Перейти к содержимому
Central European Journal of Sport Sciences and Medicine
Vol. 54, No. 2/2026
Статистика OpenAlex

Для всего журнала по данным OpenAlex.

Цитирования
1 077
Публикации
412
H-index
12
I10-index
22
Средняя цитируемость (2 года)
0,43
Полученные цитирования по годам
График цитирований журнала. Точные значения доступны в таблице ниже. 2017: 37 2018: 45 2019: 56 2020: 112 2021: 106 2022: 146 2023: 150 2024: 161 2025: 132 2026: 67 2017 2026
Показать таблицу данных
Полученные цитирования по годам
ГодПубликацииЦитирования
20174237
20183345
20193556
202038112
202138106
202231146
202330150
202429161
202530132
20261667

Данные загружены:

Что означают эти числа?

Публикации и цитирования охватывают записи, связанные с этим журналом в OpenAlex. H-индекс равен наибольшему числу h публикаций, каждая из которых цитируется не менее h раз. I10-индекс считает публикации с 10 и более цитированиями. График показывает цитирования, полученные в каждом году; текущий год неполный. Охват может отличаться от WNUS и других сервисов.

Средняя цитируемость за 2 года является показателем OpenAlex, а не Journal Impact Factor компании Clarivate.

Открыть в OpenAlex (новая вкладка)

Assessing the Impact of Assists in Offensive Efficiency and Game Outcomes in EuroLeague Basketball

DOI: 10.18276/cej.2026.2-03
Цитировать

Полный текст доступен

Абстракт

Assists in basketball are considered a key metric of team play and offensive efficiency. The purpose of the study was to examine how assist performance influences team offensive efficiency and game outcomes in the EuroLeague. Data from 224 games of the 2024-2025 EuroLeague regular season were analyzed, including game context, basic and advanced assist metrics, and shooting and performance efficiency metrics. The Mann-Whitney U test measured the differences in these variables between the winning and losing teams, and Spearman's rank-order correlation assessed the strength and direction of associations between assist-related variables, offensive efficiency, and game results. The findings showed that the winning teams recorded significantly higher assist-related metrics (p < .001) and higher efficiency (p < .001) compared to the losing teams. The Spearman analysis revealed a strong positive correlation between assist-related metrics and offensive efficiency (p < .001). A weaker association was observed between assist-related metrics and game outcome (p < .05). The assist-to-turnover ratio emerged as a key factor contributing to offensive output and, to a lesser extent, to winning outcomes in elite-level basketball. Data-driven analysis helps optimize team coordination, identify patterns, and promote a culture of effective ball movement, which is vital in elite basketball.
Просмотр PDF

