Gender-based differences in game-related statistics between winning and losing teams in an amateur handball league

Sveinn Þorgeirsson, Miguel Pic, Demetrio Lozano, Olafur Sigurgeirsson, Damir Sekulic, Jose M. Saavedra

Gender-based differences in game-related statistics between winning and losing teams in an amateur handball league

Číslo: 1/2022
Periodikum: Acta Gymnica
DOI: 10.5507/ag.2022.001

Klíčová slova: performance analysis, notational analysis, shots

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Anotace: Background: The literature on performance analysis in team handball has increased at the top level, but there has been far less research published at the amateur level.

Objective: The objectives of the present study were: (i) to compare handball game-related statistics by match result (winning and losing teams) for the men's and women's teams in an amateur league, (ii) to compare handball game-related statistics by gender, and (iii) to identify characteristics that discriminated performance in amateur men and women handball leagues.

Methods: The game-related statistics of the 190 matches (113 men, 77 women) played in the 2018/19 Icelandic League by 12 men and 8 women teams were analysed. Their intra- and inter-observer internal consistency and reliability were at levels considered to be good or very good for the games of both genders. Differences in the game statistics between match outcomes (winning or losing teams for each gender) and between the genders were determined using the unpaired t-test or Mann-Whitney U test, and the corresponding effect sizes were calculated.

Results: Large differences between the winning and losing teams were shown by shots, goalkeeper blocked shots, and 9 m shots for men, and by shots, goalkeeper blocked shots and 7 m shots for women. In the comparison between the genders, there were four variables that showed a moderate effect size (Cohen's d > 0.50). A discriminant analysis applying the sample-splitting method was performed for each gender to determine the game statistics that best characterized the match outcomes. The resulting predictive models correctly classified 84% of the matches using five variables for men and 87% of the matches using two variables for women.

Conclusions: The results could be used to better understand the structure of the game in amateur leagues, and to improve the performance of teams.