A cluster analysis of basketball players for each of the five traditionally defined positions

Determining the players’ playing styles and bringing the right players together are very important for winning in basketball. This study aimed to group basketball players into similar clusters according to their playing styles for each of the traditionally defined five positions (point guard (PG), shooting guard (SG), small forward (SF), power forward (PF), and center (C)). This way, teams would be able to identify their type of players to help them determine what type of players they should recruit to build a better team. The 17 game-related statistics from 15 seasons of the National Basketball Association (NBA) were analyzed using a hierarchical clustering method. The cluster validity indices (CVIs) were used to determine the optimum number of groups. Based on this analysis, four clusters were identified for PG, SG, and SF positions, while five clusters for PF position and six clusters for C position were established. In addition to the definition of the created clusters, their individual achievements were examined based on three performance indicators: adjusted plus-minus (APM), average points differential, and the percentage of clusters on winning teams. This study contributes to the evaluation of team compatibility, which is a significant part of winning, as it allows one to determine the playing styles for each position, while examining the success of position pair combinations.

Victorious and Hierarchical: Past Performance as a Determinant of Team Hierarchical Differentiation

Hierarchies emerge as collectives attempt to organize themselves toward successful performance. Consequently, research has focused on how team hierarchies affect performance. We extend existing models of the hierarchy-performance relationship by adopting an alternative: Performance is not only an output of hierarchy but also a critical input, as teams’ hierarchical differentiation may vary based on whether they are succeeding. Integrating research on exploitation and exploration with work on group attributions, we argue that teams engage in exploitation by committing to what they attribute as the cause of their performance success. Specifically, collectives tend to attribute their success to individuals who wielded greater influence within the team; these individuals are consequently granted relatively higher levels of influence, leading to a higher degree of hierarchy. We additionally suggest that the tendency to attribute, and therefore grant more influence, to members believed to be the cause of success is stronger for teams previously higher (versus lower) in hierarchy, as a higher degree of hierarchical differentiation provides clarity as to which members had a greater impact on the team outcome. We test our hypotheses experimentally with teams engaging in an online judgement task and observationally with teams from the National Basketball Association. Our work makes two primary contributions: (a) altering existing hierarchy-performance models by highlighting performance as both an input and output to hierarchy and (b) extending research on the dynamics of hierarchy beyond individual rank changes toward examining what factors increase or decrease hierarchical differentiation of the team as a whole.

Quantifying Training and Game Demands of a National Basketball Association Season

Purpose: There are currently no data describing combined practice and game load demands throughout a National Basketball Association (NBA) season. The primary objective of this study was to integrate external load data garnered from all on-court activity throughout an NBA season, according to different activity and player characteristics.Methods: Data from 14 professional male basketball players (mean ± SD; age, 27.3 ± 4.8 years; height, 201.0 ± 7.2 cm; body mass, 104.9 ± 10.6 kg) playing for the same club during the 2017–2018 NBA season were retrospectively analyzed. Game and training data were integrated to create a consolidated external load measure, which was termed integrated load. Players were categorized by years of NBA experience (1-2y, 3-5y, 6-9y, and 10 + y), position (frontcourt and backcourt), and playing rotation status (starter, rotation, and bench).Results: Total weekly duration was significantly different (p < 0.001) between years of NBA playing experience, with duration highest in 3–5 year players, compared with 6–9 (d = 0.46) and 10+ (d = 0.78) year players. Starters experienced the highest integrated load, compared with bench (d = 0.77) players. There were no significant differences in integrated load or duration between positions.Conclusion: This is the first study to describe the seasonal training loads of NBA players for an entire season and shows that a most training load is accumulated in non-game activities. This study highlights the need for integrated and unobtrusive training load monitoring, with engagement of all stakeholders to develop well-informed individualized training prescription to optimize preparation of NBA players.

Predicting Seasonal Performance in Professional Sport: A 30-Year Analysis of Sports Illustrated Predictions

In 2017, Sports Illustrated (SI) made headlines when their remarkable prediction from 2014 that the Houston Astros (a team in one of the lowest Major League Baseball divisional rankings) would win the World Series, came true. The less-publicised story was that in 2017, SI predicted the Los Angeles Dodgers to win the Major League Baseball (MLB) title. Assessing the forecasting accuracy of experts is critical as it explores the difficulty and limitations of forecasts and can help illuminate how predictions may shape sociocultural notions of sport in society. To thoroughly investigate SI’s forecasting record, predictions were collected from the four major North American sporting leagues (the National Football League, National Basketball Association, Major League Baseball, and National Hockey League) over the last 30 years (1988–2018). Kruskal–Wallis H Tests and Mann–Whitney U Tests were used to evaluate the absolute and relative accuracy of predictions. Results indicated that SI had the greatest predictive accuracy in the National Basketball Association and was significantly more likely to predict divisional winners compared to conference and league champions. Future work in this area may seek to examine multiple media outlets to gain a more comprehensive perspective on forecasting accuracy in sport.

Tendency of Driving to the Basket Is Associated With Increased Risk of Anterior Cruciate Ligament Tears in National Basketball Association Players: A Cohort Study

Background: Driving to the basket in basketball involves acceleration, deceleration, and lateral movements, which may expose players to increased anterior cruciate ligament (ACL) injury risk. It is unknown whether players who heavily rely on driving have decreased performance on returning to play after ACL reconstruction (ACLR). Hypothesis: Players with a greater tendency to drive to the basket would be more likely to tear their ACL versus noninjured controls and would experience decreased performance when returning to play after ACLR. Study Design: Case-control study; Level of evidence, 3. Methods: Season-level performance statistics and ACL injuries were aggregated for National Basketball Association (NBA) seasons between 1980 and 2017 from publicly available sources. Players’ tendency to drive was calculated using 49 common season-level performance metrics. Each ACL-injured player was matched with 2 noninjured control players by age, league experience, and style of play metrics. Points, playing minutes, driving, and 3-point shooting tendencies were compared between players with ACL injuries and matched controls. Independent-samples t test was utilized for comparisons. Results: Of 86 players with a total of 96 ACL tears identified in the NBA, 50 players were included in the final analysis. Players who experienced an ACL tear had a higher career-average drive tendency than controls ( P = .047). Players with career-average drive tendency ≥1 standard deviation above the mean were more likely to tear their ACL than players with drive tendency <1 standard deviation (5.2% vs 2.7%; P = .026). There was no significant difference in total postinjury career points ( P = .164) or career minutes ( P = .237) between cases and controls. There was also no significant change in drive tendency ( P = .152) or 3-point shooting tendency ( P = .508) after return to sport compared with controls. Conclusion: NBA players with increased drive tendency were more likely to tear their ACL. However, players who were able to return after ACLR did not underperform compared with controls and did not alter their style of play compared with the normal changes seen with age. This information can be used to target players with certain playing styles for ACL injury prevention programs.

Salary Cap, Organizational Gap, and Catch-up in the Performance of NBA Teams: A Two-Stage DEA Model Under Heterogeneity

This paper evaluates National Basketball Association teams performance using a two-stage approach under a metafrontier framework. Our approach decomposes overall efficiency into salary-cap and on-court efficiency. Organizational gaps between different organizational structures are calculated as well. The introduction of the salary cap as an important element for a competitive market preventing monopoly formation allows the examination of the effects on different types of performance. Findings reveal that teams experience increased performance while organizational gaps decline. Moreover, a [Formula: see text] and [Formula: see text] convergence analysis reveals convergence patterns. Finally, the catch-up index denotes that the speed of convergence increases, especially after the salary cap policy implementation.