Estimating locomotor demands during team play from broadcast-derived tracking data

TL;DR

This paper develops models to estimate offscreen athletic load metrics in soccer using broadcast-derived tracking data, enabling more comprehensive performance analysis without expensive hardware.

Contribution

It introduces novel models that predict offscreen load metrics from broadcast data, expanding the utility of accessible tracking information in sports science.

Findings

Models accurately predict offscreen load metrics.

Broadcast data can be used to estimate total external load.

Enhanced performance analysis without costly hardware.

Abstract

The introduction of optical tracking data across sports has given rise to the ability to dissect athletic performance at a level unfathomable a decade ago. One specific area that has seen substantial benefit is sports science, as high resolution coordinate data permits sports scientists to have to-the-second estimates of external load metrics, such as acceleration load and high speed running distance, traditionally used to understand the physical toll a game takes on an athlete. Unfortunately, collecting this data requires installation of expensive hardware and paying costly licensing fees to data providers, restricting its availability. Algorithms have been developed that allow a traditional broadcast feed to be converted to x-y coordinate data, making tracking data easier to acquire, but coordinates are available for an athlete only when that player is within the camera frame. Obviously, this leads to inaccuracies in player load estimates, limiting the usefulness of this data for sports scientists. In this research, we develop models that predict offscreen load metrics and demonstrate the viability of broadcast-derived tracking data for understanding external load in soccer.