The relative contribution of training intensity and duration to daily measures of training load in professional rugby league and union

How Do Young Soccer Players Train? A 5-Year Analysis of Weekly Training Load and its Variability Between Age Groups in an Elite Youth Academy

ABSTRACT

Connolly, DR, Stolp, S, Gualtieri, A, Ferrari Bravo, D, Sassi, R, Rampinini, E, and Coutts, AJ. How do young soccer players train? A 5-year analysis of weekly training load and its variability between age groups in an elite youth academy. J Strength Cond Res 38(8): e423-e429, 2024-The aim of this study was to quantify the session rating of perceived exertion (sRPE), duration, and training load accrued across typical training weeks undertaken by youth soccer players. Differences between starters, nonstarters, and variations in training load variables were also investigated. Data were collected from 230 elite youth players in 4 age groups (U15, U16, U17, and U19) during 5 competitive seasons. Mixed models were used to describe variation between age groups and compare starters with nonstarters, with season as a fixed covariate effect. Week-to-week variation in training load was expressed as the percentage coefficient of variation. The main findings may be used to highlight a significant effect of age and playing status on training intensity, duration, and internal training load. Weekly training load increased progressively from the U15 to U17, with significant differences between each age group (p < 0.03). Lower mean weekly perceived intensity (sRPE) was noted in U15 when compared with the older age groups (4.2 vs. 4.6-4.9 arbitrary unit for U16 to U19, p < 0.001). Low weekly training load variation was observed across the different phases of the season in each age group, with the preseason exhibiting the greatest variance (3.6-6.2%). Differences in the training load are likely more attributable to changes in training duration rather than sRPE. Control of session duration seems to play an important role when aiming to control load in the academy environment, and practitioners should closely monitor the differences in duration and load being recorded between starters and nonstarters.

The influence of physical fitness qualities, individual characteristics and contextual factors on youth basketball players' perceived exertion and recovery responses to official games

BACKGROUND

This study examined the influence of physical fitness qualities, individual characteristics, and contextual factors on perceived exertion and recovery responses to official games in youth basketball players.

METHODS

Twenty-six males (age: 15.8±1.2 years; 12 guards, 9 forwards, and 5 centers) and 7 females (age: 16.1±0.9 years; 3 guards, 4 forwards) were monitored for an entire basketball season (N.=635 observations). Yo-Yo Intermittent Recovery (level 1) and countermovement jump (CMJ) tests were administered, with players categorized as high and low Yo-Yo and CMJ groups according to test results. Ratings of perceived exertion (RPE) were collected after each official game. Before the game and the day after, the Total Quality of Recovery (scores) were collected, and the difference between post- and pregame TQR was calculated (TQRΔ). Separate linear mixed models evaluated the effects of sex (M; F), fitness qualities (high Yo-Yo; low Yo-Yo) (high CMJ; low CMJ), playing position (guard; forward; center), game outcome (won; loss) and game location (home; away).

RESULTS

Male players reported higher RPE (7.0±0.3) than females (5.5±0.4) (P=0.003, effect size [ES]: moderate). Players with high Yo-Yo performance also reported higher RPE (6.7±0.4) than low Yo-Yo (5.8±0.3) (P=0.049, ES: small). TQRΔ was higher in guards (-1.3±0.2) than forwards (-0.8±0.2) (P=0.041, ES: trivial), and lower after lost games (-0.8±0.2) compared to won games (-1.2±0.2) (P=0.002, ES: small).

CONCLUSIONS

In youth basketball, postgame perceived exertion and recovery responses are influenced by players' sex, intermittent endurance capacity, and game outcome. Current findings can help youth basketball practitioners to better understand their players' performances and perceptual responses.

The physical, technical and tactical demands of on-field training drills in professional Rugby league: a systematic scoping review

OBJECTIVES

The main objectives of this scoping review were to conduct a systematic search on the physical, technical and tactical demands of rugby league training, consolidate and summarise key findings and identify any existing gaps in knowledge.

