2013
DOI: 10.2478/hukin-2013-0016
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Identifying Basketball Performance Indicators in Regular Season and Playoff Games

Abstract: The aim of the present study was to identify basketball game performance indicators which best discriminate winners and losers in regular season and playoffs. The sample used was composed by 323 games of ACB Spanish Basketball League from the regular season (n=306) and from the playoffs (n=17). A previous cluster analysis allowed splitting the sample in balanced (equal or below 12 points), unbalanced (between 13 and 28 points) and very unbalanced games (above 28 points). A discriminant analysis was used to ide… Show more

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Cited by 124 publications
(152 citation statements)
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“…All games were first classified into three types (balanced, unbalanced and very unbalanced) according to point differential by a k-means cluster analysis 7,14,15 . The classification is shown in Table 1.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…All games were first classified into three types (balanced, unbalanced and very unbalanced) according to point differential by a k-means cluster analysis 7,14,15 . The classification is shown in Table 1.…”
Section: Discussionmentioning
confidence: 99%
“…The classification is shown in Table 1. After the classification, very unbalanced games were eliminated from further analyses 14 . This was because many minutes of very unbalanced games would be "garbage time", and game-related statistics from those minutes have little value for analysis.…”
Section: Discussionmentioning
confidence: 99%
“…The games were first classified into three types (balanced, unbalanced and very unbalanced) according to point differential by a k-means cluster analysis (5,11,14,15). The difference in the proportion of game types between the two regions was analyzed by a chi-square test.…”
Section: Discussionmentioning
confidence: 99%
“…In the literature consulted, game indicators research through notational analysis technique has been used to identify the factors that differentiate winning teams from the losing teams 1,6,7 . Currently, high numbers of field goals made (FGM) [6][7][8][9] , free throws made 1,7 , defensive rebounds 1,8 , and assists 6,7 have been pointed out as crucial factors to ensure winning in basketball. However, because game indicators represent basketball athletes' performance in a fragmented manner, sport scientists have sought methods of data collection and analysis that contextualize game indicators and enable a broader interpretation amongst the actions present in the game 2,10 .…”
Section: Introductionmentioning
confidence: 99%