Search Results

You are looking at 1 - 10 of 35 items for :

  • "player monitoring" x
Clear All
Restricted access

Nils Haller, Tobias Ehlert, Sebastian Schmidt, David Ochmann, Björn Sterzing, Franz Grus and Perikles Simon

and consequently, to maintain game performance because a lack of sufficient recovery may result in decreased performance, overtraining, illness, or injury. 4 A variety of options for player monitoring leads to a disagreement about the ideal approach. 4 Questionnaires or the assessment of load

Restricted access

Richard Akenhead and George P. Nassis

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.

Restricted access

Nick Dobbin, Jamie Highton, Samantha Louise Moss and Craig Twist

Purpose: To investigate the factors affecting the anthropometric and physical characteristics of elite academy rugby league players. Methods: One hundred ninety-seven elite academy rugby league players (age = 17.3 [1.0] y) from 5 Super League clubs completed measures of anthropometric and physical characteristics during a competitive season. The interaction between and influence of contextual factors on characteristics was assessed using linear mixed modeling. Results: All physical characteristics improved during preseason and continued to improve until midseason, whereafter 10-m sprint (η 2 = .20 cf .25), countermovement jump (CMJ) (η 2 = .28 cf .30), and prone Yo-Yo Intermittent Recovery (Yo-Yo IR) test (η 2 = .22 cf .54) performance declined. Second (η 2 = .17) and third (η 2 = .16) -year players were heavier than first-years, whereas third-years had slower 10-m sprint times (η 2 = .22). Large positional variability was observed for body mass, 20-m sprint time, medicine-ball throw, CMJ, and prone Yo-Yo IR1. Compared with bottom-ranked teams, top-ranked teams demonstrated superior 20-m (η 2 = −.22) and prone Yo-Yo IR1 (η 2 = .26) performance, whereas middle-ranked teams reported higher CMJ height (η 2 = .26) and prone Yo-Yo IR1 distance (η 2 = .20) but slower 20-m sprint times (η 2 = .20). Conclusion: These findings offer practitioners who design training programs for academy rugby league players insight into the relationships between anthropometric and physical characteristics and how they are influenced by playing year, league ranking, position, and season phase.

Restricted access

Jace A. Delaney, Tannath J. Scott, Heidi R. Thornton, Kyle J.M. Bennett, David Gay, Grant M. Duthie and Ben J. Dascombe

Rugby league coaches often prescribe training to replicate the demands of competition. The intensities of running drills are often monitored in comparison with absolute match-play measures. Such measures may not be sensitive enough to detect fluctuations in intensity across a match or to differentiate between positions.

Purpose:

To determine the position- and duration-specific running intensities of rugby league competition, using a moving-average method, for the prescription and monitoring of training.

Methods:

Data from a 15-Hz global positioning system (GPS) were collected from 32 professional rugby league players across a season. The velocity–time curve was analyzed using a rolling-average method, where maximum values were calculated for 10 different durations, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 min, for each player across each match.

Results:

There were large differences between the 1- and 2-min rolling averages and all other rolling-average durations. Smaller differences were observed for rolling averages of greater duration. Fullbacks maintained a greater velocity than outside backs and middle and edge forwards over the 1- and 2-min rolling averages (ES 0.8−1.2, P < .05). For rolling averages 3 min and greater, the running demands of the fullbacks were greater than those of the middle forwards and outside backs (ES 1.1−1.4, P < .05).

Conclusions:

These findings suggest that the running demands of rugby league fluctuate vastly across a match. Fullbacks were the only position to exhibit a greater running intensity than any other position, and therefore training prescription should reflect this.

Restricted access

Mathieu Lacome, Christopher Carling, Jean-Philippe Hager, Gerard Dine and Julien Piscione

performance, and muscle damage in players with high exposure to competition were generally unclear or small. The present findings support the need for holistic systematic player monitoring and management programs to track and inform practitioners on player recovery and readiness for forthcoming matches

Open access

Martin Buchheit and Ben Michael Simpson

With the ongoing development of microtechnology, player tracking has become one of the most important components of load monitoring in team sports. The 3 main objectives of player tracking are better understanding of practice (provide an objective, a posteriori evaluation of external load and locomotor demands of any given session or match), optimization of training-load patterns at the team level, and decision making on individual players’ training programs to improve performance and prevent injuries (eg, top-up training vs unloading sequences, return to play progression). This paper discusses the basics of a simple tracking approach and the need to integrate multiple systems. The limitations of some of the most used variables in the field (including metabolic-power measures) are debated, and innovative and potentially new powerful variables are presented. The foundations of a successful player-monitoring system are probably laid on the pitch first, in the way practitioners collect their own tracking data, given the limitations of each variable, and how they report and use all this information, rather than in the technology and the variables per se. Overall, the decision to use any tracking technology or new variable should always be considered with a cost/benefit approach (ie, cost, ease of use, portability, manpower/ability to affect the training program).

Restricted access

Lindsay T. Starling and Michael I. Lambert

-researched monitoring protocol that teams can implement with confidence. Therefore, the first aim of this study was to identify the structures rugby union teams currently use to monitor training load and the training load response. The next aim was to identify the prerequisites of a player monitoring protocol that

Restricted access

Andrew D. Govus, Aaron Coutts, Rob Duffield, Andrew Murray and Hugh Fullagar

holistic player-monitoring system in American college football. Second, these results suggest that pretraining subjective wellness ratings such as wellness Z score and energy may influence the exercise output of American college football players during in-season training sessions, while muscle soreness

Restricted access

Riana R. Pryor, Douglas J. Casa, Susan W. Yeargin and Zachary Y. Kerr

/462) 41.1 (273/664) .068 Monitor playersMonitored players for signs and symptoms of EHI 93.0 (1,048/1,127) 94.1 (434/461) 92.2 (614/666) .207  Monitored players for full length of practice 98.5 (1,109/1,126) 98.5 (452/459) 98.5 (657/667) .972 Athlete attire  Athletes required to wear lightweight

Restricted access

Mohamed Saifeddin Fessi and Wassim Moalla

confidentiality, players’ data of 52 official matches were collected and analyzed anonymously. These data arose from the daily player monitoring in which player activities are routinely measured over the course of the season. Therefore, ethics committee clearance was not required. 15 The study conformed