Statistical analyses of basketball team performance: understanding teams’ wins and losses according to a different index of ball possessions

By Jaime Sampaio, M. Janeira. 2003

219 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Nature and Landscape Conservation, Orthopedics and Sports Medicine

View Paper: https://www.semanticscholar.org/paper/39382bfee89e9544bea8aae5ee8dd07b90264582

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Nature and Landscape Conservation

In Orthopedics and Sports Medicine

Citations in the corpus

Effects of season period, team quality, and playing time on basketball players' game-related statistics (2010, background): “…players’ somatotype, as taller and stronger players secure more rebounds; (3) technical and tactical preparation, pivoting, blocking, anticipation, securing, and pulling the ball away; and (4) muscular fitness, particularly in stretch shortening cycle jumping performances (Sampaio & Janeira, 2003).”
Discriminant analysis of game-related statistics between basketball guards, forwards and centres in three professional leagues (2006, background): “…research on this subject has only addressed the identification of the most discriminant statistics between winning and losing teams (e.g., Akers, Wolff, & Buttross, 1991; Ibáñez, Sampaio, Sáenz-López, Giménez, & Janeira, 2003; Sampaio & Janeira, 2003; Trninić, Milanović, & Dizdar, 1997).”
Effects of starting score-line, game location, and quality of opposition in basketball quarter score (2010, background): “In the balanced game quarters, the results were unpredictable as already discussed in earlier literature (Sampaio & Janeira, 2003), followed by a weak effect of quality of the opponent in the second and third quarters.”
Basketball game-related statistics that discriminate between teams’ season-long success (2008, methodology): “To control for game rhythm, all variables were then normalized according to game ball possessions and multiplied by 100 (Kubatko, Oliver, Pelton, & Rosenbaum, 2007; Oliver, 2004; Sampaio & Janeira, 2003).”