Red, Amber, or Green? Athlete Monitoring in Team Sport: The Need for Decision-Support Systems

Statistical Tests for Sports Science Practitioners: Identifying Performance Gains in Individual Athletes

ABSTRACT

Harry, JR, Hurwitz, J, Agnew, C, and Bishop, C. Statistical tests for sports science practitioners: identifying performance gains in individual athletes. J Strength Cond Res 38(5): e264-e272, 2024-There is an ongoing surge of sports science professionals within sports organizations. However, when seeking to determine training-related adaptations, sports scientists have demonstrated continued reliance on group-style statistical analyses that are held to critical assumptions not achievable in smaller-sample team settings. There is justification that these team settings are better suited for replicated single-subject analyses, but there is a dearth of literature to guide sports science professionals seeking methods appropriate for their teams. In this report, we summarize 4 methods' ability to detect performance adaptations at the replicated single-subject level and provide our assessment for the ideal methods. These methods included the model statistic, smallest worthwhile change, coefficient of variation (CV), and standard error of measurement (SEM), which were discussed alongside step-by-step guides for how to conduct each test. To contextualize the methods' use in practice, real countermovement vertical jump (CMJ) test data were used from 4 (2 females and 2 males) athletes who complete 5 biweekly CMJ test sessions. Each athlete was competing in basketball at the NCAA Division 1 level. We concluded that the combined application of the model statistic and CV methods should be preferred when seeking to objectively detect meaningful training adaptations in individual athletes. This combined approach ensures that the differences between the tests are (a) not random and (b) reflect a worthwhile change. Ultimately, the use of simple and effective methods that are not restricted by group-based statistical assumptions can aid practitioners when conducting performance tests to determine athlete adaptations.

Within-Subject Principal Component Analysis of External Training Load and Intensity Measures in Youth Soccer Training

ABSTRACT

Marynowicz, J, Lango, M, Horna, D, Kikut, K, Konefał, M, Chmura, P, and Andrzejewski, M. Within-participant principal component analysis of external training load and intensity measures in youth soccer training. J Strength Cond Res 37(12): 2411-2416, 2023-The aim of this study was to identify which combination of external training load (EL) and external intensity (EI) metrics during youth soccer training sessions captured similar or unique information. Data were collected from 18 youth soccer players during an 18-week in-season competition period using a 10-Hz global positioning system, rating of perceived exertion (RPE), and session-RPE (sRPE). External training load measures included total distance (TD, in meters), PlayerLoad (PL, in arbitrary units), high-speed running distance (HSR, in meters), and number of accelerations (ACC, n). All EL metrics were also divided by session duration (minutes) to obtain EI values. A total of 804 training observations were undertaken (43 ± 17 sessions per player). The analysis was performed by use of the principal component analysis technique. The first principal component (PC) captured 49-70% and 68-89% of the total variance in EI and EL, respectively. The findings show that from the 5 EI metrics, most of the information can be explained by either TD per minute or PL per minute, with a loading from 0.87 to 0.98 and from 0.76 to 0.95, respectively. The majority of EL information can be explained by PL (loading: 0.93-0.98), TD (loading: 0.95-0.99), ACC (loading: 0.71-0.91), or sRPE (loading: 0.70-0.93). The second PC for EL metrics is most strongly correlated with HSR, with loadings from 0.53 to 0.84. The results suggest that the majority of the information contained in the EL variables can be captured in 1 PC without losing much information. The findings suggest that stakeholders who intend to provide a fast and holistic view of EL information in a daily training environment should report TD, PL, ACC, or sRPE plus HSR to coaching staff as a metrics that provides additional unique information.

