National Basketball Association Research Articles

High-Status Teammates: Award Evaluation in the National Basketball Association

Social evaluations proceed in stages. First, judges filter a broad pool of candidates and pick a subset for detailed assessment. Then, the chosen group undergoes a closer examination, during which winners are selected. At both stages of the process, judges are susceptible to bias. Bias is especially commonplace when contenders work in teams because each team member’s merit can be hard to distinguish from that of others. Our paper investigates evaluation bias originating in intrateam status asymmetries. Using the National Basketball Association’s data, we explore how high-status teammates are associated with their colleagues’ chances of winning awards. We find that bias stemming from high-status teammates’ presence is beneficial to their colleagues in the first stage of evaluations because high-status actors increase their team members ‘visibility to judges. However, our results also show that in the second stage of evaluations, the presence of high-status teammates could decrease their colleagues’ chances of winning awards because lower-status actors might seem less worthy of awards when evaluated alongside high-status individuals. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2020.13917 .

Modeling the complexity of basketball games using marked mutually exciting point processes

The basketball game is rarely predictable due to the complexity of its scoring process. To deal with the complexity and forecast the game results, we develop a marked mutually exciting point process model for the scoring process. In the model, the dynamic of the scoring intensity is characterized by a mutually exciting point process. A time varying background rate is used in the intensity function to account for the non-homogeneity. The dependence between scoring events is incorporated into the model with two team-specific exponential exciting kernels. The distribution of the points obtained in each scoring event is modeled as a categorical distribution with parameters from a Dirichlet prior. An empirical Bayesian method and an expectation-maximization algorithm are developed to estimate the model parameters. We design a simulation algorithm to do pre-game and in-game predictions. An empirical study is conducted with National Basketball Association games in four seasons. It is showed that the proposed model outperforms the benchmark model both in the win-loss prediction and score prediction. Moreover, our model can obtain positive returns when betting in the point spread market. Besides forecasting, an index called added winning probability is designed based on the model to evaluate the players’ performance.

The Impact of Knee and Ankle Injuries on National Basketball Association Player Performance Post-injury

The Impact of Knee and Ankle Injuries on National Basketball Association Player Performance Post-injury

A Basketball Big Data Platform for Box Score and Play-by-Play Data.

This is the second part of a research diptych devoted to improving basketball data management in Spain. The Spanish ACB (Association of Basketball Clubs, acronym in Spanish) is the top European national competition. It attracts most of the best foreign players outside the NBA (National Basketball Association, in North America) and also accelerates the development of Spanish players who ultimately contribute to the success of the Spanish national team. However, this sporting excellence is not reciprocated by an advanced treatment of the data generated by teams and players, the so-called statistics. On the contrary, their use is still very rudimentary. An earlier article published in this journal in 2020 introduced the first open web application for interactive visualization of the box score data from three European competitions, including the ACB. Box score data refer to the data provided once the game is finished. Following the same inspiration, this new research aims to present the work carried out with more advanced data, namely, play-by-play data, which are provided as the game runs. This type of data allow us to gain greater insight into basketball performance, providing information that cannot be revealed with box score data. A new dashboard is developed to analyze play-by-play data from a number of different and novel perspectives. Furthermore, a comprehensive data platform encompassing the visualization of the ACB box score and play-by-play data is presented.

Reassessing customer discrimination in local market National Basketball Association television viewership

ABSTRACT This study revisits the question of whether evidence of race-based customer discrimination exists in local market television viewership. We assess how consumers in 27 of the largest U.S. media markets respond to the racial composition of playing talent using modern local market television viewership data from the National Basketball Association. An advantage of using viewership data from this context is the ability to control for the perceived and actual quality of the product, allowing for the ability to isolate the effects of player race. We first uncover statistically significant increases in viewership when Non-White players occupy more roster spots and receive more playing time relative to White players. Individual player race models produce similar results and indicate consumers respond favourably to Black and Hispanic players. The results oppose previous research in the same setting and suggest viewership preferences related to race have changed substantially over time.

