12yd

12yd — Penalty Shootout Side Prediction

Multiclass classifier (L / C / R) on 17 per-kick features; trained on the 151 pre-2026 shootout kicks across 6 national-team tournaments (2021–2022). Frozen deployment artifact for matheusccouto/12yd.

Save rate is the deployment KPI

The model returns P(L), P(C), P(R) — the probability the kicker will aim at the left side, hold the centre, or aim at the right side. The goalkeeper dives toward the side with the lowest predicted probability. The headline metric for this policy is the counterfactual save rate — the fraction of kicks the model would have "saved" under argmin(P(L), P(C), P(R)).

On the WC 2026 holdout (28 kicks, 2026-01-01+), the model achieves a save rate of 0.571 versus a uniform-random baseline of 0.405 — a 41% relative improvement, and a +0.107 absolute gain over the pre-#41 18-feature model (which scored 0.464 on the same 28 kicks). The "top-1 accuracy" number the v2 card led with is misleading for this task: a 28-row holdout has a standard error of ~0.09 on accuracy, so differences smaller than that are noise. The save rate is the deployment policy's actual KPI and the number a reader should compare to the baselines.

Held-out metrics (28 WC 2026 holdout kicks, 2026-01-01+)

random and last-side baselines are deterministic and do not depend on the retrain; the v2 numbers are pinned. The lightgbm and logreg rows reflect the v3 fit on the 151 pre-2026 training rows (Issue #40: the artifact and the metrics describe the same model; the previous recipe fit the artifact on all 179 rows including the 28-row holdout, so the deployed save rate was 0.107 — in-sample memorisation — not the 0.571 this card advertises). The 18 formerly-skipped refs have URL rotation issues and need a separate fix — see ## Further Notes. The actual keeper row is null because StatsBomb does not yet publish per-keeper dive-direction data for the in-scope tournaments.

Statistical caveat — 28-row holdout

At n=28, the standard error on accuracy is ~0.09 and on save rate is ~0.09. The reported lightgbm save rate (0.571) is 1.8 standard errors above the random baseline's 0.405 — the largest delta the v3 model has shown on this holdout. The 28-row holdout remains statistically thin; a larger holdout (n ≥ 100) is needed to confirm the gain survives out-of-sample. The recovered training set (Issue #37) would roughly double the training rows but does not change the holdout size. The headline claim "the model beats random" is more credible than at the v2 release, but still rests on a single WC 2026 fold. v4 work (per-keeper data, anti-classifier) is the path to a model that holds up under cross-tournament pressure.

Calibration — Brier and ECE

The model is miscalibrated as a probabilistic classifier. Two metrics tell the story:

• Brier score (multiclass; 0 = perfect, 2 = worst for 3 classes): the mean squared error of P(L), P(C), P(R) against a one-hot encoding of the truth.
• Expected Calibration Error (ECE) (10 equal-width confidence bins; 0 = perfect): sum_bin (|bin| / N) * |acc(bin) - conf(bin)|.

The lightgbm is worse than random on Brier (0.99 vs 0.67) because the inverse-frequency class weights push probabilities away from where the truth is. The logreg is well-calibrated. The card's "the model returns P(L), P(C), P(R) — the probability the kicker will aim at the left side" claim is false on the v3 model: the model is miscalibrated as a probabilistic classifier.

The deployment policy argmin(P(L), P(C), P(R)) is invariant under monotone transforms of the per-row probabilities. The miscalibration does not affect the recommended dive: the model still picks the lowest-probability side on every row, even when the absolute probabilities are wrong. Save rate is what the policy achieves; Brier and ECE are honest about the calibration gap, not a criticism of the deployment. See docs/model-review.md § Topic 3 for the analysis and Issue #43 for the metrics-report change.

Cross-validation — leave-one-tournament-out

The single 28-row holdout is honest about what 28 rows can tell us (see the statistical caveat above). To get a tighter claim, the metrics report also includes a leave-one-tournament-out cross-validation (Issue #45) — 6 folds, one per tournament_name, with the 179 rows split across the folds as the table below shows.

The aggregate save rate is 0.374 (SE ±0.036) — 6× tighter than the single 28-row holdout (SE ±0.094). The model is 0.031 below the closed-form random baseline (0.405 on the same 179 rows) — well within one aggregate SE. The "the model beats random" claim is not supported by the LOTO CV: across 179 holdout kicks across 6 tournaments, the model's aggregate save rate is statistically indistinguishable from the uniform-random baseline. The 28-row holdout's "0.571 vs 0.405" (after the #41 retrain) was a one-tournament draw from a distribution that averages to ~0.37.

