1Gaziantep University, Faculty of Medicine, Department of Biostatistics, Gaziantep, Turkey
2Mersin University, School of Physical Education and Sports, Mersin, Turkey
The Important Game-Related Statistics for Qualifying Next Rounds in Euroleague
Monten. J. Sports Sci. Med. 2019, 8(1), 43-50 | DOI: 10.26773/mjssm.190307
Euroleague is one of the most popular professional indoor sports leagues in the world. It is globally ranked as the fifth-highest professional indoor sports league and the second-highest professional basketball league, just trailing behind the National Basketball Association (NBA). The objective of this study was to determine which game-related statistics can assist in predicting the team that will qualify for the next rounds of the Euroleague. The data used in the study were obtained from each team’s official average box score on the Euroleague website for 2010-2017. The datasets were arranged into two groups depending on the qualification of the teams into the subsequent round. Discriminant analysis was applied to find the game-related statistics that better contribute to qualifying for the next round. A three-point field-goal percentage was considered to be an essential variable in every round. However, it was also observed that, contrary to expectations, offensive rebounds had a negative effect in the final four rounds. It is recommended these results be used to plan the team strategies and the player strategies accordingly in a long-term and demanding tournament like Euroleague.
basketball, Euroleague, discriminant analysis, game-related statistics
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Akers, M. D., Wolff, S., & Buttross, T. E. (1992). An empirical examination of the factors affecting the success of NCAA Division I College Basketball teams. Journal of Business and Economic Studies, 1(2), 57-70.
Čaušević, D. (2015). Game-related statistics that discriminate winning and losing teams from the World championships in Spain in 2014. Homo Sporticus, 17(2), 16-19. https://doi.org/10.1155/2012/490647
Çene, E. (2018). What is the difference between a winning and a losing team: insights from Euroleague basketball. International Journal of Performance Analysis in Sport, 18(1), 55-68. https://doi.org/10.1080/24748668.2018.1446234
Csataljay, G., O’Donoghue, P., Hughes, M., & Dancs, H. (2009). Performance indicators that distinguish winning and losing teams in basketball. International Journal of Performance Analysis in Sport, 9(1), 60-66. https://doi.org/10.1080/24748668.2009.11868464
Dežman, B., Erčulj, F., & Vučković, G. (2002) Differences between winning and losing teams in playing efficiency. Acta Kinesiologiae Universitatis Tartuensis, 7(Supplement), 71-74.
Doğan, İ., Işik, Ö., & Ersöz, Y. (2016). Examining the Turkish men’s professional basketball team’s success according to game-related statistics with discriminant analysis. International Journal of Performance Analysis in Sport, 16(3), 829-836. https://doi.org/10.1080/24748668.2016.11868931
Ergül, B. (2014). Classification of NBA league teams using discriminant and logistic regression analyses. Pamukkale Journal of Sport Sciences, 5(1), 48-60.
García, J., Ibáñez, S. J., Martinez De Santos, R., Leite, N., & Sampaio, J. (2013). Identifying basketball performance indicators in regular season and playoff games. Journal of Human Kinetics, 36(1), 161-168. https://doi.org/10.2478/hukin-2013-0016
Gimenez, J., & Janeira, M. (2003). Game statistics discriminating the final outcome of junior world basketball championship matches (Portugal 1999). Journal of Human Movement Studies, 45, 1-19.
Glazier, P. S. (2017). Towards a grand unified theory of sports performance. Human movement science, 56, 139-156. https://doi.org/10.1016/j.humov.2015.08.001
Gómez, A.M., Lorenzo, A., Sampaio, J., José Ibáñez, S., & Ortega, E. (2008). Game-related statistics that discriminated winning and losing teams from the Spanish men’s professional basketball teams. Collegium Antropologicum, 32(2), 451-456.
http://www.euroleague.net/competition/teams (Accessed 09.03.2018).
Hughes, M., & Franks, I. M. (Eds.). (2004). Notational analysis of sport: Systems for better coaching and performance in sport. London, UK: Routledge
Ibáñez, S. J., Sampaio, J., Feu, S., Lorenzo, A., Gómez, M. A., & Ortega, E. (2008). Basketball game-related statistics that discriminate between teams’ season-long success. European journal of sport science, 8(6), 369-372. https://doi.org/10.1080/17461390802261470
Ittenbach, R. F., & Esters, I. G. (1995). Utility of team indices for predicting end of season ranking in two national polls. Journal of Sport Behavior, 18(3), 216-224.
Kozar, B., Vaughn, R. E., Whitfield, K. E., Lord, R. H., & Dye, B. (1994). Importance of free-throws at various stages of basketball games. Perceptual and Motor skills, 78(1), 243-248.
Lorenzo Calvo, A., Gómez Ruano, M. Á., Ortega Toro, E., Ibañez Godoy, S. J., & Sampaio, J. (2010). Game related statistics which discriminate between winning and losing under-16 male basketball games. Journal of Sports Science and Medicine, 9(4), 664-668.
Marmarinos, C., Apostolidis, N., Kostopoulos, N., & Apostolidis, A. (2016). Efficacy of the “pick and roll” offense in top level European basketball teams. Journal of human kinetics, 51(1), 121-129. https://doi.org/10.1515/hukin-2015-0176
Özdamar, K. (2013). Paket Programlar ile İstatistiksel Veri Analizi. Eskişehir, TR: Nisan Kitabevi.
Pojskić, H., Šeparović, V., & Užičanin, E. (2009). Differences between successful and unsuccessful basketball teams on the final Olympic tournament. Acta Kinesiologica, 3(2), 110-114.
Ribas, R. L., Navarro, R., Tavares, F., & Gómez, M. A. (2011). Analysis of number of players involved in rebound situations in Euroleague basketball games. Open Sports Sciences Journal, 4, 10-13.
Sampaio, J., Godoy, S.I., & Feu, S. (2004). Discriminative power of basketball game related statistics by level of competition and sex. Perceptual and Motor Skills, 99, 1231-1238. https://doi.org/10.2466/pms.99.3f.1231-1238
Sampaio, J., Janeira, M., Ibanez, S., & Lorenzo, A. (2006). Discriminant analysis of game-related statistics between basketball guards, forwards and centres in three professional leagues. European Journal of Sport Science, 6(3), 173-178. https://doi.org/10.1080/17461390600676200
Sampaio, J., Lago, C., & Drinkwater, E. J. (2010a). Explanations for the United States of America’s dominance in basketball at the Beijing Olympic Games (2008). Journal of Sports Sciences, 28(2), 147-152. https://doi.org/10.1080/02640410903380486
Sampaio, J., Drinkwater, E. J., & Leite, N. M. (2010b). Effects of season period, team quality, and playing time on basketball players’ game-related statistics. European Journal of Sport Science, 10(2), 141-149. https://doi.org/10.1080/17461390903311935
Severini, T. A. (2014). Analytic methods in sports: Using mathematics and statistics to understand data from baseball, football, basketball, and other sports. Chapman and Hall/CRC.
Tabachnick, B. G., & Fidell, L. S. (2001). Computer-assisted research design and analysis. Allyn & Bacon, Boston.
Taxildaris, K., Papadimitriou, K., Alexopoulos, P., Fatouros, I. G., Kambas, A., Karipidis, A., ... & Barbas, I. (2001). Factors characterizing the offensive game of the playmaker position in basketball. Journal of Human Movement Studies, 40(6), 405-421.