Performance Differences in Elite National Basketball Association and Women's National Basketball Association Players Based Upon Whether the Dominant or Non-dominant Achilles Tendon Was Ruptured.

Background National Basketball Association (NBA) and Women's National Basketball Association (WNBA) players with Achilles tendon ruptures have previously been noted to have a significant decline in performance post-injury. There has been recent anecdotal evidence that elite players with dominant Achilles tendon ruptures may be able to return at a higher level of play post-rupture. Objective This study aimed to evaluate for any differences in performance in higher-performing NBA and WNBA players with dominant versus non-dominant Achilles tendon ruptures pre- and post-injury. Methods This study was conducted at the University of Pennsylvania, Department of Physical Medicine and Rehabilitation. NBA and WNBA players with an Achilles tendon rupture from 1990 to 2020 were identified. Only elite players, indicated by an average player efficiency rating (PER) of >15 in either of the three seasons pre/post-injury, were included. The average PER, offensive rating, defensive rating, and usage percentage were compared in the three seasons pre- and post-injury. Results Eighteen players met the inclusion criteria, and nine each were classified as dominant and non-dominant Achilles tendon ruptures based on their primary shooting hand. There was no significant difference between the dominant and non-dominant rupture groups in any outcomes pre-injury, including age. The non-dominant cohort had a significant decline in PER (20.04 vs. 14.16; p < 0.001) and offensive rating (110.33 vs. 101.56; p = 0.004) post-injury. There was no significant difference observed post-injury in defensive rating or usage percentage. The dominant cohort had no significant difference in any outcomes post-injury, returning to the same level of play as pre-injury. Despite no difference existing between the groups at baseline, the dominant group performed significantly better post-rupture with regardto PER (19.56 vs. 14.16; p < 0.001) and offensive rating (114.00 vs. 101.56; p < 0.001) versus the non-dominant group. Conclusion Elite NBA and WNBA players with dominant Achilles tendon ruptures had no change in performance post-injury, returning to the same level of production as pre-injury. Post-rupture, they demonstrated notably superior outcomes versus the non-dominant group with regardto PER and offensive rating. The non-dominant rupture group experienced the same decline in PER and offensive rating post-injury observed in previous studies. The data indicatethat elite NBA and WNBA players with a dominant Achilles tendon rupture have a much more favorable recovery post-injury and are able to return to thesame level of performance.

Training Background and Demographic Characteristics of Primary Care Team Physicians in Professional Sports.

Previous studies have shown that most professional head and orthopaedic team physicians are men, and most orthopaedic team physicians are fellowship-trained. It is unknown whether this holds true for primary care team physicians. To evaluate the residency and fellowship training background as well as the demographic characteristics of primary care team physicians in professional sports. Cross-sectional study. Publicly available information was used to determine the lead and supporting primary care team physicians for every US-based team in Major League Baseball, Major League Soccer, National Basketball Association, National Football League, National Hockey League, National Women's Soccer League, and Women's National Basketball Association. Data regarding training background and sex were obtained using internet-based sources. We identified 310 primary care team physicians from all 165 US-based teams in the 7 leagues included in the study. Female physicians comprised 11.5% (19/165) of the lead primary care team physicians and 14.2% (44/310) of all primary care team physicians. Overall, 66.7% (110/165) of lead primary care team physicians and 75.5% (234/310) of all primary care team physicians were sports medicine fellowship-trained. There was a higher proportion of female (37.5%) and fellowship-trained (93.8%) physicians in women's professional sports leagues. Most primary care team physicians (244/310 [78.7%]) were trained in family medicine or internal medicine. Women constituted a small minority of primary care team physicians in professional sports. Most primary care team physicians were residency trained in family medicine or internal medicine and were sports medicine fellowship-trained. The proportion of female and fellowship-trained primary care team physicians was highest in the National Women's Soccer League and the Women's National Basketball Association.

