Beyond completion rate: evaluating the passing ability of footballers
Passing the ball is one of the key skills of a football player yet the metrics commonly used to evaluate passing ability are crude and largely limited to various forms of a pass completion rate. These metrics can be misleading for two general reasons: they do not account for the difficulty of the at...
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| Published in | Journal of the Royal Statistical Society. Series A, Statistics in society Vol. 179; no. 2; pp. 513 - 533 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.02.2016
John Wiley & Sons Ltd Oxford University Press |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0964-1998 1467-985X |
| DOI | 10.1111/rssa.12115 |
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| Abstract | Passing the ball is one of the key skills of a football player yet the metrics commonly used to evaluate passing ability are crude and largely limited to various forms of a pass completion rate. These metrics can be misleading for two general reasons: they do not account for the difficulty of the attempted pass nor the various levels of uncertainty involved in empirical observations based on different numbers of passes per player. We address both these deficiencies by building a statistical model in which the success of a pass depends on the skill of the executing player as well as other factors including the origin and destination of the pass, the skill of his teammates and the opponents, and proxies for the defensive pressure put on the executing player as well as random chance. We fit the model by using data from the 2006-2007 season of the English Premier League provided by Opta, estimate each player's passing skill and make predictions for the next season. The model predictions considerably outperform a naive method of simply using the previous season's completion rate as a predictor of the following season's completion rate. In particular, we show how a change in the difficulty of passes attempted in both seasons explains a significant proportion of the shift in the observed performance of some players—a fact that is ignored if the raw completion rate is used to evaluate player skill. |
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| AbstractList | Passing the ball is one of the key skills of a football player yet the metrics commonly used to evaluate passing ability are crude and largely limited to various forms of a pass completion rate. These metrics can be misleading for two general reasons: they do not account for the difficulty of the attempted pass nor the various levels of uncertainty involved in empirical observations based on different numbers of passes per player. We address both these deficiencies by building a statistical model in which the success of a pass depends on the skill of the executing player as well as other factors including the origin and destination of the pass, the skill of his teammates and the opponents, and proxies for the defensive pressure put on the executing player as well as random chance. We fit the model by using data from the 2006–2007 season of the English Premier League provided by Opta, estimate each player's passing skill and make predictions for the next season. The model predictions considerably outperform a naive method of simply using the previous season's completion rate as a predictor of the following season's completion rate. In particular, we show how a change in the difficulty of passes attempted in both seasons explains a significant proportion of the shift in the observed performance of some players—a fact that is ignored if the raw completion rate is used to evaluate player skill. Passing the ball is one of the key skills of a football player yet the metrics commonly used to evaluate passing ability are crude and largely limited to various forms of a pass completion rate. These metrics can be misleading for two general reasons: they do not account for the difficulty of the attempted pass nor the various levels of uncertainty involved in empirical observations based on different numbers of passes per player. We address both these deficiencies by building a statistical model in which the success of a pass depends on the skill of the executing player as well as other factors including the origin and destination of the pass, the skill of his teammates and the opponents, and proxies for the defensive pressure put on the executing player as well as random chance. We fit the model by using data from the 2006-2007 season of the English Premier League provided by Opta, estimate each player's passing skill and make predictions for the next season. The model predictions considerably outperform a naive method of simply using the previous season's completion rate as a predictor of the following season's completion rate. In particular, we show how a change in the difficulty of passes attempted in both seasons explains a significant proportion of the shift in the observed performance of some players -- a fact that is ignored if the raw completion rate is used to evaluate player skill. [web URL: http://onlinelibrary.wiley.com/doi/10.1111/rssa.12115/abstract] Summary Passing the ball is one of the key skills of a