Individualisation of time-motion analysis : a method comparison and case report series

© Georg Thieme Verlag KG. This study compared the intensity distribution of time-motion analysis data, when speed zones were categorized by different methods. 12 U18 players undertook a routine battery of laboratory- and field-based assessments to determine their running speed corresponding to the respiratory compensation threshold (RCT), maximal aerobic speed (MAS), maximal oxygen consumption (vVO 2max ) and maximal sprint speed (MSS). Players match-demands were tracked using 5 Hz GPS units in 22 fixtures (50 eligible match observations). The percentage of total distance covered running at high-speed (%HSR), very-high speed (%VHSR) and sprinting were determined using the following speed thresholds: 1) arbitrary; 2) individualised (IND) using RCT, vVO 2max and MSS; 3) individualised via MAS per se; 4) individualised via MSS per se; and 5) individualised using MAS and MSS as measures of locomotor capacities (LOCO). Using MSS in isolation resulted in 61 % and 39 % of player's % HSR and % VHSR, respectively, being incorrectly interpreted, when compared to the IND technique. Estimating the RCT from fractional values of MAS resulted in erroneous interpretations of % HSR in 50 % of cases. The present results suggest that practitioners and researchers should avoid using singular fitness characteristics to individualise the intensity distribution of time-motion analysis data. A combination of players' anaerobic threshold, MAS, and MSS characteristics are recommended to individualise player-tracking data

Combining internal- and external-training-load measures in professional rugby league

Purpose: This study investigated the effect of training mode on the relationships between measures of training load in professional rugby league players. Methods: Five measures of training load (internal: individualized training impulse, session rating of perceived exertion; external—body load, high-speed distance, total impacts) were collected from 17 professional male rugby league players over the course of two 12-week pre-season periods. Training was categorized by mode (small-sided games, conditioning, skills, speed, strongman, and wrestle) and subsequently subjected to a principal component analysis. Extraction criteria were set at an eigenvalue of greater than one. Modes that extracted more than one principal component were subjected to a varimax rotation. Results: Small-sided games and conditioning extracted one principal component, explaining 68% and 52% of the variance, respectively. Skills, wrestle, strongman, and speed extracted two principal components explaining 68%, 71%, 72%, and 67% of the variance respectively. Conclusions: In certain training modes the inclusion of both internal and external training load measures explained a greater proportion of the variance than any one individual measure. This would suggest that in those training modes where two principal components were identified, the use of only a single internal or external training load measure could potentially lead to an underestimation of the training dose. Consequently, a combination of internal and external load measures is required during certain training modes

An individualised approach to monitoring and prescribing training in elite youth football players

