blend_weight=0.0271` `history_matches=11` `season_overlap_discount=1.0` `per90_to_match_factor=0.8828` `prior_reliability=0.1333` `competition=La Liga`	totalCross: 0.6179 -> 0.649 totalShots: 0.5305 -> 0.552 wonTackle: 0.656 -> 0.6382 wasFouled: 0.809 -> 0.799 goalAssist: 0.114 -> 0.1229 interceptionWon: 1.0561 -> 1.0634
`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': 'Petar Ratkov', '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 1.20 (squad xG sum 0.9247, xG scale 1.2978, xA scale 1.0679, team xA 0.96 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.2` `squad_xg_sum=0.9247` `squad_xa_sum=0.8989` `team_expected_assists=0.96` `team_xa_to_xg_ratio=0.8`	expected_goals: 0.0281 -> 0.0365
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 13 by projected rating, fifa_overall 77.0)	`match_squad_rank=13` `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=0.0358` `goal_rate=0.0` `scorer_weight=0.0292`	-
`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=0.0174` `assist_rate=0.1229` `provider_weight=0.022`	-

Jesús Areso D 79 0 0.01 0.01 147% 0 0.1 6.7 37.5 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 0.9985, quality 0.468)	`market_value_eur=5500000` `quality_factor=0.4683` `boost_factor=0.9985` `reference_quality=0.505` `history_matches=32` `history_shrink=0.1351`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0094)	`blend_weight=0.0094` `history_matches=32` `season_overlap_discount=1.0` `per90_to_match_factor=0.8791` `prior_reliability=0.1067` `competition=La Liga`	wasFouled: 0.5308 -> 0.5413 interceptionWon: 1.1346 -> 1.1291
`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': 'Iván Romero', '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 1.20 (squad xG sum 0.9247, xG scale 1.2978, xA scale 1.0679, team xA 0.96 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.2` `squad_xg_sum=0.9247` `squad_xa_sum=0.8989` `team_expected_assists=0.96` `team_xa_to_xg_ratio=0.8`	-
`rankings_attached`	Display-only provenance: no published number changes. Global ranking attached (match squad rank 16 by projected rating, fifa_overall 80.0)	`match_squad_rank=16` `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=0.0143` `goal_rate=0.0` `scorer_weight=0.0118`	-
`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=0.0519` `assist_rate=0.0223` `provider_weight=0.0426`	-

Unai Simón G 90 0 0.00 - n/a 0.0 7.1 57.9 31 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 1.0042, quality 0.641)	`market_value_eur=21000000` `quality_factor=0.6408` `boost_factor=1.0042` `reference_quality=0.505` `history_matches=44` `history_shrink=0.102`	-
`fbref_prior_blend`	FBref 2026-27 prior blended (weight 0.0133)	`blend_weight=0.0133` `history_matches=44` `season_overlap_discount=1.0` `per90_to_match_factor=1.0` `prior_reliability=0.2` `competition=La Liga`	saves: 2.961 -> 2.9748
`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': 'Iván Romero, Petar Ratkov', 'matchup_zone': 'Goalkeeper shot-stopping context', 'opponent_lambda': 1.1714}`	-
`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 1.20 (squad xG sum 0.9247, xG scale 1.2978, xA scale 1.0679, team xA 0.96 = 0.80 x lambda)	`scoreline_reset=true` `team_lambda=1.2` `squad_xg_sum=0.9247` `squad_xa_sum=0.8989` `team_expected_assists=0.96` `team_xa_to_xg_ratio=0.8`	shots_faced: 11.1 -> 10.6 expected_saves: 2.71 -> 2.6 high_pressure_saves: 0.94 -> 0.89
`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` `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=1.2595`	-
`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=0.8679`	-

Team totals 1 1.20 12 6.9

Bench Candidates (7 modeled substitutes with empirical playing time)

Player	Pos	Exp Min	App Prob	Substitute Modeling Details
Alex Padilla	G	4.4m	10.9%	Market-value substitute prior
Dani Vivian	D	14.3m	33.0%	Market-value substitute prior
Aitor Paredes	D	13.8m	22.1%	Market-value substitute prior
Mikel Jauregizar	M	24.7m	52.3%	Market-value substitute prior
Oihan Sancet	M	22.9m	32.6%	Market-value substitute prior
Nico Williams	F	25.8m	62.0%	Market-value substitute prior

Antagonist / tactical summary

Midfield is evenly contested — Jon Ander Olasagasti vs Peio Canales will be the pivotal duel. Total antagonist xG — Home: 0.81, Away: 0.72. Home danger zone: Left Wing vs Right Back — Flank Battle. Away danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle.

Midfield — possession 48.8% / 51.2%, dominance ratio 0.48, key duel: Jon Ander Olasagasti vs Peio Canales

Iván Romero xG 0.26Víctor García xG 0.13Petar Ratkov xG 0.21Peio Canales xG 0.09Maroan Sannadi xG 0.21Robert Navarro xG 0.21

Summary

Antagonist matchup analysis for Levante vs Athletic Club. Most likely scoreline: 1-1. Lambdas derived from 6 home and 6 away individual player duels. Head-to-head: Levante 0W-3D-7L vs Athletic Club over 10 meetings.

Tactical parity between 4-3-3 and 4-2-3-1. Match outcome driven primarily by individual player quality. Midfield is evenly contested — Jon Ander Olasagasti vs Peio Canales will be the pivotal duel. Total antagonist xG — Home: 0.81, Away: 0.72. Home danger zone: Left Wing vs Right Back — Flank Battle. Away danger zone: Striker vs Two Centre Backs — 1v2 Aerial/Physical Battle.

Generated 2026-10-21 13:39:23 UTC · Soccer prediction reporting