silly-kicks/xshot-occurrence-v1

xShotOccurrence v1 (sc_extended) — Shot-Occurrence Propensity from Tracking State

Read this first. This repo serves the sc_extended (owner-tier) variant. It is NOT bundled with the silly-kicks wheel because it is trained on restricted owner-tier data that cannot be redistributed inside a PyPI package — a licensing constraint. Only the learned parameters are published here. If you do not have owner-tier access, use the bundled default variant instead — see Which variant should I use?.

Model Description

XShotOccurrenceModel is a deterministic-XGBoost classifier estimating P(a shot is attempted by the in-possession team within ~1 s of a tracking frame) — the xS surface of the GKDV research arc (TF-16).

Paper-faithful 27-feature extractor in goal-relative coordinates via the shared _geometry helper: ball r/θ/z/speed, an openGoal goal-mouth obstruction term, GK distance/bearing, and the 5 nearest defenders + 5 nearest attackers.

• Domain filter: alive-ball, attacking third
• Feature set: faithful (velocity-bearing, 27 features)
• Trained on 964,263 rows / 182,517 positives (18.9% positive rate)

Which variant should I use?

Variant	Corpus	Where it lives	Use it?
default (public)	17 matches — SkillCorner + IDSSE, redistributable	bundled in the wheel	Default choice — offline, fully reproducible, no restricted data
sc_extended	115 matches — 7 IDSSE + 108 SkillCorner (incl. 98 owner-tier)	this repo (HF-only)	Yes, if you have owner-tier access and can accept a Hub download + the corpus caveats below
sc_extended_position_only	same 115-match corpus, velocity features dropped (26-feature)	separate repo silly-kicks/xshot-occurrence-position-only-v1 (HF-only)	Yes, if scoring velocity-less frames (StatsBomb-360 freeze frames) with owner-tier access — a stronger position-only model than the bundled position_only. Reachable ONLY via from_variant("sc_extended_position_only"); asking for sc_extended still returns this faithful model (ADR-070).

Why this variant is HF-only

sc_extended is HF-only for licensing, not quality: it is trained on restricted owner-tier SkillCorner data that cannot be redistributed inside the PyPI wheel (ADR-038). Only learned parameters are published here — no raw provider tracking data (only split thresholds, feature indices and leaf values are stored — no per-sample training data).

This artifact was produced by a two-provider (IDSSE + SkillCorner) single-candidate run: the corpus is the owner-tier sc_extended tier, so there is no paired comparison against public (that comparison governs the wheel bundle selection, not this archive; ADR-071). The bundled default/public model remains the reproducible offline choice.

Held-out CV (5 folds, out-of-fold)

Metric	Value	Baseline
PR-AUC	0.5851 (± 0.0436)	base rate 0.1893
Brier	0.1131	base-rate Brier 0.1534
Log loss	0.3671	—

All four acceptance gates pass (enough_usable_folds, pr_auc_gt_base_rate, brier_lt_base_rate_brier, log_loss_lt_uniform). Estimates are CV, not the shipped fit. training_commit: 1ce63ef.

TF-19 status: the shot arm has never been measured

Unlike its xCross sibling, this model carries no GK-substitution probe in metrics.json. That is blocked, not missing: xs_substitution_probe consumes ghost-substituted targets from the GKDV engine, and the registered xS probe has not been run against this arm. A TF-19 spec must not assume the shot arm is healthy — it is unmeasured, not validated.

Usage

from silly_kicks.tracking import XShotOccurrenceModel

model = XShotOccurrenceModel.from_variant("default")       # recommended, bundled, offline
model = XShotOccurrenceModel.from_variant("sc_extended")   # this repo, downloads from the Hub

Requires pip install silly-kicks[xshot] and silly-kicks >= 4.74.0 (the sc_extended_position_only sibling repo requires >= 4.94.0, which introduced its variant key — ADR-070).

The >= 4.74.0 floor is a hard requirement. These weights are on the corrected goal-relative transform (geometry_version: goal-relative-2); ADR-051 found the previous transform was chiral (an x-only mirror at one goal end, identity at the other), so one physical scene scored differently depending which end the attacking team attacked. load()'s feature-contract prong is fail-closed, so an older silly-kicks refuses these weights with IntegrityError rather than serve them against the geometry they were not fit on. from_hub() takes no revision argument yet, so treat the library version as the pin; prior revisions are addressable by commit SHA in this repo's git history.

Integrity and load-time guards

load() is fail-closed on two independent checks: (1) SHA256SUMS verified before anything is parsed; (2) chirality fingerprint (ADR-040) — the model re-runs its own outputs on a fixed y-asymmetric probe frame and compares to the recorded fingerprint, raising on a mismatch and on a missing one. A base_score guard handles the xgboost 3.x bracketed-string serialization that 2.x silently drops to 0.5.

Limitations

• Not the bundled model (restricted corpus). This is a redistribution limit, not a performance one.
• No GK measurement exists for this arm at all — see the TF-19 section.
• Trained on 115 matches, heavily one-club — the 98 owner-tier additions are a single club, so club/style confounding is real and unquantified here.
• SkillCorner keepers are detected in only ~19.6% of frames (~80% interpolated), which is why GKDV measurement is registered to Gradient Sports frames only (ADR-038 §5).
• Estimates are cross-validated, not a held-out test of the shipped fit.

References

See the NOTICE file in the silly-kicks repository for full bibliographic citations.

• Attribution: arXiv:2512.00203.
• Decisions: ADR-011 (trained-model lifecycle), ADR-037 (TF-19 re-gate), ADR-038 (corpus + visibility), ADR-040 (chirality enforcement), ADR-070 (position-only Hub variant), ADR-071 (owner-tier archive).

Model Files

File	Purpose
model.json	XGBoost booster (pickle-free)
metadata.json	features, hyperparameters, chirality fingerprint, provenance
metrics.json	CV metrics and acceptance record
SHA256SUMS	integrity manifest, verified by load()

More Information

https://github.com/karsten-s-nielsen/silly-kicks