Modern Techniques and Technologies Applied to Training and Performance Monitoring

Abstract

Athlete preparation and performance continue to increase in complexity and costs. Modern coaches are shifting from reliance on personal memory, experience, and opinion to evidence from collected training-load data. Training-load monitoring may hold vital information for developing systems of monitoring that follow the training process with such precision that both performance prediction and day-to-day management of training become adjuncts to preparation and performance. Time-series data collection and analyses in sport are still in their infancy, with considerable efforts being applied in "big data" analytics, models of the appropriate variables to monitor, and methods for doing so. Training monitoring has already garnered important applications but lacks a theoretical framework from which to develop further. As such, we propose a framework involving the following: analyses of individuals, trend analyses, rules-based analysis, and statistical process control.Keywords: time-series analysis, dose-response, statistical process control Training is a process and a historical sequence of events. As with all processes, whether biochemical, manufacturing, raising children, or others, these processes require some type of regulation and oversight. 1 The level of oversight is related to the complexity of the process and the potential cost of losing process control. 2 Complex processes tend to have more opportunities for error resulting in a deviation from the most desired path. 3 For example, game sports are unique events that involve dynamic interactions between players, and as a result the observed behavior of an athlete or team is influenced by a situation or opponent. 4 The "unstable" nature of game sports creates a challenge to quantify performance indicators. Statistical process control is one method used to identify stable performance traits. 4 Statistical process control uses normative profiles or the averages of variables from several games, as well as tolerance limits expressed in confidence intervals based on a mean and variance estimates, to determine "reliable" or typical indicators of performance. 5 Mathematical modeling, such as probability analysis, also has been used for estimating the impact of a single player on team performance, predicting future behavior and identifying optimal decision-making strategies. 6 Processes can operate along a continuum from deterministically controlled to utterly chaotic 7-9 with both sharing a potential for unexpected problems and considerable expense in terms of money and threats to life and health. 2,10 Historically, more effort has been expended in assessing the athlete as a system. However, the preparation and performance of an athlete can also be addressed as a system. When viewed from the perspective of dynamic systems, athletes are described as "non-ergodic, out of equilibrium systems, exploring larger and larger regions of the state space but eventually getting trapped within some relatively small set of the whole state space by the constraints of their sport disci...

Heart Rate Monitoring in Team Sports—A Conceptual Framework for Contextualizing Heart Rate Measures for Training and Recovery Prescription

“…In training load management, it has become best practice to evaluate short-term ( acute , usually ~5–10 days) and long-term ( chronic , usually ~4–6 weeks) accumulated loads using (exponentially weighted) rolling averages and acute-to-chronic ratios (Bourdon et al, 2017 ). Also, mid- to long-term changes and trends could be evaluated with (linear) trend analysis (i.e., the slope of the regression; Plews et al, 2012 ; Hopkins, 2017 ; Sands et al, 2017 ). Moreover, a more advanced approach was recently introduced by Hecksteden et al ( 2017 ), using Bayesian statistics to compile individualized reference ranges to differentiate between two states of muscle recovery.…”

Maximum Strength, Rate of Force Development, Jump Height, and Peak Power Alterations in Weightlifters across Five Months of Training

“…An athlete monitoring system can provide the coach with invaluable data concerning athlete preparation and preparedness. While completely accurate predictions of an athlete’s response to a given training stimuli may not be possible, [ 5 , 6 ] the general direction of the adaptation process can be predicted based on the training prescription and the manner in which training stress is being directed [ 5 , 6 ]. Appropriate monitoring can provide the coach with quantitative information that allow comparisons to be made between the theoretically based, pre-determined expectations, and the actual results of the training prescription.…”

“…that affect an athlete’s performance. Qualitative performance outcomes relate to the fitness-fatigue paradigm and the level of athlete “preparedness”, and thus can provide an estimate of athletes’ potential to perform well [ 5 , 6 ]. Quantitative measures deal with the magnitude of specific adaptations, as well as actual performance outcomes.…”

“…Quantitative measures deal with the magnitude of specific adaptations, as well as actual performance outcomes. Although long-term athlete monitoring is still in its infancy, a comprehensive athlete monitoring system can provide a framework for the creation of a sport science-coach feedback system, by which evidence-based adjustments to training can be made [ 5 , 6 , 7 ].…”

The Development of a Personalised Training Framework: Implementation of Emerging Technologies for Performance

“…Technologies can also be utilised to quantify training load, which is useful in assessing fatigue and readiness to train [172][173][174]. This occurs via the quantification of both external (e.g., running velocity, duration and intensity, and weightlifting sets, reps and weight) and internal (e.g., heart rate (HR) and heart rate variability (HRV)) loads, along with the determination of environmental aspects that might affect such loads, such as temperature and altitude [175,176].…”