METHODS

A systematic online search of Scopus, PubMed, MEDLINE and SPORTDiscus was conducted from earliest record to 6 August 2023 and supplemented by manually searching reference lists. The Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist was followed. Studies were eligible for inclusion if they investigated the physical, technical and/or tactical demands of rugby league training within all levels of competition and included either male or female participants.

RESULTS

The initial search yielded 637 papers, 25 of which were included in the review. Of these studies, the majority (n = 19) exclusively examined the physical demands of training, one paper exclusively examined the technical demands of training, five studies included both physical and technical demands, and no studies examined the tactical demands of training. Small-sided games was the most prevalent drill included within investigations examining the physical and technical demands of various rugby league training drills.

CONCLUSIONS

The present review was the first to scope peer-reviewed literature on the multifaceted demands (i.e. physical, technical and tactical) demands of rugby league training. It is apparent that this area is under researched, specifically in literature examining the technical and tactical elements of rugby league training.

Innovation in sport medicine and science: a global social network analysis of stakeholder collaboration in rugby union

Objectives

To investigate the network of stakeholders involved in rugby union research across the globe.

Methods

Using author affiliations listed on scientific publications, we identified the organisations that contributed to rugby union research from 1977 to 2022 and examine collaboration through coauthorship indicators. We determined the locations and sectors of identified organisations and constructed a collaboration network. Network metrics, including degree centrality and betweenness centrality, are computed to identify influential organisations and measure intersector collaboration.

Results

There is an increase in scientific knowledge creation and collaboration between organisations for rugby union research over time. Among the sectors, the university, professional sports team and sports governing body sectors exhibit the highest intersectoral and intrasectoral density. Predominantly, influential actors are located in England, Australia, France, New Zealand, Ireland and South Africa. Australian Catholic University, Leeds Beckett University, Stellenbosch University, Swansea University, University College London and the University of Cape Town emerge as influential actors between 2016 and 2022.

Conclusions

Our study underscores the ongoing growth of scientific knowledge generation in rugby union, primarily led by organisations in tier 1 rugby-playing nations within the university sector. Intersectoral collaboration with sports governing bodies plays a crucial role, acting as a broker between sectors. However, the overall collaboration landscape between and within sectors is low. These results highlight an opportunity for improved collaboration opportunities, as the organisations driving knowledge creation have been identified.

The Relationships Between External and Internal Training Loads in Mixed Martial Arts

PURPOSE

As a multidisciplined combat sport, relationships between external and internal training loads and intensities of mixed martial arts (MMA) have not been described. The aim of this study was to determine the external loads and intensities of MMA training categories and their relationship to internal loads and intensities.

METHODS

Twenty MMA athletes (age = 23.3 [5.3] y, mass = 72.1 [7.2] kg, stature = 171.5 [8.4] cm) were observed for 2 consecutive weeks. Internal load and intensity (session rating of perceived exertion [sRPE]) were calculated using the Foster RPE for the session overall (sRPE-training load [TL]) and segmented RPE (segRPE-TL) for each training category: warm-up, striking drills, wrestling drills, Brazilian jiujitsu (BJJ) drills, striking sparring, wrestling sparring, BJJ sparring, and MMA sparring. External load and intensity were measured via Catapult OptimEye S5 for the full duration of each session using accumulated Playerload (PLdACC) and PLdACC per minute (PLdACC·min-1). Differences in loads between categories and days were assessed via Bayesian analysis of variance (BF10 ≥ 3). Predictive relationships between internal and external variables were calculated using Bayesian regression.

RESULTS

Session overall sRPE-TL = 448.6 (191.1) arbitrary units (AU); PLdACC = 310.6 (112) AU. Category segRPE-TL range = 33.8 (22.6) AU (warm-up) to 122.8 (54.6) AU (BJJ drills). Category PLdACC range = 44 (36.3) AU (warm-up) to 125 (58.8) AU (MMA sparring). Neither sRPE-TL nor PLdACC changed between days. PLdACC was different between categories. Evidence for regressions was strong-decisive except for BJJ drills (BF10 = 7, moderate). R2 range = .50 to .77, except for warm-up (R2 = .17), BJJ drills (R2 = .27), BJJ sparring (R2 = .49), and session overall (R2 = .13).

