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Skill importance in women’s soccer

Author

Listed:
  • Heiner Matthew
  • Fellingham Gilbert W.

    (Statistics, Brigham Young University, Provo, UT, USA)

  • Thomas Camille

    (Physical Education and Human Performance, Southern Utah University, Cedar City, UT, USA)

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.

Suggested Citation

  • Heiner Matthew & Fellingham Gilbert W. & Thomas Camille, 2014. "Skill importance in women’s soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 1-16, June.
  • Handle: RePEc:bpj:jqsprt:v:10:y:2014:i:2:p:16:n:16
    DOI: 10.1515/jqas-2013-0119
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    References listed on IDEAS

    as
    1. Miskin Michelle A & Fellingham Gilbert W & Florence Lindsay W, 2010. "Skill Importance in Women's Volleyball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-14, April.
    2. Ian McHale & Phil Scarf, 2007. "Modelling soccer matches using bivariate discrete distributions with general dependence structure," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(4), pages 432-445, November.
    3. Brillinger David R, 2007. "A Potential Function Approach to the Flow of Play in Soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(1), pages 1-21, January.
    4. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
    5. Hamilton Howard H, 2011. "An Extension of the Pythagorean Expectation for Association Football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(2), pages 1-18, May.
    6. Goldner Keith, 2012. "A Markov Model of Football: Using Stochastic Processes to Model a Football Drive," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-18, March.
    7. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, vol. 21(2), pages 331-340.
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