Uncovering Determinants of Victory and Defeat in Men's UEFA Champions League: An Analytical Exploration Using Logistic Regression

Vol.18,No.2(2024)

Abstract

This study aimed to explore the factors influencing outcomes in men's UEFA Championship matches. The sample comprised 201 UEFA Championship games, and the primary objective was to identify key components significantly associated with success in the UEFA Champions League through logistic regression analysis. The game outcome was treated as the dependent variable in a Binary Logistic Regression (Forward: LR Method). Logistic regression, a statistical technique assessing the relationship between variables, employed predictor variables as covariates, with calculations of β, standard error β, and Wald’s χ2. Model evaluation involved the likelihood ratio test, Cox & Snell (R2), and Nagelkerke (R2) tests, while the fit of the models to the data was assessed using the Hosmer & Lemeshow test. The analysis revealed six variables linked to winning matches. The study highlights a significant correlation between crucial variables and success in UEFA Champions League matches. Players and coaches can gain valuable insights into essential elements contributing to victory in this prestigious championship.


Keywords:
UEFA Champions League; Factors Determining Outcomes; Logistical Regression Analysis; Winning and Losing Determinants; Prediction
References

Almeida, C. H., Ferreira, A. P., & Volossovitch, A. (2014). Effects of Match Location, Match Status and Quality of Opposition on Regaining Possession in UEFA Champions League. Journal of Human Kinetics, 41, 203–214. https://doi.org/10.2478/hukin-2014-0048

Bar-Eli, M., Tenenbaum, G., & Geister, S. (2006). Consequences of players’ dismissal in professional soccer: A crisis-related analysis of group-size effects. Journal of Sports Sciences, 24(10), 1083–1094. https://doi.org/10.1080/02640410500432599

Carling, C., Wright, C., Nelson, L., & Bradley, P. (2013). Comment on “Performance analysis in football: A critical review and implications for future research.” Journal of Sports Sciences, 32. https://doi.org/10.1080/02640414.2013.807352

Castellano, J., & Alvarez, D. (2013). Uso defensivo del espacio de interacción en fútbol. (Defensive use of the interaction space in soccer). RICYDE. Revista Internacional de Ciencias Del Deporte, 9, 126–136. https://doi.org/10.5232/ricyde2013.03203

Castellano, J., Casamichana, D., & Lago, C. (2012). The Use of Match Statistics that Discriminate Between Successful and Unsuccessful Soccer Teams. Journal of Human Kinetics, 31(2012), 137–147. https://doi.org/10.2478/v10078-012-0015-7

Clemente, F. M., Sarmento, H., & Aquino, R. (2020). Player position relationships with centrality in the passing network of world cup soccer teams: Win/loss match comparisons. Chaos, Solitons & Fractals, 133, 109625. https://doi.org/10.1016/j.chaos.2020.109625

Glazier, P., & Davids, K. (2009). On analysing and interpreting variability in motor output. Journal of Science and Medicine in Sport / Sports Medicine Australia, 12, e2-3; author reply e4. https://doi.org/10.1016/j.jsams.2009.03.010

Higham, D., Hopkins, W., Pyne, D., & Anson, J. (2014). Performance Indicators Related to Points Scoring and Winning in International Rugby Sevens. Journal of Sports Science & Medicine, 13, 358–364.

Hughes, M. D., & Bartlett, R. M. (2002). The use of performance indicators in performance analysis. Journal of Sports Sciences, 20(10), 739–754. https://doi.org/10.1080/026404102320675602

Ievoli, R., Palazzo, L., & Ragozini, G. (2021). On the use of passing network indicators to predict football outcomes. Knowledge-Based Systems, 222, 106997. https://doi.org/10.1016/j.knosys.2021.106997

Igiri, Chinwe Peace1 & ; Nwachukwu, Enoch Okechukwu. (2014). An Improved Prediction System for Football a Match Result. http://ir.mtu.edu.ng/xmlui/bitstream/handle/123456789/113/an-improved-football-prediction-systempdf.pdf?sequence=1&isAllowed=y

Karanfil, F. (2017). An empirical analysis of European football rivalries based on on-field performances. Sport Management Review, 20(5), 468–482. https://doi.org/10.1016/j.smr.2016.12.003

Lago-Ballesteros, J., Lago-Peñas, C., & Rey, E. (2012). The effect of playing tactics and situational variables on achieving score-box possessions in a professional soccer team. Journal of Sports Sciences, 30(14), 1455–1461. https://doi.org/10.1080/02640414.2012.712715

Lago-Peñas, C., & Lago-Ballesteros, J. (2011). Game Location and Team Quality Effects on Performance Profiles in Professional Soccer. Journal of Sports Science & Medicine, 10(3), 465–471.

Lago-Peñas, C., Lago-Ballesteros, J., Dellal, A., & Gómez, M. (2010). Game-Related Statistics that Discriminated Winning, Drawing and Losing Teams from the Spanish Soccer League. Journal of Sports Science & Medicine, 9(2), 288–293.

