Skill importance in women’s soccer

Heiner Matthew (), Fellingham Gilbert W. and Thomas Camille
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Heiner Matthew: Statistics, Brigham Young University, Provo, UT, USA
Fellingham Gilbert W.: Statistics, Brigham Young University, Provo, UT, USA
Thomas Camille: Physical Education and Human Performance, Southern Utah University, Cedar City, UT, USA

Journal of Quantitative Analysis in Sports, 2014, vol. 10, issue 2, 287-302

Abstract: Soccer analytics often follow one of two approaches: 1) regression models on number of shots taken or goals scored to predict match winners, or 2) spatial and/or temporal analysis of plays for evaluation of strategy. We propose a new model to evaluate skill importance in soccer. Play by play data were collected on 22 NCAA Division I Women’s Soccer matches with a new skill notation system. Using a Bayesian approach, we model play sequences as discrete absorbing Markov chains. Using posterior distributions, we estimate the probability of 35 distinct offensive skills leading to a shot during a single possession.

Keywords: absorbing state; Bayesian methods; Markov chain (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/jqas-2013-0119

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