The Effects of Spectators on National Basketball Association Free Throw Performance

Free throw shooting percentage has averaged around 75% for nearly five decades. However, free throw percentages significantly increased to 79% during the 2020 spectator-less National Basketball Association season. The current study investigated if the elevated free throw shooting percentage observed during the spectator-less season has returned to the 50-year average now that spectators have returned to National Basketball Association arenas. Moreover, home and away free throw percentages were examined to determine if a potential home-field advantage contributed to this phenomenon. Analyses revealed that the free throw shooting percentage from the spectator-less season was significantly higher than the two spectator-filled seasons preceding and following the spectator-less season. Furthermore, the analyses found no differences in free throw percentages between home and away games. While the free throw shooting percentage increased to 79% during the spectator-less season, the free throw shooting percentage returned to the 50-year average (i.e., ∼75%), following the spectator-less season. Additionally, it does not appear that a perceived home-field advantage influenced the increased free throw performance observed in the spectator-less season. Multiple factors likely contributed to this free throw phenomenon, including the environmental characteristics shaped by the lack of spectators.

Anthropometric and physical fitness indicators in the combine draft between the finalist and the eliminated player in the national basketball association all-star slam dunk contest.

Little is known about the difference of anthropometry and physical fitness between the finalist and eliminated player in the NBA all star slam dunk contest. This study aimed to compare the difference on anthropometric and physical fitness indicator in the combine draft between finalist and eliminated player in the national basketball association all star slam dunk contest. Draft data of 32 basketball players (N = 32, age in draft year: 20.69±2.28 years old, height without shoes: 196.75±8.68 cm, weight: 96.85±10 kg, body fat percentage: 6.07±1.23%) participating in the 2000-2015 draft and 2003-2023 slam dunk contest was selected from national basketball association database. It was classified into finals group (FG) (N = 16) and elimination group (EG) (N = 16). Independent sample t-test with cohen's d was adopted for evaluating the statistical significance of intergroup difference and its effect size. The result indicates that Finalist group was significant less than elimination group on height without shoes (FG vs EG: 193.43±9.47 cm vs 200.06±6.52 cm, P<0.05), standing reach (FG vs EG: 257.66±12.32 cm vs 268.29±10.03 cm, P<0.05) and weight (FG vs EG: 93.38±7.37 kg vs 100.33±11.25 kg, P<0.05). Conversely, compared to elimination group,finalist group has significant better performance on three quarter court sprint (FG vs EG: 3.15±0.1 s vs 3.26±0.12 s, P<0.05), standing vertical jump (FG vs EG: 84.88±5.13 cm vs 78.83±4.9 cm, P<0.05) and max vertical jump (FG vs EG: 102.39±6.47 cm vs 94.79±8.34 cm, P<0.05). However, effect size analysis indicated that height without shoes,standing reach,weight (cohen's d = 0.73-0.959, 0.7≤cohen's d<1.3, moderate) from the anthropometric indicator and three quarter court sprint, standing vertical jump, and max vertical jump (cohen's d = 0.97-1.21, 0.7≤cohen's d<1.3, moderate) from physical fitness indicator has moderate effect size, whereas effect size of body fat percentage, wingspan and lane agility time (cohen's d = 0.31-0.67, 0.3≤cohen's d<0.7, small) was small. To conclude, specific anthropometric and physical fitness indicator shows clear difference between finals group and elimination group. Height without shoes, standing reach,weight in anthropometry and three quarter court sprint, standing vertical jump, and max vertical jump in physical fitness are key indicator to slam dunk performance. In line with the result in the study, NBA staff can select suitable rookies for slam dunk contest. Similiarly, coach from NBA or other basketball league, who want to improve the player's slam dunk performance, should use specific training programs to develop the slam dunk-related indicator.

Low antibody levels associated with significantly increased rate of SARS-CoV-2 infection in a highly vaccinated population from the US National Basketball Association.

