بررسی شاخص‌های عملکردی متمایزکننده پیروزی و شکست در بسکتبال از طریق تحلیل شبکه اجتماعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه علوم رفتاری، شناختی و فناوری ورزشی، دانشکده علوم ورزشی و تندرستی، دانشگاه شهید بهشتی، تهران، ایران

2 استاد گروه علوم رفتاری، شناختی و فناوری ورزشی، دانشکده علوم ورزشی و تندرستی، دانشگاه شهید بهشتی، تهران، ایران

3 استاد دانشکده تربیت بدنی، دانشگاه فدرال سانتا ماریا، برزیل

چکیده
ارتباط بین هم‌تیمی‌ها در ورزشهای تیمی دارای ویژگیهایی است که میتوان آنها را با تجزیه و تحلیل شبکههای اجتماعی بررسی کرد. هدف مطالعه حاضر بررسی شاخصهای عملکردی متمایزکننده پیروزی و شکست از طریق تحلیل شبکه اجتماعی در دو سطح میکرو (درجه مرکزیت، نزدیکی، میان‌گذری، بردار ویژه) و ماکرو (چگالی تیمی) بود؛ بر این اساس،  24 بازی تیم بسکتبال شیمیدر در رقابتهای لیگ برتر بسکتبال ایران به صورت نمونهگیری دردسترس انتخاب شد. این تیم متشکل از 12 بازیکن با میانگین سن 24 و انحراف معیار 5 ± بود که براساس شماره پیراهن شناسایی شدند. نتایج به‌دست‌آمده در هفتههای منجر به پیروزی یا شکست تفاوت معنا‌داری را بین شاخصهای درجه مرکزیت (001/0 P=، 95/12= (5،66)F) و بردار ویژه (025/0 P=، 77/2= (5،66)F) نشان داد (05/0P<). در تحلیل چگالی شبکههای موفق و ناموفق تفاوت معنا‌داری بین شبکههای مختلف تیمی مشاهده نشد (05/0P>). همچنین درمورد نقش متمایز عملکرد موفق و ناموفق و تأثیر آن بر شبکه کلی تیمی نتایج نشان داد که تنها در شاخص درجه مرکزیت (001/0 P=،13/197= (2،69)F) اختلاف معنا‌دار بود. نتایج پژوهش حاضر نشان داد، در بسکتبال درجه مرکزیت به موفقیت عملکرد تیم کمک میکند؛ چراکه بازیکنان توپ را به بهترین پخشکننده تیم پاس میدهند و سپس آن بازیکن تصمیم میگیرد توپ را به سمت کدام بازیکن به بهترین شکل هدایت کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله English

Investigating the Performance Indicators Differentiating Winning and Losing in Basketball Through the Social Network Analysis

نویسندگان English

mohammad mehdi kheirkhiz 1
behrouz abdoli 2
Lorenzo Laporta 3
Alireza Farsi 2
1 Ph.D. Student in Motor Behavior, Department of Cognitive and Behavioral science and Technology in sport, Shahid Beheshti University, Tehran, Iran
2 Professor in Motor Behavior, Department of Cognitive and Behavioral science and Technology in sport, Shahid Beheshti University, Tehran, Iran
3 Professor in Centro de Educação Física e Desportos, Universidade Federal de Santa Maria (Santa Maria, Brazil).
چکیده English

