# Continuous football player tracking from discrete broadcast data

arXiv: https://arxiv.org/abs/2311.14642 · HTML: https://mlsanity.com/paper/2311.14642/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 24 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Multi-Modal Trajectory Prediction of NBA Players](https://mlsanity.com/paper/2008.07870/) (2020)
- [Automatic event detection in football using tracking data](https://mlsanity.com/paper/2202.00804/) (2022)
- [Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations](https://mlsanity.com/paper/2302.06569/) (2023)

## Built on by (most cited first, 1 of 1)

- [Training-Free Off-Screen Player Imputation for Broadcast-Based Spatial Football Analytics](https://mlsanity.com/paper/2607.11548/) (2026)

## Abstract (arXiv)

Player tracking data remains out of reach for many professional football teams as their video feeds are not sufficiently high quality for computer vision technologies to be used. To help bridge this gap, we present a method that can estimate continuous full-pitch tracking data from discrete data made from broadcast footage. Such data could be collected by clubs or players at a similar cost to event data, which is widely available down to semi-professional level. We test our method using open-source tracking data, and include a version that can be applied to a large set of over 200 games with such discrete data.