Hungarian male water polo players' body composition can predict specific playing positions and highlight different nutritional needs for optimal sports performance.

BACKGROUND

Water polo is unique among aquatic-and generally other-sports as it includes cyclic elements typical in swimming and acyclic elements occurring mainly in ball games. Moreover, water polo demands high level of technical and tactical skills. Players need an optimal nutritional and physical condition to achieve high athletic performance, which is to a great extend influenced by nutritional habits. We aim to highlight possible shortfalls in players' nutritional intake in relation to positions played within the team.

METHODS

In the present study, we determined the anthropometric and body composition characteristics, dietary habits and laboratory parameters of elite adult male water polo players (n = 19) before the start of the championship and at the end of the regular season, which meant a 4-month intervention period. Analyses of body composition characteristics and nutritional habits were performed using bioimpedance analyzer InBody 770 and a 3-day nutrition diary, respectively. Paired-sample t-test were used to determine the differences between the variables measured before and after the championship. Correlations between the anthropometric and body composition characteristics and different serum parameters were analyzed using linear correlation calculation. K-mean cluster analysis was performed using the anthropometric and body composition characteristics of the athletes.

RESULTS

Based on anthropometric and body composition characteristics, players can be divided into two significantly different clusters that shows an association with specific playing positions. Cluster I included goalkeepers and wing players, while defenders, centers, and shooters belonged to Cluster II. We observed significant differences in the physical composition and slight but not significant differences in nutritional habits of the clusters. Cluster I players were 5 cm shorter on average, while their mean body weight, skeletal muscle mass and body fat mass data were lower by 19 kg, 7 kg, and 7 kg, respectively. We studied the correlation between initial anthropometric and body composition parameters and the changes in laboratory parameters before and after the regular season. As a result, we detected numerous significant differences between the two clusters, such as the changes in glucose and magnesium levels, which showed a strong correlation with several body composition parameters in cluster II, but did not in cluster I.

CONCLUSIONS

Cluster differences between anthropometric and body compositional characteristics, and the changes in laboratory parameters can help to develop position-specific training and nutritional recommendations in the future. Therefore, the results may be applicable in sport sciences for elite athletes and sports coaches.