Relationship of Pre-season Training Load With In-Season Biochemical Markers, Injuries and Performance in Professional Soccer Players

Coppalle, S., Rave, G., Ben Abderrahman, A., Ali, A., Salhi, I., Zouita, S., Zouita, A., Brughelli, M., Granacher, U., Zouhal, H.

Introduction: There is controversy in the literature regarding the link between training load and injury rate. Thus, the aims of this non-interventional study were to evaluate relationships between pre-season training load with biochemical markers, injury incidence, and performance during the first month of the competitive period in professional soccer players.

Materials and Methods: Healthy professional soccer players were enrolled in this study over two pre-season periods. Data sets were available from 26 players during the first season (2014–2015) and 24 players during the second season (2015–2016) who completed two pre-season periods (6 weeks each). External training load was assessed from all athletes during training using Global Positioning System (GPS). Internal training load was monitored after each training session using rate of perceived exertion (RPE). Before and after each pre-season, blood samples were taken to determine plasma lactate dehydrogenase (LDH), creatine kinase (CK), and C-reactive protein (CRP). Injury incidence and overall performance (ranking of the team after the first five official games of the championship) were recorded for both seasons separately.

Results: There was no statistically significant difference in mean RPE values of the two-preparation periods (2737 ± 452 and 2629 ± 786 AU, p = 0.492). The correlational analysis did not reveal significant associations between internal and external training load (RPE and GPS data) and biological markers. There was a significant positive correlation between RPE and LDH during the 2015/2016 season (r = 0.974, p = 0.001). In addition, a significant negative correlation was found between total distance >20 km/h and CRP during the 2015–2016 season (r = −0.863, p = 0.027). The injury rates for the two seasons were 1.76 and 1.06 per 1000 h exposure for the 2014–2015 and 2015–2016 seasons, respectively (p = 0.127).

Conclusion: Our study showed that pre-season training load is not associated with overall team performance. This association is most likely multifactorial, and other factors (e.g., technical and tactical level of the team, opponents, environment) may play an important role for the collective team performance. Our findings may help coaches to better prepare their athletes during pre-season.

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