The Impact of Diverse Predictors on Show Jumping Competition Outcomes

Even though its role in the development of equestrian sports has been confirmed, a small number of authors have conducted a thematic analyses of the interaction between errors and sports performance in the show jumping discipline. This may indicate that performance analysis is still insufficiently emphasized in equestrian sports, particularly in relation to sports performance. The aim of this study was to identify types of errors as predictors of total penalty points in a competition and to determine their predictive capacity as components of performance. A total of 7,285 jumping actions were analyzed during the “Balkan Championship 2022” across 512 starts. The identified errors (independent variables – predictors) included: obstacle knockdown (OP), stepping into water (SV), first refusal (PN), second refusal (DN), first closed circle (PZK), second closed circle (DZK), fall of the rider and/or horse (PJ/K), exceeding the allowed time (PDV), exceeding the maximum allowed time (PMDV), and jumping the wrong obstacle (PSP). Their impact was assessed on the total penalty points (UKP) (dependent variable). The data obtained were processed using the SPSS 19 statistical package. Descriptive statistical parameters were calculated, followed by Pearson’s correlation analysis to determine the strength and direction of existing correlations. Multiple regression analysis was then applied to assess the influence of the independent variables (errors) on the dependent variable (UKP). The results of this model indicate that the cumulative effect of the predictors significantly (Sig=0.000ª) explains 88% of the total variance in UKP. With statistical significance confirmed for all independent variables (Sig≤0.027), their individual positive correlations with the dependent variable ranged from r=0.85 to r=0.774. Individually, all predictors exerted a differentiated yet statistically significant influence, with statistical significance (Sig=0.000), with effects ranging from minimal [PDV (t=3.708; Sig=0.000; β=0.063; 0.34%)] to maximal [DN (t=19.889; Sig=0.000; β=0.424; 9.54%)]. It can be concluded that all predictors, when viewed individually, are significant, while they simultaneously exert a cumulative significant influence on UKP. Furthermore, each predictor individually exerts a statistically significant predictive effect on UKP. This analysis provides valuable data for all stakeholders and may support the development and implementation of more effective competition strategies aimed at improving performance.