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Modeling team compatibility factors using a semi-Markov decision process: a data-driven approach to player selection in soccer

Author

Listed:
  • Jarvandi Ali

    (George Washington University, Engineering Management and Systems Engineering 45742 Smoketree Terrace, Sterling, VA 20166, USA)

  • Sarkani Shahram

    (George Washington University, Engineering Management and Systems Engineering, Washington, DC, USA)

  • Mazzuchi Thomas

    (George Washington University, Engineering Management and Systems Engineering, Washington, DC, USA)

Abstract

Player selection is one of the great challenges of professional soccer clubs. Despite extensive use of performance data, a large number of player transfers at the highest level of club soccer have less than satisfactory outcome. This study uses player performance and decision making data to estimate team performance in terms of goal differential and model the effects of team compatibility on player and team performance. In this methodology, players’ attributes are assessed with respect to the potential contribution to team performance, given the attributes of surrounding players. The study is using a semi-Markov decision process to model game flow. Performance data from the English Premier League between seasons 2008/2009 and 2011/2012 is used to predict the outcome of 69 transfers. The model provides an average error of 7.86 in predicting teams’ goal differential in a season with current squad and 18.91 in estimating the effect of a future transfer on team performance.

Suggested Citation

  • Jarvandi Ali & Sarkani Shahram & Mazzuchi Thomas, 2013. "Modeling team compatibility factors using a semi-Markov decision process: a data-driven approach to player selection in soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(4), pages 347-366, December.
    • Handle: RePEc:bpj:jqsprt:v:9:y:2013:i:4:p:347-366:n:2
    DOI: 10.1515/jqas-2012-0054
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    References listed on IDEAS

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    1. Oberstone Joel, 2011. "Comparing Team Performance of the English Premier League, Serie A, and La Liga for the 2008-2009 Season," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(1), pages 1-18, January.
    2. Nikolaus Beck & Mark Meyer, 2012. "Modeling team performance," Empirical Economics, Springer, vol. 43(1), pages 335-356, August.
    3. 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.
    4. Boon, Bart H. & Sierksma, Gerard, 2003. "Team formation: Matching quality supply and quality demand," European Journal of Operational Research, Elsevier, vol. 148(2), pages 277-292, July.
    5. Mohsen Shafizadeh & Shirley Gray & John Sproule & Terry McMorris, 2012. "An exploratory analysis of losing possession in professional soccer," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 12(1), pages 14-23, April.
    6. N Hirotsu & M Wright, 2003. "Determining the best strategy for changing the configuration of a football team," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 878-887, August.
    7. Dobson, Stephen & Goddard, John, 2010. "Optimizing strategic behaviour in a dynamic setting in professional team sports," European Journal of Operational Research, Elsevier, vol. 205(3), pages 661-669, September.
    8. Albin Tenga & Einar Sigmundstad, 2011. "Characteristics of goal-scoring possessions in open play: Comparing the top, in-between and bottom teams from professional soccer league," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 11(3), pages 545-552, December.
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