silly-kicks/ghost-outfield-v1

Ghost-Outfield v1 — League-Average Rearguard Positioning (Rest-Defense)

Predicts where a league-average outfield rearguard defender would position themselves, per tracking frame, team, and lateral slot. The outfield sibling of Ghost-GK v1: a point estimate (ghost_gr_x/y, goal-relative) served as the exact pickle-free boosted HistGradientBoostingRegressor mean.

Part of the silly-kicks soccer analytics library (TF-60 rest-defense; ADR-087). It is the league-average baseline the rest-defense outfield counterfactual arm differences against ("how far does the actual rest-defense shape sit from average?").

What it models

For an in-possession team's rest defense (the rearguard it keeps while attacking, to blunt the counter after a loss), the model predicts the league-average position of each of the deepest-n rearguard defenders, ranked left-to-right by a lateral slot_index. It is possession-conditioned: one model serves both regimes via a live team_in_possession feature — the ball-carrier's rest-defense rearguard and the defending line facing an attack.

The feature vector is leakage-safe by construction: no input encodes the modeled team's own rearguard coordinates (the prediction target). It is ball state + opponent counter-threat geometry + game context + the lateral slot rank.

Variants

Variant	Repo	Features	Use
default (this repo)	silly-kicks/ghost-outfield-v1	20 (velocity-bearing)	Continuous tracking (Sportec, SkillCorner, Gradient Sports)
position_only	silly-kicks/ghost-outfield-position-only-v1	16 (4 velocity features dropped)	Velocity-less StatsBomb-360 freeze-frames

The serve seam auto-selects the variant from the frames' declared velocity availability; a velocity-less freeze-frame with no bundled position_only yields honest-NaN, never the (invalid) velocity model.

Model description

• Point estimate only. ghost_gr_x/y serve the exact boosted HGBR mean, reconstructed pickle-free (baseline + Σ_trees leaf_value; no sklearn at inference). There is no KDE / density read-out (a point estimate is what the counterfactual arm differences).
• Parameters-only, pickle-free: npz (two boosted x/y ensembles + baselines) + JSON metadata + SHA-256 checksums. No per-sample training data, no raw provider tracking data (ADR-011/044).
• Fail-closed load: SHA-256 + a behavioural chirality fingerprint (a y-mirrored model is rejected) + a feature-contract fingerprint. The chirality frame hash is pandas-major-invariant (loads identically under pandas 2 and 3).
• FOV honest-NaN (StatsBomb-360): a served frame whose rearguard region is not sufficiently observed is returned NaN (ghost_outfield_source="fov_cropped"), never a fabricated ghost from the deepest-n visible players.
• Hyperparameters: HistGradientBoostingRegressor, 500 trees, max depth 8, 5-fold CV; trained at 1 frame/second (25 fps tracking is highly autocorrelated — 1 fps is ~25× fewer, near-duplicate rows with no meaningful signal loss for a mean-positioning model).

Metrics

Metric	Value
Held-out CV euclidean MAE	6.00 m (per-provider: Gradient Sports 6.14 / SkillCorner 5.92 / Sportec 6.33)
Per-possession CV MAE	in-possession 6.97 m / out-of-possession 5.04 m
Per-slot CV MAE (slots 1–4)	5.96 / 6.05 / 6.02 / 5.98 m
Rearguard coherence (slot ordering)	ordering_fraction = 1.0 (the independently-predicted slots order as a line)
Boosted-reconstruction parity vs sklearn	exact (round-trip verified — safe to publish)

Training data

179 matches / ~4.17M per-(frame, team, slot) rows (at 1 fps) 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.

Usage

import silly_kicks.tracking as tracking

model = tracking.GhostOutfieldModel.from_variant("default")
# One row per (frame, team, slot) with goal-relative ghost_gr_x / ghost_gr_y + a provenance token.
served = tracking.serve_ghost_outfield_positions(frames, model=model, home_team_id=1)

Input frames must be LTR-normalized (home attacks right). All coordinates are goal-relative to the modeled team's defended goal.

Limitations

• League-average positioning, not a specific player's or team's tactical style.
• Static per-frame estimate; inherits tracking-system noise.
• LTR normalization required.
• Unlike the goalkeeper model, this model is not affected by the Gradient Sports keeper clamp (outfielders are tracked over the full pitch).

References

Le et al. 2017 (Data-Driven Ghosting, MIT Sloan). This model is the boosted-mean point-estimate variant of the ghosting concept (no density estimation). See the silly-kicks NOTICE for the full citation.

More information

• License: MIT · Library: silly-kicks (v4.109.0+) · GitHub · ADR-087.