Defining and quantifying fatigue in the rugby codes

The rugby codes (i.e., rugby union, rugby league, rugby sevens [termed 'rugby']) are team-sports that impose multiple complex physical, perceptual, and technical demands on players which leads to substantial player fatigue post-match. In the post-match period, fatigue manifests through multiple domains and negatively influences recovery. There is, however, currently no definition of fatigue contextualised to the unique characteristics of rugby (e.g., locomotor and collision loads). Similarly, the methods and metrics which practitioners consider when quantifying the components of post-match fatigue and subsequent recovery are not known. The aims of this study were to develop a definition of fatigue in rugby, to determine agreement with this common definition of fatigue, and to outline which methods and metrics are considered important and feasible to implement to quantify post-match fatigue. Subject matter experts (SME) undertook a two-round online Delphi questionnaire (round one; n = 42, round two; n = 23). SME responses in round one were analysed to derive a definition of fatigue, which after discussion and agreement by the investigators, obtained 96% agreement in round two. The SME agreed that fatigue in rugby refers to a reduction in performance-related task ability which is underpinned by time-dependent negative changes within and between cognitive, neuromuscular, perceptual, physiological, emotional, and technical/tactical domains. Further, there were 33 items in the neuromuscular performance, cardio-autonomic, or self-report domains achieved consensus for importance and/or feasibility to implement. Highly rated methods and metrics included countermovement jump force/power (neuromuscular performance), heart rate variability (cardio-autonomic measures), and soreness, mood, stress, and sleep quality (self-reported assessments). A monitoring system including highly-rated fatigue monitoring objective and subjective methods and metrics in rugby is presented. Practical recommendations of objective and subjective measures, and broader considerations for testing and analysing the resulting data in relation to monitoring fatigue are provided.

Operational Insights into Analysing Team and Player Performance in Elite Rugby League: A Narrative Review with Case Examples

In professional team sports, like Rugby League, performance analysis has become an integral part of operational practices. This has helped practitioners gain deeper insight into phenomena like team and athlete behaviour and understanding how such behaviour may be influenced by various contextual factors. This information can then be used by coaches to design representative practice tasks, inform game principles and opposition strategies, and even support team recruitment practices. At the elite level, the constant evolution of sports technology (both hardware and software) has enabled greater access to information, making the role of the performance analyst even more valuable. However, this increase in information can create challenges regarding which variables to use to help guide decision-making, and how to present it in ways that can be utilised by coaches and other support staff. While there are published works exploring aspects of performance analysis in team sports like Rugby League, there is yet to be a perspective that explores the various operational uses of performance analysis in Rugby League, the addition of which could help guide the practices of emerging performance analysts in elite organisations like the Australian National Rugby League and the European Super League. Thus, this narrative review—with accompanying case examples—explores the various ways performance analysis can help address pertinent operational questions commonly encountered when working in high-performance sport.

Force Plate-Derived Countermovement Jump Normative Data and Benchmarks for Professional Rugby League Players

The countermovement jump (CMJ) is an important test in rugby league (RL), and the force plate is the recommended assessment device, as it permits the calculation of several variables that explain jump strategy, alongside jump height. The purpose of this study was to produce normative CMJ data and objective benchmarks for professional RL forwards and backs. Normative data for jump height, modified reactive strength index, and jump momentum are provided for 121 professional RL players (66 forwards and 55 backs) who completed CMJ testing on a portable force plate during preseason training. Standardized T-scores (scaled from 0 to 100) were calculated from the respective positional group mean and standard deviation to create CMJ performance bands that were combined with a qualitative description (ranging from extremely poor to excellent) and a traffic light system to facilitate data interpretation and objective benchmark setting by RL practitioners. The jump height and modified reactive strength index benchmarks were larger for the lighter backs, whereas the jump momentum benchmarks were larger for the heavier forwards. The presented novel approach to compiling and presenting normative data and objective benchmarks may also be applied to other data (i.e., from other tests or devices) and populations.

Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care

There is growing interest in the role of predictive analytics in sport, where such extensive data collection provides an exciting opportunity for the development and utilisation of prediction models for medical and performance purposes. Clinical prediction models have traditionally been developed using regression-based approaches, although newer machine learning methods are becoming increasingly popular. Machine learning models are considered 'black box'. In parallel with the increase in machine learning, there is also an emergence of proprietary prediction models that have been developed by researchers with the aim of becoming commercially available. Consequently, because of the profitable nature of proprietary systems, developers are often reluctant to transparently report (or make freely available) the development and validation of their prediction algorithms; the term 'black box' also applies to these systems. The lack of transparency and unavailability of algorithms to allow implementation by others of 'black box' approaches is concerning as it prevents independent evaluation of model performance, interpretability, utility, and generalisability prior to implementation within a sports medicine and performance environment. Therefore, in this Current Opinion article, we: (1) critically examine the use of black box prediction methodology and discuss its limited applicability in sport, and (2) argue that black box methods may pose a threat to delivery and development of effective athlete care and, instead, highlight why transparency and collaboration in prediction research and product development are essential to improve the integration of prediction models into sports medicine and performance.

Digitally Optimizing the Information Flows Necessary to Manage Professional Athletes: A Case Study in Rugby Union

Practical case studies elaborating end-to-end attempts to improve the quality of information flows associated with athlete management processes are scarce in the current sport literature. Therefore, guided by a Business Process Management (BPM) approach, the current study presents the outcomes from a case study to optimize the quality of strength and conditioning (S&C) information flow in the performance department of a professional rugby union club. Initially, the S&C information flow was redesigned using integral technology, activity elimination and activity automation redesign heuristics. Utilizing the Lean Startup framework, the redesigned information flow was digitally transformed by designing data collection, management and visualization systems. Statistical tests used to assess the usability of the data collection systems against industry benchmarks using the System Usability Scale (SUS) administered to 55 players highlighted that its usability (mean SUS score of 87.6 ± 10.76) was well above average industry benchmarks of similar systems (Grade A from SUS scale). In the data visualization system, 14 minor usability problems were identified from 9 cognitive walkthroughs conducted with the High-Performance Unit (HPU) staff. Pre-post optimization information quality was subjectively assessed by administering a standardized questionnaire to the HPU members. The results indicated positive improvements in all of the information quality dimensions (with major improvements to the accessibility) relating to the S&C information flow. Additionally, the methods utilized in the study would be especially beneficial for sporting environments requiring cost effective and easily adoptable information flow digitization initiatives which need to be implemented by its internal staff members.

Risk or benefit? Side effects of caffeine supplementation in sport: a systematic review

PURPOSE

The aim of this study was to systematically review evidence on the prevalence and magnitude of side effects associated with caffeine supplementation in athletes.

METHODS

Systematic searches through the PubMed, VHL, Scopus, and Web of Science databases were conducted according to the PRISMA guidelines. Peer-reviewed articles written in English that reported the prevalence/percentage or magnitude/effect size of side effects after caffeine supplementation in athletes in a sports context were included. Studies were grouped by the dose of caffeine administered as follows: low =  ≤ 3.0 mg/kg; moderate = from 3.1 to 6.0 mg/kg; high =  ≥ 6.1 mg/kg. The magnitude of the side effects was calculated with effect sizes.

RESULTS

The search retrieved 25 studies that met the inclusion/exclusion criteria with a pooled sample of 421 participants. The supplementation with caffeine produced a higher prevalence or magnitude of all side effects under investigation when compared to placebo/control situations. The prevalence (magnitude) was between 6 and 34% (ES between 0.13 and 1.11) for low doses of caffeine, between 0 and 34% (ES between -0.13 and 1.20) for moderate doses of caffeine, and between 8 and 83% (ES between 0.04 and 1.52) with high doses of caffeine. The presence of tachycardia/heart palpitations and the negative effects on sleep onset had the highest prevalence and magnitude, in athletes using supplementation with caffeine.

CONCLUSION

In summary, caffeine supplementation in the doses habitually used to enhance physical performance produces several side effects, both after exercise and at least 24 h after the ingestion. However, the prevalence and magnitude of side effects with high doses of caffeine were habitually higher than with low doses of caffeine. From a practical perspective, using ~3.0 mg/kg of caffeine may be the dose of choice to obtain the ergogenic benefits of caffeine with the lowest prevalence and magnitude of side effects.