Extended Abstract
Background and Purpose
Communication among teammates in team sports exhibits complex characteristics that can be effectively explored using social network analysis (SNA). In basketball, five positions—Point Guard, Shooting Guard, Small Forward, Power Forward, and Center—are traditionally distinguished by players’ physical attributes such as height and weight. However, modern coaching trends favor player versatility, allowing athletes more freedom to assume varied roles during gameplay. Social network analysis has emerged as a complementary tool to traditional scouting in team sports like soccer, offering rapid assessment of player interactions and an ecological overview of team and match dynamics that conventional analyses often lack. Importantly, SNA facilitates understanding of players’ decision-making ability through metrics assessing their capacity to create (e.g., degree prestige) and locate (e.g., degree centrality) passing opportunities, thereby enhancing tactical comprehension.
The present study aimed to investigate key performance indicators distinguishing winning and losing outcomes in basketball through SNA at two analytical levels: micro-level metrics (degree centrality, closeness, between nesses, eigenvector centrality) and macro-level metrics (team density). Furthermore, this research examined differences between successful and unsuccessful team networks to identify factors underpinning competitive performance. The study provides a qualitative baseline describing player role expectations by position and establishes quantitative standards for measuring these metrics.
 
Methods
A men’s basketball team competing in the 2019–20 Iranian Basketball Premier League was selected via convenience sampling. The roster included 12 players (mean age 24 ± 5 years) each with at least ten years of Premier League experience. Players were identified consistently by their shirt numbers, which remained unchanged throughout the season. Players were categorized into five positions consistent with their on-court roles: Point Guard, Shooting Guard, Small Forward, Power Forward, and Center.
The analysis encompassed 1,800 offensive phases across 24 matches. For each game, a comprehensive network was constructed based on player positions and ball movement. The linkage between teammates was operationalized as passes made during offensive sequences, considered as the network edges. Video analysis was employed to extract pass distribution data and performance indicators for each player position. Using this data, adjacency matrices representing the interactions between players were generated. These matrices were processed with Socnet software to calculate network measures and their corresponding criteria.
 
Results
Analysis showed significant differences in degree centrality indices between winning and losing weeks (F(5,66) = 12.95, p=0.001), as well as in eigenvector centrality (F(5,66) = 7.77, p=0.025), with both metrics higher during winning matches. However, no significant differences were detected in overall team network density when comparing successful and unsuccessful games (p>0.05). Focusing on the distinct influence of individual player performance on the broader team network, only degree centrality showed a significant disparity between successful and unsuccessful performances (F=197.13, p=0.001).
 
Conclusion
Coaches often categorize players by position to organize teams and assign roles and responsibilities effectively. This study's application of social network centrality metrics revealed that guards, particularly point guards, serve as pivotal facilitators establishing the greatest number of interactions with teammates during play. Typically, players tend to pass the ball to the team’s most capable players, who then direct play by deciding the optimal subsequent passes.
Interestingly, no correlation was found between players’ degree centrality and their points scored per game, suggesting that measures of success beyond scoring—such as decision-making impact and team facilitation—may be related to network position. A deeper understanding of passing behavior through social network metrics can thus assist coaches and players in refining strategies and identifying team vulnerabilities.
Overall, this research provides both qualitative and quantitative foundations regarding player roles across positions, contributing valuable insights into team dynamics measurement.
Article Message
This study highlights a significant association between the structure of player communication networks—characterized via social network analysis—and match outcomes (winning vs. losing) in basketball teams. Specifically, network measures such as degree centrality and eigenvector centrality significantly distinguish periods of winning and losing, illustrating that players in central, influential network positions critically shape team success by facilitating information flow and ball movement.
Ethical Considerations
the study was approved by the Research Ethics Committee of the Sport Sciences Research Institute (Approval Number IR.SSRI.REC.1401.1988).
Authors’ Contributions
·         Conceptualization: Mohammad Mehdi Kheirkhiz, Behrouz Abdoli
·         Data Collection: Mohammad Mehdi Kheirkhiz
·         Data Analysis: Mohammad Mehdi Kheirkhiz
·         Manuscript Writing: Mohammad Mehdi Kheirkhiz, Behrouz Abdoli
·         Review and Editing: Mohammad Mehdi Kheirkhiz, Behrouz Abdoli, Lorenzo Laporta, Alireza Farsi
·         Funding Responsibility: Mohammad Mehdi Kheirkhiz
·         Literature Review: Mohammad Mehdi Kheirkhiz, Behrouz Abdoli
·         Project Management: Behrouz Abdoli
 
