LAUSR

Aim of this study was to determine variables of elite athletes’ competition seasons in vertical jumping events and to determine relationships between these variables. Also, to constitute prediction models of season best (SB) and season performance average based on season first performance to offer a new insight for coaching education. Research group consisted of male and female elite athletes who ranked in the top 100 in high jump (HJ) and pole vault (PV), during 2018 season. Athletes’ competition information was reached from 2018 world rankings. Ages, total number of days in season, days between competitions, total number of competitions, number of competitions that season’s best score was performed, ratio of SB to total number of competitions, percentages of first, end, average scores were calculated. Statistical comparison of gender groups was analyzed using Independent Samples t-Test. Pearson correlation coefficients were used to express relationships. Polynomial regression analysis was used to find coefficients of determination for relationships. Quadratic equations were calculated to predictive SB performance and season average performances according average of first two performances by gender/events. In PV, there were differences between genders for season first, end, and average percentages calculated according to SB (p < 0.05). Strong relationships were determined between season average and season first performances in female athletes. Predicted models created according to season first performance may be considered as early evaluations for coaches. Coach can use these prediction models as a new and different education material for their training plans. By doing so, in case of calculating a prediction far away from the coach’s aim it is possible that coach can take necessary measures at the beginning of season.