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A finite mixture latent trajectory model for modeling ultrarunners' behavior in a 24-hour race


Francesco Bartolucci, Brendan Murphy

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Refereed Original Article
A mixed latent trajectory model is developed to study the performance and strategy of runners in a 24-hour long ultra running race. The model facilitates clustering of runners based on their speed and propensity to rest and thus reveals the strate- gies used in the race. Inference for the mixed latent trajectory model is achieved using an expectation-maximization algorithm. Fitting the model to data from the 2013 World Championships reveals three clearly separated clusters of runners who exhibit di erent strategies throughout the race. The strategies show that runners can be grouped in terms of their average moving speed and their propensity to rest during the race. The e ect of age and gender on the probability of belonging to each cluster is also investigated.
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Publication Date: 
Journal of Quantitative Analysis in Sports
National University of Ireland, Dublin (UCD)
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