Fitting motion models to contextual player behavior

TL;DR

This paper introduces a novel approach to modeling player movements in Australian Football by integrating contextual commitment data, revealing distinct spatial and passing behaviors, and clustering pass types for better understanding of game dynamics.

Contribution

The study develops commitment-based motion models that incorporate contextual passing information, providing new insights into player behavior and spatial strategies in Australian Football.

Findings

Commitment-based models differ significantly from displacement-based models.

Player commitment arrays reveal spatial occupancy and dominance.

Passes are mostly to one-on-one contests or unmarked players, rarely exceeding 25 meters.

Abstract

The objective of this study was to incorporate contextual information into the modelling of player movements. This was achieved by combining the distributions of forthcoming passing contests that players committed to and those they did not. The resultant array measures the probability a player would commit to forthcoming contests in their vicinity. Commitment-based motion models were fit on 46220 samples of player behavior in the Australian Football League. It was found that the shape of commitment-based models differed greatly to displacement-based models for Australian footballers. Player commitment arrays were used to measure the spatial occupancy and dominance of the attacking team. The spatial characteristics of pass receivers were extracted for 2934 passes. Positional trends in passing were identified. Furthermore, passes were clustered into three components using Gaussian mixture models. Passes in the AFL are most commonly to one-on-one contests or unmarked players. Furthermore, passes were rarely greater than 25 m.