{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:58:39Z","timestamp":1780502319329,"version":"3.54.1"},"reference-count":76,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Escuela Polit\u00e9cnica Nacional"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>This paper examines the integration of machine learning (ML) techniques in professional football, focusing on two key areas: (i) player and team performance, and (ii) match outcome prediction. Using a systematic methodology, this study reviews 172 papers from a five-year observation period (2019\u20132024) to identify relevant applications, focusing on the analysis of game actions (free kicks, passes, and penalties), individual and collective performance, and player position. A predominance of supervised learning, deep learning, and hybrid models (which integrate several ML techniques) is observed in the ML categories. Among the most widely used algorithms are decision trees, extreme gradient boosting, and artificial neural networks, which focus on optimizing sports performance and predicting outcomes. This paper discusses challenges such as the limited availability of public datasets due to access and cost restrictions, the restricted use of advanced visualization tools, and the poor integration of data acquisition devices, such as sensors. However, it also highlights the role of ML in addressing these challenges, thereby representing future research opportunities. Furthermore, this paper includes two illustrative case studies: (i) predicting the date Cristiano Ronaldo will reach 1000 goals, and (ii) an example of predicting penalty shoots; these examples demonstrate the practical potential of ML for performance monitoring and tactical decision-making in real-world football environments.<\/jats:p>","DOI":"10.3390\/make7030085","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T15:44:21Z","timestamp":1755186261000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning Applied to Professional Football: Performance Improvement and Results Prediction"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7391-4597","authenticated-orcid":false,"given":"Diego","family":"Moya","sequence":"first","affiliation":[{"name":"Department of Electronics, Telecommunications and Information Networks, Escuela Polit\u00e9cnica Nacional, Quito 170525, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8655-325X","authenticated-orcid":false,"given":"Christian","family":"Tipantu\u00f1a","sequence":"additional","affiliation":[{"name":"Department of Electronics, Telecommunications and Information Networks, Escuela Polit\u00e9cnica Nacional, Quito 170525, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00e9nesis","family":"Villa","sequence":"additional","affiliation":[{"name":"Telef\u00f3nica, Quito 170135, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8498-4080","authenticated-orcid":false,"given":"Xavier","family":"Calder\u00f3n-Hinojosa","sequence":"additional","affiliation":[{"name":"Department of Electronics, Telecommunications and Information Networks, Escuela Polit\u00e9cnica Nacional, Quito 170525, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7941-2287","authenticated-orcid":false,"given":"Bel\u00e9n","family":"Rivadeneira","sequence":"additional","affiliation":[{"name":"Department of Electronics, Telecommunications and Information Networks, Escuela Polit\u00e9cnica Nacional, Quito 170525, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7413-9895","authenticated-orcid":false,"given":"Robin","family":"\u00c1lvarez","sequence":"additional","affiliation":[{"name":"Department of Electronics, Telecommunications and Information Networks, Escuela Polit\u00e9cnica Nacional, Quito 170525, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1080\/14763141.2021.1910334","article-title":"Machine learning in sports science: Challenges and opportunities","volume":"23","author":"Delahunt","year":"2024","journal-title":"Sports Biomech."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.5114\/biolsport.2023.112970","article-title":"Machine learning application in soccer: A systematic review","volume":"40","author":"Clemente","year":"2023","journal-title":"Biol. 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