The concept of how training load affects performance is founded in the notion that training contributes to two specific outcomes, these are developed simultaneously by repeated bouts of training and act in conflict of each other; fitness and fatigue (Banister et al., 1975). The ability to understand these two components and how they interact with training load is commonly termed the “dose-response relationship” (Banister, 1991). The accurate quantification of training load, fitness and fatigue are therefore of paramount importance to coaches and practitioners looking to examine this relationship. In recent years, the advancement in technology has seen a rise in the number of methodologies used to assess training load and specific training outcomes. However, there is a general lack of evidence regarding the reliability, sensitivity and usefulness of these methods to help inform the training process. The aim of this thesis was therefore to improve the current understanding around the monitoring and prescription of training, with special reference to the relationship between training load, fitness and fatigue. Chapter 4 of this thesis looked to establish test re-test reliability. Variables selected for investigation were measures of subjective wellness; fatigue, muscle soreness, sleep quality, stress levels and mood state, assessments of physical performance; countermovement jump (CMJ), squat jump (SJ) and drop jump (DJ) and the assessment of tri-axial accelerometer data; PlayerLoadTM and individual component planes anterior-posterior (PLAP), mediolateral (PLML), and vertical (PLV), were collected during a sub-maximal shuttle run. The results from this investigation suggest that a short three minute sub-maximal shuttle run can be used as a reliable method to collect accelerometer data. Additionally, assessments of CMJ height, SJ height, DJ contact time (DJ-CT) and DJ reactive strength index (DJ-RSI) were all deemed to have good reliability. In contrast, this chapter highlighted the poor test re-test reliability of the subjective wellness questionnaire. Importantly, the minimum detectable change (MDC) was also calculated for all measures within this study to provide an estimate of measurement error and a threshold for changes that can be considered ‘real’. Chapter 5 assessed the sensitivity and reproducibility of these measures following a standardised training session. To assess sensitivity, the signal-to-noise (S: N) ratio was calculated by using the post training fatigue response (signal) and the MDC derived from Chapter 4 (noise). The fatigue response was considered reproducible if the S: N ratio was greater than one following two standardised training sessions. Three measures met the criteria to be considered both sensitive and reproducible; DJ-RSI, PLML and %PLV. All other measures did not meet the criteria. Subjective ratings of fatigue, muscle soreness and sleep quality did show a sensitive response on one occasion, however, this was not reproducible. This might be due to the categorical nature of the data, making detectable group changes hard to accomplish. The subjective wellness questionnaire was subsequently adapted to include three items; subjective fatigue, muscle soreness and sleep quality on a 10-point scale. The test re-test reliability of these three questions was established in Chapter 6, demonstrating that subjective fatigue and muscle soreness have good test re-test reliability. Chapter 6 was comprised of two studies looking to simultaneously establish the dose-response relationship between training load, measures of fatigue (Part I) and measures of fitness (Part II). In Part I training load was strategically altered on three occasions during a standardised training session in a randomised crossover design. In Part II training and match load was monitored over a 6-week training period with maximal aerobic speed (MAS) assessed pre and post. A key objective for both studies was to assess differences in the training load-fitness-fatigue relationship when using various training load measures, in particular differences between arbitrary and individualised speed thresholds. Results from Part I showed a large to very large relationship between training load and subjective fatigue, muscle soreness and DJ-RSI performance. No differences were found between arbitrary and individualised thresholds. In Part II however, individual external training load, assessed via time above MAS (t>MAS), showed a very large relationship with changes in aerobic fitness. This was in contrast to the unclear relationships with arbitrary thresholds. Taking the results from both studies into consideration it was concluded that t>MAS is a key measure of training load if the objective is to assess the relationship with both fitness and fatigue concurrently with one measure. Chapter 7 subsequently looked to validate the training load-fitness-fatigue relationships established in Chapter 6 via an intervention study. The aim was to develop a novel intervention that prescribed t>MAS, in order to improve aerobic fitness, based on the findings from Chapter 6. Additionally, the fatigue response following a standardised training session was assessed pre and post intervention to evaluate the effect the predicted improvements in aerobic fitness would have on measures of fatigue. Results from Chapter 7 indicate a highly predictable improvement in aerobic fitness from the training load completed during the study, validating the use of t>MAS as a monitoring and intervention tool. Furthermore, this improvement in aerobic fitness attenuated the fatigue response following a standardised training session. The final key finding was the very strong relationship between improvements in aerobic fitness and reductions in fatigue response. This further highlights the relationship between t>MAS, fitness and fatigue. In summary, this thesis has helped further current understanding on the monitoring and prescription of training load, with reference to fitness and fatigue. Firstly, a rigorous approach was used to identify fatigue monitoring measures that are reliable, sensitive and reproducible. Secondly, the relationship between training load, fatigue and fitness was clearly established. And finally, it has contributed new knowledge to the existing literature by establishing the efficacy of a novel MAS intervention to improve aerobic fitness and attenuate a fatigue response in elite youth football players

Relationship between External and Internal Workloads in Elite Soccer Players : Comparison between Rate of Perceived Exertion and Training Load

The use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and SRPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and SRPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports

The effect of training mode on the validity of training load measures for quantifying the training dose in professional rugby league

Establishing the accurate quantification of the training load is a key focus for researchers and sport scientists to maximise the likelihood of appropriate training prescription. In the field, there are numerous methods adopted to quantify the physiological, physical, mechanical, and other loads placed on team sports athletes, including global positioning systems, accelerometry, heart rate and session rating of perceived exertion. Each method can be classified within one of two theoretical constructs: the external or internal training load. Due to the lack of a gold standard criterion, previous research has investigated validity through relationships with criterion measures of load or dose-response associations with chronic changes in physical fitness. The current research designs within investigations into the validity of those methods have failed to consider the influence of the mode of training on the validity of the measures. As strength and conditioning coaches utilise a variety of training modes to stress the various physiological systems to promote the adaptations required to succeed in competition, investigating the influence of training type on training load validity is warranted.To achieve this, the research (Chapters 3-6) was conducted within two professional rugby league clubs, where training load data (global positioning system, accelerometry, heart rate, session rating of perceived exertion) were collected across three twelve week pre-season preparatory periods. Training sessions were demarcated by training mode. The results of the first study showed that meaningful differences in the distances covered within arbitrary speed-and metabolic power-derived-thresholds exist between field-based training modes (small-sided games, conditioning, skills, speed). These differences in external load also led to differences in the perceptual- and heart-rate-derived internal load response. Establishing how those differences in demands influence the relationships between multiple external and internal training load methods is important to establish the validity of individual methods across different modes of training. In our case study approach in study two, the main finding was that when session rating of perceived exertion (sRPE) demonstrated trivial differences across multiple skills training sessions, large variation was present (coefficient of variation range 31-93%) in other training load methods (individualised training impulse [iTRIMP], Body Load™, Total Number of Impacts, high-speed distance) which reduced (coefficient of variation range 3-78%) when sRPE demonstrated trivial differences during small-sided games. This provided initial evidence that training load measures provide different information which might be influenced by the training mode. However, a more comprehensive investigation was needed. In the third study we aimed to examine the influence of training mode on the variance explained between measures of external (arbitrary high-speed distance, Body Load™, total-impacts) and internal (iTRIMP, sRPE) training load over two twelve week pre-season preparatory periods. This was replicated in our fourth study, across a shorter period of training from a different team utilising different methods in which to represent the external (individualised high-speed distance, PlayerLoad™) and internal (heart rate exertion index [HREI], sRPE) training load. During both investigations, we determined the structure of the interrelationships of multiple internal and external load methods via a principal-component analysis (PCA). Within the findings of both investigations, the extraction of multiple dimensions (two principal components) in certain modes of training suggests a single training load measure cannot explain all the information provided by multiple measures used to represent the training load in professional rugby league players. Therefore, if a single measure is used this could underrepresent the actual load imposed onto players. However, establishing the ‘dose-response’ associations between training load and the changes in training outcomes, such as physical fitness is also needed to establish validity. As a result, during study five, we aimed to determine the influence of training mode on the ‘dose-response’ relationship between measures of external (PlayerLoad™ ) and internal (sRPE, HREI) training load and acute changes in physical performance (countermovement jump, 10- and 20-m sprint, Yo-Yo intermittent recovery test level 1) following conditioning and speed training. sRPE was the only training load measure to provide meaningful relationships with changes in Yo-Yo intermittent recovery test level 1 performance. This provides the first evidence of the acute dose-response validity of the sRPE method. No measure provided meaningful relationships with all changes in performance. Therefore, further investigation is warranted to establish whether a combination of measures reflect better those changes than individual measures. The findings of the thesis suggests that practitioner should consider the implementation of both external and internal training load methods within their monitoring practices and researchers should establish multivariate and mode-specific relationships between training load methods to elucidate appropriate evidence of validity

