Arntzen, H. & Hvattum, L.M. (2020). Predicting match outcomes in association football using team ratings and player ratings. Statistical Modelling, forthcoming.10.1177/1471082X20929881
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, 97–116.10.1515/jqas-2018-0020
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 6.10.1145/3105576
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 785–794.10.1145/2939672.2939785
Decroos, T. (2020). Soccer analytics meets artificial intelligence: learning value and style from soccer event stream data. PhD Dissertation, KU Leuven, Belgium.
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 1851–1861.10.1145/3292500.3330758
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 4696–4700.10.24963/ijcai.2020/648
Engelmann, J. (2011). A new player evaluation technique for players of the National Basketball Association (NBA), Proceedings of the MIT Sloan Sports Analytics Conference.
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, 151–165.10.1515/jqas-2016-0098
Gelade, G.A. & Hvattum, L.M. (2020). On the relationship between +/ − ratings and event -level performance statistics. Journal of Sports Analytics, 6, 85–97.10.3233/JSA-200432
Gramacy, R., Jensen, S., & Taddy, M. (2013). Estimating player contribution in hockey with regularized logistic regression. Journal of Quantitative Analysis in Sports, 9, 97–111.10.1515/jqas-2012-0001
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, 1–23.10.2478/ijcss-2019-0001
Hvattum, L.M. & Arntzen, H. (2010). Using ELO ratings for match result prediction in association football. International Journal of Forecasting, 26, 460–470.10.1016/j.ijforecast.2009.10.002
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, 102132.10.1016/j.joep.2018.12.006
Kharrat, T., Peña, J., & McHale, I. (2020). Plus-minus player ratings for soccer. European Journal of Operational Research, 283, 726–736.10.1016/j.ejor.2019.11.026
Link, D., Lang, S., & Seidenschwarz, P. (2016). Real time quantification of dangerousity in football using spatiotemporal tracking data. PLoS ONE, 11, e0168768.10.1371/journal.pone.0168768520129128036407
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, 339–347.10.1016/j.ejor.2018.01.018
McHale, I., Scarf, P., & Folker, D. (2012). On the development of a soccer player performance rating system for the English Premier League. Interfaces, 42, 339–351.10.1287/inte.1110.0589
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.10.1007/s11750-020-00584-9
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, 59.10.1145/3343172
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, 236.10.1038/s41597-019-0247-7681787131659162
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, 1605–1613.10.1145/3097983.3098051
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.
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.
Sæbø, O. & Hvattum, L. (2019). Modelling the financial contribution of soccer players to their clubs. Journal of Sports Analytics, 5, 23–34.10.3233/JSA-170235
Schultze, S. & Wellbrock, C. (2018). A weighted plus/minus metric for individual soccer player performance. Journal of Sports Analytics, 4, 121–131.10.3233/JSA-170225
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.10.2139/ssrn.2828090
Szymanski, S. (2000). A market test for discrimination in the English professional soccer leagues. Journal of Political Economy, 108, 590–603.10.1086/262130
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, 1497–1524.10.1214/13-AOAS646
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, 571–587.10.1007/s10100-010-0146-7
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
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.
Witten, I., Frank, E., & Hall, M.A. (2011). Data mining: practical machine learning tools and techniques. Morgan Kaufmann Publishers, 3rd edition.10.1016/B978-0-12-374856-0.00001-8