The Quantification of Training Load in Professional American Soccer Players

Training load (TL) has become a common tool to measure the exertion of athletes over the course of a session or season. However, there is still limited understanding of the load placement throughout training weeks within American professional soccer. The purpose of this study was to quantify potential differences in the external [duration, total distance (DT), explosive distance (DE), sprint distance (DS), maximum speed (SpeedMax) and number of sprints (#S)] and internal [session rating of perceived exertion load (S-RPE Load), average heart rate (HRAvg), peak heart rate (HRPeak), time spent above 90% heart rate max (time \u3e 90% HRMax)] demands of seasonal training on a single Professional American soccer team. A USL 1 soccer team (n=22; 23.3yr, 80.5kg, 181.9cm) was outfitted with GPS bio-harnesses and had all training sessions monitored and recorded. Training sessions were categorized by their distance away from the next competition [match days (MDs) out]. A Kruskal-Wallis analysis of variance (ANOVA) was used to analyze main effect differences. A Fisher’s least significant difference post hoc pairwise analysis determined intervariable differences. Alpha was set at 0.05 for all significant main effect findings. Following the analysis of the main effect differences for all variables (p \u3c 0.01), various trends in TL were discovered. All external load variables and S-RPE load represented a bell-shaped curve, where TL increased from MD-4 to MD-3, and then decreased toward MD-1. HRAvg, HRPeak and time \u3e 90% HRMax portrayed a linear taper beginning with the highest load on MD-1 and gradually declining toward MD. Interestingly, HR responses did not fall in line with the external load variables, which may indicate athletes were not fully recovered when they resumed training following the previous game. Across all variables analyzed in the present study, MD-1 presented the lowest load across all MDs. Although the data analysis was extensive (10 variables), it is our recommendation to include a regular and concise list of variables when communicating to coaches. Based on these results coaches should be mindful of the recovered state of players returning to training following a game, to not overstress them before they are sufficiently recovered