Training load (TL) is monitored with the aim of making evidence-based decisions on appropriate loading schemes to reduce injuries and enhance team performance. However, little is known in detail about the variables of load and methods of analysis used in high-level football. Therefore, the aim of this study was to provide information on the practices and practitioners’ perceptions of monitoring in professional clubs. Eighty-two high-level football clubs from Europe, the United States, and Australia were invited to answer questions relating to how TL is quantified, how players’ responses are monitored, and their perceptions of the effectiveness of monitoring. Forty-one responses were received. All teams used GPS and heart-rate monitors during all training sessions, and 28 used rating of perceived exertion. The top-5-ranking TL variables were acceleration (various thresholds), total distance, distance covered above 5.5 m/s, estimated metabolic power, and heart-rate exertion. Players’ responses to training are monitored using questionnaires (68% of clubs) and submaximal exercise protocols (41%). Differences in expected vs actual effectiveness of monitoring were 23% and 20% for injury prevention and performance enhancement, respectively (P < .001 d = 1.0−1.4). Of the perceived barriers to effectiveness, limited human resources scored highest, followed by coach buy-in. The discrepancy between expected and actual effectiveness appears to be due to suboptimal integration with coaches, insufficient human resources, and concerns over the reliability of assessment tools. Future approaches should critically evaluate the usefulness of current monitoring tools and explore methods of reducing the identified barriers to effectiveness.
Richard Akenhead and George P. Nassis
Darren J. Paul and George P. Nassis
Pedro Figueiredo, George P. Nassis, and João Brito
Purpose: To quantify the association between salivary secretory immunoglobulin A (sIgA) and training load in elite football players. Methods: Data were obtained on 4 consecutive days during the preparation camp for the Rio 2016 Olympic Games. Saliva samples of 18 elite male football players were collected prior to breakfast. The session rating of perceived exertion (s-RPE) and external training-load metrics from global positioning systems (GPS) were recorded. Within-subject correlation coefficients between training load and sIgA concentration, and magnitude of relationships, were calculated. Results: sIgA presented moderate to large negative correlations with s-RPE (r = −.39), total distance covered (r = −.55), accelerations (r = −.52), and decelerations (r = −.48). Trivial to small associations were detected between sIgA and distance covered per minute (r = .01), high-speed distance (r = −.23), and number of sprints (r = −.18). sIgA displayed a likely moderate decrease from day 1 to day 2 (d = −0.7) but increased on day 3 (d = 0.6). The training-load variables had moderate to very large rises from day 1 to day 2 (d = 0.7 to 3.2) but lowered from day 2 to day 3 (d = −5.0 to −0.4), except for distance per minute (d = 0.8) and sprints (unclear). On day 3, all training-load variables had small to large increments compared with day 1 (d = 0.4 to 1.5), except for accelerations (d = −0.8) and decelerations (unclear). Conclusions: In elite football, sIgA might be more responsive to training volume than to intensity. External load such as GPS-derived variables presented stronger association with sIgA than with s-RPE. sIgA can be used as an additional objective tool in monitoring football players.
Darren J. Paul, Gustavo Tomazoli, and George P. Nassis
Purpose: To examine the reproducibility of the Perceived Recovery Status (PRS) scale in football players and describe the time course of the PRS in response to a football match. Methods: Twenty trained youth players (mean [SD] age = 16.2 [1.2] y, height = 1.75 [0.07] m, body mass = 64.0 [7.8] kg) took part in the study. PRS was collected −2 h and −30 min before and +15 min, +3 h, and +24 h after an international football match. Players were categorized into 2 groups based on their playing time (≤45 and 90 min). Results: Reproducibility of the PRS was high (intraclass correlation coefficient = .83, typical error = 0.59, coefficient of variation = 9.9%) between the 2 prematch measures. Overall, PRS was lower at +15 min (4.0 [1.5]; P < .01, effect size = 2.2) and +3 h (4.7 [1.6]; P < .01, effect size = 1.5) compared with −30 min (7.1 [1.3]); +15 min was lower than +24 h (6.1 [1.3]; P < .01, effect size = 1.5). No differences between groups for PRS scores at any of the time points were found. Conclusions: The PRS is a reproducible tool for monitoring perceptions of recovery to football activity and is sensitive to time-course changes relating to a match. The scale is an easy and efficient tool that can be used to monitor an aspect of recovery.
Darren J. Paul, Paul S. Bradley, and George P. Nassis
Time-motion analysis is a valuable data-collection technique used to quantify the match running performance of elite soccer players. However, interpreting the reductions in running performance in the 2nd half or temporarily after the most intense period of games is highly complex, as it could be attributed to physical or mental fatigue, pacing strategies, contextual factors, or a combination of mutually inclusive factors. Given that research in this domain typically uses a reductionist approach whereby match running performance is examined in isolation without integrating other factors, this ultimately leads to a 1-dimensional insight into match performance. Subsequently, a cohesive review of influencing factors does not yet exist. The aim of this commentary is to provide a detailed insight into the complexity of match running performance and its most influential factors.