1. bookVolume 20 (2021): Issue 1 (July 2021)
Journal Details
License
Format
Journal
First Published
16 Apr 2016
Publication timeframe
2 times per year
Languages
English
access type Open Access

Comparing bottom-up and top-down ratings for individual soccer players

Published Online: 08 May 2021
Page range: 23 - 42
Journal Details
License
Format
Journal
First Published
16 Apr 2016
Publication timeframe
2 times per year
Languages
English
Abstract

Correctly assessing the contributions of an individual player in a team sport is challenging. However, an ability to better evaluate each player can translate into improved team performance, through better recruitment or team selection decisions. Two main ideas have emerged for using data to evaluate players: Top-down ratings observe the performance of the team as a whole and then distribute credit for this performance onto the players involved. Bottom-up ratings assign a value to each action performed, and then evaluate a player based on the sum of values for actions performed by that player. This paper compares a variant of plus-minus ratings, which is a top-down rating, and a bottom-up rating based on valuing actions by estimating probabilities. The reliability of ratings is measured by whether similar ratings are produced when using different data sets, while the validity of ratings is evaluated through the quality of match outcome forecasts generated when the ratings are used as predictor variables. The results indicate that the plus-minus ratings perform better than the bottom-up ratings with respect to the reliability and validity measures chosen and that plus-minus ratings have certain advantages that may be difficult to replicate in bottom-up ratings.

Keywords

Arntzen, H. & Hvattum, L.M. (2020). Predicting match outcomes in association football using team ratings and player ratings. Statistical Modelling, forthcoming. Search in Google Scholar

Bransen, L., Van Haaren, J. (2020). Player chemistry: striving for a perfectly balanced soccer team. ArXiv: 2003.01712v1. Search in Google Scholar

Bransen, L., Van Haaren, J., & van de Velden, M. (2019). Measuring soccer players’ contributions to chance creation by valuing their passes, Journal of Quantitative Analysis in Sports, 15, 97–116. Search in Google Scholar

Chawla, S., Estephan, J., Gudmundsson, J., & Horton, M. (2017). Classification of passes in football matches using spatiotemporal data. ACM Transactions on Spatial Algorithms and Systems, 3, Article 6. Search in Google Scholar

Chen, T. & Guestrin, C. (2016). XGBoost: a scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery, New York, NY, USA, pages 785–794. Search in Google Scholar

Decroos, T. (2020). Soccer analytics meets artificial intelligence: learning value and style from soccer event stream data. PhD Dissertation, KU Leuven, Belgium. Search in Google Scholar

Decroos, T., Bransen, L., Van Haaren, J., & Davis, J. (2019). Actions speak louder than goals: valuing player actions in soccer. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery, New York, NY, USA, pages 1851–1861. Search in Google Scholar

Decroos, T., Bransen, L., Van Haaren, J., & Davis, J. (2020). VAEP: an objective approach to valuing on-the-ball actions in soccer (Extended Abstract). In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), pages 4696–4700. Search in Google Scholar

Dobson, S. & Goddard, J. (2001). The Economics of Football. Cambridge University Press, Cambridge. Search in Google Scholar

Engelmann, J. (2011). A new player evaluation technique for players of the National Basketball Association (NBA), Proceedings of the MIT Sloan Sports Analytics Conference. Search in Google Scholar

Franks, A., D’Amour, A., Cervone, D., & Bornn, L. (2016). Meta-analytics: tools for understanding the statistical properties of sports metrics. Journal of Quantitative Analysis in Sports, 12, 151–165. Search in Google Scholar

Gelade, G.A. & Hvattum, L.M. (2020). On the relationship between +/ − ratings and event -level performance statistics. Journal of Sports Analytics, 6, 85–97. Search in Google Scholar

Gramacy, R., Jensen, S., & Taddy, M. (2013). Estimating player contribution in hockey with regularized logistic regression. Journal of Quantitative Analysis in Sports, 9, 97–111. Search in Google Scholar

Greene, W. (2012). Econometric Analysis. Pearson, Harlow, England, 7th edition. Search in Google Scholar

Gyarmati, L. & Stanojevic, R. (2016). QPass: a merit-based evaluation of soccer passes. In KDD 2016 Workshop on Large-Scale Sports Analytics. Search in Google Scholar

Hvattum, L.M. (2019). A comprehensive review of plus-minus ratings for evaluating individual players in team sports. International Journal of Computer Science in Sport, 18, 1–23. Search in Google Scholar

Hvattum, L.M. (2020). Offensive and defensive plus-minus player ratings for soccer. Applied Sciences, 10, 7345. Search in Google Scholar

Hvattum, L.M. & Arntzen, H. (2010). Using ELO ratings for match result prediction in association football. International Journal of Forecasting, 26, 460–470. Search in Google Scholar