Открыть PDF

Библиография (39)
  1. Akinwande, M. O., Dikko, H. G., & Samson, A. (2015). Variance inflation factor: as a condition for the inclusion of suppressor variable (s) in regression analysis. Open Journal of Statistics, 5(07), 754. http://dx.doi.org/10.4236/ojs.2015.57075
  2. Cabarkapa, D., Cabarkapa, D. V., & Fry, A. C. (2024). Game-related statistics that discriminate winning from losing in NCAA Division-I men's basketball. Frontiers in Sports and Active Living, 6, 1387918. https://doi.org/10.3389/fspor.2024.1387918
  3. 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
  4. Cervone, D., D’Amour, A., Bornn, L., & Goldsberry, K. (2016). A multiresolution stochastic process model for predicting basketball possession outcomes. Journal of the American Statistical Association, 111(514), 585–599. https://doi.org/10.1080/01621459.2016.1141685
  5. Cohen J. (1988). Statistical power analysis for the behavioral sciences. 2nd ed. Routledge, New York, USA. https://doi.org/10.4324/9780203771587
  6. Euroleaguebasketball.net/Statistics. [online] Available at: https://www.euroleaguebasketball.net/en/euroleague/stats/ [Accessed 10 April 2025].
  7. Foteinakis, P. F., Pavlidou, S. P. (2024). Evolution of three-point field goals shooting trends in EuroLeague basketball. TRENDS in Sport Sciences, 31(4). https://doi.org/10.23829/TSS.2024.31.4-2
  8. Foteinakis, P., & Pavlidou, S. (2024). Positional Differences in the Efficacy of Critical End-of-Game Possessions in EuroLeague Basketball. Sport Mont, 22(2), 25–31. https://doi.org/10.26773/smj.240704
  9. Foteinakis, P., Pavlidou, S., & Stavropoulos, N. (2024). Analysis of the effectiveness of different play types in the end of game possessions of close EuroLeague matches. Journal of Human Sport and Exercise, 19(2), 617–630. https://doi.org/10.55860/rj38hm02
  10. Foteinakis, P., Pavlidou, S., & Stavropoulos, N. (2024). Performance analysis of switching defense to ball screens in the fourth quarter of close Euroleague matches. Journal of Physical Education and Sport, 24(2), 425–432. https://doi.org/10.7752/jpes.2024.02052
  11. García, J., Ibáñez, S. J., De Santos, R. M., Leite, N., & Sampaio, J. (2013). Identifying basketball performance indicators in regular season and playoff games. Journal of Human Kinetics, 36, 161–168. https://doi.org/10.2478/hukin-2013-0016
  12. Gkotsis, P. (2024). The value of the three point shot throughout the 2010-2020 era of NBA basketball (Doctoral dissertation, Lietuvos sporto universitetas). https://vb.lsu.lt/object/elaba:198035380/ (accessed May 15, 2025).
  13. Goldsberry, K. (2012). Courtvision: New visual and spatial analytics for the NBA. MIT Sloan Sports Analytics Conference, 9, 12–15. https://www.sloansportsconference.com/research-papers/courtvision-new-visual-and-spatial-analytics-for-the-nba (accessed May 15, 2025).
  14. Gomez-Ruano, M. A., Ibáñez, S. J., & Leicht, A. S. (2020). Performance analysis in sport. Frontiers in Psychology, 11, 611–634. https://doi.org/10.3389/fpsyg.2020.611634
  15. Karamousalidis, G., Agathokleous, M., Tsimpouris, I., Michailidis, I., & Foteinakis, P. (2025). Examining gender differences in ball reversal and paint touch usage in NCAA division I basketball games. Journal of Physical Education and Sport, 25(7), 1438–1444. https://doi.org/10.7752/jpes.2025.07160
  16. Karamousalidis, G., Foteinakis, P., Konstantinidis, G., Kifonidis, D., Kourtis-Doulkeridis, A., & Symeonidou, S. (2025). A comparison of three-point shooting effectiveness in the Euroleague and NCAA DI basketball. Journal of Human Sport and Exercise, 20(4), 1293–1302. https://doi.org/10.55860/d3bzsf39
  17. Karipidis, A., Fotinakis, P., Taxildaris, K., & Fatouros, J. (2001). Factors characterizing a successful performance in basketball. Journal of Human Movement Studies, 41(5), 385–398.
  18. Kinrade, N. P., Jackson, R. C., & Ashford, K. J. (2015). Reinvestment, task complexity and decision making under pressure in basketball. Psychology of Sport and Exercise, 20, 11–19. https://doi.org/10.1016/j.psychsport.2015.03.007
  19. Kubatko, J., Oliver, D., Pelton, K., & Rosenbaum, D. T. (2007). A starting point for analyzing basketball statistics. Journal of Quantitative Analysis in Sports, 3, 1–22. https://doi.org/10.2202/1559-0410.1070
