{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T22:48:54Z","timestamp":1772837334272,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>One of the great challenges for football coaches is to choose the football line-up that gives more guarantees of success. Even though there are several dimensions to analyse the problem, such as the opposing team characteristics. The objective of this study is to identify, based on the players\u2019 physiological variables collected using Global Positioning Systems (GPS), which players are the most suitable to be part of the starting team\/line-up. The work was developed in two stages, first with the choice of the most important variables using the Recursive Feature Elimination algorithm, and then using logistic regression on these chosen variables. The logistic regression resulted in an index, called the line-up preparedness index, for the following player positions: Fullbacks, Central Midfielders and Wingers. For the other players\u2019 positions, the model results were not satisfactory.<\/jats:p>","DOI":"10.3390\/computers11030040","type":"journal-article","created":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T12:58:36Z","timestamp":1647003516000},"page":"40","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Football Match Line-Up Prediction Based on Physiological Variables: A Machine Learning Approach"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6139-2595","authenticated-orcid":false,"given":"Alberto","family":"Cortez","sequence":"first","affiliation":[{"name":"Coimbra Business School Research Centre|ISCAC, Polytechnic of Coimbra, 3045-601 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0506-4284","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Trigo","sequence":"additional","affiliation":[{"name":"Coimbra Business School Research Centre|ISCAC, Polytechnic of Coimbra, 3045-601 Coimbra, Portugal"},{"name":"ALGORITMI Research Center, University of Minho, Campus de Azur\u00e9m, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6558-1956","authenticated-orcid":false,"given":"Nuno","family":"Loureiro","sequence":"additional","affiliation":[{"name":"Sport Sciences School of Rio Maior, 2040-413 Rio Maior, Portugal"},{"name":"Life Quality Research Centre (CIEQV), Polytechnic Institute of Santarem, 2040-413 Rio Maior, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, Y. (2020). Evaluation Model of Soccer Training Technology Based on Artificial Intelligence. J. Phys. Conf. Ser., 1648.","DOI":"10.1088\/1742-6596\/1648\/4\/042085"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"90266","DOI":"10.1109\/ACCESS.2020.2992025","article-title":"Machine Learning Enabled Team Performance Analysis in the Dynamical Environment of Soccer","volume":"8","author":"Kusmakar","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1016\/j.jsams.2020.04.021","article-title":"Using Machine Learning to Improve Our Understanding of Injury Risk and Prediction in Elite Male Youth Football Players","volume":"23","author":"Oliver","year":"2020","journal-title":"J. Sci. Med. 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Proceedings of the Computational Science and Its Applications\u2014ICCSA 2021: 21st International Conference, Cagliari, Italy.","DOI":"10.1007\/978-3-030-86970-0_1"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1249\/MSS.0000000000001535","article-title":"A Preventive Model for Muscle Injuries: A Novel Approach Based on Learning Algorithms","volume":"50","author":"Ayala","year":"2018","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_10","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_11","first-page":"148","article-title":"In-Game Behaviour Analysis of Football Players Using Machine Learning Techniques Based on Player Statistics","volume":"16","author":"Marquina","year":"2020","journal-title":"Int. J. Sports Sci. Coach."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1007\/s00500-020-05319-3","article-title":"A Novel Machine Learning Method for Estimating Football Players\u2019 Value in the Transfer Market","volume":"25","author":"Behravan","year":"2021","journal-title":"Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"\u0106wiklinski, B., Gie\u0142czyk, A., and Chora\u015b, M. (2021). Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers. Entropy, 23.","DOI":"10.3390\/e23010090"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s10994-018-5703-7","article-title":"Dolores: A Model That Predicts Football Match Outcomes from All over the World","volume":"108","author":"Constantinou","year":"2019","journal-title":"Mach. Learn."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"St\u00fcbinger, J., Mangold, B., and Knoll, J. (2019). Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics. Appl. Sci., 10.","DOI":"10.3390\/app10010046"},{"key":"ref_16","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. Sports Sci. Coach."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1111\/dsji.12222","article-title":"Applying the CRISP-DM Framework for Teaching Business Analytics","volume":"18","author":"Jaggia","year":"2020","journal-title":"Decis. Sci. J. Innov. Educ."},{"key":"ref_18","first-page":"27","article-title":"A Machine Learning Framework for Sport Result Prediction","volume":"15","author":"Bunker","year":"2019","journal-title":"Appl. Comput. Inf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1080\/24748668.2020.1726158","article-title":"Comparative Analysis of Game Parameters between Italian League and Israeli League Football Matches","volume":"20","author":"Elyakim","year":"2020","journal-title":"Int. J. Perform. Anal. Sport"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tanizaka Filho, M.O., Cordeiro Marujo, E., and Calasans Dos Santos, T. (2019, January 15\u201318). Identification of Features for Profit Forecasting of Soccer Matches. Proceedings of the 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), Salvador, Brazil.","DOI":"10.1109\/BRACIS.2019.00013"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Miguel, M., Oliveira, R., Loureiro, N., Garc\u00eda-Rubio, J., and Ib\u00e1\u00f1ez, S.J. (2021). Load Measures in Training\/Match Monitoring in Soccer: A Systematic Review. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18052721"},{"key":"ref_22","first-page":"2007","article-title":"The Physical Effort Required from Professional Football Players in Different Playing Positions","volume":"17","author":"Altavilla","year":"2017","journal-title":"J. Phys. Educ. Sport"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"213695","DOI":"10.1109\/ACCESS.2020.3038601","article-title":"Machine Learning Models Reveal Key Performance Metrics of Football Players to Win Matches in Qatar Stars League","volume":"8","author":"Almulla","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Baptista, I., Johansen, D., Seabra, A., and Pettersen, S.A. (2018). Position Specific Player Load during Matchplay in a Professional Football Club. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0198115"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1080\/15438627.2020.1815201","article-title":"Differences in GPS Variables According to Playing Formations and Playing Positions in U19 Male Soccer Players","volume":"29","author":"Borghi","year":"2020","journal-title":"Res. Sports Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2774","DOI":"10.1080\/02640414.2019.1652541","article-title":"Where to Go: Computational and Visual What-If Analyses in Soccer","volume":"37","author":"Stein","year":"2019","journal-title":"J. Sports Sci."},{"key":"ref_27","first-page":"18","article-title":"Applications of Artificial Intelligence in the Game of Football: The Global Applications of Artificial Intelligence in the Game of Football: The Global Perspective","volume":"11","author":"Keshav","year":"2020","journal-title":"Int. Refereed Soc. Sci. 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