Purpose: To predict the session rating of perceived exertion (sRPE) in soccer and determine its main predictive indicators. Methods: A total of 70 external-load indicators (ELIs), internal-load indicators, individual characteristics, and supplementary variables were used to build a predictive model. Results: The analysis using gradient-boosting machines showed a mean absolute error of 0.67 (0.09) arbitrary units (AU) and a root-mean-square error of 0.93 (0.16) AU. ELIs were found to be the strongest predictors of the sRPE, accounting for 61.5% of the total normalized importance (NI), with total distance as the strongest predictor. The included internal-load indicators and individual characteristics accounted only for 1.0% and 4.5%, respectively, of the total NI. Predictive accuracy improved when including supplementary variables such as group-based sRPE predictions (10.5% of NI), individual deviation variables (5.8% of NI), and individual player markers (17.0% of NI). Conclusions: The results showed that the sRPE can be predicted quite accurately using only a relatively limited number of training observations. ELIs are the strongest predictors of the sRPE. However, it is useful to include a broad range of variables other than ELIs, because the accumulated importance of these variables accounts for a reasonable component of the total NI. Applications resulting from predictive modeling of the sRPE can help coaching staff plan, monitor, and evaluate both the external and internal training load.

Medicine & Science in Sports & Exercise ® Published ahead of Print contains articles in unedited manuscript form that have been peer reviewed and accepted for publication. This manuscript will undergo copyediting, page composition, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered that could affect the content.

Purpose: To determine if there is a link between the demands of competitive game activity and performance profiles of elite female field hockey players. Methods: Global positioning systems (GPS) were used to quantify running performance of elite female field hockey players (N = 20) during 26 competitive games. Performance profiles were assessed at 2 time points (preseason and midseason) for 2 competitive seasons. A battery of anthropometric and performance field-based tests (30–15 intermittent fitness test, incremental run test, 10–30-m speed test, T test, and vertical jump test) were used to determine the performance profiles of the players. Results: Players covered a mean total distance of 5384 (835) m, of which 19% was spent at high intensities (zone 5: 796 [221] m; zone 6: 274 [105] m). Forwards covered the lowest mean total distance (estimated marginal means 4586 m; 95% confidence interval, 4275–4897), whereas work rate was higher in forwards compared with midfielders (P = .006, d = 0.43) and central defenders (P = .001, d = 1.41). Players showed an improvement in body composition and anaerobic performance from preseason to midseason. Aerobic performance capacity (maximal oxygen uptake and speed at the 4-mM lactate threshold) was positively correlated with high-intensity activities. Conclusions: There is a clear relationship between running performance and aerobic performance profiles in elite female hockey players. These results highlight the importance of a well-developed aerobic performance capacity in order to maintain a high performance level during hockey games.

Vermeire, KM, Vandewiele, G, Caen, K, Lievens, M, Bourgois, JG, and Boone, J. Training progression in recreational cyclists: no linear dose-response relationship with training load. J Strength Cond Res 35(12): 3500–3505, 2021—The purpose of the study was to assess the relationship between training load (TL) and performance improvement in a homogeneous group of recreational cyclists, training with a self-oriented training plan. Training data from 11 recreational cyclists were collected over a 12-week period. Before and after the training period, subjects underwent a laboratory incremental exercise test with blood lactate measurements to determine the power output associated with the aerobic threshold (PAT) and the anaerobic threshold (PANT), and the maximal power output (PMAX) was also determined. Mean weekly TL (calculated using the training impulse (TRIMP) of Banister, Edwards TRIMP, Lucia TRIMP and the individualized TRIMP) were correlated to the progression in fitness parameters using Pearson Correlation. Training intensity distribution (TID) was also determined (% in zone 1 as <AT; % in zone 2 as between AT and ANT; % in zone 3 as >ANT). No significant correlations between mean weekly TRIMP values and the improvement on PMAX (r = −0.22 to 0.08), PANT (r = −0.56 to −0.31) and PAT (r = −0.08 to 0.41) were found. The TID was significant in a multiple regression with PANT as dependent variable (y = 0.0088 + 0.1094 × Z1 − 0.2704 × Z2 + 1.0416 × Z3; p = 0.02; R 2 = 0.62). In conclusion, this study shows that the commonly used TRIMP methods to quantify TL do not show a linear dose-response relationship with performance improvement in recreational cyclists. Furthermore, the study shows that TID might be a key factor to establish a relationship with performance improvement.