Search Results

You are looking at 1 - 7 of 7 items for

  • Author: Michel S. Brink x
Clear All Modify Search
Restricted access

Michel S. Brink, Anna W. Kersten and Wouter G.P. Frencken

A mismatch between the training exertion intended by a coach and the exertion perceived by players is well established in sports. However, it is unknown whether coaches can accurately observe exertion of individual players during training. Furthermore, the discrepancy in coaches’ and players’ perceptions has not been explained.

Purpose:

To determine the relation between intended and observed training exertion by the coach and perceived training exertion by the players and establish whether on-field training characteristics, intermittent endurance capacity, and maturity status explain the mismatch.

Methods:

During 2 mesocycles of 4 wk (in November and March), rating of intended exertion (RIE), rating of observed exertion (ROE), and rating of perceived exertion (RPE) were monitored in 31 elite young soccer players. External and internal training loads were objectively quantified with accelerometers (PlayerLoad) and heart-rate monitors (TRIMPmod). Results of an interval shuttle-run test (ISRT) and age at peak height velocity (APHV) were determined for all players.

Results:

RIE, ROE, and RPE were monitored in 977 training sessions. The correlations between RIE and RPE (r = .58; P < .01) and between ROE and RPE (r = .64; P < .01) were moderate. The mean difference between RIE and RPE was –0.31 ± 1.99 and between ROE and RPE was –0.37 ± 1.87. Multilevel analyses showed that PlayerLoad and ISRT predicted RIE and ROE.

Conclusion:

Coaches base their intended and observed exertion on what they expect players will do and what they actually did on the field. When doing this, they consider the intermittent endurance capacity of individual players.

Restricted access

Michel S. Brink, Wouter G.P. Frencken, Geir Jordet and Koen A.P.M. Lemmink

Purpose:

The aim of the current study was to investigate and compare coaches’ and players’ perceptions of training dose for a full competitive season.

Methods:

Session rating of perceived exertion (RPE), duration, and training load (session RPE × duration) of 33 professional soccer players (height 178.2 ± 6.6 cm, weight 70.5 ± 6.4 kg, percentage body fat 12.2 ± 1.6) from an under-19 and under-17 (U17) squad were compared with the planned periodization of their professional coaches. Before training, coaches filled in the session rating of intended exertion (RIE) and duration (min) for each player. Players rated session RPE and training duration after each training session.

Results:

Players perceived their intensity and training load (2446 sessions in total) as significantly harder than what was intended by their coaches (P < .0001). The correlations between coaches’ and players’ intensity (r = .24), duration (r = .49), and load (r = .41) were weak (P < .0001). Furthermore, for coach-intended easy and intermediate training days, players reported higher intensity and training load (P < .0001). For hard days as intended by the coach, players reported lower intensity, duration, and training load (P < .0001). Finally, first-year players from the U17 squad perceived training sessions as harder than second-year players (P < .0001).

Conclusion:

The results indicate that young elite soccer players perceive training as harder than what was intended by the coach. These differences could lead to maladaptation to training. Monitoring of the planned and perceived training load of coaches and players may optimize performance and prevent players from overtraining.

Restricted access

James J. Malone, Arne Jaspers, Werner Helsen, Brenda Merks, Wouter G.P. Frencken and Michel S. Brink

The purpose of this investigation was to (1) quantify the training load practices of a professional soccer goalkeeper and (2) investigate the relationship between the training load observed and the subsequent self-reported wellness response. One male goalkeeper playing for a team in the top league of the Netherlands participated in this case study. Training load data were collected across a full season using a global positioning system device and session-RPE (rating of perceived exertion). Data were assessed in relation to the number of days to a match (MD− and MD+). In addition, self-reported wellness response was assessed using a questionnaire. Duration, total distance, average speed, PlayerLoad™, and load (derived from session-RPE) were highest on MD. The lowest values for duration, total distance, and PlayerLoad™ were observed on MD−1 and MD+1. Total wellness scores were highest on MD and MD−3 and were lowest on MD+1 and MD−4. Small to moderate correlations between training load measures (duration, total distance covered, high deceleration efforts, and load) and the self-reported wellness response scores were found. This exploratory case study provides novel data about the physical load undertaken by a goalkeeper during 1 competitive season. The data suggest that there are small to moderate relationships between training load indicators and self-reported wellness response. This weak relation indicates that the association is not meaningful. This may be due to the lack of position-specific training load parameters that practitioners can currently measure in the applied context.

Restricted access

Steven H. Doeven, Michel S. Brink, Wouter G.P. Frencken and Koen A.P.M. Lemmink

During intensified phases of competition, attunement of exertion and recovery is crucial to maintain performance. Although a mismatch between coach and player perceptions of training load is demonstrated, it is unknown if these discrepancies also exist for match exertion and recovery.

Purpose:

To determine match exertion and subsequent recovery and to investigate the extent to which the coach is able to estimate players’ match exertion and recovery.

Methods:

Rating of perceived exertion (RPE) and total quality of recovery (TQR) of 14 professional basketball players (age 26.7 ± 3.8 y, height 197.2 ± 9.1 cm, weight 100.3 ± 15.2 kg, body fat 10.3% ± 3.6%) were compared with observations of the coach. During an in-season phase of 15 matches within 6 wk, players gave RPEs after each match. TQR scores were filled out before the first training session after the match. The coach rated observed exertion (ROE) and recovery (TQ-OR) of the players.

