EuroBasket Yarı Final ve Final Aşamasındaki Takımlarda Maç Performans Göstergeleri ile Takım Başarısı Arasındaki İlişkinin İncelenmesi

Öz

Bu çalışma, 2017, 2022 ve 2025 yıllarında düzenlenen EuroBasket Final Four takımlarının maç performans göstergeleri ile turnuva sıralamaları arasındaki ilişkileri incelemeyi amaçlayan betimsel ve ilişkisel tarama modelinde tasarlanmıştır. Araştırma grubunu, her yıl final aşamasında yer alan dört takım (toplam 12 takım) oluşturmaktadır. Performans göstergeleri, resmi maç istatistiklerinden (box-score) elde edilmiş ve her takımın turnuva süresince sergilediği ortalama değerler hesaplanmıştır. Verilerin normal dağılıma uygunluğu Shapiro–Wilk testi ile değerlendirilmiş, normal dağılım göstermediği için Spearman sıra farkları korelasyonu kullanılmıştır. Analizler, 2017 yılı takımlarında sıralama ile herhangi bir parametre arasında anlamlı ilişki bulunmadığını göstermiştir (p>0,05); ancak sayı üretimi ile 3 sayılık isabet yüzdesi (p<0,05), sayı üretim yüzdesi, serbest atış yüzdesi, hücum ve savunma ribaundları, asist, top kaybı, top çalma ve blok parametreleri arasında pozitif yönde anlamlı ilişkiler gözlemlenmiştir (p<0,01). 2022 yılı takımlarında, sıralama ile rakip sayı üretimi arasında pozitif ilişki bulunmuş (p<0,01) ve sayı üretimi ile 3 sayılık isabet yüzdesi (p<0,05), ribaundlar, asist, top kaybı ve top çalma arasında da pozitif ilişkiler görülmüştür (p<0,01). 2025 yılı takımlarında ise sıralama ile rakip sayı üretimi arasında pozitif ilişki (p<0,01) ve sayı üretimi ile 3 sayılık isabet, serbest atış yüzdesi, hücum ve savunma ribaundları, asist, top kaybı, top çalma ve blok parametreleri arasında pozitif ilişkiler (p<0,01) bulunmuştur. Bu bulgular, üst düzey basketbol turnuvalarında takım başarısının yalnızca sayı üretimine değil, çok boyutlu performans unsurlarına bağlı olduğunu göstermektedir.

Anahtar Kelimeler

• EuroBasket
• performans göstergeleri
• maç analizi
• spor analitiği

Analysis of the Relationship Between Match Performance Indicators and Team Success in EuroBasket Semi-Final and Final Teams

Öz

This study was designed as a descriptive-relational survey to examine the match performance indicators of EuroBasket Final Four teams in 2017, 2022, and 2025 and their relationships with tournament rankings. The study group comprised four teams from each final stage, totaling 12 teams. Performance indicators were obtained from official match statistics (box-scores), and each team's average performance throughout the tournament was calculated. Normality of the data was assessed using the Shapiro–Wilk test, and due to non-normal distribution, Spearman rank-order correlation was employed. The analyses showed that in 2017, ranking was not significantly related to any performance parameter (p>0.05); however, scoring was positively correlated with three-point field goal percentage (p<0.05), scoring percentage, free-throw percentage, offensive and defensive rebounds, assists, turnovers, steals, and blocks (p<0.01). In 2022, ranking was positively associated with opponent scoring (p<0.01), and scoring was positively correlated with three-point field goal percentage (p<0.05), rebounds, assists, turnovers, and steals (p<0.01). In 2025, ranking was positively correlated with opponent scoring (p<0.01), and scoring was positively related to three-point and free-throw percentages, offensive and defensive rebounds, assists, turnovers, steals, and blocks (p<0.01). These findings indicate that success in elite basketball tournaments depends on multidimensional performance factors beyond scoring alone.