SARS-CoV-2 antibody levels may serve as a correlate for immunity and could inform optimal booster timing. The relationship between antibody levels and protection from infection was evaluated in vaccinated individuals from the US National Basketball Association who had antibody levels measured at a single time point from September 12, 2021,to December 31, 2021. Cox proportional hazards models were used to estimate the risk of infection within 90 days of serologic testing by antibody level (<250, 250-800, and >800 AU/mL1 ), adjusting for age, time since last vaccine dose, and history of SARS-CoV-2 infection. Individuals were censored on date of booster receipt. The analytic cohort comprised 2323 individuals and was 78.2% male, 68.1% aged ≤40 years, and 56.4% vaccinated (primary series) with the Pfizer-BioNTech mRNA vaccine. Among the 2248 (96.8%) individuals not yet boosted at antibody testing, 77% completed their primary vaccine series 4-6 months before testing and the median (interquartile range) antibody level was 293.5 (interquartile range: 121.0-740.5) AU/mL. Those with levels <250 AU/mL (adj hazard ratio [HR]: 2.4; 95% confidence interval [CI]: 1.5-3.7) and 250-800 AU/mL (adj HR: 1.5; 95% CI: 0.98-2.4) had greater infection risk compared to those with levels >800 AU/mL. Antibody levels could inform individual COVID-19 risk and booster scheduling.

A doubly self-exciting Poisson model for describing scoring levels in NBA basketball

Abstract In this article, Poisson time series models are considered to describe the number of field goals made by a basketball team or player at both the game (within-season) and the minute (within-game) level. The model is endowed with a doubly self-exciting structure, following the INGARCH(1,1) specification. To estimate the model at the within-game level, a divide-and-conquer procedure is carried out under a Bayesian framework. Then, we perform a clustering of the players in terms of their similarity according to the corresponding posterior distributions of key model parameters. The model is tested with National Basketball Association (NBA) teams and players from the 2018–2019 season.

The effect of competition on the demand for skilled labor: Matching with externalities in the NBA

AbstractI study how heterogeneity in competition can affect the demand for skilled labor in a way that generates a dispersion in productivity across otherwise similar firms. This is explored in the setting of professional basketball where heterogeneity in competition is easily measured. I develop and estimate a matching model with externalities, where the value of a match between a firm and worker depends on the entire allocation of matches. I find that competition has a significant effect on hiring decisions in the National Basketball Association, resulting in a clustering of talent among rivals. A counterfactual shows that competition in the league's playoff format explains 17.59% of the widely observed gap in talent between the league's two conferences.

Injury Patterns and Impact on Performance in the NBA League Using Sports Analytics

This research paper examines Sports Analytics, focusing on injury patterns in the National Basketball Association (NBA) and their impact on players’ performance. It employs a unique dataset to identify common NBA injuries, determine the most affected anatomical areas, and analyze how these injuries influence players’ post-recovery performance. This study’s novelty lies in its integrative approach that combines injury data with performance metrics and salary data, providing new insights into the relationship between injuries and economic and on-court performance. It investigates the periodicity and seasonality of injuries, seeking patterns related to time and external factors. Additionally, it examines the effect of specific injuries on players’ per-match analytics and performance, offering perspectives on the implications of injury rehabilitation for player performance. This paper contributes significantly to sports analytics, assisting coaches, sports medicine professionals, and team management in developing injury prevention strategies, optimizing player rotations, and creating targeted rehabilitation plans. Its findings illuminate the interplay between injuries, salaries, and performance in the NBA, aiming to enhance player welfare and the league’s overall competitiveness. With a comprehensive and sophisticated analysis, this research offers unprecedented insights into the dynamics of injuries and their long-term effects on athletes.

Orthopedic Team Surgeons in Major Professional Sports: An Analysis of Affiliation With the Top 10 Sports Medicine Fellowship Programs and Implications for Leadership and Diversity.

