A Deep Learning Approach Based on Interpretable Feature Importance for Predicting Sports Results

Aslan, B. G., and Inceoglu, M. M. (2007), “A comparative study on neural network based soccer result prediction”, Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), pp. 545–550, IEEE. AslanB. G. InceogluM. M. 2007 “A comparative study on neural network based soccer result prediction” Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007) 545 550 IEEE Search in Google Scholar

Bilek, G., and Ulas, E. (2019), “Predicting match outcome according to the quality of opponent in the English Premier League using situational variables and team performance indicators”, International Journal of Performance Analysis in Sport, Vol. 24(7), pp. 1–12. BilekG. UlasE. 2019 “Predicting match outcome according to the quality of opponent in the English Premier League using situational variables and team performance indicators” International Journal of Performance Analysis in Sport 24 7 1 12 Search in Google Scholar

Constantinou, A. C. (2019), “Dolores: a model that predicts football match outcomes from all over the world”, Machine Learning, Vol. 108, pp. 49–75. ConstantinouA. C. 2019 “Dolores: a model that predicts football match outcomes from all over the world” Machine Learning 108 49 75 Search in Google Scholar

Danisik, N., Lacko, P., and Farkas, M. (2018), “Football match prediction using players attributes”, 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA), pp. 201–206. DanisikN. LackoP. FarkasM. 2018 “Football match prediction using players attributes” 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) 201 206 Search in Google Scholar

Dobson, S., and Goddard, J. (2011), “The economics of football”, Cambridge University Press. DobsonS. GoddardJ. 2011 “The economics of football” Cambridge University Press Search in Google Scholar

Fawcett, T. (2006), “An introduction to ROC analysis”, Pattern recognition letters, Volume 27, Issue 8, pp. 861–874, ISSN 0167-8655. FawcettT. 2006 “An introduction to ROC analysis” Pattern recognition letters 27 8 861 874 ISSN 0167-8655. Search in Google Scholar

Football-Data, available at: https://www.football-data.co.uk/englandm.php/ (accessed August 2023). Football-Data available at: https://www.football-data.co.uk/englandm.php/ (accessed August 2023). Search in Google Scholar

Hucaljuk, J., and Rakipovic, A. (2011), “Predicting football scores using machine learning techniques”, In MIPRO, 2011 proceedings of the 34th international convention, pp. 1623–1627, IEEE. HucaljukJ. RakipovicA. 2011 “Predicting football scores using machine learning techniques” In MIPRO, 2011 proceedings of the 34th international convention 1623 1627 IEEE Search in Google Scholar

Igiri, C. P. (2015), “Support vector machine-based prediction system for a football match result”, IOSR Journal of Computer Engineering (IOSR-JCE), 17(3), pp. 21–26. IgiriC. P. 2015 “Support vector machine-based prediction system for a football match result” IOSR Journal of Computer Engineering (IOSR-JCE) 17 3 21 26 Search in Google Scholar

Kelum, S., Shanika, A., Valery, O., Kenjabek, U., and Upaka, R. (2024), “Explainable artificial intelligence for fitness prediction of young athletes living in unfavorable environmental conditions”, Results in Engineering, Volume 23, 102592, ISSN 2590-1230, https://doi.org/10.1016/j.rineng.2024.102592. KelumS. ShanikaA. ValeryO. KenjabekU. UpakaR. 2024 “Explainable artificial intelligence for fitness prediction of young athletes living in unfavorable environmental conditions” Results in Engineering 23 102592 ISSN 2590-1230, https://doi.org/10.1016/j.rineng.2024.102592. Search in Google Scholar

Lundberg, S. M., and Lee, S.-I. (2017), “A unified approach to interpreting model predictions”, Advances in Neural Information Processing Systems 30 (NIPS 2017). LundbergS. M. LeeS.-I. 2017 “A unified approach to interpreting model predictions” Advances in Neural Information Processing Systems 30 (NIPS 2017) Search in Google Scholar

Martins, R. G., Martins, A. S., Neves, L. A., Lima, L. V., Flores, E. L., and do Nascimento, M. Z. (2017), “Exploring polynomial classifier to predict match results in football championships”, Expert Systems with Applications, 83, 79–93. MartinsR. G. MartinsA. S. NevesL. A. LimaL. V. FloresE. L. do NascimentoM. Z. 2017 “Exploring polynomial classifier to predict match results in football championships” Expert Systems with Applications 83 79 93 Search in Google Scholar

