Kinesiology, Vol. 54 No. 1, 2022.
Original scientific paper
https://doi.org/10.26582/k.54.1.12
Women's beach handball game statistics: differences and predictive power for winning and losing teams
Sveinn Þorgeirsson
orcid.org/0000-0001-5047-7147
; 1Physical Activity, Physical Education, Sport and Health Research Centre, Sports Science Department, School of Social Sciences, Reykjavik University, Iceland ; Faculty of Kinesiology, University of Split, Split, Croatia
Demetrio Lozano
; Universidad San Jorge, Zaragoza, Spain
Juan Carlos Zapardiel
orcid.org/0000-0002-1835-2085
; Biomedic Sciences Department, University of Alcalá, Alcalá de Henares, Spain
Francisco Jimenez
; Group of Research and Teaching Innovation in Physical Activity and Sport,
Damir Sekulić
; Faculty of Kinesiology, University of Split, Split, Croatia
Jose M. Saavedra
; Physical Activity, Physical Education, Sport and Health Research Centre, Sports Science Department, School of Social Sciences, Reykjavik University, Iceland
Abstract
The objectives of the present study were: (i) to compare beach handball game-related statistics by match outcome (winning and losing teams), and (ii) to identify characteristics that discriminate performances in the match. The game-related statistics of the 72 women’s matches played in the VIII Women’s Beach Handball World Championship (2018) were analysed. The game-related statistics were taken from the official Web page. A validation of the data showed their reliability to be very good (the inter-observer mean reliability was α=0.82 and the intra-observer mean was α=0.86). For the differences between winning/losing teams a parametric (unpaired t-test) or non-parametric (Mann-Whitney U test) test was applied depending on whether the variable met or did not meet normality, respectively. A stepwise discriminant analysis was then performed to determine the variables that predicted performance (victory or defeat). Five variables showed differences between the winning and losing teams: total points (p<.001; ES=1.09), technical faults (p<.001; ES=‑0.96), the number of players with either negative (p<.001; ES=‑0.86) or positive (p<.001; ES=1.05) valuations and overall valuation (p<.001; ES=1.29). The predictive model correctly classified 80.6% of the matches using two variables (Wilks’s λ=0.618; canonical correlation index=0.618): overall valuation and GK shots.
Keywords
performance; goal; goalkeeper; shot; block
Hrčak ID:
278682
URI
Publication date:
30.6.2022.
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