Results:

RPE was lower than ROE (15.6 ± 2.3 and 16.1 ± 1.4; P = .029). Furthermore, TQR was lower than TQ-OR (12.7 ± 3.0 and 15.3 ± 1.3; P < .001). Correlations between coach- and player-perceived exertion and recovery were r = .25 and r = .21, respectively. For recovery within 1 d the correlation was r = .68, but for recovery after 1–2 d no association existed.

Conclusion:

Players perceive match exertion as hard to very hard and subsequent recovery reasonable. The coach overestimates match exertion and underestimates degree of recovery. Correspondence between coach and players is thus not optimal. This mismatch potentially leads to inadequate planning of training sessions and decreases in performance during fixture congestion in basketball.

Restricted access

Steven H. Doeven, Michel S. Brink, Barbara C.H. Huijgen, Johan de Jong and Koen A.P.M. Lemmink

During rugby sevens tournaments, it is crucial to balance match load and recovery to strive for optimal performance. Purpose: To determine changes in well-being, recovery, and neuromuscular performance during and after an elite women’s rugby sevens tournament and assess the influence of match-load indicators. Methods: Twelve elite women rugby sevens players (age = 25.3 [4.1]y, height = 169.0 [4.0] cm, weight = 63.9 [4.9] kg, and body fat = 18.6% [2.7%]) performed 5 matches during a 2-d tournament of the Women’s Rugby Sevens World Series. Perceived well-being (fatigue, sleep quality, general muscle soreness, stress levels, and mood), total quality of recovery, and countermovement-jump flight time were measured on match days 1 and 2, 1 d posttournament, and 2 d posttournament. Total distance; low-, moderate-, and high-intensity running; and physical contacts during matches were derived from global positioning system–based time–motion analysis and video-based notational analysis, respectively. Internal match load was calculated by session rating of perceived exertion and playing time (rating of perceived exertion × duration). Results: Well-being (P < .001), fatigue (P < .001), general muscle soreness (P < .001), stress levels (P < .001), mood (P = .005), and total quality of recovery (P < .001) were significantly impaired after match day 1 and did not return to baseline values until 2 d posttournament. More high-intensity running was related to more fatigue (r = −.60, P = .049) and a larger number of physical contacts with more general muscle soreness (r = −.69, P = .013). Conclusion: Perceived well-being and total quality of recovery were already impaired after match day 1, although performance was maintained. High-intensity running and physical contacts were predominantly related to fatigue and general muscle soreness, respectively.

Restricted access

Arne Jaspers, Tim Op De Beéck, Michel S. Brink, Wouter G.P. Frencken, Filip Staes, Jesse J. Davis and Werner F. Helsen

Purpose: Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators (ELIs) and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. Methods: Training data were collected from 38 professional soccer players over 2 seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using 2 machine learning techniques, artificial neural networks and least absolute shrinkage and selection operator (LASSO) models, and 1 naive baseline method. The predictions were based on a large set of ELIs. Using each technique, 1 group model involving all players and 1 individual model for each player were constructed. These models’ performance on predicting the reported RPE values for future training sessions was compared with the naive baseline’s performance. Results: Both the artificial neural network and LASSO models outperformed the baseline. In addition, the LASSO model made more accurate predictions for the RPE than did the artificial neural network model. Furthermore, decelerations were identified as important ELIs. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models. Conclusions: Machine learning techniques may have added value in predicting RPE for future sessions to optimize training design and evaluation. These techniques may also be used in conjunction with expert knowledge to select key ELIs for load monitoring.

Restricted access

Tim Op De Beéck, Arne Jaspers, Michel S. Brink, Wouter G.P. Frencken, Filip Staes, Jesse J. Davis and Werner F. Helsen

Purpose: The influence of preceding load and future perceived wellness of professional soccer players is unexamined. This paper simultaneously evaluates the external load (EL) and internal load (IL) for different time frames in combination with presession wellness to predict future perceived wellness using machine learning techniques. Methods: Training and match data were collected from a professional soccer team. The EL was measured using global positioning system technology and accelerometry. The IL was obtained using the rating of perceived exertion multiplied by duration. Predictive models were constructed using gradient-boosted regression trees (GBRT) and one naive baseline method. The individual predictions of future wellness items (ie, fatigue, sleep quality, general muscle soreness, stress levels, and mood) were based on a set of EL and IL indicators in combination with presession wellness. The EL and IL were computed for acute and cumulative time frames. The GBRT model’s performance on predicting the reported future wellness was compared with the naive baseline’s performance by means of absolute prediction error and effect size. Results: The GBRT model outperformed the baseline for the wellness items such as fatigue, general muscle soreness, stress levels, and mood. In addition, only the combination of EL, IL, and presession perceived wellness resulted in nontrivial effects for predicting future wellness. Including the cumulative load did not improve the predictive performances. Conclusions: The findings may indicate the importance of including both acute load and presession perceived wellness in a broad monitoring approach in professional soccer.