Chronic Workload, Subjective Arm Health, and Throwing Injury in High School Baseball Players: 3-Year Retrospective Pilot Study

BACKGROUND

Baseball workloads are monitored by pitch counts, appearances, innings per appearance, ball velocity, and distance, whereas current workload standards neglect throws made during nongame situations. The association between total workloads, subjective measures, and injury in baseball is poorly understood. The question remains whether baseball athletes are at higher risk of injury by throwing more often or if they generate injury resilience when appropriately transitioned into the higher demands of throwing.

HYPOTHESIS

Increased chronic load, along with subjective arm health measures, are related to decreased injury risk.

STUDY DESIGN

Clinical research.

LEVEL OF EVIDENCE

Level 3.

METHODS

A total of 49 male baseball players (age 17.9 ± 0.4 years, height 181.8 ± 6.8 cm, body mass 80.6 ± 9.1 kg) competing at the varsity high school level were included in this 3-year retrospective data analysis from 2016 to 2019. Players wore the motusTHROW sleeve and sensor during all throwing activities.

RESULTS

A total of 898,492 throws and 9455 athletic exposures were captured with the motusTHROW sensors. There were 24 injuries recorded throughout the 3-year analysis, with 11 throwing-related and 13 non-throwing related injuries. Results of the 1-way analysis of variance found chronic load was significantly related to throwing-injury occurrence (P < 0.01). Six of the throwing-related injuries occurred when athletes had a chronic load greater than 11.3, marking 75th percentile across all observations. There was a relationship between arm health and throwing arm-related injury occurrence (P < 0.01). Higher chronic load was associated with increased throwing-related injuries even when adjusted for arm health (P = 0.01). Specifically, injuries were more likely to occur in pitchers (either as a combination player or pitcher only) with a chronic load greater than 9.2.

CONCLUSION

This study revealed a significant relationship between chronic load, subjective arm health, and throwing-related injury in varsity high school baseball players. Contrary to our hypothesis, increased chronic load was associated with increased injury risk. However, subjective arm health measures remain a relevant factor in assessing injury risk. Normative data for this population also provide key information around positional demands along with overall demands of the sport during the competitive season and off-season.

Quantifying Fatigue in the Rugby Codes: The Interplay Between Collision Characteristics and Neuromuscular Performance, Biochemical Measures, and Self-Reported Assessments of Fatigue

Locomotor and collision actions that rugby players complete during match-play often lead to substantial fatigue, and in turn, delays in recovery. The methods used to quantify post-match fatigue and recovery can be categorised as subjective and objective, with match-related collision characteristics thought to have a primary role in modulating these recovery measures. The aim of this review was to (1) evaluate how post-match recovery has been quantified in the rugby football codes (i.e., rugby league, rugby union, and rugby sevens), (2) to explore the time-course of commonly used measures of fatigue post-match, and (3) to investigate the relationships between game-related collisions and fatigue metrics. The available evidence suggests that upper-, and lower-body neuromuscular performance are negatively affected, and biomarkers of muscular damage and inflammation increase in the hours and days following match-play, with the largest differences being at 12–36 h post-match. The magnitude of such responses varies within and between neuromuscular performance (Δ ≤ 36%, n = 13 studies) and tissue biomarker (Δ ≤ 585%, n = 18 studies) measures, but nevertheless appears strongly related to collision frequency and intensity. Likewise, the increase in perceived soreness in the hours and days post-match strongly correlate to collision characteristics across the rugby football codes. Within these findings, there are specific differences in positional groups and recovery trajectories between the codes which relate to athlete characteristics, and/or locomotor and collision characteristics. Finally, based on these findings, we offer a conceptual model of fatigue which details the multidimensional latent structure of the load to fatigue relationship contextualised to rugby. Research to date has been limited to univariate associations to explore relationships between collision characteristics and recovery, and multivariate methods are necessary and recommended to account for the latent structures of match-play external load and post-match fatigue constructs. Practitioners should be aware of the typical time windows of fatigue recovery and utilise both subjective and objective metrics to holistically quantify post-match recovery in rugby.