football player yet the metrics commonly used to evaluate passing ability are crude and largely limited to various forms of a pass completion rate. These metrics can be misleading for two general reasons: they do not account for the difficulty of the attempted pass nor the various levels of uncertainty involved in empirical observations based on different numbers of passes per player. We address both these deficiencies by building a statistical model in which the success of a pass depends on the skill of the executing player as well as other factors including the origin and destination of the pass, the skill of his teammates and the opponents, and proxies for the defensive pressure put on the executing player as well as random chance. We fit the model by using data from the 2006–2007 season of the English Premier League provided by Opta, estimate each player's passing skill and make predictions for the next season. The model predictions considerably outperform a naive method of simply using the previous season's completion rate as a predictor of the following season's completion rate. In particular, we show how a change in the difficulty of passes attempted in both seasons explains a significant proportion of the shift in the observed performance of some players—a fact that is ignored if the raw completion rate is used to evaluate player skill. |
| Author | McHale, Ian Szczepański, Łukasz |
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| References | Breslow, N. E. and Clayton, D. G. (1993) Approximate inference in generalized linear mixed models. J. Am. Statist. Ass., 88, 9-25. R Core Team (2012) R: a Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing McHale, I. G. and Szczepański, Ł. (2014) A mixed effects model for identifying goal scoring ability of footballers. J. R. Statist. Soc. A, 177, 397-417. Wood, S. N. (2006) Generalized Additive Models: an Introduction with R. Boca Raton: Chapman and Hall-CRC. Efron, B. and Morris, C. (1975) Data analysis using Stein's estimator and its generalizations. J. Am. Statist. Ass., 70, 311-319. Lin, X. and Zhang, D. (1999) Inference in generalized additive mixed models by using smoothing splines. J. R. Statist. Soc. B, 61, 381-400. Duch, J., Waitzman, J. S. and Amaral, L. A. N. (2010) Quantifying the performance of individual players in a team activity. PLOS ONE, 5, no. 6, article e10937. Loughin, T. M. and Bargen, J. L. (2008) Assessing pitcher and catcher influences on base stealing in Major League Baseball. J. Sprts Sci., 26, 15-20. Albert, J. (1992) A Bayesian analysis of a Poisson random effects model for home run hitters. Am. Statistn, 46, 246-253. Albert, J. (2006) Pitching statistics, talent and luck, and the best strikeout seasons of all-time. J. Quant. Anal. Sprts, 2, no. 1 Jensen, S. T., Shirley, K. E. and Wyner, A. J. (2009) Bayesball: a Bayesian hierarchical model for evaluating fielding in major league baseball. Ann. App. Statist., 3, 491-520. McHale, I. G., Scarf, P. and Folker, D. (2012) On the development of a soccer player performance rating system for the English Premier League. Interfaces, 42, 339-351. Schall, R. (1991) Estimation in generalized linear models with random effects. Biometrika, 78, 719-727. Oberstone, J. (2011) Evaluating English Premier League player performance using the MAP model. In Proc. 3rd Int Conf. Mathematics in Sport (eds D. Percy, J. Reade and P. Scarf), pp. 153-159. Southend-on-sea: Institute of Mathematics and Its Applications. 2012 1991; 78 2011 1993; 88 2008; 26 2006 1992; 46 2006; 2 1999; 61 2009; 3 1975; 70 2014; 177 2010; 5 2012; 42 Efron (2023030310213150500_cit5) 1975; 70 McHale (2023030310213150500_cit9) 2012; 42 Core Team (2023030310213150500_cit12) 2012 Albert (2023030310213150500_cit2) 2006; 2 Breslow (2023030310213150500_cit3) 1993; 88 Oberstone (2023030310213150500_cit11) 2011 Albert (2023030310213150500_cit1) 1992; 46 McHale (2023030310213150500_cit10) 2014; 177 Jensen (2023030310213150500_cit6) 2009; 3 Wood (2023030310213150500_cit14) 2006 Duch (2023030310213150500_cit4) 2010; 5 Lin (2023030310213150500_cit7) 1999; 61 Schall (2023030310213150500_cit13) 1991; 78 Loughin (2023030310213150500_cit8) 2008; 26 |
| References_xml | – reference: Efron, B. and Morris, C. (1975) Data analysis using Stein's estimator and its generalizations. J. Am. Statist. Ass., 70, 311-319. – reference: R Core Team (2012) R: a Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing, – reference: McHale, I. G., Scarf, P. and Folker, D. (2012) On the development of a soccer player performance rating system for the English Premier League. Interfaces, 42, 339-351. – reference: Wood, S. N. (2006) Generalized Additive Models: an Introduction with R. Boca Raton: Chapman and Hall-CRC. – reference: Schall, R. (1991) Estimation in generalized linear models with random effects. Biometrika, 78, 719-727. – reference: Breslow, N. E. and Clayton, D. G. (1993) Approximate inference in generalized linear mixed models. J. Am. Statist. Ass., 88, 9-25. – reference: McHale, I. G. and Szczepański, Ł. (2014) A mixed effects model for identifying goal scoring ability of footballers. J. R. Statist. Soc. A, 177, 397-417. – reference: Lin, X. and Zhang, D. (1999) Inference in generalized additive mixed models by