Conflict of Interest
the authors declare no commercial or financial conflicts of interest related to this study.
Acknowledgments
The authors gratefully acknowledge the cooperation of the participating players and coaching staff supporting this research
 

کلیدواژه‌ها English

Basketball, Performance Indicators, Social Network Analysis, Team Interactions
1.       Lorenzo J, Lorenzo A, Conte D, Giménez M. Long-term analysis of elite basketball players’ game-related statistics throughout their careers. Frontiers in Psychology. 2019;10:421. https://doi.org/10.3389/fpsyg.2019.00421
2.       Hughes MD, Bartlett RM. The use of performance indicators in performance analysis. Journal of sports sciences. 2002;20(10):739-54. https://doi.org/10.1080/026404102320675602
3.       Bartlett R. Performance analysis: can bringing together biomechanics and notational analysis benefit coaches? International Journal of Performance Analysis in Sport. 2001;1(1):122-6. https://doi.org/10.1080/24748668.2001.11868254
4.       Hughes M, Franks I. The essentials of performance analysis: an introduction: London: Routledge. https://doi.org/200710.4324/9780203938065
5.       Dimitros E, Garopoulou V, Bakirtzoglou P, Maltezos C. Differences and discriminant analysis by location in A1 Greek women’s basketball league. Sport Science. 2013;6(1):33-7. https://doi.org/10.1080/054704102320675602
6.       Leicht AS, Gomez MA, Woods CT. Team performance indicators explain outcome during women’s basketball matches at the Olympic Games. Sports. 2017;5(4):96. https://doi.org/10.3390/sports5040096
7.       Koon Teck K, Wang C, Mallett C. Discriminating factors between successful and unsuccessful elite youth Olympic female basketball teams. International Journal of Performance Analysis in Sport. 2012;12(1):119-31. https://doi.org/10.1080/24748668.2012.11868588
8.       Sampaio J, Janeira M, Ibáñez S, Lorenzo A. Discriminant analysis of game-related statistics between basketball guards, forwards and centres in three professional leagues. European Journal of Sport Science. 2006;6(3):173-8. https://doi.org/10.1080/17461390600676200
9.       Conte D, Favero TG, Niederhausen M, Capranica L, Tessitore A. Determinants of the effectiveness of fast break actions in elite and sub-elite Italian men’s basketball games. Biology of Sport. 2017;34(2):177-83. https://doi.org/10.5114/biolsport.2017.65337
10.    Trninić S, Dizdar D, Lukšić E. Differences between winning and defeated top quality basketball teams in final tournaments of European club championship. Collegium Antropologicum. 2002;26(2):521-31. https://doi.org/10.1108/17461390600676200
11.    Mendes L, Janeira M. Basketball performance-multivariate study in Portuguese professional male basketball teams. Notational Analysis of sport–IV. 2001;103:111. https://doi.org/10.1080/054704102320675602
12.    Tsamourtzis E, Salonikidis K, Taxildaris K, Mawromatis G. Technical and tactical characteristics of winners and losers in basketball. Leistungssport. 2002;32(1):54-8. https://doi.org/10.1080/054704102320675602
13.    Lidor R, Arnon M. Developing indexes of efficiency in basketball: talk with the coaches in their own language. Kinesiology. 2000;32(2):31-41. https://doi.org/10.1016/j.socnet.2012.08.006
14.    Sampaio J, Ibáñez S, Lorenzo A, Gómez M. Discriminative game-related statistics between basketball starters and nonstarters when related to team quality and game outcome. Perceptual and Motor Skills. 2006;103(2):486-94. https://doi.org/10.2466/pms.103.2.486-494
