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

被引：8

作者：

Jarvandi, Ali ^{[1
]}

Sarkani, Shahram ^{[2
]}

Mazzuchi, Thomas ^{[2
]}

机构：

[1] George Washington Univ, Engn Management & Syst Engn, 45742 Smoketree Terrace, Sterling, VA 20166 USA

[2] George Washington Univ, Engn Management & Syst Engn, Washington, DC USA

来源：

JOURNAL OF QUANTITATIVE ANALYSIS IN SPORTS | 2013年 / 9卷 / 04期

关键词：

decision process; Markov modeling; simulation; soccer; team compatibility;

D O I：

10.1515/jqas-2012-0054

中图分类号：

O1 [数学]; C [社会科学总论];

学科分类号：

03 ; 0303 ; 0701 ; 070101 ;

摘要：

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.

引用

页码：347 / 366

页数：20

共 16 条

[1]

Beck N., Meyer M., Modeling team performance

[2]

Theoretical and empirical annotations on the analysis of football, Empirical Economics, 43, pp. 335-356, (2011)

[3]

Beetz M., Kirchlechner B., Lames M., Computerized real-time analysis of football games, IEEE Pervasive Computing, 4, 3, pp. 33-39, (2005)

[4]

Boon B.H., Sierksma G., Team formation: Matching quality supply and quality demand, European Journal of Operational Research, 148, pp. 277-292, (2003)

[5]

Dobson S., Goddard J., Optimizing strategic behaviour in a dynamic setting in professional team sports, European Journal of Operational Research, 205, pp. 661-669, (2010)

[6]

Goldner K., A markov model of football: Using stochastic processes to model a football drive, Journal of Quantitative Analysis in Sports, 8, 1, (2012)

[7]

Hirotsu N., Wright M., Determining the best strategy for changing the configuration of a football team, Journal of the Operational Research Society, 54, pp. 878-887, (2003)

[8]

Hughes M., Franks I., Analysis of passing sequences, shots and goals in soccer, Journal of Sports Sciences, 23, 5, pp. 509-514, (2005)

[9]

Hughes M., Caudrelier T., James N., Redwood-Brown A., Donnelly I., Kirkbride A., Duschesne C., Moneyball and soccer-an analysis of the key performance indicators of elite male soccer players by position, Journal of Human Sport & Exercise, 7, 1, pp. 402-412, (2011)

[10]

Kang C.-H., Hwang J., Li K., Trajectory analysis for soccer players, Sixth IEEE International Conference on Data Mining, (2006)

← 1 2 →