Carlos Francisco Touriño González

Offensive Performance Indicators in a Regular Season of Water-Polo

The purpose of the present study was to identify offensive performance indicators that may discriminate by match score (favourable, balanced or unfavourable). The sample comprised 88 regular season games (2011-2014) from the Spanish Professional Water-Polo League. Univariate (ANOVA test; Kruskal-Wallis test and Generalized Linear Model test (GLM)) and multivariate (Discriminant) analysis to compare favourable, balanced or unfavourable and confidence intervals and effect sizes of the differences were calculated. The results showed that favourable games had averages that were significantly higher for counterattack attacks (p<0.001) and shots (p<0.001), goals (p<0.001), and shots from zones close to goal (zone 5 (p<0.01) and 6 (p<0.01)). Whereas unfavourable games had significantly higher averages in even attacks (p<0.001) and shots (p<0.001), no goals shots (p<0.001), and shots originated from zones far from goal (zone 2 (p<0.05) and 4 (p<0.01)). The variables that most discriminated were the goals, counterattack attacks and shots; shots originated from areas close to goal. These results could help coaches when planning training and competition.

Performance Assessment in Water Polo Using Compositional Data Analysis

Abstract The aim of the present study was to identify groups of offensive performance indicators which best discriminated between a match score (favourable, balanced or unfavourable) in water polo. The sample comprised 88 regular season games (2011-2014) from the Spanish Professional Water Polo League. The offensive performance indicators were clustered in five groups: Attacks in relation to the different playing situations; Shots in relation to the different playing situations; Attacks outcome; Origin of shots; Technical execution of shots. The variables of each group had a constant sum which equalled 100%. The data were compositional data, therefore the variables were changed by means of the additive log-ratio (alr) transformation. Multivariate discriminant analyses to compare the match scores were calculated using the transformed variables. With regard to the percentage of right classification, the results showed the group that discriminated the most between the match scores was “Attacks outcome” (60.4% for the original sample and 52.2% for cross-validation). The performance indicators that discriminated the most between the match scores in games with penalties were goals (structure coefficient (SC) = .761), counterattack shots (SC = .541) and counterattacks (SC = .481). In matches without penalties, goals were the primary discriminating factor (SC = .576). This approach provides a new tool to compare the importance of the offensive performance groups and their effect on the match score discrimination.

Keys to Success in High Level Water Polo Teams

The aim of this study was to identify specific offensive performance indicators that distinguish the top clubs from the others in the Male Honour Division of the Spanish Water Polo League. A total of 88 matches from the 2011-2014 seasons have been analysed. The offensive performance variables were divided into four groups, namely, playing situations, attacks outcome, origin of shots, and technical execution of shots. Univariate analyses (ANOVA, Kruskal-Wallis and generalised linear model (GLM) tests) were conducted to identify differences in the offensive variables between “strong”, “medium” or “weak” teams’ performances. A multinomial logistic regression was carried out to determine the variables that best distinguish the team level. The offensive variables that best classified the team level (61.9% of correct classification rate) were counterattack shots (p<0.010), goals (p<0.011), no goal shots (p<0.013), drive shots (p<0.020), penalties (p<0.000), and shots originated from zones close to goal (zone 5 (p<0.010) and 6 (p<0.000)). As a conclusion, this paper presents values of reference for the offensive variables that best distinguish between the strong, medium and weak teams’ performances. This information can help coaches to evaluate their teams and to design training sessions aimed at improving their weakest skills.