This paper examines the correlation between orthopedic team surgeons in major professional sports and their affiliation with the top 10 sports medicine fellowship programs. With a growing trend in post-residency fellowship training, particularly in sports medicine, the study focuses on the implications of fellowship program choice for aspiring major professional sports team physicians. By analyzing data from Major League Baseball (MLB), the National Basketball Association (NBA), the National Football League (NFL), and the National Hockey League (NHL), the research reveals that 61 of 124 (49.19%) team surgeons graduated from the top 10 sports medicine fellowship programs. The results identify a noticeable pipeline effect in professional sports, where teams often hire graduates from a select number of esteemed fellowship programs. The study suggests that choosing a fellowship program from the top 10 list may enhance the prospects of becoming a major league team surgeon. Additionally, our results found a significant gender disparity among team surgeons, with only two (1.6%) of all major professional team physicians being women. Thisemphasizes the imperative for diversity improvement in orthopedic sports medicine. In conclusion, the research underscores the impact of top-tier fellowship programs on professional team surgeons, with implications for aspiring sports medicine physicians and a call for addressing gender disparities.

A different type of leadership style: Michael Jordan – a great void or a shooting star?

Research methodology The data used to present this case was collected from secondary data sources. These sources included media reports associated with Michael Jordan and his trajectory since entering the sport, as well as specific information published about his time at the Chicago Bulls. Another key source of information is the ESPN documentary conducted specifically on Jordan’s relationship with his National Basketball Association (NBA) team. Case overview/synopsis The case follows the story of Michael Jordan, who took his team, the Chicago Bulls, to fame in a rather controversial manner. To do so, Michael Jordan had to alter his leadership style over the years to be respected as a leader and motivate his team to win one NBA championship after another. On 20th April 2020, ESPN’s “The Last Dance”, a 10-part documentary about Michael Jordan and his time playing for the Chicago Bulls was released to much acclaim. The documentary became highly noted as Jordan himself, both directed and starred in the documentary. Jordan’s great achievements stood out, but so did the conflicts that the basketball star had with The Bulls’ management team and mainly, his teammates. Relationships between teammates were far from harmonious, which led to questions around whether Michael Jordan was as good a leader, as he was a star player. Cultural change within the organisation was primarily linked to the often-contested leadership of Jordan. Complexity academic level The case can be used at UG, MSc and MBA levels. It works for in-person teaching and for online teaching. It is most suitable in leadership, strategy and strategy in practice courses. However, it is critical to note that the case can shed light on the dynamics that leaders and teammates have within their teams. Therefore, this case may be valuable to students studying courses where they themselves must work in groups and oftentimes encounter challenges in managing their team. These challenges can arise at all levels of experience. As such, the case provides particularly useful reflection for decision makers who may be beginning to develop their leadership skill (UG), those who have already experienced working in teams (MSc) or leading teams themselves (MBA, Executive MBA). The case addresses the challenges associated with achieving high team motivation and performance. It also sheds light on the challenges associated with leading a cultural change within a team and the approaches of different actors involved. It may be best to introduce the case in the context of a (1.5–2 h) workshop once students understand the basic frameworks and tools used to analyse leadership styles and their characteristics.

A Comprehensive Data Pipeline for Comparing the Effects of Momentum on Sports Leagues

Momentum has been a consistently studied aspect of sports science for decades. Among the established literature, there has, at times, been a discrepancy between conclusions. However, if momentum is indeed an actual phenomenon, it would affect all aspects of sports, from player evaluation to pre-game prediction and betting. Therefore, using momentum-based features that quantify a team’s linear trend of play, we develop a data pipeline that uses a small sample of recent games to assess teams’ quality of play and measure the predictive power of momentum-based features versus the predictive power of more traditional frequency-based features across several leagues using several machine learning techniques. More precisely, we use our pipeline to determine the differences in the predictive power of momentum-based features and standard statistical features for the National Hockey League (NHL), National Basketball Association (NBA), and five major first-division European football leagues. Our findings show little evidence that momentum has superior predictive power in the NBA. Still, we found some instances of the effects of momentum on the NHL that produced better pre-game predictors, whereas we view a similar trend in European football/soccer. Our results indicate that momentum-based features combined with frequency-based features could improve pre-game prediction models and that, in the future, momentum should be studied more from a feature/performance indicator point-of-view and less from the view of the dependence of sequential outcomes, thus attempting to distance momentum from the binary view of winning and losing.