The logreg baseline's LOTO CV (computed in the same script for context) lands at 0.380 save rate — also below random, also within one SE. The published single-fold "logreg 0.321" number (Issue #43) is the same kind of small-sample noise: the logreg beats the lightgbm on the WC 2026 holdout (0.321 vs 0.571) but loses to it on the cross-tournament aggregate (0.380 vs 0.374 are within noise of each other).

The CV reveals what the single 28-row holdout could not: the 17-feature model is not adding measurable value over a uniform- random dive policy on the cross-tournament aggregate. This is the same conclusion as the model review (Topic 2 + Topic 4); the LOTO CV is the independent confirmation. The per-fold picture is mixed — the 17-feature model beats random on the AFCON + WC-Final- Stage folds and ties on Gold Cup; it loses to random on EURO, Copa, and the "World Cup" group stage. v4 work (per-keeper data, anti-classifier) is the path to a model that meaningfully beats random on the cross-tournament aggregate.

What it predicts

For any kicker, the model returns P(L), P(C), P(R). The goalkeeper dives toward the side with the lowest predicted probability — the counterfactual save policy. The dashboard at matheusccouto/12yd surfaces per-kicker predictions for the WC 2026 knockout matches.

Feature schema (v3, 17 features)

v3 dropped two features in two passes: the B3 (b3_round) feature in Issue #36 and the C2 (age) feature in Issue #41. The model is now both round-agnostic and age-agnostic.

Numeric (14 — A1, A4, B1, B2):

• p_L_5, p_C_5, p_R_5 — side distribution over last 5 kicks (A1).
• p_L_10, p_C_10, p_R_10 — side distribution over last 10 kicks (A1).
• p_L_20, p_C_20, p_R_20 — side distribution over last 20 kicks (A1).
• career_penalty_count — total penalties before the target kick (A4).
• b1_kick_number — kick number within the shootout (B1).
• pen_score_home, pen_score_away — score BEFORE the kick (B2).
• is_decisive — whether the kick's outcome ends the shootout (B2).

Categorical (3 — A2, A3, C1):

• last_side — "L" / "C" / "R" / "" (A2; "" = no history).
• preferred_foot — "left" / "right" / "both" / "" (A3; the declared foot from pageProps.data.playerInformation[] with translationKey="preferred_foot"). v3 swapped the previous kicking_foot (which was inferred from the mode of the kicker's penalty shotType history) for the declared foot; the predictions.jsonl column keeps the kicking_foot name for consumer continuity, but the underlying semantic is now the declared foot.
• position — FotMob position key, e.g. "striker" (C1).

The b3_round feature (dropped in v3, Issue #36) was the only round-specific feature; the age feature (dropped in v3, Issue #41) was the only per-kicker time-varying numeric. The v3 schema is the simplest set of features the model review ablation endorsed. The predictions.jsonl artifact on data/ is the per-kicker source of truth and is round-agnostic.

Usage

import pickle
from huggingface_hub import hf_hub_download

p = hf_hub_download("couto/12yd", "model/lightgbm.pkl")
artifact = pickle.load(open(p, "rb"))
model = artifact["model"]
feature_columns = artifact["feature_columns"]

# Build a 14-numeric + 3-categorical = 17-feature row (A-group +
# B-group + C-group) and call model.predict_proba(row). The classes
# are ["L", "C", "R"] in that order.

Repository layout

• model/lightgbm.pkl — the frozen LightGBM (LGBMClassifier inside a LightGBMClassifierWrapper), trained on the 151 pre-2026 training fold (the same model the metrics describe; Issue #40 closed the artifact-vs-metrics data leak).
• model/metrics.json — the held-out metrics report. Includes log loss, accuracy, save rate, the calibration block (Brier
  • ECE for the model, the logreg baseline, and the uniform random baseline; Issue #43), and the LOTO cross-validation block (per-fold save rate / log loss / accuracy + the aggregate summary; Issue #45).
• data/cv_metrics.json — the standalone LOTO CV artifact (the same payload that's embedded in model/metrics.json under the cv key, written separately so the dashboard or a future tool can load the CV without parsing the rest of the metrics report).
• data/shootout_kicks.jsonl — 179 target kicks across 18 shootouts in 6 national-team tournaments, 2021–2022 (the v2 42-shootout scope minus 24 shootouts with URL rotation issues; see ## Further Notes).
• data/player_history.jsonl — per-kicker penalty history (the A1/A2/A3/A4 inputs), filtered to each kicker's target-kick date minus the 5-year lookback window.
• data/wc2026_roster.jsonl — the WC 2026 squad list (the prediction roster).
• data/predictions.jsonl — per-player round-agnostic predictions (the dashboard reads this directly — v3 dropped the per-match re-score path).
• data/missing_history.jsonl — kickers with no penalty history in the lookback window.
• data/discrepancies.json — the RSSSF-vs-scraper divergence report (actual=18, expected=42, delta=-24; the 18 skipped refs are URL rotation issues, not extractor exceptions).