Managing the Training Process in Elite Sports: From Descriptive to Prescriptive Data Analytics

Elite sport practitioners increasingly use data to support training process decisions related to athletes' health and performance. A careful application of data analytics is essential to gain valuable insights and recommendations that can guide decision making. In business organizations, data analytics are developed based on conceptual data analytics frameworks. The translation of such a framework to elite sport may benefit the use of data to support training process decisions. Purpose: The authors aim to present and discuss a conceptual data analytics framework, based on a taxonomy used in business analytics literature to help develop data analytics within elite sport organizations. Conclusions: The presented framework consists of 4 analytical steps structured by value and difficulty/complexity. While descriptive (step 1) and diagnostic analytics (step 2) focus on understanding the past training process, predictive (step 3) and prescriptive analytics (step 4) provide more guidance in planning the future. Although descriptive, diagnostic, and predictive analytics generate insights to inform decisions, prescriptive analytics can be used to drive decisions. However, the application of this type of advanced analytics is still challenging in elite sport. Thus, the current use of data in elite sport is more focused on informing decisions rather than driving them. The presented conceptual framework may help practitioners develop their analytical reasoning by providing new insights and guidance and may stimulate future collaborations between practitioners, researchers, and analytics experts.

Load Monitoring Practice in Elite Women Association Football

The description of current load monitoring practices may serve to highlight developmental needs for both the training ground, academia and related industries. While previous studies described these practices in elite men's football, no study has provided an overview of load monitoring practices in elite women's football. Given the clear organizational differences (i.e., professionalization and infrastructure) between men's and women's clubs, making inferences based on men's data is not appropriate. Therefore, this study aims to provide a first overview of the current load monitoring practices in elite women's football. Twenty-two elite European women's football clubs participated in a closed online survey (40% response rate). The survey consisted of 33 questions using multiple choice or Likert scales. The questions covered three topics; type of data collected and collection purpose, analysis methods, and staff member involvement. All 22 clubs collected data related to different load monitoring purposes, with 18 (82%), 21 (95%), and 22 (100%) clubs collecting external load, internal load, and training outcome data, respectively. Most respondents indicated that their club use training models and take into account multiple indicators to analyse and interpret the data. While sports-science staff members were most involved in the monitoring process, coaching, and sports-medicine staff members also contributed to the discussion of the data. Overall, the results of this study show that most elite women's clubs apply load monitoring practices extensively. Despite the organizational challenges compared to men's football, these observations indicate that women's clubs have a vested interest in load monitoring. We hope these findings encourage future developments within women's football.

A Wearable System for the Estimation of Performance-Related Metrics during Running and Jumping Tasks

Athletic performance, technique assessment, and injury prevention are all important aspects in sports for both professional and amateur athletes. Wearable technology is attracting the research community’s interest because of its capability to provide real-time biofeedback to coaches and athletes when on the field and outside of more restrictive laboratory conditions. In this paper, a novel wearable motion sensor-based system has been designed and developed for athletic performance assessment during running and jumping tasks. The system consists of a number of components involving embedded systems (hardware and software), back-end analytics, information and communications technology (ICT) platforms, and a graphical user interface for data visualization by the coach. The system is able to provide automatic activity recognition, estimation of running and jumping metrics, as well as vertical ground reaction force (GRF) predictions, with sufficient accuracy to provide valuable information as regards training outcomes. The developed system is low-power, sufficiently small for real-world scenarios, easy to use, and achieves the specified communication range. The system’s high sampling rate, levels of accuracy and performance enables it as a performance evaluation tool able to support coaches and athletes in their real-world practice.

