title: Exploring Game Performance in the National Basketball Association Using Player Tracking Data.
abstract: Recent player tracking technology provides new information about basketball game performance. The aim of this study was to (i) compare the game performances of all-star and non all-star basketball players from the National Basketball Association (NBA), and (ii) describe the different basketball game performance profiles based on the different game roles. Archival data were obtained from all 2013-2014 regular season games (n = 1230). The variables analyzed included the points per game, minutes played and the game actions recorded by the player tracking system. To accomplish the first aim, the performance per minute of play was analyzed using a descriptive discriminant analysis to identify which variables best predict the all-star and non all-star playing categories. The all-star players showed slower velocities in defense and performed better in elbow touches, defensive rebounds, close touches, close points and pull-up points, possibly due to optimized attention processes that are key for perceiving the required appropriate environmental information. The second aim was addressed using a k-means cluster analysis, with the aim of creating maximal different performance profile groupings. Afterwards, a descriptive discriminant analysis identified which variables best predict the different playing clusters. The results identified different playing profile of performers, particularly related to the game roles of scoring, passing, defensive and all-round game behavior. Coaching staffs may apply this information to different players, while accounting for individual differences and functional variability, to optimize practice planning and, consequently, the game performances of individuals and teams.
authors.affiliation: Research Center in Sports Sciences, Health and Human Development, CIDESD, CreativeLab Research Community, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal.
authors.forename: Jaime
authors.initials: J
authors.lastname: Sampaio
authors.affiliation: Faculty of Kinesiology, University of New Brunswick, Fredericton, Canada.
authors.forename: Tim
authors.initials: T
authors.lastname: McGarry
authors.affiliation: Faculty of Physical Activity Sport Sciences, University of the Basque Country, Vitoria, Spain.
authors.forename: Julio
authors.initials: J
authors.lastname: Calleja-González
authors.affiliation: Facultad de Ciencias de la Actividad Física y el Deporte, Universidad Europea de Madrid, Madrid, Spain.
authors.forename: Sergio
authors.initials: S
authors.lastname: Jiménez Sáiz
authors.affiliation: Complex Systems and Sport Research Group, National Institute of Physical Education of Catalonia (INEFC), Barcelona, Spain.
authors.forename: Xavi
authors.initials: X
authors.lastname: Schelling I Del Alcázar
authors.affiliation: Lithuanian Sports University, Kaunas, Lithuania.
authors.forename: Mindaugas
authors.initials: M
authors.lastname: Balciunas
mesh_terms: D054874:Athletic Performance / Q000706:statistics & numerical data*; D001490:Basketball / Q000706:statistics & numerical data*; D006801:Humans; D013223:Statistics as Topic / Q000379:methods*