Summary
Tracking soccer players not on screen during TV broadcasts is tricky because their speeds aren’t directly measured. The authors studied how knowing players’ velocities (speeds and directions) off-screen helps improve analysis at three stages: when filling in missing data, when estimating control over areas on the field, and when judging team performance. They found that velocity data is most helpful for estimating control but less so for filling in missing player positions. Perfect knowledge of off-screen players’ speeds adds only a small improvement. Their method also shows that errors grow less when the broadcast camera covers a wider field view.
What this means in practice
- •For sports analytics teams: Improve soccer team performance analysis by focusing velocity modeling on visible players rather than off-screen ones to better estimate pitch control.
- •For sports video processing engineers: Optimize broadcast-based player tracking by fixing off-screen position estimates with imputation and focusing velocity computation on wide-view footage where it matters most.
Tested on one dataset.
Abstract
Velocity-aware pitch control is standard, but under a broadcast viewport half the players are off screen and on-screen velocities come from a drifting calibration. We ask at which layer of broadcast off-ball analysis velocity changes the answer. Inheriting our off-screen imputation protocol (three Metrica matches, 44 m viewport, block-bootstrap CIs), we score four velocity regimes -- none, viewport-legal observed, true-for-visible, true-for-all -- against a velocity-aware ground truth at three layers: imputation, the control surface, and team verdicts. Velocity is nearly useless for imputation (-0.2 pp against a 12--14 pp velocity-free surface MAE), first-order for the surface (-1.5 to -1.8 pp, 11--15% of that MAE), and ten times smaller for verdicts (-0.12 to -0.19 pp). The velocity that matters is the visible channel: perfect occluded-player velocity adds 2--6% of the visible gain, and no last-seen decay policy we tested exceeds that. Omitting velocity blurs the surface (per-frame |e| 2.2--2.6 pp) with small time-averaged bias (per cell <=0.4 pp), whereas imputation error is a structured bias against the defending team's deep zone (5--9 pp). At a fixed velocity window, a noise ladder of eleven jitter settings, including sigma_v-matched pairs, is ordered to first order by one velocity-noise axis sigma_v with break-even ~1 m/s; eleven SoccerNet-GSR clips from one match through our pipeline measure sigma_v=1.65 m/s yet recover 24--36% of the benefit: 43% of the variance is frame-common, which the surface tolerates, and the residual is heavy-tailed and clustered, which Gaussian controls matched on component RMS do not reproduce (+0.03 vs. +0.36). The share of velocity-free error that velocity removes grows with viewport width (7% at 36 m, 21% at 60 m): fix imputation on tight shots, velocity on wide ones. Code and logs are released.