CONCLUSIONS

While MMA training categories may be differentiated in terms of external load, overall session external load does not change within or between weeks. Resultant regression equations may be used to appropriately plan MMA technical/tactical training loads.

Female basketball game performance is influenced by menstrual cycle phase, age, perceived demands and game-related contextual factors

This study evaluated the influence of physical and perceived game demands, menstrual cycle phase, perceived recovery, individual and game-related contextual factors on competitive performance in professional, female basketball players.11 professional female players (age: 20.6 ± 2.7 years) were monitored for game-related statistics (Performance Index Rating, PIR; rebounds, REB; effective field goal %, eFG%; turnovers, TO), objective (PlayerLoad per minute, PL·min-1) and subjective (RPE) game loads, pre-game perceived recovery (Total Quality Recovery, TQRpre), menstrual phase (follicular; luteal) and game-related contextual factors (game location; game outcome; score differential; opponent level) during 12 official games. Separate linear mixed models were used to evaluate the influence of RPE, PL·min-1, TQRpre, menstrual phase, contextual factors, and individual characteristics (age; playing position) on game-related statistics.Higher PIR and eFG% were found for older players and those who reported higher RPE (all p < 0.05). Higher age also led to less TO (p = 0.042). eFG% was higher when players reported higher TQRpre ;(p = 0.010). Better shooting (eFG%) and rebounding (REB) performances were found during the follicular menstrual phase (p < 0.05). More REB were collected in won games (p = 0.002).This study suggests that the co-influences of perceptual, menstrual-related, individual and game-related contextual factors should be considered to optimise female basketball players' performance.

Relationship Between Athlete-Reported Outcome Measures and Subsequent Match Performance in Team Sports: A Systematic Review

ABSTRACT

Sansone, P, Rago, V, Kellmann, M, and Alcaraz, PE. Relationship between athlete-reported outcome measures and subsequent match performance in team sports: A systematic review. J Strength Cond Res 37(11): 2302-2313, 2023-Athlete-reported outcome measures (AROMs; e.g., fatigue, stress, readiness, recovery, and sleep quality) are commonly implemented in team sports to monitor the athlete status. However, the relationship between AROMs and match performance indicators is unclear and warrants further investigation. This systematic review examined the relationship between precompetitive AROMs and subsequent match performances of team sport athletes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 3 (PubMed, Scopus, and Web of Science) databases were systematically searched to retrieve studies investigating the effects or association of AROMs and match: (a) technical-tactical performance (match-related statistics), (b) physical performance, (c) physiological and (d) perceptual demands, and (e) other measures of performance in adult team sport athletes. Quality assessment of included studies was performed using a modified Black and Downs checklist. Fifteen articles representing 289 team sport athletes were included. Mean quality of included studies was 7.6 ± 1.0 (of 11). Across the included studies, 22 AROMs parameters were used, and 16 different statistical approaches were identified. Approximately 11 of 15 studies used nonvalidated AROMs. Overall, associations or effects of AROMs were found consistently for match-related statistics (7/9 studies), whereas results were unclear for physical performances (3/7 studies), perceptual demands (1/2 studies), or other measures of performance (2/4 studies). Considering the importance of key match-related statistics for success in team sports, this review suggests that monitoring precompetitive AROMs has potential to provide valuable information to coaches. However, it is indispensable to validate AROMs questionnaires and to uniform data collection and statistical procedures before substantiated indications to practitioners can be made.

Resultant equations for training load monitoring during a standard microcycle in sub-elite youth football: a principal components approach