Training Load Monitoring Considerations for Female Gaelic Team Sports: From Theory to Practice

Athlete monitoring enables sports science practitioners to collect information to determine how athletes respond to training loads (TL) and the demands of competition. To date, recommendations for females are often adapted from their male counterparts. There is currently limited information available on TL monitoring in female Gaelic team sports in Ireland. The collection and analysis of female athlete monitoring data can provide valuable information to support the development of female team sports. Athletic monitoring can also support practitioners to help minimize risk of excessive TL and optimize potential athletic performance. The aims of this narrative review are to provide: (i) an overview of TL athlete monitoring in female team sports, (ii) a discussion of the potential metrics and tools used to monitor external TL and internal TL, (iii) the advantages and disadvantages of TL modalities for use in Gaelic team sports, and (iv) practical considerations on how to monitor TL to aid in the determination of meaningful change with female Gaelic team sports athletes.

Assessment of Protocol Impact on Subjectivity Uncertainty When Analyzing Peripheral Blood Mononuclear Cell Flow Cytometry Data Files

Measured variability of product within Cell and Gene Therapy (CGT) manufacturing arises from numerous sources across pre-analytical to post-analytical phases of testing. Operators are a function of the manufacturing process and are an important source of variability as a result of personal differences impacted by numerous factors. This research uses measurement uncertainty in comparison to Coefficient of Variation to quantify variation of participants when they complete Flow Cytometry data analysis through a 5-step gating sequence. Two study stages captured participants applying gates using their own judgement, and then following a diagrammatical protocol, respectively. Measurement uncertainty was quantified for each participant (and analysis phase) by following Guide to the Expression of Uncertainty in Measurement protocols, combining their standard deviations in quadrature from each gating step in the respective protocols. When participants followed a diagrammatical protocol, variation between participants reduced by 57%, increasing confidence in a more uniform reported cell count percentage. Measurement uncertainty provided greater resolution to the analysis processes, identifying that most variability contributed in the Flow Cytometry gating process is from the very first gate, where isolating target cells from dead or dying cells is required. This work has demonstrated the potential for greater usage of measurement uncertainty within CGT manufacturing scenarios, due to the resolution it provides for root cause analysis and continuous improvement.

A framework for effective knowledge translation and performance delivery of Sport Scientists in professional sport

Sport Science is considered the study and application of scientific principles and techniques to improve sporting performance. Thus, a key role of the Sport Scientist is to translate complex information into usable and contextual performance solutions for a range of different stakeholders. These stakeholders consist of athletes, coaches, recruiting, performance support, medical, administration and operations staff and have varying interests and priorities meaning the information required can be vastly different. In addition to these different needs, sport is fast-moving, diverse and complex meaning there are a number of potential translational barriers. Sport Science training programmes entail the development of technical knowledge and practical skills; however, little is considered in view of interpersonal craft skill development and knowledge translation (KT). Given the reported barriers and challenges to effective KT in sport, this lack of specific training may render KT as ineffective and suboptimal. Accordingly, in this article, we propose a framework and work-based training model with the aim of developing the KT process and performance delivery of Sport Scientists operating in professional sport. Firstly, we define the current perspectives and challenges for Sport Scientists in the context of KT, before proposing a framework that focusses on Evidence-Based-Practice, Philosophy, Recipients and Facilitation, in which Sport Scientists can use to develop their interpersonal craft and subsequent KT approach. We finish by presenting a model of sport science practitioner training; the professional sport-doctoral training programme, that combined with the framework, can be effective in developing Sport Scientists.Highlights Knowledge translation is a key component of the sport scientist's role, however, there is a lack of defined training processes on how to develop and improve knowledge translation in the context and environment of professional sport.We propose a framework that focuses on the development of sport scientist's knowledge translation capacity by emphasizing and concentrating efforts towards the main constituents of Evidence-Based Practice, Philosophy, Recipients and Facilitation.Sport science research-practitioner professional sport training models are effective in developing knowledge translation strategies through simultaneously acquiring new technical knowledge, developing practical coaching skills and evolving interpersonal craft skills that collectively work together to improve knowledge translation and organizational decision making.

