{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:11:24Z","timestamp":1767337884846,"version":"3.40.3"},"reference-count":14,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"University of the Pacific at Stockton"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>This study analyzes performance data from training sessions of male soccer players to predict their match performance. Regression analysis was conducted to examine the relationship between training metrics\u2014such as energy expenditure, sprint count, and power plays\u2014and match performance features. Additionally, a binary classification model was employed to determine whether a player\u2019s match performance could be predicted based on their training sessions leading up to the game. A correlation analysis using multivariate regression further explored the relationship between training session data and match outcomes.<\/jats:p>\n          <jats:p>To support training optimization, a drill selection application was developed to maintain a database of past drills, including metrics such as average player load, intensity, and duration. For each of the two training sessions prior to a match, the most relevant drill was identified based on the coach\u2019s criteria, ensuring that training exercises closely aligned with match demands.<\/jats:p>\n          <jats:p>The findings of this study provide insights into how varying training loads impact match performance and offer a data-driven approach for coaches to refine training sessions, optimize player development, and make informed decisions regarding starting lineups and substitutions.<\/jats:p>","DOI":"10.1007\/s42979-025-03870-0","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T02:45:03Z","timestamp":1743389103000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["From Practice To Performance: Predicting Soccer Match Outcomes from Training Data"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4004-1950","authenticated-orcid":false,"given":"Leili","family":"Javadpour","sequence":"first","affiliation":[]},{"given":"Mehdi","family":"Khazaeli","sequence":"additional","affiliation":[]},{"given":"Ryanne","family":"Molenaar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"3870_CR1","doi-asserted-by":"publisher","unstructured":"Elmiligi H, Saad S. Predicting the Outcome of Soccer Matches Using Machine Learning and Statistical Analysis. In :IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022, pp. 1\u20138. IEEE. https:\/\/doi.org\/10.1109\/CCWC54503.2022.9720896 (2022).","DOI":"10.1109\/CCWC54503.2022.9720896"},{"issue":"4","key":"3870_CR2","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1177\/1747954121995610","volume":"16","author":"B Guerrero-Calder\u00f3n","year":"2021","unstructured":"Guerrero-Calder\u00f3n B, Klemp M, Morcillo JA, Memmert D. How does the workload applied during the training week and the contextual factors affect the physical responses of professional soccer players in the match? Int J Sports Sci Coaching. 2021;16(4):994\u20131003.","journal-title":"Int J Sports Sci Coaching"},{"issue":"7","key":"3870_CR3","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.knosys.2006.04.011","volume":"19","author":"A Joseph","year":"2006","unstructured":"Joseph A, Fenton N, Neil M. Predicting football results using bayesian Nets and other machine learning techniques. Knowl Based Syst. 2006;19(7):544\u201353.","journal-title":"Knowl Based Syst"},{"issue":"3","key":"3870_CR4","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.ijforecast.2009.10.002","volume":"26","author":"LM Hvattum","year":"2010","unstructured":"Hvattum LM, Arntzen H. Using ELO ratings for match result prediction in association football. Int J Forecast. 2010;26(3):460\u201370.","journal-title":"Int J Forecast"},{"issue":"1","key":"3870_CR5","first-page":"19","volume":"2","author":"D Berrar","year":"2019","unstructured":"Berrar D, Lopes P, Dubitzky W. Ensemble learning for soccer match prediction. Artif Intell Sports. 2019;2(1):19\u201328.","journal-title":"Artif Intell Sports"},{"issue":"1","key":"3870_CR6","first-page":"35","volume":"8","author":"J Kahn","year":"2022","unstructured":"Kahn J. Deep learning for predictive modeling in Soccer: an LSTM approach. J Sports Analytics. 2022;8(1):35\u201347.","journal-title":"J Sports Analytics"},{"issue":"11","key":"3870_CR7","first-page":"1234","volume":"39","author":"S Santos","year":"2021","unstructured":"Santos S, Jones D, Figueiredo A. Integrating biometric data to enhance personalized training workload models in professional soccer. J Sports Sci. 2021;39(11):1234\u201342.","journal-title":"J Sports Sci"},{"issue":"1","key":"3870_CR8","first-page":"56","volume":"15","author":"P Miller","year":"2022","unstructured":"Miller P, Smith A, Garret J. The impact of wearable technology on training load management and player performance in elite soccer. Sports Technol. 2022;15(1):56\u201367.","journal-title":"Sports Technol"},{"issue":"4","key":"3870_CR9","first-page":"251","volume":"18","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Lee A. Machine learning models for predicting performance responses to training loads in professional soccer players. J Quant Anal Sports. 2022;18(4):251\u201363.","journal-title":"J Quant Anal Sports"},{"issue":"3","key":"3870_CR10","first-page":"308","volume":"24","author":"C Lago-Pe\u00f1as","year":"2016","unstructured":"Lago-Pe\u00f1as C, G\u00f3mez-Ruano M\u00c1. Assessment of offensive performance in Soccer: a systematic review. Res Sports Med. 2016;24(3):308\u201329.","journal-title":"Res Sports Med"},{"key":"3870_CR11","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1007\/s11205-020-02323-w","volume":"156","author":"M Carpita","year":"2021","unstructured":"Carpita M, Ciavolino E, Pasca P. Players\u2019 Role-Based performance composite indicators of soccer teams: A statistical perspective. Soc Indic Res. 2021;156:815\u201330.","journal-title":"Soc Indic Res"},{"key":"3870_CR12","unstructured":"GPS Player Tracking System. PlayerTek. [Online]. Available: https:\/\/www.playertek.com\/us\/. Accessed: January 25, 2024."},{"key":"3870_CR13","unstructured":"Understanding Playertek Metrics. [Online]. Available: https:\/\/medium.com\/playertek\/understanding-playertek-metrics-898c90f12127. Accessed: December 10, 2023."},{"issue":"7","key":"3870_CR14","doi-asserted-by":"publisher","first-page":"e0289374","DOI":"10.1371\/journal.pone.0289374","volume":"18","author":"RFS Oliveira","year":"2023","unstructured":"Oliveira RFS, Can\u00e1rio-Lemos R, Peixoto R, Vila\u00e7a-Alves J, Morgans R, Brito JP. The relationship between wellness and training and match load in professional male soccer players. PLoS ONE. 2023;18(7):e0289374. https:\/\/doi.org\/10.1371\/journal.pone.0289374.","journal-title":"PLoS ONE"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03870-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-03870-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03870-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T02:45:27Z","timestamp":1743389127000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-03870-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,28]]},"references-count":14,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["3870"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-03870-0","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,28]]},"assertion":[{"value":"21 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human and \/or Animals"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"324"}}