The influence of substitution decisions made by national team coaches on final match outcomes at UEFA EURO 2024

. 2025 ; 7 () : 1573823. [epub] 20250710

Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic-ecollection

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid40708603

INTRODUCTION: Coaches leading national football teams during championship tournaments make decisions about tactical substitutions of players in critical match phases. This may be an attempt to change or defend a favorable score. This study focused on the time of decision-making of forced and planned substitutions, considering its characteristics: neutral, offensive, and defensive. The point of analysis of the substitutions was the match outcomes at the time of the substitutions and the final result and impact of the substitution concerning the result. METHODS: A total of 51 matches played during the UEFA EURO 2024 football tournament were analyzed, during which 466 player substitutions were made. For the statistical analysis of the degree and strength of the relationship between the variables, the chi-square test, Cramer's V coefficient, and machine learning were used accordingly. RESULTS: 72% of coaches' decisions to player substitutions resulted from the decision to change the team's tactics by changing the team's setup or the players' positions. The most common negative (69%) or positive (61%) impact occurred from the substitution of a player after the 20th minute. DISCUSSION: The decision trees used in the analysis determined the most advantageous time periods for coaches to make decisions about substitutions. The highest substitution effectiveness rate is obtained when the substitution is made between 60 and 85 min, and the lowest is made between 45 and 60 min.

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Lago C. The influence of match location, quality of opposition, and match status on possession strategies in professional association football. J Sports Sci. (2009) 27:1463–9. 10.1080/02640410903131681 PubMed DOI

Black GM, Gabbett TJ, Johnston RD, Naughton G, Cole MH, Dawson B. The influence of rotations on match running performance in female Australian football midfielders. Int J Sports Physiol Perform. (2018) 13:434–41. 10.1123/ijspp.2017-0175 PubMed DOI

Carling C, Espié V, Le Gall F, Bloomfield J, Jullien H. Work-rate of substitutes in elite soccer: a preliminary study. J Sci Med Sport. (2010) 13:253–5. 10.1016/j.jsams.2009.02.012 PubMed DOI

Bradley PS, Noakes TD. Match running performance fluctuations in elite soccer: indicative of fatigue, pacing or situational influences? J Sports Sci. (2013) 31:1627–38. 10.1080/02640414.2013.796062 PubMed DOI

Bradley PS, Lago-Peñas C, Rey E. Evaluation of the match performances of substitution players in elite soccer. Int J Sports Physiol Perform. (2014a) 9:415–24. 10.1123/ijspp.2013-0304 PubMed DOI

FIFA. 2018 FIFA World Cup Russia™ (n.d.). Available online at: https://www.fifa.com/worldcup/ (Accessed December 21, 2024).

Chmura P, Konefał M, Andrzejewski M, Kosowski J, Rokita A, Chmura J. Physical activity profile of 2014 FIFA World Cup players, with regard to different ranges of air temperature and relative humidity. Int J Biometeorol. (2017) 61:677–84. 10.1007/s00484-016-1245-5 PubMed DOI

Pan P, Li F, Han B, Yuan B, Liu T. Exploring the impact of professional soccer substitute players on physical and technical performance. BMC Sports Sci Med Rehabil. (2023) 15:143. 10.1186/s13102-023-00752-x PubMed DOI PMC

Di Salvo V, Baron R, Tschan H, Calderon Montero F, Bachl N, Pigozzi F. Performance characteristics according to playing position in elite soccer. Int J Sports Med. (2007) 28:222–7. 10.1055/s-2006-924294 PubMed DOI

Lago-Peñas C, Rey E, Lago-Ballesteros J, Casais L, Domínguez E. Analysis of work-rate in soccer according to playing positions. Int J Perform Anal Sport. (2009) 9:218–27. 10.1080/24748668.2009.11868478 DOI

Trewin J. Match-running performance of elite female soccer players: the factors affecting performance and training applications (2018). Available online at: https://www.researchgate.net/publication/325930381 (Accessed October 5, 2024).

Hills SP, Barwood MJ, Radcliffe JN, Cooke CB, Kilduff LP, Cook CJ, et al. Profiling the responses of soccer substitutes: a review of current literature. Sports Med. (2018) 48:2255–69. 10.1007/s40279-018-0962-9 PubMed DOI

Mohr M, Krustrup P, Bangsbo J. Match performance of high-standard soccer players with special reference to development of fatigue. J Sports Sci. (2003) 21:519–28. 10.1080/0264041031000071182 PubMed DOI

Reilly T, Drust B, Clarke N. Muscle fatigue during football match-play. Sports Med. (2008) 38:357–67. 10.2165/00007256-200838050-00001 PubMed DOI