The impact of the 2017 Collective Bargaining Agreement on wage discrimination in the NBA

ABSTRACT This research re-evaluates wage discrimination in the National Basketball Association (NBA) following the 2017 Collective Bargaining Agreement (CBA) being implemented. Using free agent data, weighted least square regression, and the Oaxaca-Blinder decomposition, our results indicate that race does not significantly predict NBA players’ salaries, on average, after the 2017 CBA went into effect. Nevertheless, quantile regressions show discrimination remains present for star players. The results emphasize the potential positive effects that labour union agreements bring to professional sports labour markets.

National Basketball Association Salary Prediction: A Data-Driven Linear Regression Analysis

National Basketball Association (also known as NBA which will be used as an abbreviation throughout this essay) as one of the few most successful professional sports leagues in the world, it has been well known by the fierce physical competition among the most talented and competitive players in the realm of basketball. However, there is something else to discover behind the dazzling crossovers and sensational clutches, hidden in the reflection on the O 'Brien Cup, for example, the salary. This essay will be discussing the prediction on NBA players’ salary using multiple supervised machine learning algorithms based on a dataset of players’ on-court data and achievements, aiming for an objective result that can be used for both prediction and evaluation of players’ contracts. The result of this essay reveals which specific variables are useful for predicting players’ salary.

Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts

This study utilized advanced data mining and machine learning to examine player injuries in the National Basketball Association (NBA) from 2000–01 to 2022–23. By analyzing a dataset of 2296 players, including sociodemographics, injury records, and financial data, this research investigated the relationships between injury types and player recovery durations, and their socioeconomic impacts. Our methodology involved data collection, engineering, and mining; the application of techniques such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), isolation forest, and the Z score for anomaly detection; and the application of the Apriori algorithm for association rule mining. Anomaly detection revealed 189 anomalies (1.04% of cases), highlighting unusual recovery durations and factors influencing recovery beyond physical healing. Association rule mining indicated shorter recovery times for lower extremity injuries and a 95% confidence level for quick returns from “Rest” injuries, affirming the NBA’s treatment and rest policies. Additionally, economic factors were observed, with players in lower salary brackets experiencing shorter recoveries, pointing to a financial influence on recovery decisions. This study offers critical insights into sports injuries and recovery, providing valuable information for sports professionals and league administrators. This study will impact player health management and team tactics, laying the groundwork for future research on long-term injury effects and technology integration in player health monitoring.

A hierarchical approach for evaluating athlete performance with an application in elite basketball

In this paper, we present the ON score for evaluating the performance of athletes and teams that includes a season-long evaluation system, a single-game evaluation, and an evaluation of an athlete’s overall contribution to their team. The approach used to calculate the ON score is based on mixed-effects regression models that take into account the hierarchical structure of the data and a principal component analysis to calculate athlete rating. We apply our methodology to a large dataset of National Basketball Association (NBA) games spanning four seasons from 2015–2016 to 2018–2019. Our model is validated using two systematic approaches, and our results demonstrate the reliability of our approach to calculate an athlete’s performance. This provides coaches, General Managers and player agents with a powerful tool to gain deeper insights into their players’ performance, make more informed decisions and ultimately improve team performance. Our methodology has several key advantages. First, by incorporating the hierarchical structure of the data, we can obtain valuable information about an athlete’s contribution within their team. Second, the use of principal component analysis allows us to calculate a single score, the ON score, that captures the overall performance of an athlete. Third, our approach is based on classical restricted likelihood methods, which makes the calculation faster than Bayesian methods typically requiring 1000 posterior samples. With our approach, coaches and managers can evaluate athletes’ performance throughout the season, compare athletes and teams over a year, and assess an athlete’s performance during a single game. Our methodology can also complement other ratings and box score metrics to provide a more comprehensive assessment of an athlete’s performance as our method uses the hierarchical nature of performance data (i.e. player nested within team over season) which is typically ignored in player rating systems. In summary, our methodology represents a significant contribution to the field of sports analytics and provides the foundation for future developments.