Establishing a Duration Standard for the Calculation of Session Rating of Perceived Exertion in Ncaa Division I Men’s Soccer

Objectives: The purpose of this study was to determine the best predictor of training and/or match load using session RPE. Design and Methods: 20 NCAA DI male soccer players participated in the study during the 2014 and 2015 competitive seasons. Players completed 15.20 ± 1.05 matches for a total of 304 individual data points and 29.90 ± 1.89. training sessions for a total of 598 individual data points. GPS variables (total distance, High-intensity running distance, and Player load) were analyzed with session RPE using Pearson product-moment correlations. To evaluate various methods of session RPE, “match duration” was recorded using eight different definitions: total match duration including warm-up and half-time, total match duration and warm-up, total match duration and half-time, total match duration only, minutes played including warm-up and half-time, minutes played and warm-up, minutes played and half-time, and minutes played only. A one-way ANOVA with repeated measures was used to determine if differences existed between the eight session RPE calculations. Results: Results from the ANOVA showed that all session RPE measures were significantly different from one another (P \u3c 0.05). Very large correlations were reported between session RPE calculated using minutes played and total distance (0.81), while session RPE calculated using match duration showed less magnitude (0.57). Conclusions: Minutes played should be used to calculate session RPE as it was found to most closely reflect the actual workloads incurred during competitive matches

Does training affect match performance? A study using data mining and tracking devices

FIFA has recently allowed the use of electronic performance and tracking systems (EPTS) in professional football competition, providing teams with novel and more accurate data. Physical performance has not yet taken much attention from the research community, due to the difficulty of accessing this information with the same devices during training and competition. This study provides a methodology based on machine learning and statistical methods to relate the physical performance variation of players during time-framed training sessions, and their performance in the following matches. The analysis is carried out over F.C. Barcelona B, season 2015-2016 data, and makes emphasis on exploiting the design characteristics of the structured training methodology implemented within the club. The use of summarized physical variation data has provided a remarkable relation between higher magnitudes of variation in 3-week time frames during training, and higher physical values in the following matches. With increased data availability this and new approaches could provide a new frontier in physical performance analysis. This is, up to our knowledge, the first study to relate training and matches performance through the same EPTS devices in professional football.Peer ReviewedPostprint (published version

Comparison of Static and Countermovement Jump Variables in Relation to Estimated Training Load and Subjective Measures of Fatigue

The purpose of this study was to compare changes in static and countermovement jump variables across a competitive season of collegiate soccer to estimated training load and subjective measures of fatigue. Monitoring data from 21 male collegiate soccer players were retrospectively examined. Nine vertical jump sessions occurred across the season in addition to daily training load assessment and daily mood-state assessment. Group average changes from the first testing session were calculated and compared to the group average training load for the 7 days preceding each vertical jump testing session for static and countermovement jump height and allometrically scaled peak power. Statistical analysis demonstrated strong relationships between changes in vertical jump height for both conditions, allometrically scaled peak power for static jumps, and estimated training load. The results indicate changes in static jump height and allometrically scaled peak power may be more useful athlete fatigue monitoring tools than countermovement jump variables