Abstract
This study aimed to investigate the changes in internal training intensity, well-being, and countermovement jump (CMJ) performance and to determine their relationship across five weeks of the pre-season training phase in professional soccer players. A total of 22 professional male soccer players (age = 21.7 ± 4 years, body height = 185.9 ± 6.3 cm, body weight = 79 ± 6.3 kg, BMI = 22.8 ± 1.4 kg·m−2; VO2max = 52.9 ± 3.2) from the Croatian Second League voluntary participated in this study. The players spent 2230 ± 117 min in 32 technical/tactical and strength/conditioning training sessions, mostly at the low intensity zone (61%), and played 8 friendly matches at a high intensity (>90%). A one-way repeated measure of analysis ANOVA revealed a significant difference between weeks in CMJ performance (F(1,22) = 11.8, p < 0.001), with CMJ height in weeks 4 and 5 being likely to very likely higher than that noted in week 1. Moreover, significant differences between weeks were found in all internal training intensity measures (average [F(1,22) = 74.8, p < 0.001] and accumulated weekly internal training intensity [F(1,22) = 55.4, p < 0.001], training monotony [F(1,22) = 23.9, p < 0.001], and training strain [F(1,22) = 34.5, p < 0.001]). Likewise, differences were observed for wellness status categories (fatigue [F(1,22) = 4.3, p = 0.003], sleep [F(1,22) = 7.1, p < 0.001], DOMS [F(1,22) = 5.7, p < 0.001], stress [F(1,22) = 15.6, p < 0.001]), mood [F(1,22) = 12.7, p < 0.001], and overall well-being status score (F(1,22) = 13.2, p < 0.001). Correlation analysis showed large negative correlations between average weekly internal training intensity and fatigue (r = −0.63, p = 0.002), DOMS (r = −0.61, p = 0.003), and WBI (r = −0.53, p = 0.011). Additionally, fatigue was significantly associated (large negative correlation) with accumulated weekly internal training intensity (r = −0.51, p = 0.014) and training strain (r = −0.61, p = 0.003). Small, but non-significant, correlations were found between CMJ performance and wellness status measures. These findings highlight the utility and simplicity of monitoring tools to improve athletes’ performance.