Montesinos López, O.A., Montesinos Lóopez, A., Crossa, J. (2022). “Overfitting, Model Tuning, and Evaluation of Prediction Performance“. In: Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer, Cham. https://doi.org/10.1007/978-3-030-89010-0_4 Montesinos LópezO.A. Montesinos LóopezA. CrossaJ. 2022 “Overfitting, Model Tuning, and Evaluation of Prediction Performance“ In: Multivariate Statistical Machine Learning Methods for Genomic Prediction Springer Cham. https://doi.org/10.1007/978-3-030-89010-0_4 Search in Google Scholar

Moustakidis, S., Plakias, S., Kokkotis, C., Tsatalas, T., and Tsaopoulos, D. (2023), “Predicting Football Team Performance with Explainable AI: Leveraging SHAP to Identify Key Team-Level Performance Metrics”, Future Internet, 15, no. 5: 174, https://doi.org/10.3390/fi15050174. MoustakidisS. PlakiasS. KokkotisC. TsatalasT. TsaopoulosD. 2023 “Predicting Football Team Performance with Explainable AI: Leveraging SHAP to Identify Key Team-Level Performance Metrics” Future Internet 15 5 174 https://doi.org/10.3390/fi15050174 Search in Google Scholar

Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011), “Scikit-learn: Machine learning in Python”, Journal of machine learning research, Vol. 12, pp. 2825–2830. PedregosaF. VaroquauxG. GramfortA. 2011 “Scikit-learn: Machine learning in Python” Journal of machine learning research 12 2825 2830 Search in Google Scholar

Premier League, available at: https://www.premierleague.com/ (accessed August 2023). Premier League available at: https://www.premierleague.com/ (accessed August 2023). Search in Google Scholar

Procopiou, A. and Piki, A. (2023), “The 12th Player: Explainable Artificial Intelligence (XAI) in Football: Conceptualisation, Applications, Challenges and Future Directions”, Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support - icSPORTS; ISBN 978-989-758-673-6, ISSN 2184-3201, SciTePress, pages 213–220, DOI: 10.5220/0012233800003587. ProcopiouA. PikiA. 2023 “The 12th Player: Explainable Artificial Intelligence (XAI) in Football: Conceptualisation, Applications, Challenges and Future Directions” Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support - icSPORTS; ISBN 978-989-758-673-6, ISSN 2184-3201, SciTePress 213 220 10.5220/0012233800003587 Open DOISearch in Google Scholar

Razali, N., Mustapha, A., Yatim, F., and Aziz, R. (2017), “Predicting football matches results using Bayesian networks for English Premier League (EPL)”, IOP Conference Series: Materials Science and Engineering, 226, DOI: 10.1088/1757-899X/226/1/012099. RazaliN. MustaphaA. YatimF. AzizR. 2017 “Predicting football matches results using Bayesian networks for English Premier League (EPL)” IOP Conference Series: Materials Science and Engineering 226 10.1088/1757-899X/226/1/012099 Open DOISearch in Google Scholar

Ren, Y., and Susnjak, T. (2022), “Predicting football match outcomes with explainable machine learning and the Kelly Index”, arXiv preprint arXiv:2211.15734. RenY. SusnjakT. 2022 “Predicting football match outcomes with explainable machine learning and the Kelly Index” arXiv preprint arXiv:2211.15734. Search in Google Scholar

Smith, A. (2017), “Sky Sports bust common football myths: Home advantage?”, available at: https://www.skysports.com/football/news/11096/10955089/sky-sports-bust-common-football-myths-home-advantage/ (accessed August 2023). SmithA. 2017 “Sky Sports bust common football myths: Home advantage?” available at: https://www.skysports.com/football/news/11096/10955089/sky-sports-bust-common-football-myths-home-advantage/ (accessed August 2023). Search in Google Scholar

Ulmer, B., and Fernandez, M. (2013), “Predicting soccer match results in the English Premier League”, Stanford, Ph.D. thesis. UlmerB. FernandezM. 2013 “Predicting soccer match results in the English Premier League” Stanford Ph.D. thesis. Search in Google Scholar

Winter, E. (2002), “The Shapley value”, Handbook of game theory with economic applications, Vol. 3, pp. 2025–2054, Elsevier. WinterE. 2002 “The Shapley value” Handbook of game theory with economic applications 3 2025 2054 Elsevier Search in Google Scholar

Yuanchen, W., Weibo, L., and Xiaohui, L. (2022), “Explainable AI techniques with application to NBA gameplay prediction“, Neurocomputing, Volume 483, Pages 59–71, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2022.01.098. YuanchenW. WeiboL. XiaohuiL. 2022 “Explainable AI techniques with application to NBA gameplay prediction“ Neurocomputing 483 59 71 ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2022.01.098. Search in Google Scholar