{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T23:48:48Z","timestamp":1784332128479,"version":"3.55.0"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Background: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. Methods: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. Results: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). Conclusion: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.<\/jats:p>","DOI":"10.3390\/e23010090","type":"journal-article","created":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T19:55:56Z","timestamp":1610308556000},"page":"90","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3808-3265","authenticated-orcid":false,"given":"Bartosz","family":"\u0106wiklinski","sequence":"first","affiliation":[{"name":"Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5630-7461","authenticated-orcid":false,"given":"Agata","family":"Gie\u0142czyk","sequence":"additional","affiliation":[{"name":"Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Micha\u0142","family":"Chora\u015b","sequence":"additional","affiliation":[{"name":"Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101562","DOI":"10.1016\/j.is.2020.101562","article-title":"Sports analytics\u2013Evaluation of basketball players and team performance","volume":"93","author":"Sarlis","year":"2020","journal-title":"Inf. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.aci.2017.09.005","article-title":"A machine learning framework for sport result prediction","volume":"15","author":"Bunker","year":"2019","journal-title":"Appl. Comput. Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s00779-019-01307-z","article-title":"Study on the intelligent system of sports culture centers by combining machine learning with big data","volume":"24","year":"2020","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s40745-018-00189-x","article-title":"NBA game result prediction using feature analysis and machine learning","volume":"6","author":"Thabtah","year":"2019","journal-title":"Ann. Data Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1016\/j.ijforecast.2018.01.003","article-title":"Predictive analysis and modelling football results using machine learning approach for English Premier League","volume":"35","author":"Baboota","year":"2019","journal-title":"Int. J. Forecast."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Horvat, T., Hava\u0161, L., and Srpak, D. (2020). The Impact of Selecting a Validation Method in Machine Learning on Predicting Basketball Game Outcomes. Symmetry, 12.","DOI":"10.3390\/sym12030431"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sharma, M., Kumar, N., and Kumar, P. (2020). Badminton match outcome prediction model using Na\u00efve Bayes and Feature Weighting technique. J. Ambient. Intell. Humaniz. Comput., 1\u201315.","DOI":"10.1007\/s12652-020-02578-8"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"St\u00fcbinger, J., Mangold, B., and Knoll, J. (2020). Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics. Appl. Sci., 10.","DOI":"10.3390\/app10010046"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.eswa.2019.04.025","article-title":"A game-predicting expert system using big data and machine learning","volume":"130","author":"Gu","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chora\u015b, M., Pawlicki, M., Kozik, R., Demestichas, K., Kosmides, P., and Gupta, M. (2019, January 26\u201329). SocialTruth project approach to online disinformation (fake news) detection and mitigation. Proceedings of the 14th International Conference on Availability, Reliability and Security, Canterbury, UK.","DOI":"10.1145\/3339252.3341497"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.dss.2016.05.010","article-title":"Predicting wins and spread in the Premier League using a sentiment analysis of twitter","volume":"88","author":"Schumaker","year":"2016","journal-title":"Decis. Support Syst."},{"key":"ref_12","first-page":"330","article-title":"Training load prior to injury in professional rugby league players: Analysing injury risk with machine learning","volume":"36","author":"Welch","year":"2018","journal-title":"ISBS Proc. Arch."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Vallance, E., Sutton-Charani, N., Imoussaten, A., Montmain, J., and Perrey, S. (2020). Combining Internal-and External-Training-Loads to Predict Non-Contact Injuries in Soccer. Appl. Sci., 10.","DOI":"10.3390\/app10155261"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"10008","DOI":"10.1051\/matecconf\/201818910008","article-title":"A learning-based system for predicting sport injuries","volume":"Volume 189","author":"Liu","year":"2018","journal-title":"MATEC Web of Conferences"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1177\/1747954119879350","article-title":"Machine learning in men\u2019s professional football: Current applications and future directions for improving attacking play","volume":"14","author":"Herold","year":"2019","journal-title":"Int. J. Sport Sci. Coach."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3343172","article-title":"PlayeRank: Data-driven performance evaluation and player ranking in soccer via a machine learning approach","volume":"10","author":"Pappalardo","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9157","DOI":"10.1007\/s00521-019-04036-9","article-title":"Introducing an expert system for prediction of soccer player ranking using ensemble learning","volume":"31","author":"Maanijou","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"St\u00fcbinger, J., and Knoll, J. (2018). Beat the Bookmaker\u2013Winning Football Bets with Machine Learning (Best Application Paper). International Conference on Innovative Techniques and Applications of Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-030-04191-5_21"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s13218-019-00610-4","article-title":"Machine-learning-based statistical arbitrage football betting","volume":"34","author":"Knoll","year":"2020","journal-title":"KI-K\u00fcnstliche Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"138743","DOI":"10.1109\/ACCESS.2020.3012456","article-title":"Research on Basketball Shooting Action Based on Image Feature Extraction and Machine Learning","volume":"8","author":"Ji","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","first-page":"14","article-title":"Prediction of tennis performance in junior elite tennis players","volume":"16","author":"Kramer","year":"2017","journal-title":"J. Sport Sci. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"116321","DOI":"10.1109\/ACCESS.2020.3004182","article-title":"Performance Prediction and Evaluation in Female Handball Players Using Machine Learning Models","volume":"8","author":"Oytun","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e241","DOI":"10.1016\/j.scispo.2019.02.006","article-title":"The application of Artificial Neural Network and k-Nearest Neighbour classification models in the scouting of high-performance archers from a selected fitness and motor skill performance parameters","volume":"34","author":"Musa","year":"2019","journal-title":"Sci. Sport"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.comcom.2020.04.025","article-title":"Continuous health monitoring of sportsperson using IoT devices based wearable technology","volume":"160","author":"Huifeng","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zahran, L., El-Beltagy, M., and Saleh, M. (2019). A Conceptual Framework for the Generation of Adaptive Training Plans in Sports Coaching. International Conference on Advanced Intelligent Systems and Informatics, Springer.","DOI":"10.1007\/978-3-030-31129-2_62"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chora\u015b, M., and Pawlicki, M. (2020). Intrusion Detection Approach based on Optimised Artificial Neural Network. Neurocomputing, in press.","DOI":"10.1016\/j.neucom.2020.07.138"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e1380","DOI":"10.1002\/widm.1380","article-title":"The use of machine learning in sport outcome prediction: A review","volume":"10","author":"Horvat","year":"2020","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Behravan, I., and Razavi, S.M. (2020). A novel machine learning method for estimating football players\u2019 value in the transfer market. Soft Comput., 1\u201313.","DOI":"10.1007\/s00500-020-05319-3"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/1\/90\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:09:28Z","timestamp":1760159368000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/1\/90"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,10]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["e23010090"],"URL":"https:\/\/doi.org\/10.3390\/e23010090","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,10]]}}}