Kausel, E.E., Ventura, S., & Rodríguez, A. (2019). Outcome bias in subjective ratings of performance: Evidence from the (football) field. Journal of Economic Psychology, 75, 102132. Search in Google Scholar

Kharrat, T., Peña, J., & McHale, I. (2020). Plus-minus player ratings for soccer. European Journal of Operational Research, 283, 726–736. Search in Google Scholar

Link, D., Lang, S., & Seidenschwarz, P. (2016). Real time quantification of dangerousity in football using spatiotemporal tracking data. PLoS ONE, 11, e0168768. Search in Google Scholar

Macdonald, B. (2012). An expected goals model for evaluating NHL teams and players. Proceedings of the 2012 MIT Sloan Sports Analytics Conference. Search in Google Scholar

Matano, F., Richardson, L., Pospisil, T., Eubanks, C., & Qin, J. (2018). Augmenting adjusted plus-minus in soccer with FIFA ratings. ArXiv:1810.08032v1. Search in Google Scholar

McHale, I.G. & Relton, S.D. (2018). Identifying key players in soccer teams using network analysis and pass difficulty. European Journal of Operational Research, 268, 339–347. Search in Google Scholar

McHale, I., Scarf, P., & Folker, D. (2012). On the development of a soccer player performance rating system for the English Premier League. Interfaces, 42, 339–351. Search in Google Scholar

Pantuso, G. & Hvattum, L.M. (2020). Maximizing performance with an eye on the finances: a chance constrained model for football transfer market decisions. TOP, forthcoming. Search in Google Scholar

Pappalardo, L., Cintia, P., Ferragina, P., Massucco, E., Pedreschi, D., & Giannotti, F. (2019a). PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach. ACM Transactions on Intelligent Systems and Technology, 10, 59. Search in Google Scholar

Pappalardo, L., Cintia, P., Rossi, A., Massucco, E., Ferragina, P., Pedreschi, D., & Giannotti, F. (2019b). A public data set of spatio-temporal match events in soccer competitions. Scientific Data, 6, 236. Search in Google Scholar

Power, P, Ruiz, H., Wei, X., & Lucey, P. (2017). Not all passes are created equal: objectively measuring the risk and reward of passes in soccer from tracking data. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘17). Association for Computing Machinery, New York, NY, USA, 1605–1613. Search in Google Scholar

Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. In Advances in Neural Information Processing Systems, pages 6639–6649. Search in Google Scholar

Sæbø, O. & Hvattum, L. (2015). Evaluating the efficiency of the association football transfer market using regression based player ratings. In: NIK: Norsk Informatikkonferanse, Bibsys Open Journal Systems, 12 pages. Search in Google Scholar

Sæbø, O. & Hvattum, L. (2019). Modelling the financial contribution of soccer players to their clubs. Journal of Sports Analytics, 5, 23–34. Search in Google Scholar

Schultze, S. & Wellbrock, C. (2018). A weighted plus/minus metric for individual soccer player performance. Journal of Sports Analytics, 4, 121–131. Search in Google Scholar

Singh, K. (2019). Introducing expected threat. https://karun.in/blog/expected-threat.html. Accessed: 2021-01-25. Search in Google Scholar

Sittl, R. & Warnke, A. (2016). Competitive balance and assortative matching in the German Bundesliga. Discussion Paper No. 16-058, ZEW Centre for European Economic Research, Mannheim. Search in Google Scholar

Szymanski, S. (2000). A market test for discrimination in the English professional soccer leagues. Journal of Political Economy, 108, 590–603. Search in Google Scholar

Thomas, A., Ventura, S., Jensen, S., & Ma, S. (2013). Competing process hazard function models for player ratings in ice hockey. The Annals of Applied Statistics, 7, 1497–1524. Search in Google Scholar

Tiedemann, T., Francksen, T., & Latacz-Lohmann, U. (2011). Assessing the performance of German Bundesliga football players: a non-parametric metafrontier approach. Central European Journal of Operations Research, 19, 571–587. Search in Google Scholar

Van Roy, M., Robberecths, P., Decroos, T., & Davis, J. (2020). Valuing on-the-ball actions in soccer: a critical comparison of xT and VAEP. AAAI-20 Workshop on AI in Team Sports. (https://ai-teamsports.weebly.com/uploads/1/2/7/0/127046800/paper11.pdf Search in Google Scholar

Vilain, J. & Kolkovsky, R. (2016). Estimating individual productivity in football. http://econ.sciences-po.fr/sites/default/files/file/jbvilain.pdf, accessed 2019-08-03. Search in Google Scholar

Witten, I., Frank, E., & Hall, M.A. (2011). Data mining: practical machine learning tools and techniques. Morgan Kaufmann Publishers, 3rd edition. Search in Google Scholar

Wolf, S., Schmitt, M., & Schuller, B. (2020). A football player rating system. Journal of Sports Analytics, 6, 243–257. Search in Google Scholar

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