silly-kicks/ghost-gk-sweeper-v1

Ghost-GK Sweeper v1 — Extended-Grid Goalkeeper Positioning (Rest-Defense)

Predicts where a league-average goalkeeper would position themselves, including the in-possession high-sweeper regime that the standard Ghost-GK v1 model cannot represent. Point-estimate (ghost_gk_x/y) served as the exact pickle-free boosted HistGradientBoostingRegressor mean.

Part of the silly-kicks soccer analytics library (TF-60 rest-defense; ADR-083).

Why a separate model

The bundled Ghost-GK default/full variants train on a fixed goal-relative box x ∈ [0, 30] m from the defended goal: a keeper who has swept far upfield falls outside it and is dropped as a "sweeper rush". That is correct for normal in-goal positioning, but it makes the model hard-saturate at 30 m — it cannot place an in-possession keeper who has pushed up to 30–45 m to blunt a counter-attack (the rest-defense regime). This sweeper variant lifts the label ceiling to x_max = 52.5 m so it represents the high-sweeper regime, for TF-60 rest-defense / TF-19 GKDV counterfactual arms.

The frozen default/position_only/full variants are unchanged — this is an additive variant; consumers opt in via from_variant("sweeper").

Variants

Variant	Repo	Features	Use
sweeper (this repo)	silly-kicks/ghost-gk-sweeper-v1	26 (velocity-bearing)	Continuous tracking (Sportec, SkillCorner, Gradient Sports)
sweeper_position_only	silly-kicks/ghost-gk-sweeper-position-only-v1	21 (5 velocity features dropped)	Velocity-less StatsBomb-360 freeze-frames

Model description

• Point estimate only. ghost_gk_x/y serve the exact boosted HGBR mean, reconstructed pickle-free (baseline + Σ_trees leaf_value; no sklearn at inference). The KDE density read-out (predict_density) is not supported on this extended grid and raises — the density path stays on the default 30 m grid (ADR-083). Use compute_ghost_gk / serve_ghost_gk_positions.
• Grid: first-class per-model GhostGridSpec — x ∈ [0, 52.5] m, y ∈ [18, 50] m, 0.5 m resolution (105 × 64 cells).
• Parameters-only, pickle-free: npz (tree ensembles + baselines) + JSON metadata + SHA-256 checksums. No per-sample training data, no raw provider tracking data (ADR-044).
• Hyperparameters: HistGradientBoostingRegressor, 500 trees, max depth 8, 5-fold CV.

Metrics

Metric	Value
Held-out CV euclidean MAE	1.142 m (per-provider: Gradient Sports 1.078 / SkillCorner 1.167 / Sportec 1.734)
Boosted-reconstruction parity vs sklearn	1.21e-13 (exact — safe to publish)
> 30 m high-sweeper stratum MAE	~2.06 m (the sweeper places high keepers where the default is blind)

The > 30 m (high-sweeper) coverage of the training corpus is IDSSE/Sportec-dominated (11.5 %); SkillCorner 0.24 %; Gradient Sports 0.0 % — see the data caveat below.

Training data

179 matches / ~1.05M frames of licensed professional tracking (Sportec/DFL Bundesliga, SkillCorner, Gradient Sports FIFA World Cup 2022). Only the learned parameters are published; no raw provider tracking data is redistributed (parameters-only artifact). Trained from a clean, CI-green commit.

⚠ Gradient Sports goalkeeper clamp. Gradient Sports' tracking clamps the goalkeeper's position to a hard 27.5 m from goal (a source-data limitation, verified on the raw provider data; silly-kicks passes it through faithfully). Consequently GS contributes no high-sweeper training signal, and any GS goalkeeper-depth analysis is invalid past 27.5 m. The high-sweeper regime this model represents is learned from IDSSE/Sportec. silly-kicks flags this at conversion time via validate_gk_position_clamp / GoalkeeperClampWarning. See docs/research/gs_keeper_clamp/.

Usage

import silly_kicks.tracking as tracking

# The extended-grid sweeper (velocity-keyed within its family on velocity-less frames)
model = tracking.GhostGkModel.from_variant("sweeper")
out = tracking.compute_ghost_gk(frames, model=model, home_team_id=1)  # ghost_gk_x / ghost_gk_y

# predict_density is NOT supported on the extended grid (raises); use the mean/serve path above.

Input frames must be LTR-normalized (home attacks right). All coordinates are goal-relative.

Limitations

• League-average positioning, not a specific keeper's style; no shot-stopping ability.
• No KDE density on the extended grid (mean/serve path only).
• Inherits tracking-system noise; Gradient Sports keeper positions are unusable past 27.5 m (above).
• LTR normalization required; static per-frame estimate.

References

Le et al. 2017 (Data-Driven Ghosting, MIT Sloan); Dutta et al. 2024 (NFL Ghosts, arXiv:2406.17220); Pospisil & Lee 2018 (RFCDE, arXiv:1804.05753). See the silly-kicks NOTICE for full citations.

More information

• License: MIT · Library: silly-kicks (v4.105.0+) · GitHub · ADR-083.