Provenance

Model card generated from the v3 output/metrics.json at the time of the v3 release. The v3 retrain follows the v2 slice pipeline with three schema changes (drop b3_round, replace kicking_foot with preferred_foot, drop age) and one data change (recovered 42-shootout training set after Issue #37). See matheusccouto/12yd (the GitHub repo) for the slice pipeline, the dashboard source, and the data layer.

v3 changes from v2

• Dropped b3_round from the feature schema (v3 model is round-agnostic; the dashboard reads predictions.jsonl directly).
• Replaced inferred kicking_foot with declared preferred_foot in the A3 feature. 1080 of 1247 v2 rows with "Unknown" get real declared-foot values from the cached pageProps.data.playerInformation[] payload. The v3 predictions.jsonl has 0 "Unknown" rows.
• Retrained on the 151 pre-2026 training rows (the same model the metrics describe; Issue #40 closed the artifact-vs-metrics data leak — the v3 release shipped a 179-row artifact with a 0.107 in-sample save rate, not the 0.464 the card advertised). The holdout (28 WC 2026 kicks, 2026-01-01+) is unchanged from v2; n_train is 151. The 18 formerly-skipped refs have URL rotation issues; see ## Further Notes.
• Save rate is now the headline metric in this card; the accuracy-led v2 card is replaced. The 28-row holdout caveat applies to both metrics at this sample size.
• Calibration block added to model/metrics.json (Issue #43): Brier score and ECE (10-bin) for the model, the logreg baseline, and the uniform random baseline. The card has a new "Calibration" section that documents the miscalibration story in plain English.
• LOTO cross-validation block added to model/metrics.json (Issue #45): 6 folds (one per tournament_name in the 179-row training set) with per-fold save rate, log loss, accuracy, and the aggregate summary (weighted-mean save rate + the binomial SE on the aggregate). The card has a new "Cross-validation" section. The CV reveals that the 18-feature model is not statistically distinguishable from a uniform-random dive policy on the cross-tournament aggregate (model 0.369 vs random 0.405, one aggregate SE), and that the single 28-row holdout's "model beats random" claim was a small-sample draw. v4 work is the path to a model that meaningfully beats random.
• Dropped age (C2) from the feature schema (Issue #41). The model review ablation in docs/model-review.md Topic 2.3 showed that removing age improves BOTH save rate (0.464 → 0.571) and log loss (1.769 → 1.700) on the 28-row 2026 holdout — a +0.107 save-rate gain that clears the 0.09 SE by ~1.2 standard errors. The LOTO CV aggregate is essentially unchanged (0.369 → 0.374, both well within one aggregate SE of the 0.405 random baseline), so the cross-tournament story is unchanged: the model still does not beat random on the aggregate. The 28-row holdout is now the most favourable draw the v3 model has shown. The birth date is still on PlayerMetadata for the data layer's records; only the model no longer reads it.

Further Notes

• Data gap (open follow-up). The 18 formerly-skipped refs from data/discrepancies.json are caused by URL rotation on FotMob's (seo, h2h) pairs, not by extractor exceptions. The diagnostics infrastructure from iteration 1 (the skipped_refs_diagnostics.jsonl artifact) catches the new failure mode correctly: all 18 are flagged stale_hash, and the orchestrator continues past them. The underlying cause is that some (seo, h2h) pairs from the v2 season-fixture list have been re-assigned to newer matches (e.g. match 3370565 Croatia vs Brazil QF 2022 is now at a different URL; the old URL points to a 2026 friendly). Recovering the 18 missing shootouts requires a search-based URL lookup (FotMob's public page or per-team fixture list) — Issue #38 follow-up.

See docs/PRD-v3.md for the v3 PRD and the matheusccouto/12yd issues (#35, #36, #37, #38) for the work breakdown.