A generative approach to frame-level multi-competitor races

Stokes Tyrel (), Bagga Gurashish (), Kroetch Kimberly (), Kumagai Brendan () and Welsh Liam ()
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Stokes Tyrel: Department of Biostatistics, NYU Langone, New York, USA
Bagga Gurashish: Department of Statistics and Actuarial Science, 1763 Simon Fraser University , Burnaby, Canada
Kroetch Kimberly: Department of Statistics and Actuarial Science, 1763 Simon Fraser University , Burnaby, Canada
Kumagai Brendan: Department of Statistics and Actuarial Science, 1763 Simon Fraser University , Burnaby, Canada
Welsh Liam: Department of Statistical Sciences, 7938 University of Toronto , Toronto, Canada

Journal of Quantitative Analysis in Sports, 2024, vol. 20, issue 4, 365-383

Abstract: Multi-competitor races often feature complicated within-race strategies that are difficult to capture when training data on race outcome level data. Models which do not account for race-level strategy may suffer from confounded inferences and predictions. We develop a generative model for multi-competitor races which explicitly models race-level effects like drafting and separates strategy from competitor ability. The model allows one to simulate full races from any real or created starting position opening new avenues for attributing value to within-race actions and performing counter-factual analyses. This methodology is sufficiently general to apply to any track based multi-competitor races where both tracking data is available and competitor movement is well described by simultaneous forward and lateral movements. We apply this methodology to one-mile horse races using frame-level tracking data provided by the New York Racing Association (NYRA) and the New York Thoroughbred Horsemen’s Association (NYTHA) for the Big Data Derby 2022 Kaggle Competition. We demonstrate how this model can yield new inferences, such as the estimation of horse-specific speed profiles and examples of posterior predictive counterfactual simulations to answer questions of interest such as starting lane impacts on race outcomes.

Keywords: multi-competitor races; Bayesian model; simulation analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/jqas-2023-0091

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