Can physical performance predict future success in football? : predictions of future playing level

Abstract

The study objectives were to follow up on participants from Oddsson et al. (2024) and: (i) analyze the participants' current playing level and identify whether anthropometry, physical fitness, and football skills assessed at the age of 15 distinguish between playing levels three to four years later. (ii) Determine if these factors can predict dropout or playing level within the same period. Current playing levels of 826 participants were analyzed, using test results from 2020-2021 for anthropometry, physical fitness, and football skills. Statistical analysis involved Kruskal-Wallis, one-way ANOVA, and binary logistic regression. Differences were found between playing levels for both genders with top-two levels outperforming lower levels or retired in: Males: 30 m sprint (F=3.624,p=0.006), 5x30 m RSA (χ²=35.915,p<0.001), IAG (χ²=50.017,p<0.001), CMJ (F=5.466,p<0.001), YoYo IE2 (F=21.795,p<0.001), and kicking velocity with both legs (F=15.751,p<0.001) and (F=18.371,p<0.001). Females: 10 m (F=4.095,p=0.003) and 30 m sprints (F=5.777,p<0.001), 5x30 m RSA (F=8.418,p<0.001), IAG (F=11.755,p<0.001), CMJ (F=6.785,p<0.001), AJ (F=2.858,p=0.025) and YoYo IE2 (F=19.587,p<0.001). Predictors of continued play included YoYo IE2 performance (p<0.001) and non-dominant leg kicking velocity (p=0.002) for males, explaining 18.1% of variance. For females, YoYo IE2 (p=0.036) predicted continued play, with a model explanation of 15.9%. To play at the top-level, dominant leg kicking velocity (p=0.014) and YoYo IE2 (p=0.038) were predictors for males, explaining 23.4%. For females, 10 m sprint (p=0.022) and YoYo IE2 (p=0.027) predicted top-level play, explaining 41.5% of the variance. Findings demonstrate that physical fitness influences future playing level and YoYo IE2 significantly predicts both playing level and dropout.

Markmið rannsóknarinnar voru að fylgja eftir þátttakendum úr Oddsson et al. (2024) og: (i) greina núverandi getustig einstaklings og ákvarða hvort líkamlegt atgervi, líkamshreysti og tæknilegir þættir sem mældir voru á 15.aldursári geti skorið úr um getustig einstaklings þremur til fjórum árum síðar, (ii) kanna hvort líkamlegt atgervi, líkamshreysti og tæknilegir þættir geti spáð fyrir um brottfall einstaklings eða á hvaða getustigi hann spilar þremur til fjórum árum síðar. Núverandi getustig 826 þátttakenda var greint og boðrið saman við frammistöðumælingar á líkamlegu atgervi,líkamshreysti og tæknilegum þáttum frá 2020-2021. Kruskal-Wallis, one-way ANOVA og binary logistic regression voru notuð. Hjá báðum kynjum skoruðu leikmenn á tveimur efstu getustigunum betur en hættir eða leikmenn á lægri getustigum: Karlar: 30 m sprettur (F=3.624,p=0.006), 5x30 m hraðaþol (χ²=35.915,p<0.001), IAG (χ²=50.017,p<0.001), CMJ (F=5.466,p<0.001), YoYo IE2 (F=21.795,p<0.001) og spyrnukraftur með báðum fótum (F=15.751,p<0.001)og(F=18.371,p<0.001). Konur: 10 m (F=4.095,p=0.003) and 30 m sprettir (F=5.777,p<0.001), 5x30m hraðaþol (F=8.418,p<0.001), IAG (F=11.755,p<0.001), CMJ (F=6.785,p<0.001), AJ(F=2.858,p=0.025) og YoYo IE2 (F=19.587,p<0.001). Forspárgildi áframhaldandi iðkunar karla voru frammistaða í YoYo IE2 (p<0.001) og spyrnukraftur með víkjandi fæti (p=0.002), módelið útskýrir 18.1% breytileikans en YoYo IE2 (p=0.036) hjá konum, útskýrir 15.9% breytileikans. Forspárgildi um hvort leikmaður spilar á efstu tveimur getustigum voru spyrnukraftur með ríkjandi fæti (p=0.014) og YoYo IE2 (p=0.038) hjá körlum, útskýrir 23.4% breytileikans, en 10 m sprettur(p=0.022) og YoYo IE2 (p=0.027) hjá konum, útskýrir 41.5% breytileikans. Frammistaða í líkamlegum mælingum hefur áhrif á framtíðar getustig og YoYo IE2 getur spáð fyrir um getustig og brottfall.