1. XXV International Conference on Sports Rehabilitation and Traumatology
13-15 May 2017
GPS FEATURES REFLECT THE PLAYERS’ RATE OF PERCEIVED EXERTION OF FOOTBALL MATCH
Rossi A., Perri E., Trecroci A., Formenti D., Cavaggioni L., Iaia F.M., Alberti G.
Department of Biomedical Sciences for Health, Università degli Studi di Milano, Italy
Background:
Assessing the external load during physical activity during football trainings and matches is now possible tanks to the recent innovations in Global Position System (GPS) technology. However, to schedule an effective training program, coaches and athletic trainers have to assess the internal response to external stimuli as well [1]. The Rate of Perceived Exertion (RPE) is as a simple and non-invasive technique for assessing the players’ internal load. Previous studies showed that the external training load features are moderately predictive of the internal ones [2,3]. Because the information obtained from match is obliviously crucial to achieve individual and team efficacy during the game and it constitutes a basic criterion for training program, assessing the influence of GPS features on RPE is required in match as well.
Table 1. Multiple regression analysis
Features
Correlation (r)
p-value
RPE
Distance covered between 20 and 25 W/kg
0.411
<0.001
Distance covered in deceleration between 2 and 3 m/s/s
0.244
0.024
Top Speed (m/s)
-0.211
0.033
Aim:
The aim of this study was to detect the GPS features that influence the players’ RPE during the sub-elite match.
Figure 1. Underestimation and overestimation of RPE predicted vs RPE observed.
Method:
Twenty-two football players (age=21.96±4.53 yrs; height=180.68±5.23 cm; weight=72.36±4.19) competing in an Italian Serie D team took part in this study. Three central backs, five fullbacks, five midfielders, five wingers and four forwards were recruited. Goalkeepers were not included in the study. Players’ physical activity were recorded during 16 matches in the season by using a portable non-differential 10 Hz global position system (GPS) integrated with 400 Hz Tri-Axial Accelerometer and 10 Hz Tri-Axial magnetometer (PLAYERTEK®, Dundalk, Ireland). Eighty-eight features (i.e. 21 Kinematic, 37 Metabolic and 30 Mechanical) were computed from the GPS raw data to describe each players’ match. Each player’s RPE was collected in isolation ~20 minutes after the match using the CR-10 Borg scale. For the data analysis, the GPS data were normalized in accordance to the time that players had played during the match in order to avoid intra subjects variability induced by the playtime. A stepwise multiple regression analysis using forward selection were performed in order to detect the relationship between GPS features and the RPE. The relationship between RPE and GPS features was assessed by using Pearson’s Correlation Coefficient. The level of statistical significance was set at p < .05 for all tests.
Results:
The RPE is described by a multiple regression model computed using the distance covered between 20 and 25 W/kg, the distance covered in deceleration between 2 and 3 m/s/s and the top speed (m/s) as predictors (r=0.485, F(1,88)=4.171, p=0.044). These features respectively explained 13.4, 6.8 and 3.3% of the RPE variance. Table 1 shows the correlations between the RPE and the features selected by stepwise multiple regression.
Conclusion:
The results of this study provide evidences that the internal load is related to several external load predictors. As a matter of fact, we found that the higher is the distance covered between 20 and 25 W/kg and the distance covered in deceleration between 2 and 3 m/s/s, the higher is the players’ perceived exertion (Table 1). Conversely, the lover is the top speed performed during matches the higher is the RPE (Table 1). Beside these trends discrepancy, the combination of these GPS features describes the RPE during matches better than each feature alone. Hence, we can conclude that the RPE reflects the external load and may be useful to coaches and athletic trainers to schedule training load and monitor the athlete effort during trainings and matches.
References
Gallo T, Cormack S, Gabbett T, Williams M, Lorenzen C. Characteristics impacting on session rating of perceived exertion training load in Australian footballers. J Sports Sci. 2015;33: 467–475.
Gaudino P, Iaia FM, Strudwick AJ, Hawkins RD, Alberti G, Atkinson G, et al. Factors influencing perception of effort (session rating of perceived exertion) during elite soccer training. Int J Sports Physiol Perform. 2015;10: 860–864.
Casamichana D, Castellano J, Calleja-Gonzalez J, San Román J, Castagna C. Relationship between indicators of training load in soccer players. J Strength Cond Res. 2013;27: 369–374.