Bradley PS, Sheldon W, Wooster B, Olsen P, Boanas P, Krustrup P. High-intensity running in English FA Premier League soccer matches. J Sports Sci. (2009) 27:159–68. 10.1080/02640410802512775 PubMed DOI

Myers BR. A proposed decision rule for the timing of soccer substitutions. J Quant Anal Sports. (2012) 8(1). 10.1515/1559-0410.1349 DOI

Hirotsu N, Wright M. Using a Markov process model of an association football match to determine the optimal timing of substitution and tactical decisions. J Operat Res Soc. (2002) 53:1174–1174. 10.1057/palgrave.jors.2601414 DOI

Del Corral J, Barros CP, Prieto-Rodríguez J. The determinants of soccer player substitutions. J Sports Econom. (2008) 9:160–72. 10.1177/1527002507308309 DOI

Rey E, Lago-Ballesteros J, Padrón-Cabo A. Timing and tactical analysis of player substitutions in the UEFA Champions League. Int J Perform Anal Sport. (2015) 15:840–50. 10.1080/24748668.2015.11868835 DOI

Hills SP, Barrett S, Feltbower RG, Barwood MJ, Radcliffe JN, Cooke CB, et al. A match-day analysis of the movement profiles of substitutes from a professional soccer club before and after pitch-entry. PLoS One. (2019) 14:e0211563. 10.1371/journal.pone.0211563 PubMed DOI PMC

Schneemann S, Deutscher C. Intermediate information, loss aversion, and effort: empirical evidence. Econ Inq. (2017) 55:1759–70. 10.1111/ecin.12420 DOI

Raab M, Bar-Eli M, Plessner H, Araújo D. The past, present and future of research on judgment and decision making in sport. Psychol Sport Exerc. (2019) 42:25–32. 10.1016/j.psychsport.2018.10.004 DOI

Bar-Eli M, Tenenbaum G, Geister S. Consequences of players’ dismissal in professional soccer: a crisis-related analysis of group-size effects. J Sports Sci. (2006) 24:1083–94. 10.1080/02640410500432599 PubMed DOI

Mechtel M, Bäker A, Brändle T, Vetter K. Red cards: not such bad news for penalized guest teams. J Sports Econom. (2011) 12:621–46. 10.1177/1527002510388478 DOI

Courneya KS, Carron AV. The home advantage in sport competitions: a literature review. J Sport Exerc Psychol. (1992) 14:13–27. 10.1123/jsep.14.1.13 DOI

Carron AV, Loughhead TM, Bray SR. The home advantage in sport competitions: Courneya and Carron’s (1992) conceptual framework a decade later. J Sports Sci. (2005) 23:395–407. 10.1080/02640410400021542 PubMed DOI

Van Damme N, Baert S. Home advantage in European international soccer: which dimension of distance matters? Economics. (2019) 13(1):20190050. 10.5018/economics-ejournal.ja.2019-50 DOI

Nevill AM, Balmer NJ, Mark Williams A. The influence of crowd noise and experience upon refereeing decisions in football. Psychol Sport Exerc. (2002) 3:261–72. 10.1016/S1469-0292(01)00033-4 DOI

Dohmen TJ. The influence of social forces: evidence from the behavior of football referees. Econ Inq. (2008) 46:411–24. 10.1111/j.1465-7295.2007.00112.x DOI

Garicano L, Palacios-Huerta I, Prendergast C. Favoritism under social pressure. SSRN Electron J. (2001) 87(2):1–34. 10.2139/ssrn.274934 DOI

Lago C, Casais L, Dominguez E, Sampaio J. The effects of situational variables on distance covered at various speeds in elite soccer. Eur J Sport Sci. (2010) 10:103–9. 10.1080/17461390903273994 DOI

Bradley PS, Lago-Peñas C, Rey E, Sampaio J. The influence of situational variables on ball possession in the English Premier League. J Sports Sci. (2014) 32:1867–73. 10.1080/02640414.2014.887850 PubMed DOI

Almeida CH, Ferreira AP, Volossovitch A. Effects of match location, match status and quality of opposition on regaining possession in UEFA Champions League. J Hum Kinet. (2014) 41:203–14. 10.2478/hukin-2014-0048 PubMed DOI PMC

Iglesias B, García-Ceberino JM, García-Rubio J, Ibáñez SJ. How do player substitutions influence men’s UEFA Champions League soccer matches? Appl Sci. (2022) 12:11371. 10.3390/app122211371 DOI

Milanović M, Stamenković M. CHAID Decision tree: methodological frame and application. Economic Themes. (2016) 54:563–86. 10.1515/ethemes-2016-0029 DOI

Crewson P. Applied Statistics Handbook, Version 1.2. Leesburg, VA, USA: AcaStat Software; (2006).

