Skoči na glavni sadržaj

Kineziologija, Vol. 50. No. 1., 2018.

Izvorni znanstveni članak

https://doi.org/10.26582/k.50.1.7

Exploring game performance in nba playoffs

Nuno Mateus orcid id orcid.org/0000-0001-7275-9161 ; Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, CreativeLab Research Community, Portugal
Bruno Gonçalves orcid id orcid.org/0000-0001-7874-4104 ; Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, CreativeLab Research Community, Portugal
Eduardo Abade ; Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, CreativeLab Research Community, University Institute of Maia, ISMAI, Portugal
Nuno Leite orcid id orcid.org/0000-0001-5181-6390 ; Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, CreativeLab Research Community, Portugal
Miguel Angel Gomez orcid id orcid.org/0000-0002-9585-3158 ; Faculty of Physical Activity and Sport Sciences, Polytechnic University of Madrid, Spain
Jaime Sampaio ; Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, CreativeLab Research Community, Portugal

Puni tekst: engleski pdf 811 Kb

str. 89-96

preuzimanja: 1.489

citiraj

Sažetak

This study aimed to identify performance profiles in NBA playoffs and describe performances in the first and the last games of the series. Thirty games from the official box-scores and player-tracking data of the 2014/15 NBA playoffs were considered. A k-means cluster analysis was performed to group the players according to their game performance profiles and a discriminant analysis was conducted to identify the game-related statistics that best discriminated the groups. The first function correctly classified 64.2% of the cases and the second function classified 29.9% of the cases. The cluster analysis identified four different performance profiles and from the discriminant analysis emerged several offensive and defensive variables to classify the players in the clusters. The identified trends help to improve understanding of the game during different stages of playoffs. Coaches may use this information to better prepare their teams for different game scenarios.

Ključne riječi

game statistics; players profile; discriminant analysis

Hrčak ID:

192569

URI

https://hrcak.srce.hr/192569

Datum izdavanja:

21.6.2018.

Posjeta: 3.296 *