Applying data-reduction techniques to extract meaningful information from electronic performance and tracking systems (EPTS) has become a hot topic in football training load (TL) monitoring. The aim of this study was to reduce the dimensionality of the internal and external load measures, by a principal component approach, to describe and explain the resultant equations for TL monitoring during a standard in-season microcycle in sub-elite youth football. Additionally, it is intended to identify the most representative measure for each principal component. A principal component analysis (PCA) was conducted with a Monte Carlo parallel analysis and VariMax rotation to extract baseline characteristics, external TL, heart rate (HR)-based measures and perceived exertion. Training data were collected from sixty sub-elite young football players during a 6-week training period using 18 Hz global positioning system (GPS) with inertial sensors, 1 Hz short-range telemetry system, total quality recovery (TQR) and rating of perceived exertion (RPE). Five principal components accounted for 68.7% of the total variance explained in the training data. Resultant equations from PCA was subdivided into: (1) explosiveness, accelerations and impacts (27.4%); (2) high-speed running (16.2%); (3) HR-based measures (10.0%); (4) baseline characteristics (8.3%); and (5) average running velocity (6.7%). Considering the highest factor in each principal component, decelerations (PCA 1), sprint distance (PCA 2), average HR (PCA 3), chronological age (PCA 4) and maximal speed (PCA 5) are the conditional dimension to be considered in TL monitoring during a standard microcycle in sub-elite youth football players. Current research provides the first composite equations to extract the most representative components during a standard in-season microcycle in sub-elite youth football players. Futures research should expand the resultant equations within training days, by considering other well-being measures, technical-tactical skills and match-related contextual factors.

Validity of the Training-Load Concept

Training load (TL) is a widely used concept in training prescription and monitoring and is also recognized as as an important tool for avoiding athlete injury, illness, and overtraining. With the widespread adoption of wearable devices, TL metrics are used increasingly by researchers and practitioners worldwide. Conceptually, TL was proposed as a means to quantify a dose of training and used to predict its resulting training effect. However, TL has never been validated as a measure of training dose, and there is a risk that fundamental problems related to its calculation are preventing advances in training prescription and monitoring. Specifically, we highlight recent studies from our research groups where we compare the acute performance decrement measured following a session with its TL metrics. These studies suggest that most TL metrics are not consistent with their notional training dose and that the exercise duration confounds their calculation. These studies also show that total work done is not an appropriate way to compare training interventions that differ in duration and intensity. We encourage scientists and practitioners to critically evaluate the validity of current TL metrics and suggest that new TL metrics need to be developed.

A four-season study quantifying the weekly external training loads during different between match microcycle lengths in professional rugby league

This study investigated differences in external training load between microcycle lengths and its variation between microcycles, players, and head coaches. Commonly used external training load variables including total-, high-speed- (5-7 m∙s-1), and sprint-distance (> 7 m∙s-1) alongside combined high acceleration and deceleration distance (> 2 m∙s-2). Which were also expressed relative to time were collected using microtechnology within a repeated measures design from 54 male rugby league players from one Super League team over four seasons. 4337 individual observations across ninety-one separate microcycles and six individual microcycle lengths (5 to 10 day) were included. Linear mixed effects models established the differences in training load between microcycle-length and the variation between-microcycles, players and head coaches. The largest magnitude of difference in training load was seen when comparing 5-day with 9-day (ES = 0.31 to 0.53) and 10-day (ES = 0.19 to 0.66) microcycles. The greatest number of differences between microcycles were observed in high- (ES = 0.3 to 0.53) and sprint-speed (ES = 0.2 to 0.42) variables. Between-microcycle variability ranged between 11% to 35% dependent on training load variable. Training load also varied between players (5-65%) and head coaches (6-20%) with most variability existing within high-speed (19-43%) and sprinting (19-65%). Overall, differences in training load between microcycle lengths exist, likely due to manipulation of session duration. Furthermore, training load varies between microcycle, player and head coach.

Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis

Seeking to obtain a competitive advantage and manage the risk of injury, team sport organisations are investing in tracking systems that can quantify training and competition characteristics. It is expected that such information can support objective decision-making for the prescription and manipulation of training load. This narrative review aims to summarise, and critically evaluate, different tracking systems and their use within team sports. The selection of systems should be dependent upon the context of the sport and needs careful consideration by practitioners. The selection of metrics requires a critical process to be able to describe, plan, monitor and evaluate training and competition characteristics of each sport. An emerging consideration for tracking systems data is the selection of suitable time analysis, such as temporal durations, peak demands or time series segmentation, whose best use depends on the temporal characteristics of the sport. Finally, examples of characteristics and the application of tracking data across seven popular team sports are presented. Practitioners working in specific team sports are advised to follow a critical thinking process, with a healthy dose of scepticism and awareness of appropriate theoretical frameworks, where possible, when creating new or selecting an existing metric to profile team sport athletes.