  20. Liang, Z., Gao, Y., Wang, J., & Liu, Z. (2025). Data-driven insights into basketball performance: Unveiling the impact of advanced analytics on player and team efficiency. Molecular & Cellular Biomechanics, 22(1), 1083. https://doi.org/10.62617/mcb1083
  21. Mandić, R., Jakovljević, S., Erčulj F., & Štrumbelj, E. (2019). Trends in NBA and Euroleague basketball: Analysis and comparison of statistical data from 2000 to 2017. PLoS ONE, 14(10), e0223524. https://doi.org/10.1371/journal.pone.0223524
  22. McKim, E., Werner, J., & Werner, K. (2023). Analyzing the Correlation Between Offensive and Defensive Efficiency in the NBA. Unpublished Thesis, Sport Management Student Projects, Taylor University, USA. https://pillars.taylor.edu/sport-management-student-projects/2
  23. Melnick M. J. (2001). Relationship between team assists and win-loss record in The National Basketball Association. Perceptual and Motor Skills, 92(2), 595–602. https://doi.org/10.2466/pms.2001.92.2.595
  24. Mikołajec, K., Banyś, D., Żurowska-Cegielska, J., Zawartka, M., & Gryko, K. (2021). How To Win the Basketball Euroleague? Game Performance Determining Sports Results During 2003-2016 Matches. Journal of Human Kinetics, 77, 287–296. https://doi.org/10.2478/hukin-2021-0050
  25. Navalta, J. W., Stone, W. J., & Lyons, S. (2019). Ethical Issues Relating to Scientific Discovery in Exercise Science, International Journal of Exercise Science, 12 (1), 1–8. https://doi.org/10.70252/EYCD6235
  26. Oliver, D. (2004). Basketball on Paper: Rules and Tools for Performance Analysis. Potomac Books.
  27. Özmen, M. U. (2016). Marginal contribution of game statistics to probability of winning at different levels of competition in basketball: Evidence from the Euroleague. International Journal of Sports Science & Coaching, 11(1), 98–107. https://doi.org/10.1177/1747954115624828
  28. 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. https://doi.org/10.7752/jpes.2024.06154
  29. Sampaio, J., Lago, C., & Drinkwater, E. J. (2010). 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
  30. Sarlis, V., & Tjortjis, C. (2020). Sports analytics—Evaluation of basketball players and team performance. Information Systems, 93, 101562. https://doi.org/10.1016/j.is.2020.101562
  31. Skinner, B. (2010). The Price of Anarchy in Basketball. Journal of Quantitative Analysis in Sports, 6(1). https://doi.org/10.2202/1559-0410.1217
  32. Stamiris, F., Karamousalidis, G., & Stavropoulos, N. (2020). Inside game in Euroleague basketball. Journal of Physical Education and Sport, 20(6), 3222–3228. https://doi.org/10.7752/jpes.2020.s6437
  33. Stancin, I., & Jovic, A. (2018). Analyzing the influence of player tracking statistics on winning basketball teams. 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 1533–1538. https://doi.org/10.23919/MIPRO.2018.8400276
  34. Supola, B., Hoch, T., & Baca, A. (2022). The role of secondary assists in basketball – an analysis of its characteristics and effect on scoring. International Journal of Performance Analysis in Sport, 22(2), 261–276. https://doi.org/10.1080/24748668.2022.2039090
  35. Terner, Z., & Franks, A. (2021). Modeling player and team performance in basketball. Annual Review of Statistics and Its Application, 8(1), 1–23. https://doi.org/10.1146/annurev-statistics-040720-015536
  36. Wang, Z., Zhu, M., & Wei, J. (2025). Evaluating the Utility of Hierarchical Multiple Regression and Quantile Regression in Determining Critical Factors for Success in Elite Men’s Basketball. (2025). Journal of Education, Humanities, and Social Research, 2(1), 145–154. https://doi.org/10.71222/sqqnjf72
  37. Zając, T., Mikołajec, K., Chmura, P., Konefał, M., Krzysztofik, M., & Makar, P. (2023). Long-Term Trends in Shooting Performance in the NBA: An Analysis of Two- and Three-Point Shooting across 40 Consecutive Seasons. International Journal of Environmental Research and Public Health, 20(3), 1924. https://doi.org/10.3390/ijerph20031924
  38. Ziv, G., Lidor, R., & Arnon, M. (2010). Predicting team rankings in basketball: The questionable use of on-court performance statistics. International Journal of Performance Analysis in Sport, 10(2), 103–114. https://doi.org/10.1080/24748668.2010.11868506
  39. Zuccolotto, P., Manisera, M., & Sandri, M. (2017). Big data analytics for modeling scoring probability in basketball: The effect of shooting under high-pressure conditions. International Journal of Sports Science & Coaching, 13(4), 569–589. https://doi.org/10.1177/1747954117737492