Lago-Peñas, C., Lago-Ballesteros, J., & Rey, E. (2011). Differences in performance indicators between winning and losing teams in the UEFA Champions League. Journal of Human Kinetics, 27(2011), 135–146. https://doi.org/10.2478/v10078-011-0011-3

Latash, M. L., Scholz, J. P., & Schöner, G. (2002). Motor control strategies revealed in the structure of motor variability. Exercise and Sport Sciences Reviews, 30(1), 26–31. https://doi.org/10.1097/00003677-200201000-00006

Li, H. (2020). Analysis on the construction of sports match prediction model using neural network. Soft Computing, 24(11), 8343–8353. https://doi.org/10.1007/s00500-020-04823-w

Liu, H., Hopkins, W., Ruano, M., & Molinuevo, J. (2013). Inter-operator reliability of live football match statistics from OPTA Sportsdata. International Journal of Performance Analysis in Sport, 13, 803–821. https://doi.org/10.1080/24748668.2013.11868690

Luhtanen, P., Belinskij, A., Häyrinen, M., & Vänttinen, T. (2001). A comparative tournament analysis between the EURO 1996 and 2000 in soccer. International Journal of Performance Analysis in Sport, 1. https://doi.org/10.1080/24748668.2001.11868250

Mackenzie, R., & Cushion, C. (2013). Performance analysis in football: A critical review and implications for future research. Journal of Sports Sciences, 31(6), 639–676. https://doi.org/10.1080/02640414.2012.746720

Moura, F. A., Martins, L. E. B., & Cunha, S. A. (2014). Analysis of football game-related statistics using multivariate techniques. Journal of Sports Sciences, 32(20), 1881–1887. https://doi.org/10.1080/02640414.2013.853130

Oberstone, J. (2009). Differentiating the Top English Premier League Football Clubs from the Rest of the Pack: Identifying the Keys to Success. Journal of Quantitative Analysis in Sports, 5(3). https://doi.org/10.2202/1559-0410.1183

Opsahl, T., & Panzarasa, P. (2009). Clustering in weighted networks. Social Networks, 31(2), 155–163. https://doi.org/10.1016/j.socnet.2009.02.002

Peña, J., Rodríguez-Guerra, J., Buscà, B., & Serra, N. (2013). Which skills and factors better predict winning and losing in high-level men’s volleyball? Journal of Strength and Conditioning Research, 27(9), 2487–2493. https://doi.org/10.1519/JSC.0b013e31827f4dbe

Peñas, C., Lago Ballesteros, J., & Rey, E. (2011). Section III – Sport, Physical Education & Recreation Differences in performance indicators between winning and losing teams in the UEFA Champions League. Journal of Human Kinetics, 27, 135–146. https://doi.org/10.2478/v10078-011-0011-3

Reilly, T., Cabri, J., & Araújo, D. (2005). Science and Football V: The Proceedings of the Fifth World Congress on Sports Science and Football. Routledge.

Ruano, M., Gómez López, M., Peñas, C., & Sampaio, J. (2012). Effects of game location and final outcome on game-related statistics in each zone of the pitch in professional football. European Journal of Sport Science, 12, 393–398. https://doi.org/10.1080/17461391.2011.566373

Ruano, M., Lorenzo Calvo, A., Ibáñez, S., & Sampaio, J. (2013). Ball possession effectiveness in men’s and women’s elite basketball according to situational variables in different game periods. Journal of Sports Sciences, 31, 1578–1587. https://doi.org/10.1080/02640414.2013.792942

Sargent, J., & Bedford, A. (2013). Evaluating Australian Football League Player Contributions Using Interactive Network Simulation. Journal of Sports Science & Medicine, 12(1), 116–121.

Stewart, M., Mitchell, H., & Stavros, C. (2007). Moneyball Applied: Econometrics and the Identification and Recruitment of Elite Australian Footballers. International Journal of Sport Finance, 2, 231–248.

Tenga, A., Holme, I., Ronglan, L., & Bahr, R. (2010a). Effect of playing tactics on achieving score-box possessions in a random series of team possessions from Norwegian professional soccer matches. Journal of Sports Sciences, 28, 245–255. https://doi.org/10.1080/02640410903502766

Tenga, A., Holme, I., Ronglan, L. T., & Bahr, R. (2010b). Effect of playing tactics on goal scoring in Norwegian professional soccer. Journal of Sports Sciences, 28(3), 237–244. https://doi.org/10.1080/02640410903502774

Tenga, A., Ronglan, L., & Bahr, R. (2010). Measuring the effectiveness of offensive match-play in professional soccer. European Journal of Sport Science, 10, 269–277. https://doi.org/10.1080/17461390903515170

Tim McGarry , David I. Anderson , Stephen A. Wallace , Mike D. Hughes & Ian & M. Franks. (2002). Sport competition as a dynamical self organizing system. https://moldham74.github.io/AussieCAS/papers/McGarry.pdf

UEFA.com. (n.d.). UEFA Champions League. UEFA.Com. Retrieved July 9, 2023, from https://www.uefa.com/uefachampionsleague/

Vogelbein, M., Nopp, S., & Hökelmann, A. (2014). Defensive transition in soccer – are prompt possession regains a measure of success? A quantitative analysis of German Fußball-Bundesliga 2010/2011. Journal of Sports Sciences, 32(11), 1076–1083. https://doi.org/10.1080/02640414.2013.879671

Wheatcroft, E. (2021). Forecasting football matches by predicting match statistics. Journal of Sports Analytics, 7(2), 77–97.

Williams, A. M., Ward, P., Bell-Walker, J., & Ford, P. R. (2012). Perceptual-cognitive expertise, practice history profiles and recall performance in soccer: Perceptual-cognitive expertise. British Journal of Psychology, 103(3), 393–411. https://doi.org/10.1111/j.2044-8295.2011.02081.x

Willoughby, K. A. (2002). Winning Games in Canadian Football: A Logistic Regression Analysis. The College Mathematics Journal, 33(3), 215–220. https://doi.org/10.1080/07468342.2002.11921944

Zheng, S., & Man, X. (2022). An Improved Logistic Regression Method for Assessing the Performance of Track and Field Sports. Computational Intelligence and Neuroscience, 2022, e6341495. https://doi.org/10.1155/2022/6341495

Metrics

0

Crossref logo

0


67

Views

13

PDF views