using smoothing splines. J. R. Statist. Soc. B, 61, 381-400. – reference: Duch, J., Waitzman, J. S. and Amaral, L. A. N. (2010) Quantifying the performance of individual players in a team activity. PLOS ONE, 5, no. 6, article e10937. – reference: Albert, J. (2006) Pitching statistics, talent and luck, and the best strikeout seasons of all-time. J. Quant. Anal. Sprts, 2, no. 1 – reference: Albert, J. (1992) A Bayesian analysis of a Poisson random effects model for home run hitters. Am. Statistn, 46, 246-253. – reference: Loughin, T. M. and Bargen, J. L. (2008) Assessing pitcher and catcher influences on base stealing in Major League Baseball. J. Sprts Sci., 26, 15-20. – reference: Jensen, S. T., Shirley, K. E. and Wyner, A. J. (2009) Bayesball: a Bayesian hierarchical model for evaluating fielding in major league baseball. Ann. App. Statist., 3, 491-520. – reference: Oberstone, J. (2011) Evaluating English Premier League player performance using the MAP model. In Proc. 3rd Int Conf. Mathematics in Sport (eds D. Percy, J. Reade and P. Scarf), pp. 153-159. Southend-on-sea: Institute of Mathematics and Its Applications. – volume: 3 start-page: 491 year: 2009 end-page: 520 article-title: Bayesball: a Bayesian hierarchical model for evaluating fielding in major league baseball publication-title: Ann. App. Statist. – start-page: 153 year: 2011 end-page: 159 – volume: 2 year: 2006 article-title: Pitching statistics, talent and luck, and the best strikeout seasons of all‐time publication-title: J. Quant. Anal. Sprts – volume: 61 start-page: 381 year: 1999 end-page: 400 article-title: Inference in generalized additive mixed models by using smoothing splines publication-title: J. R. Statist. Soc. B – volume: 42 start-page: 339 year: 2012 end-page: 351 article-title: On the development of a soccer player performance rating system for the English Premier League publication-title: Interfaces – volume: 78 start-page: 719 year: 1991 end-page: 727 article-title: Estimation in generalized linear models with random effects publication-title: Biometrika – year: 2006 – volume: 26 start-page: 15 year: 2008 end-page: 20 article-title: Assessing pitcher and catcher influences on base stealing in Major League Baseball publication-title: J. Sprts Sci. – volume: 5 year: 2010 article-title: Quantifying the performance of individual players in a team activity publication-title: PLOS ONE – volume: 88 start-page: 9 year: 1993 end-page: 25 article-title: Approximate inference in generalized linear mixed models publication-title: J. Am. Statist. Ass. – volume: 46 start-page: 246 year: 1992 end-page: 253 article-title: A Bayesian analysis of a Poisson random effects model for home run hitters publication-title: Am. Statistn – volume: 70 start-page: 311 year: 1975 end-page: 319 article-title: Data analysis using Stein's estimator and its generalizations publication-title: J. Am. Statist. Ass. – volume: 177 start-page: 397 year: 2014 end-page: 417 article-title: A mixed effects model for identifying goal scoring ability of footballers publication-title: J. R. Statist. Soc. A – year: 2012 – volume: 26 start-page: 15 year: 2008 ident: 2023030310213150500_cit8 article-title: Assessing pitcher and catcher influences on base stealing in Major League Baseball publication-title: J. 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App. Statist. – volume: 46 start-page: 246 year: 1992 ident: 2023030310213150500_cit1 article-title: A Bayesian analysis of a Poisson random effects model for home run hitters publication-title: Am. Statistn doi: 10.1080/00031305.1992.10475898 – volume: 2 issue: 1 year: 2006 ident: 2023030310213150500_cit2 article-title: Pitching statistics, talent and luck, and the best strikeout seasons of all-time publication-title: J. Quant. Anal. Sprts – volume-title: Generalized Additive Models: an Introduction with R year: 2006 ident: 2023030310213150500_cit14 doi: 10.1201/9781420010404 – volume: 42 start-page: 339 year: 2012 ident: 2023030310213150500_cit9 article-title: On the development of a soccer player performance rating system for the English Premier League publication-title: Interfaces doi: 10.1287/inte.1110.0589 – start-page: 153 volume-title: Proc. 3rd Int Conf. Mathematics in Sport year: 2011 ident: 2023030310213150500_cit11 – volume-title: R: a Language and Environment for Statistical Computing year: 2012 ident: 2023030310213150500_cit12 – volume: 61 start-page: 381 year: 1999 ident: 2023030310213150500_cit7 article-title: Inference in generalized additive mixed models by using smoothing splines publication-title: J. R. Statist. Soc. B doi: 10.1111/1467-9868.00183 – volume: 177 start-page: 397 year: 2014 ident: 2023030310213150500_cit10 article-title: A mixed effects model for identifying goal scoring ability of footballers publication-title: J. R. Statist. Soc. A doi: 10.1111/rssa.12015 – volume: 88 start-page: 9 year: 1993 ident: 2023030310213150500_cit3 article-title: Approximate inference in generalized linear mixed models publication-title: J. Am. Statist. Ass. doi: 10.1080/01621459.1993.10594284 |
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| Title | Beyond completion rate: evaluating the passing ability of footballers |
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