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The national basketball association
The National Basketball Association (NBA) is widely recognized as an entertaining and innovative league whose teams play regular season and postseason games in packed arenas at home and away sites in the United States and Canada. This book discusses the development, growth, and success of the 61-year-old NBA from a business perspective. Covering the late 1940s to 2009, it focuses on the league's expansions and mergers, team territories and relocations, franchise organizations and operations, basketball arenas and markets, and NBA domestic and international affairs. Readers will gain an insight into when, how, and why the NBA emerged, reformed, and gradually matured to become one of the world's most dominant, prosperous, and popular professional sports organizations today.
eBook
Measuring players’ importance in basketball using the generalized Shapley value
Measuring players’ importance in team sports to help coaches and staff with the aim of winning the game is gaining relevance, mainly because of the advent of new data and advanced technologies. In this paper we evaluate each player’s importance - for the first time in basketball - as his/her average marginal contribution to the utility of an ordered subset of players, through a generalized version of the Shapley value, where the value assumed by the generalized characteristic function of the generalized coalitional game is expressed in terms of the probability a certain lineup has to win the game. In turn, such probability is estimated by applying a logistic regression model in which the response is represented by the game outcome and the Dean’s factors are used as explanatory features. Then, we estimate the generalized Shapley values of the players, with associated bootstrap confidence intervals. A novelty, allowed by explicitly considering single lineups, is represented by the possibility of forming best lineups based on players’ estimated generalized Shapley values conditional on specific constraints, such as an injury or an “a-priori” coach’s decision. A comparison of our proposed approach with industry-standard counterparts shows a strong linear relation. We show the application of our proposed method to seventeen full NBA seasons (from 2004/2005 to 2020/21). We eventually estimate generalized Shapley values for Utah Jazz players and we show how our method is allowed to be used to form best lineups.
Journal Article
Basketball (and other things) : a collection of questions asked, answered, illustrated
\"Thirty-three chapters, each chapter ... a different basketball question that needs to be answered. Some of them are obviously crucial ... and some of them are secretly crucial. But all of them are approached in ways that ([the author] hope[s] you think) are smart and fun and nuanced\"--Back cover.
Book
Hybrid Basketball Game Outcome Prediction Model by Integrating Data Mining Methods for the National Basketball Association
The sports market has grown rapidly over the last several decades. Sports outcomes prediction is an attractive sports analytic challenge as it provides useful information for operations in the sports market. In this study, a hybrid basketball game outcomes prediction scheme is developed for predicting the final score of the National Basketball Association (NBA) games by integrating five data mining techniques, including extreme learning machine, multivariate adaptive regression splines, k-nearest neighbors, eXtreme gradient boosting (XGBoost), and stochastic gradient boosting. Designed features are generated by merging different game-lags information from fundamental basketball statistics and used in the proposed scheme. This study collected data from all the games of the NBA 2018–2019 seasons. There are 30 teams in the NBA and each team play 82 games per season. A total of 2460 NBA game data points were collected. Empirical results illustrated that the proposed hybrid basketball game prediction scheme achieves high prediction performance and identifies suitable game-lag information and relevant game features (statistics). Our findings suggested that a two-stage XGBoost model using four pieces of game-lags information achieves the best prediction performance among all competing models. The six designed features, including averaged defensive rebounds, averaged two-point field goal percentage, averaged free throw percentage, averaged offensive rebounds, averaged assists, and averaged three-point field goal attempts, from four game-lags have a greater effect on the prediction of final scores of NBA games than other game-lags. The findings of this study provide relevant insights and guidance for other team or individual sports outcomes prediction research.
Journal Article
Characterization of Ankle Injuries and Associated Risk Factors in the National Basketball Association: Minutes Per Game and Usage Rate Associated With Time Loss
Background:
Ankle injuries are more common in the National Basketball Association (NBA) compared with other professional sports.
Purpose/Hypothesis:
The purpose of this study was to report the incidence and associated risk factors of ankle injuries in NBA athletes. It was hypothesized that factors associated with an increased physiologic burden, such as minutes per game (MPG), usage rate, and associated lower extremity injury, would be associated with increased ankle injury risk and time loss.
Study Design:
Descriptive epidemiology study.
Methods:
Ankle injury data from the 2015-2016 through 2020-2021 NBA seasons were evaluated. The truncated 2019-2020 season due to the COVID-19 pandemic was omitted. The primary outcome was the incidence of ankle injuries, reported per 1000 game-exposures (GEs). Secondary analysis was performed to identify risk factors for ankle injuries through bivariate analysis and multivariable logistic regression of player demographic characteristics, performance statistics, injury characteristics, and previous lower extremity injuries. Factors influencing the time loss after injury were assessed via a negative binomial regression analysis.
Results:
A total of 554 ankle injuries (4.06 injuries per 1000 GEs) were sustained by NBA players over 5 NBA seasons, with sprain/strain the most common injury type (3.71 injuries per 1000 GEs). The majority of ankle injury events (55%) resulted in 2 to 10 game absences. The likelihood of sustaining an ankle injury was significantly associated with a greater number of games played (P = .029) and previous injury to the hip, hamstring, or quadriceps (P = .004). Increased length of absence due to ankle injury was associated with greater height (P = .019), MPG (P < .001), usage rate (P = .025), points per game (P = .011), and a prior history of foot (P = .003), ankle (P < .001), and knee injuries (P < .001).
Conclusion:
The incidence of ankle injuries was 4.06 per 1000 GEs in professional basketball players. Games played and prior history of hip, hamstring, or quadriceps injuries were found to be risk factors for ankle injuries. Factors associated with physiologic burden such as MPG and usage rate were associated with an increased time loss after injury.
Journal Article
Basketball now! : the stars and stories of the NBA
\"The greatest players in the NBA... NOW! Like Hockey Now! and Football Now!, Basketball Now! has earned its place as an anticipated release, giving fans the inside stories about their favorite superstars. This third edition is packed with 130 action images and 50 profiles, including a fresh crop of young players whose swagger and skills launched them to league-wide stardom. Bona fide superstars, rim-rocking rookies and future Hall of Famers, plus the all stars of tomorrow, the best international imports and the underrated players that can change a game -- they're all here. Look out for elite names like Stephen Curry, Kevin Durant, Giannis Antetokounmpo, James Harden, LeBron James, Kawhi Leonard and many, many more! Author Adam Elliott Segal gives readers an inside tour of all things NBA, including essays on the Draft, the Dunk Contest and the best clutch and playoff performances in the history of the league, as well as a summary of MVPs (regular season, All-Star Game and Finals) up to the end of the 2018-19 season. Mind-boggling athleticism, career-changing plays and pure magic -- Basketball Now! has it all, straight from the hardwood.\"-- Provided by publisher.
Book
\Hot hand\ in the National Basketball Association point spread betting market: A 34-year analysis
Several articles have looked at factors that affect the adjustments of point spreads, based on hot hands or streaks, for smaller durations of time. This study examines these effects for 34 regular seasons in the National Basketball Association (NBA). Estimating a Seemingly Unrelated Regression model using all 34 seasons, all streaks significantly impacted point spreads and difference in actual points. When estimating each season individually, differences emerged particularly examining winning and losing streaks of six games or more. The results indicate both the presence of momentum effects and the gambler's fallacy.
Journal Article