15.    Zaccaro SJ, Rittman AL, Marks MA. Team leadership. The Leadership Quarterly. 2001;12(4): 451-83. https://doi.org/10.1016/S1048-9843(01)00093-5
16.    Anderson C, Franks NR. Teams in animal societies. Behavioral Ecology. 2001;12(5):534-40. https://doi.org/10.1093/beheco/12.5.534
17.    Vilar L, Araújo D, Davids K, Button C. The role of ecological dynamics in analysing performance in team sports. Sports Medicine. 2012;42:1-10. https://doi.org/10.2165/11596520-000000000-00000  
18.    Araújo D, Davids K. Team synergies in sport: theory and measures. Frontiers in psychology. 2016;7:1449. https://doi.org/10.3389/fpsyg.2016.01449
19.    Pena JL, Touchette H. A network theory analysis of football strategies. arXiv preprint arXiv: 12066904.2012. https://doi.org/10.48550/arXiv.1206.6904
20.    Gudmundsson J, Wolle T, editors. Football analysis using spatio-temporal tools. Proceedings of the 20th International Conference on Advances in Geographic Information Systems; 2012. https://doi.org/10.3389/fpsyg.2016.01449
21.    Glazier PS. Game, set and match? Substantive issues and future directions in performance analysis. Sports Medicine. 2010;40:625-34. 10.2165/11534970-000000000-00000
22.    Latash ML, Gorniak S, Zatsiorsky VM. Hierarchies of synergies in human movements. Kinesiology (Zagreb, Croatia). 2008;40(1):29. https://doi.org/10.2566/pms.103.2.486-494
23.    Komar J, Seifert L, Thouvarecq R. What variability tells us about motor expertise: measurements and perspectives from a complex system approach. Movement & Sport Sciences-Science & Motricité. 2015;(89):65-77. https://doi.org/10.1051/sm/2015020
24.    Cummings JN, Cross R. Structural properties of work groups and their consequences for performance. Social Networks. 2003 25(3):197-210. https://doi.org/10.1016/S0378-8733(02)00049-7
25.    Warner S, Bowers MT, Dixon MA. Team dynamics: A social network perspective. Journal of Sport Management. 2012;26(1):53-66. https://doi.org/10.1123/jsm.26.1.53
26.    Gaston ME, DesJardins M. The effect of network structure on dynamic team formation in multiagent systems. Computational Intelligence. 2008; 24(2):122-57. https://doi.org/10.1111/j.1467-8640.2008.00325  
27.    Vazquez-Guerrero J, Fernández-Valdés B, Jones B, Moras G, Reche X, Sampaio J. Changes in physical demands between game quarters of U18 elite official basketball games. PLoS One. 20193;14(9):e0221818. https://doi.org/10.1371/journal.pone.0221818
28.    Pollard R, Pollard G. Home advantage in soccer: a review of its existence and causes. 2005. https://doi.org/10.3389/fpsyg.2016.01449
29.    Praça G, Diniz L, Clemente F, Bredt SGT, Couto B, Andrade A, et al. The influence of playing position on the physical, technical, and network variables of sub-elite professional soccer athletes. Human Movement. 2021;22(2):22-31. https://doi.org/10.1371/journal.pone.0198888
30.    Grund TU. Network structure and team performance: The case of English Premier League soccer teams. Social Networks. 2012; 34(4):682-90. https://doi.org/10.1016/j.socnet.2012.08.004
31.    Travassos B, Davids K, Araújo D, and Esteves TP. Performance analysis in team sports: Advances from an Ecological Dynamics approach. International Journal of Performance Analysis in Sport. 2013;13(1):83-95. https://doi.org/10.1080/24748668.2013.11868633
32.    Correia V, Araújo D, Duarte R, Travassos B, Passos P, Davids K. Changes in practice task constraints shape decision-making behaviours of team games players. Journal of Science and Medicine in Sport. 2012;15(3): 244-9. https://doi.org/10.1016/j.jsams.2011.10.004