Misuse of the term 'load' in sport and exercise science

Despite the International System of Units (SI), as well as several publications guiding researchers on correct use of terminology, there continues to be widespread misuse of mechanical terms such as 'work' in sport and exercise science. A growing concern is the misuse of the term 'load'. Terms such as 'training load' and 'PlayerLoad' are popular in sport and exercise science vernacular. However, a 'load' is a mechanical variable which, when used appropriately, describes a force and therefore should be accompanied with the SI-derived unit of the newton (N). It is tempting to accept popular terms and nomenclature as scientific. However, scientists are obliged to abide by the SI and must pay close attention to scientific constructs. This communication presents a critical reflection on the use of the term 'load' in sport and exercise science. We present ways in which the use of this term breaches principles of science and provide practical solutions for ongoing use in research and practice.

A Novel Approach to Training Monotony and Acute-Chronic Workload Index: A Comparative Study in Soccer

Load is a multifactorial construct, but usually reduced to parameters of volume and intensity. In the last decades, other constructs have been proposed for assessing load, but also relying on relationships between volume and intensity. For example, Foster's Training Monotony has been used in athletes' load management simply by computing mean weekly load divided by its standard deviation, often multiplied by session rate of perceived exertion. Meanwhile, the Acute to Chronic Workload Ratio (ACWR) has been debated by the sport scientists as a useful monitoring metric and related to so-called injury prevention. None of these models includes parameters that are representative of training specificity, namely load orientation. The aim of this study is to present broader conceptual approaches translated by new indices for assessing Intraweek Training Monotony (ITM) and Acute to Chronic Workload Index (ACWI) while incorporating load orientation, session duration and weekly density (frequency normalized) in addition to parameters related to proxies of external and/or internal load. Our ITM and Foster's Training Monotony were similar in terms of average values, but very different for individualized analysis, illustrating how average values may be deceiving. While Foster's model provided clusters of values, ITM provided more scattered, individualized data. ACWI and ACWR provided very distinct qualitative information, and the two models were uncorrelated. Therefore, the models incorporating training load orientation presented in this study provide distinct and not redundant information when compared to previous models. More importantly, ITM and ACWI are metrics that are compatible to each other and might fit to coaches' monitoring targets in the short and medium terms, respectively. Because our models include several parameters, including load orientation, we contend that might provide a more complete monitoring tool. However, we suggest they are used for intraindividual comparisons and not so strongly for interindividual comparisons.

Continuous Versus Intermittent Running: Acute Performance Decrement and Training Load

PURPOSE

To examine the effect of continuous (CON) and intermittent (INT) running training sessions of different durations and intensities on subsequent performance and calculated training load (TL).

METHODS

Runners (N = 11) performed a 1500-m time trial as a baseline and after completing 4 different running training sessions. The training sessions were performed in a randomized order and were either maximal for 10 minutes (10CON and 10INT) or submaximal for 25 minutes (25CON and 25INT). An acute performance decrement (APD) was calculated as the percentage change in 1500-m time-trial speed measured after training compared with baseline. The pattern of APD response was compared with that for several TL metrics (bTRIMP, eTRIMP, iTRIMP, running training stress score, and session rating of perceived exertion) for the respective training sessions.

RESULTS

Average speed (P < .001, ηp2=.924) was different for each of the initial training sessions, which all resulted in a significant APD. This APD was similar when compared across the sessions except for a greater APD found after 10INT versus 25CON (P = .02). In contrast, most TL metrics were different and showed the opposite response to APD, being higher for CON versus INT and lower for 10- versus 25-minute sessions (P < .001, ηp2>.563).

CONCLUSION

An APD was observed consistently after running training sessions, but it was not consistent with most of the calculated TL metrics. The lack of agreement found between APD and TL suggests that current methods for quantifying TL are flawed when used to compare CON and INT running training sessions of different durations and intensities.