Williams C, Wragg C. Data Analysis and Research for Sport and Exercise Science. New York, NY, USA: Routledge; (2003).

Love J, Selker R, Marsman M, Jamil T, Dropmann D, Verhagen J, et al. Graphical statistical software for common statistical designs. J Stat Softw. (2019) 88(2):1–17. 10.18637/jss.v088.i02 DOI

Gomez M-A, Lago-Peñas C, Owen LA. The influence of substitutions on elite soccer teams’ performance. Int J Perform Anal Sport. (2016) 16:553–68. 10.1080/24748668.2016.11868908 DOI

Padrón-Cabo A, Rey E, Vidal B, García-Nuñez J. Work-rate analysis of substitute players in professional soccer: analysis of seasonal variations. J Hum Kinet. (2018) 65:165–74. 10.2478/hukin-2018-0025 PubMed DOI PMC

Lorenzo-Martínez M, Rey E, Padrón-Cabo A. The effect of age on between-match physical performance variability in professional soccer players. Res Sports Med. (2020) 28:351–9. 10.1080/15438627.2019.1680985 PubMed DOI

Smith MR, Thompson C, Marcora SM, Skorski S, Meyer T, Coutts AJ. Mental fatigue and soccer: current knowledge and future directions. Sports Med. (2018) 48:1525–32. 10.1007/s40279-018-0908-2 PubMed DOI

Maneiro R, Casal CA, Ardá A, Losada JL. Application of multivariant decision tree technique in high performance football: the female and male corner kick. PLoS One. (2019) 14:e0212549. 10.1371/journal.pone.0212549 PubMed DOI PMC

Gifford M, Bayrak T. A predictive analytics model for forecasting outcomes in the National Football League games using decision tree and logistic regression. Decis Analyt J. (2023) 8:100296. 10.1016/j.dajour.2023.100296 DOI

Kabakchieva D. Predicting student performance by using data mining methods for classification. Cybernet Inform Technol. (2013) 13:61–72. 10.2478/cait-2013-0006 DOI

Pandey M, Kumar Sharma V. A decision tree algorithm pertaining to the student performance analysis and prediction. Int J Comput Appl. (2013) 61:1–5. 10.5120/9985-4822 DOI

Gliznitsa M, Silkina N. Using decision trees to determine the important characteristics of ice hockey players. Lect Notes Electr Eng. (2022) 857:359–69. 10.1007/978-3-030-94202-1_34 DOI

Kim M-C. A study on the winning and losing factors of para ice hockey using data mining-based decision tree analysis. Appl Sci. (2023) 13:1334. 10.3390/app13031334 DOI

Cabrera Quercini I, González-Ramírez A, García Tormo JV, Martínez I. Performance indicator selection through decision trees in elite handball. Revista Internacional de Medicina y Ciencias de la Actividad Física y del Deporte. (2022) 22:753–64. 10.15366/rimcafd2022.88.003 DOI

Oytun M, Yavuz HU, Sekeroglu B. The performance analysis of male handball players using tree-based machine learning models. Int J Hum Move Sports Sci. (2024) 12:571–9. 10.13189/saj.2024.120313 DOI

Horvat T, Havaš L, Srpak D. The impact of selecting a validation method in machine learning on predicting basketball game outcomes. Symmetry (Basel). (2020) 12:431. 10.3390/sym12030431 DOI

Young CM, Luo W, Gastin P, Tran J, Dwyer DB. The relationship between match performance indicators and outcome in Australian football. J Sci Med Sport. (2019) 22:467–71. 10.1016/j.jsams.2018.09.235 PubMed DOI

Oh Y, Kim H, Yun J, Lee J-S. Using data mining techniques to predict win-loss in Korean professional baseball games. J Korean Institute Industr Engin. (2014) 40:8–17. 10.7232/JKIIE.2014.40.1.008 DOI

Koseler K, Stephan M. Machine learning applications in baseball: a systematic literature review. Appl Artif Intell. (2017) 31:745–63. 10.1080/08839514.2018.1442991 DOI

Schauberger G, Groll A. Predicting matches in international football tournaments with random forests. Stat Modelling. (2018) 18:460–82. 10.1177/1471082X18799934 DOI

Baboota R, Kaur H. Predictive analysis and modelling football results using machine learning approach for English Premier League. Int J Forecast. (2019) 35:741–55. 10.1016/j.ijforecast.2018.01.003 DOI

Min DK. Contribution analysis of scoring in the soccer game: using decision tree. J Korean Data Inform Sci Soc. (2019) 30:1385–97. 10.7465/jkdi.2019.30.6.1385 DOI

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