33.    Ramos J, Lopes RJ, Araújo D. What’s next in complex networks? Capturing the concept of attacking play in invasive team sports. Sports Medicine. 2018;48(1):17-28. https://doi.org/10.1007/s40279-017-0786-z
34.    Clemente FM, Couceiro MS, Martins FML, Mendes RS. Using network metrics in soccer: a macro-analysis. Journal of Human Kinetics. 2015; 45:123. https://doi.org/10.1515/hukin-2015-0013
35.    Bonacich P. Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology. 1972; 2(1):113-20. https://doi.org/10.1080/0022250X.1972.9989806
36.    Balkundi P, Harrison DA. Ties, leaders, and time in teams: Strong inference about network structure’s effects on team viability and performance. Academy of Management Journal. 2006;49(1):49-68. https://doi.org/10.5465/amj.2006.20785500
37.    Wasserman S, Faust K. Social network analysis: Methods and applications. Published by the Press Syndicate of the University of Cambridge; 1994. https://doi.org/10.1017/CBO9780511815478
38.    Fewell JH, Armbruster D, Ingraham J, Petersen A, Waters JS. Basketball teams as strategic networks. PloS one. 2012;7(11):e47445. https://doi.org/10.1371/journal.pone.0047445
39.    Xu CK. Social Network Analysis of College and Professional Basketball (Doctoral dissertation). 2018. https://doi.org/10.1017/CBO9780511815478
40.    Diambra NJ. Using topological clustering to identify emerging positions and strategies in NCAA men’s basketball. 2018. https://doi.org/10.1017/O9780511815478
41.    Sparrowe RT, Liden RC, Wayne SJ, Kraimer ML. Social networks and the performance of individuals andgroups.Academyofmanagementjournal.2001;44(2):316-25. https://doi.org/10.5465/3069458
42.    Leavitt HJ. Some effects of certain communication patterns on group performance. The Journal of Abnormal and Social Psychology. 1951;46(1):38. https://doi.org/10.1037/h0057189
43.    Rose T. The end of average: How to succeed in a world that values sameness. Penguin UK; 2016. https://doi.org/10.1236/h0057189
44.    Pena JL, Touchette H. A network theory analysis of football strategies. arXiv preprint arXiv:1206.6904. 2012. https://doi.org/10.48550/arXiv.1206.6904
45.    Gonçalves B, Coutinho D, Santos S, Lago-Penas C, Jiménez S, Sampaio J. Exploring team passing networks and player movement dynamics in youth association football. PloS one. 2017;12(1):e0171156. https://doi.org/10.1371/journal.pone.0171156
46.    Sasaki K, Yamamoto T, Miyao M, Katsuta T, Kono I. Network centrality analysis to determine the tactical leader of a sports team. International Journal of Performance Analysis in Sport. 2017;17(6):822-31. https://doi.org/10.1080/24748668.2017.1402283
47.    Zuo XN, Ehmke R, Mennes M, Imperati D, Castellanos FX, Sporns O, Milham MP. Network centrality in the human functional connectome. Cerebral cortex. 2012;22(8):1862-75. https://doi.org/10.1093/cercor/bhr269
48.    Pina TJ, Paulo A, Araújo D. Network characteristics of successful performance in association football. A study on the UEFA champions league. 2017. https://doi.org/10.3389/fpsyg.2017.01173
49.    Hughes M, Franks I. Analysis of passing sequences, shots and goals in soccer. Journal of sports sciences. 2005;23(5):509-14. https://doi.org/10.1080/02640410410001716779
50.    Karipidis A, Mavridis G, Tsamourtzis E, Rokka S. The effectiveness of control offense, following an outside game in European Championships. Inquiries in Sport & Physical Education. 2010;8(1):99-106. https://doi.org/10.26253/heal.uth.ojs.ispe.2010.1347
 
 
 
 
 
 
 
دوره 17، شماره 59
اردیبهشت 1404
صفحه 101-120

  • تاریخ دریافت 25 فروردین 1402
  • تاریخ بازنگری 23 مهر 1402
  • تاریخ پذیرش 30 آبان 1402