Inside game effectiveness in NBA basketball

Courel-Ibáñez, Javier; McRobert, Allistair P.; Ortega-Toro, Enrique; Vélez, David Cárdenas

doi:10.26582/k.50.2.5

Cited by 13 publications

(11 citation statements)

References 30 publications

(47 reference statements)

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“…Previously, winning teams were reported to be more disciplined when performing collective tactical actions (i.e., teamwork) during both short and long attacks (i.e., increasing scoring opportunities) [31]. The current results can provide further basis for these group-tactical behaviours that lead to open 3-point shots during set plays and transitions (e.g., match pace variations and inside-outside passes), and easy scoring during fast breaks (e.g., quick actions after defensive rebounds) [32][33][34].…”

Section: Discussionmentioning

confidence: 77%

The Performance Evolution of Match Play Styles in the Spanish Professional Basketball League

et al. 2020

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The aim of this study is to analyse the performance evolution of all, and the dominant, team’s performances throughout an eight-season period within the Spanish professional basketball league. Match-related statistics were gathered from all regular season matches (n = 2426) played during the period 2009–2010 to 2016–2017. The non-metric multidimensional scaling model was used to examine the team’s profiles across seasons and for the most successful (playoff) teams. The main results showed that: 3-point field goals made (effect size, d = 0.61; 90% confidence interval, CI = 0.23; 1.37) and missed (d = 0.72; 90% CI = 0.35; 1.46), and assists (d = 1.27; 90% CI = 0.82; 1.86) presented a positive trend with an increased number of actions across the seasons; 2-point field goals made (d = 0.21; 90% CI = −1.25; 2.02) and missed (d = 0.27; 90% CI = −0.52; 0.92) were decreased; free throws made and missed, rebounds, fouls, blocks, steals and turnovers showed a relatively stable performance. The matrix solution (stress = 0.22, rmse (root mean squared error) = 7.9 × 104, maximum residual = 5.8 × 103) indicated minimal season-to-season evolution with the ordination plot and convex hulls overlapping. The two most dominant teams exhibited unique match patterns with the most successful team’s pattern, a potential benchmark for others who exhibited more dynamic evolutions (and less success). The current findings identified the different performances of teams within the Spanish professional basketball league over eight seasons with further statistical modelling of match play performances useful to identify temporal trends and support coaches with training and competition preparations.

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Section: Discussionmentioning

confidence: 77%

The Performance Evolution of Match Play Styles in the Spanish Professional Basketball League

et al. 2020

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“…Besides scoring points, they significantly reduce the opponent's chance to create a quick and favorable transition. A regained possession of the ball logically increases a shot opportunity for the offense [15,16,38].…”

Section: Most Efficient Ends Of the Ball Possessionmentioning

confidence: 99%

Prediction of Offensive Possession Ends in Elite Basketball Teams

Matulaitis

Bietkis

2021

IJERPH

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In basketball, the end of the ball possession has been described as one of the most important determinants of successful offensive play by a team. The present study aimed to: (i) investigate outcomes according to the play types of ends of the ball possession; (ii) find the most efficient ball possessions during the game; (iii) predict most efficient ends of the ball possession by time in an elite basketball competition. The sample was composed of 38,640 situations of ends of the ball possession from 240 games of the 2017–2018 regular season of the men’s Euroleague that were quantitatively analyzed. According to the results, the predictive model can be used in modern basketball. The most efficient ends of the ball possession are the 2-point field goals on the fast break (78.2%), cuts (64.8%), pick and roll (P&R) screener (61.5%), and transition and offensive rebound (57.4%) situations. This information allows a better collective understanding of basketball, and it could be a great tool to use for coaches to prove which tactical solutions are to be considered when improving offense and defense strategies. It also contributes to the design of precise practice tasks of the coach that improve the game.

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“…The collective behaviour of players for the inside pass events was analysed in [8], where it was shown that the interactions combining passer's previous actions (dribbling or faking) with receiver's cuts towards the basket achieved the highest offensive effectiveness. Performing screens in favour to the receiver was an effective alternative to increase inside passing options since it reduces the defensive [8]. The effect of space among players towards team performance is analysed in [26].…”

Section: Data Driven Performance Analysis In Basketballmentioning

confidence: 99%

“…In the past, researchers have predominantly used two types of data to analyse basketball game strategies, i.e., play-by-play data that describe different events that happen on court such as shots, passes, dribbles, and fouls and player and ball tracking data. For example, play-by-play data can be used to learn the effectiveness of different types of PNR plays [7], or the factors that influence the effectiveness of inside-passes [8]. Wu and Swartz [9] proposed corrections for substitution errors using logistic classification models based on play-by-play data obtained from the National Basketball Association (NBA), in the USA.…”

Section: Introductionmentioning

confidence: 99%

Use of Machine Learning to Automate the Identification of Basketball Strategies Using Whole Team Player Tracking Data

et al. 2019

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The use of machine learning to identify and classify offensive and defensive strategies in team sports through spatio-temporal tracking data has received significant interest recently in the literature and the global sport industry. This paper focuses on data-driven defensive strategy learning in basketball. Most research to date on basketball strategy learning has focused on offensive effectiveness and is based on the interaction between the on-ball player and principle on-ball defender, thereby ignoring the contribution of the remaining players. Furthermore, most sports analytical systems that provide play-by-play data is heavily biased towards offensive metrics such as passes, dribbles, and shots. The aim of the current study was to use machine learning to classify the different defensive strategies basketball players adopt when deviating from their initial defensive action. An analytical model was developed to recognise the one-on-one (matched) relationships of the players, which is utilised to automatically identify any change of defensive strategy. A classification model is developed based on a player and ball tracking dataset from National Basketball Association (NBA) game play to classify the adopted defensive strategy against pick-and-roll play. The methodology described is the first to analyse the defensive strategy of all in-game players (both on-ball players and off-ball players). The cross-validation results indicate that the proposed technique for automatic defensive strategy identification can achieve up to 69% accuracy of classification. Machine learning techniques, such as the one adopted here, have the potential to enable a deeper understanding of player decision making and defensive game strategies in basketball and other sports, by leveraging the player and ball tracking data.

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Inside game effectiveness in NBA basketball

Cited by 13 publications

References 30 publications

The Performance Evolution of Match Play Styles in the Spanish Professional Basketball League

The Performance Evolution of Match Play Styles in the Spanish Professional Basketball League

Prediction of Offensive Possession Ends in Elite Basketball Teams

Use of Machine Learning to Automate the Identification of Basketball Strategies Using Whole Team Player Tracking Data

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