The Effect of Regular-Season Rest on Playoff Performance Among Players in the National Basketball Association

Belk, John W.; Marshall, Hayden; McCarty, Eric C.; Kraeutler, Matthew J.

doi:10.1177/2325967117729798

Cited by 23 publications

(27 citation statements)

References 8 publications

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“…NBA teams rarely practice during the season and practices that occur are typically less than 1 h [ 1 , 2 ]. In response to teams resting players during back-to-back (two games within a 2-day span) games [ 8 ], the league extended the duration of the regular season by 7 days with the purpose of scheduling fewer back-to-back games [ 6 ]. During the 2017–2018 season, NBA teams played an average of 14.4 ± 0.9 back-to-back games, which was the lowest on record compared to any previous season in the NBA [ 2 ].…”

Section: National Basketball Association: Schedule and Travel Requmentioning

confidence: 99%

“…While coaches have presumed withdrawing high-minute players from entire games may reduce injury risk and enhance performance, a tactic which is often seen nearing the conclusion of the regular season, data to support this approach is lacking. In fact, existing data revealed the average minutes played per game did not influence on-court performance or injury risk ( p < 0.001) in 811 NBA players competing between 2000 and 2015 [ 8 , 9 ]. However, it should be noted these data are not reflective of performance and injury risk in players who were rested for entire games but rather are indicative of players completing reduced game minutes.…”

Section: Scheduling and Recovery Opportunitiesmentioning

confidence: 99%

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The Negative Influence of Air Travel on Health and Performance in the National Basketball Association: A Narrative Review

Huyghe¹,

Scanlan

Dalbo

et al. 2018

Sports

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Air travel requirements are a concern for National Basketball Association (NBA) coaches, players, and owners, as sport-based research has demonstrated short-haul flights (≤6 h) increase injury risk and impede performance. However, examination of the impact of air travel on player health and performance specifically in the NBA is scarce. Therefore, we conducted a narrative review of literature examining the influence of air travel on health and performance in team sport athletes with suggestions for future research directions in the NBA. Prominent empirical findings and practical recommendations are highlighted pertaining to sleep, nutrition, recovery, and scheduling strategies to alleviate the negative effects of air travel on health and performance in NBA players.

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Section: National Basketball Association: Schedule and Travel Requmentioning

confidence: 99%

Section: Scheduling and Recovery Opportunitiesmentioning

confidence: 99%

The Negative Influence of Air Travel on Health and Performance in the National Basketball Association: A Narrative Review

Huyghe¹,

Scanlan

Dalbo

et al. 2018

Sports

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“…Basketball studies that analyze trends based on the impact of congested fixtures on the players’ performance are scarce and the few studies available reports contradictions. 14,15 Moreover, Ibáñez et al. 16 noticed that the players’ performance variations resulting from different break periods were not congruent among all game-statistics.…”

Section: Introductionmentioning

confidence: 99%

Clustering performance in the European Basketball according to players’ characteristics and contextual variables

Mateus¹,

Esteves

Gonçalves³

et al. 2020

International Journal of Sports Science & Coaching

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The aim of this study was two-fold: (i) to identify differences in the game performance profiles of basketball players in Euroleague and European national championships; and (ii) to analyse the influence of player-related (e.g. age or court-position) and contextual variables (e.g. distance travelled or game break) in players’ game-related statistics. Archival data from 2014/2015 and 2015/2016 seasons ( n = 16,268 individual game performance records) of Euroleague and European national championships were separately analyzed using a two-step cluster model and then a discriminant analysis to differentiate the obtained groups. The clustering process allowed identifying three and five different performance player profiles for Euroleague and national championships, respectively. The discriminant analysis identified differences in Euroleague playing profiles, particularly related to performers’ age and court-position. In national championships, this trend was not observed. Regardless of the competition, a relationship was perceived between players’ performance and minutes played; conversely, the players’ performance seems to not be affected by contextual variables. The current results provide evidence of different players’ profiles in elite basketball to coaches regarding the influence of player-related and contextual variables. Coaching staffs may account for this information for better management and understanding of their team composition when playing in different competitions.

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“…Logistic regression (LR) represents the most primitive form of ML and has been frequently applied in the literature. 6,7 However, regression analysis is static and not predictive, meaning that it does not autoregulate to "learn" from complex data relationships, especially when more data inputs are added. This study represents the first foray, to our knowledge, in the sports medicine literature applying complex ML algorithms in which LR is compared against different ML algorithms.…”

mentioning

confidence: 99%

Machine Learning Outperforms Regression Analysis to Predict Next-Season Major League Baseball Player Injuries: Epidemiology and Validation of 13,982 Player-Years From Performance and Injury Profile Trends, 2000-2017

Karnuta

Luu

Haeberle

et al. 2020

Orthopaedic Journal of Sports Medicine

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Background: Machine learning (ML) allows for the development of a predictive algorithm capable of imbibing historical data on a Major League Baseball (MLB) player to accurately project the player's future availability. Purpose: To determine the validity of an ML model in predicting the next-season injury risk and anatomic injury location for both position players and pitchers in the MLB. Study Design: Descriptive epidemiology study. Methods: Using 4 online baseball databases, we compiled MLB player data, including age, performance metrics, and injury history. A total of 84 ML algorithms were developed. The output of each algorithm reported whether the player would sustain an injury the following season as well as the injury’s anatomic site. The area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 1931 position players and 1245 pitchers, with a mean follow-up of 4.40 years (13,982 player-years) between the years of 2000 and 2017. Injured players spent a total of 108,656 days on the disabled list, with a mean of 34.21 total days per player. The mean AUC for predicting next-season injuries was 0.76 among position players and 0.65 among pitchers using the top 3 ensemble classification. Back injuries had the highest AUC among both position players and pitchers, at 0.73. Advanced ML models outperformed logistic regression in 13 of 14 cases. Conclusion: Advanced ML models generally outperformed logistic regression and demonstrated fair capability in predicting publicly reportable next-season injuries, including the anatomic region for position players, although not for pitchers.

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The Effect of Regular-Season Rest on Playoff Performance Among Players in the National Basketball Association

Cited by 23 publications

References 8 publications

The Negative Influence of Air Travel on Health and Performance in the National Basketball Association: A Narrative Review

The Negative Influence of Air Travel on Health and Performance in the National Basketball Association: A Narrative Review

Clustering performance in the European Basketball according to players’ characteristics and contextual variables

Machine Learning Outperforms Regression Analysis to Predict Next-Season Major League Baseball Player Injuries: Epidemiology and Validation of 13,982 Player-Years From Performance and Injury Profile Trends, 2000-2017

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