Effects of coaches' timeouts on basketball teams' offensive and defensive performances according to momentary differences in score and game period.

The aim of the present study was to assess the effects of game timeouts on basketball teams? offensive and defensive performances according to momentary differences in score and game period. The sample consisted of 144 timeouts registered during 18 basketball games randomly selected from the 2007 European Basketball Championship (Spain). For each timeout, five ball possessions were registered before (n?493) and after the timeout (n?475). The offensive and defensive efficiencies were registered across the first 35 min and last 5 min of games. A k-means cluster analysis classified the timeouts according to momentary score status as follows: losing ( ?10 to ?3 points), balanced ( ?2 to 3 points), and winning (4 to 10 points). Repeated-measures analysis of variance identified statistically significant main effects between pre and post timeout offensive and defensive values. Chi-square analysis of game period identified a higher percentage of timeouts called during the last 5 min of a game compared with the first 35 min (64.999.1% vs. 35.1910.3%; x ?5.4, PB0.05). Results showed higher post timeout offensive and defensive performances. No other effect or interaction was found for defensive performances. Offensive performances were better in the last 5 min of games, with the least differences when in balanced situations and greater differences when in winning situations. Results also showed one interaction between timeouts and momentary differences in score, with increased values when in losing and balanced situations but decreased values when in winning situations. Overall, the results suggest that coaches should examine offensive and defensive performances according to game period and differences in score when considering whether to call a timeout.

Performance profiles of football teams in the UEFA Champions League considering situational efficiency

Performance of football teams varies constantly due to the dynamic nature of this sport, whilst the typical performance and its spread can be represented by profiles combining different performance-related variables based on data from multiple matches. The current study aims to use a profiling technique to evaluate and compare match performance of football teams in the UEFA Champions League incorporating three situational variables (i.e. strength of team and opponent, match outcome and match location). Match statistics of 72 teams, 496 games across four seasons (2008-09 to 2012-13) of this competition were analysed. Sixteen performance-related events were included: shots, shots on target, shots from open play, shots from set piece, shots from counter attack, passes, pass accuracy (%), crosses, through balls, corners, dribbles, possession, aerial success (%), fouls, tackles, and yellow cards. Teams were classified into three levels of strength by a k-cluster analysis. Profiles of overall performance and profiles incorporating three situational variables for teams of all three levels of strength were set up by presenting the mean, standard deviation, median, lower and upper quartiles of the counts of each event to represent their typical performances and spreads. Means were compared by using one-way ANOVA and independent sample t test (for match location, home and away differences), and were plotted into the same radar charts after unifying all the event counts by standardised score. Established profiles can present straightforwardly typical performances of football teams of different levels playing in different situations, which could provide detailed references for coaches and analysts to evaluate performances of upcoming opposition and of their own.