Single-Item Self-Report Measures of Team-Sport Athlete Wellbeing and Their Relationship With Training Load: A Systematic Review

BACKGROUND

Single-item athlete self-report measures consist of a single question to assess a dimension of wellbeing. These methods are recommended and frequently used for athlete monitoring, yet their uniformity has not been well assessed, and we have a limited understanding of their relationship with measures of training load.

OBJECTIVE

To investigate the applications and designs of single-item self-report measures used in monitoring team-sport athletes and present the relationship between these measures and measures of training load.

DATA SOURCES

PubMed, Scopus, and SPORTDiscus were searched between inception and March 2019.

STUDY SELECTION

Articles were included if they concerned adult athletes from field- or court-sport domains, if athlete well-being was measured using a single-item self-report, and if the relationship with a measure of modifiable training load was investigated over at least 7 days.

DATA EXTRACTION

Data related to participant characteristics, self-report measures, training load measures, and statistical analysis and outcomes were extracted by 2 authors (C.D. and C.D.).

DATA SYNTHESIS

A total of 21 studies were included in the analysis. A narrative synthesis was conducted. The measures used most frequently were muscle soreness, fatigue, sleep quality, stress, and mood. All measures presented various relationships with metrics of training load from no association to a very large association, and the associations were predominantly trivial to moderate in the studies with the largest numbers of observations. Relationships were largely negative associations.

CONCLUSIONS

The implications of this review should be considered by users in the application and clinical utility of single-item self-report measures in athlete monitoring. Great emphasis has been placed on examining the relationship between subjective and objective measures of training load. Although the relationship is still unclear, such an association may not be expected or useful. Researchers should consider the measurement properties of single-item self-report measures and seek to establish their relationship with clinically meaningful outcomes. As such, further study is required to inform practitioners on the appropriate objective application of data from single-item self-report measures.

Does an Optimal Relationship Between Injury Risk and Workload Represented by the "Sweet Spot" Really Exist? An Example From Elite French Soccer Players and Pentathletes

Objective

To examine the relationships between the occurrence and severity of injuries using three workload ratios (ACWR, EWMA, REDI) in elite female soccer players and international male and female pentathletes.

Materials and Methods

Female soccer players in the U16 to U18 national French teams (n = 24) and international athletes (n = 12, 4 women and 8 men) in the French modern pentathlon team were monitored throughout an entire season. The Acute Chronic Workload Ratio (ACWR), the Exponentially Weighted Moving Averages (EWMA), and the Robust Exponential Decreasing Index (REDI) were calculated for internal load by the ROE method in soccer and external load in pentathlon. The occurrence and severity of injuries (determined according to time-loss) were quantified in the sweet spot zone [0.8; 1.3] and compared to the other zones of load variation: [0; 0.8], [1.3; 1.5], [1.5; +8], using the three ratios.

Results

Over the study period, a total of sixty-six injuries (2.75 per athlete) were reported in the soccer players and twelve in pentathletes (1 per athlete). The cumulative severity of all injuries was 788 days lost in soccer and 36 in pentathlon: respectively, 11.9 days lost per injury in soccer player and 3.0 per pentathlete. The mean values across the three methods in soccer showed a higher number of injuries detected in the [0; 0.8] workload ratio zone: 22.3 ± 6.4. They were 17.3 ± 3.5 in the sweet spot ([0.8-1.3] zone) and 17.6 ± 5.5 in the [1.5; +8] zone. In comparison to the [1.5; +8] zone, soccer players reported a higher number of days lost to injuries in the presumed sweet spot and in the [0-0.8] zone: 204.7 ± 28.7 and 275.0 ± 120.7 days, respectively. In pentathletes, ten of the twelve injuries (83.3%) occurred in the presumed sweet spot. REDI was the only method capable of tracking workloads over all-time series.

Conclusion

In the present cohort of elite soccer players and pentathletes, acute chronic workload calculations showed an association with injury occurrence and severity but did not provide evidence supporting existence of a sweet spot diminishing injury risk.

