Background:

Training control is essential to optimize performance. Therefore, methodologies that improve the preparation of national teams in events such as the Olympic Games should be documented.

Purpose:

To determine whether GPS data in combination with subjective measures of well-being, fatigue and recovery are appropriate for load monitoring during a preparatory period for the Olympic Games.

Methodology:

Twenty-two under-23 professional players participated during 5 micro-cycles and 27 training sessions. External load data was collected via a global positioning system (GPS): Total distance (DT), performance zones Z0 (0-15 km/h), Z1 (15.1-18 km/h), Z2 (18.1 -24 km/h), Z3 (>24.1 km/h), maximum speed (km/h), accelerations (>2.5m/s.) and decelerations

(<2.5m/s.). Also, internal load was obtained through subjective measures of Rating Perceived Exertion (RPE), Total Quality Recovery (TQR), Readiness to Train (RTT%) obtained from the sleep quality, muscle pain, energy levels, mood, stress, food quality and health. The subjective rate of fatigue-recovery (F-R) was then calculated. An ANOVA test, Principal Component Analysis (PCA) and multiple linear regression were applied.

Results:

the variables DT (p=0.00 ES=0.22), Z0 (p= 0.00 TE=0.08), Z2 (p=0.00 ES= 0.05), maximum speed (p= 0.00 ES=0.42), sum of acceleration and deceleration (p=0.00 ES=0.08) and values relative to load/min (p=0.00 ES=0.17) were identified as variables more sensitive to load change between micro-cycles. RTT% and subjective rate F-R showed a moderate effect size (p=0.04 ES=0.06 and p=0.06 ES=0.06), but were sensitive to change between micro-cycles. PCA extracted 15 GPS variables and 11 subjective variables that explained 78% of the training load variance.

Conclusion:

Using GPS data together with subjective measures involved in fatigue-recovery may be a good strategy to control training load in footballers.