Anahtar Kelimeler

• EuroBasket
• performance indicators
• match analysis
• Sports analytics

Kaynakça

1. Cabarkapa, D., Deane, M. A., Fry, A. C., Jones, G. T., Cabarkapa, D. V., Philipp, N. M.& Yu, D. (2022). Game statistics that discriminate winning and losing at the NBA level of basketball competition. Plos One, 17(8), e0273427.
2. Canuto, S. C., &de Almeida, M. B. (2022). Determinants of basketball match outcome based on game-related statistics: a systematic review and meta analysis. European Journal of Human Movement, (48), 4-20.
3. Christmann, J., Akamphuber, M., Müllenbach, A. L., &Güllich, A. (2018). Crunch time in the NBA–the effectiveness of different play types in the endgame of close matches in professional basketball. International Journal of Sports Science & Coaching, 13(6), 1090-1099.
4. Conte, D., Tessitore, A., Gjullin, A., Mackinnon, D., Lupo, C., &Favero, T. (2018). Investigating the game-related statistics and tactical profile in NCAA division I men’s basketball games. Biology of Sport, 35(2), 137-143.
5. Csataljay, G., James, N., Hughes, M. D.,& Dancs, H. (2012). Performance differences between winning and losing basketball teams during close, balanced and unbalanced quarters. Journnal of Human Sport & Exercise, 7(2), 356-364.
6. 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.
7. Dogan, O.,& Savaş, S. (2021). Effect of an 8-weeks core training program applied to 12-14 years old basketball players on strength, balance and basketball skill. Pakistan Journal Of Medical & Health Sciences, 15(2).
8. Gómez-Carmona, C. D., Gómez-Ramos, E., Piñar, M. I., Contreras, J. M., &Ibáñez, S. J. (2026). Technical-tactical performance of Spanish female basketball players in first division (2013–2022): effects of match outcome, location, and playing position. Biology of Sport, 43(1), 243-256.

9. Gryko, K., Mikołajec, K., Maszczyk, A., Cao, R.,& Adamczyk, J. G. (2018). Structural analysis of shooting performance in elite basketball players during FIBA EuroBasket 2015. International Journal of Performance Analysis in Sport, 18(2), 380-392.
10. Jiménez, J. L., Valido, J., &Jiménez, M. (2025). The effects of crowds on professional European basketball teams. Journal of Sports Economics, 26(4), 377-396.
11. Kamitsis, A.,& Politis, K. (2025). Performance indicators for qualification to the playoffs and the final four in Euroleague 2000–2022. Journal of Sports Analytics, 11, 22150218251365222
12. Kvasnytsya, O., Tyshchenko, V., Latyshev, M., Kvasnytsia, I., Timasheva, O., Rybak, L., Flerchuk, V.,& Omelchuk, M. (2025). Trends in performance indicators of English Premiership rugby teams. TRENDS in Sport Sciences, 32(4).
13. Lamas, L., Barrera, J., Otranto, G., &Ugrinowitsch, C. (2014). Invasion team sports: strategy and match modeling. International Journal of Performance Analysis in Sport, 14(1), 307-329.
14. Malarranha, J., Figueira, B., Leite, N., &Sampaio, J. (2013). Dynamic modeling of performance in basketball. International Journal of Performance Analysis in Sport, 13(2),377-387.
15. Martinho, D. V., Clemente, F. M., Ángel-Gomez, M., Rebelo, A., Field, A., Santos, C. C., Gouveia, É. R., Alfonos, J., &Sarmento, H. (2025). Physical, physiological, technical and tactical responses according to the playing position in male basketball: a systematic scoping review. Journal of Human Kinetics, 96(Spec Issue), 5.
16. Mateus, N., Esteves, P., Goncalves, B., Torres, I., Gomez, M. A., Arede, J., &Leite, N. (2020). Clustering performance in the European basketball according to players’ characteristics and contextual variables. International Journal of Sports Science & Coaching, 15(3), 405-411.
17. Matulaitis, K., &Bietkis, T. (2021). Prediction of offensive possession ends in elite basketball teams. International Journal of Environmental Research and Public Health, 18(3), 1083.
18. Morillo-Baro, J. P., Troyano-Gallegos, B., Pastrana-Brincones, J. L., Vázquez-Diz, J. A., Reigal, R. E., Quiñones-Rodríguez, Y., Vázquez-Diz, J. A., Reigal, R. E., Quiñones- Rodríguez, Y., Hernández-Mendo, A., Falcó, C., &Morales-Sánchez, V. (2021). Data mining and polar coordinates in the analysis by gender of finishing behaviors in professional basketball pick and roll. Frontiers in Sports and Active Living, 3, 742609.
19. O’Connor, S., Murchan, E., Dolan, C., Bergin, C., Teahan, C.,& Devaney, C. (2024). Irish basketball players: current practices, attitudes, barriers, and facilitators to injury prevention. Physical Activity and Health, 8(1), 197–209.
20. O’Donoghue, P. (2005). Normative profiles of sports performance. International Journal of Performance Analysis in Sport, 5(1), 104-119.
21. Pino-Ortega, J., Rojas-Valverde, D., Gómez-Carmona, C. D., Bastida-Castillo, A., Hernández-Belmonte, A., García-Rubio, J., Nakamura, F. Y.,& Ibáñez, S. J. (2019). Impact of contextual factors on external load during a congested-fixture tournament in elite U’18 basketball players. Frontiers in Psychology, 10, 1100.
22. Plakias, S., Kokkotis, C., Pantazis, D., &Tsatalas, T. (2024). Comparative analysis of key performance indicators in Euroleague and national basketball leagues. Journal of Physical Education and Sport, 24(6), 1360-1372.