Seasonal Changes In Training Load And Performance Metrics In American Professional Soccer

PURPOSE: Duration of running, accelerations, decelerations, changes of direction, explosive distance, number of sprints, and sprint distance are all external training load metrics used to monitor training load to ensure proper periodization and reduce overtraining. GPS technology provides various internal and external metrics to quantify training load. Training load helps coaches and staff monitor performance and injury trends in players. Therefore, the purpose of this study was to investigate how training load metrics, and performance metrics change throughout a season in soccer players. METHODS: 15 United Soccer League players participated in the study. GPS technology was used with each GPS operating on a 10 Hertz frequency. GPS metrics were quantified for each participant at preseason (pre), midseason (mid), and lateseason(late) time points through an averaging calculation process. Preseason involved training sessions and non-point matches; midseason covered matches 1-25; late season included the final seven and a half weeks. Performance metrics included average top speed in km/h (ATS) and average metabolic power in w/kg/s (AMP). Training load metrics included average total distance in km (ATD), average high intensity accelerations (AHIA), and average high intensity decelerations (AHID) per session. Separate one-way repeated measures ANOVAs determined main effects for all measures across the three time points. RESULTS: There were significant main effects for AMP (p = 0.041), ATD (p = 0.003), AHIA (p < 0.001), and AHID (p < 0.001). Post hoc paired samples t-tests showed a significant decrease between AMPPre and AMPMid (p = 0.029), and between AMPPre and AMPLate (p = 0.048), a significant decrease between ATDPre and ATDMid (p = 0.011) and between ATDPre and ATDLate (p = 0.010), significant decrease between AHIAPre and AHIAMid (p < 0.001), and AHIAPre and AHIALate (p = 0.003), a significant increase between AHIAMid and AHIALate (p = 0.011), a significant decrease between AHIDPre and AHIDMid (p < 0.001) and AHIDPre and AHIDLate (p = 0.005). CONCLUSIONS: The results of this study demonstrate the importance of tracking and monitoring GPS metrics throughout a season to observe the changes that occur and ensure proper load management is taking place to enhance performance measures, such as metabolic power.