بررسی شاخص های عملکردی متمایزکننده پیروزی و شکست در بسکتبال از طریق تحلیل شبکه اجتماعی (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
ارتباط بین هم تیمی ها در ورزش های تیمی دارای ویژگی هایی است که می توان آن ها را با تجزیه و تحلیل شبکه های اجتماعی بررسی کرد. هدف مطالعه حاضر بررسی شاخص های عملکردی متمایزکننده پیروزی و شکست از طریق تحلیل شبکه اجتماعی در دو سطح میکرو (درجه مرکزیت، نزدیکی، میان گذری، بردار ویژه) و ماکرو (چگالی تیمی) بود؛ بر این اساس، 24 بازی تیم بسکتبال شیمیدر در رقابت های لیگ برتر بسکتبال ایران به صورت نمونه گیری 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 به دست آمده در هفته های منجر به پیروزی یا شکست تفاوت معنا داری را بین شاخص های 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 شبکه های موفق و ناموفق تفاوت معنا داری بین شبکه های 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 معنا دار بود. نتایج پژوهش حاضر نشان داد، در بسکتبال درجه مرکزیت به موفقیت عملکرد تیم کمک می کند؛ چراکه بازیکنان توپ را به بهترین پخش کننده تیم پاس می دهند و سپس آن بازیکن تصمیم می گیرد توپ را به سمت کدام بازیکن به بهترین شکل هدایت کند.Investigating the Performance Indicators Differentiating Winning and Losing in Basketball Through the Social Network Analysis
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
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