[Monitoring and load management in team handball]

BACKGROUND

 The monitoring of athletes has become a focus in elite-level team sports and research in recent years. The goal is to optimise load management for maximising performance. Due to the considerable amount of data gathered during monitoring in team sports, the trend is towards integrating different monitoring methods in a software-based tool. In addition, decision-support systems are used in order to enable coaches to make quick decisions to control further burdens. This article is intended to give an overview of the large number of monitoring methods used in contact team sports, to consider their test quality criteria and to check a useful application of monitoring methods for team handball, taking into account the sports-specific requirements.

METHODS

 PubMed-based review of the literature. Narrative review focusing on current data from the years 2010 to 2019.

RESULTS

 Demands on athletes in elite team handball vary depending on the playing position. Therefore, individualised load control is necessary. Monitoring methods with proven validity and reliability should ideally be used in a multivariate approach to represent the various aspects of external and internal load, recovery and fatigue, well-being and readiness. It is important to know the meaningful change for each athlete while using a monitoring method. This circumstance necessitates an individualised approach for each parameter. There are not many studies on the use of monitoring methods in team handball, neither in the amateur field nor in an elite sports environment. Nevertheless, based on evidence from other contact team sports, a recommendation for a combined use of monitoring methods can be given. Burden, time and cost/benefit should always be considered.

CONCLUSION

 There is a considerable need for research regarding the use of athlete monitoring in team handball. The combination of monitoring methods proposed in this article can be used as a framework for studies with handball players in an elite sports environment in order to implement an increasingly handball-specific monitoring concept in the future.

Do Athlete Monitoring Tools Improve a Coach's Understanding of Performance Change?

PURPOSE

To assess a coach's subjective assessment of their athletes' performances and whether the use of athlete-monitoring tools could improve on the coach's prediction to identify performance changes.

METHODS

Eight highly trained swimmers (7 male and 1 female, age 21.6 [2.0] y) recorded perceived fatigue, total quality recovery, and heart-rate variability over a 9-month period. Prior to each race of the swimmers' main 2 events, the coach (n = 1) was presented with their previous race results and asked to predict their race time. All race results (n = 93) with aligning coach's predictions were recorded and classified as a dichotomous outcome (0 = no change; 1 = performance decrement or improvement [change +/- > or < smallest meaningful change]). A generalized estimating equation was used to assess the coach's accuracy and the contribution of monitoring variables to the model fit. The probability from generalized estimating equation models was assessed with receiver operating characteristic curves to identify the model's accuracy from the area under the curve analysis.

RESULTS

The coach's predictions had the highest diagnostic accuracy to identify both decrements (area under the curve: 0.93; 95% confidence interval, 0.88-0.99) and improvements (area under the curve: 0.89; 95% confidence interval, 0.83-0.96) in performance.

CONCLUSIONS

These findings highlight the high accuracy of a coach's subjective assessment of performance. Furthermore, the findings provide a future benchmark for athlete-monitoring systems to be able to improve on a coach's existing understanding of swimming performance.

Deceleration, Acceleration, and Impacts Are Strong Contributors to Muscle Damage in Professional Australian Football

Gastin, PB, Hunkin, SL, Fahrner, B, and Robertson, S. Deceleration, acceleration, and impacts are strong contributors to muscle damage in professional Australian football. J Strength Cond Res 33(12): 3374-3383, 2019-The purpose of this study was to investigate the relationships between serum creatine kinase [CK], an indirect marker of muscle damage, and specific indices of match load in elite Australian football. Twenty-six professional players were assessed during a competitive Australian Football League (AFL) season. [CK] was collected 24-36 hours before match and 34-40 hours after match during 8 in-season rounds. An athlete-tracking technology was used to quantify match load. Generalized estimating equations and random forest models were constructed to determine the extent to which match-load indices and pre-match [CK] explained post-match [CK]. There was a 129 ± 152% increase in [CK] in response to AFL competition. Generalized estimating equations found that number of impacts >3g (p = 0.004) and game time (p = 0.016) were most strongly associated with post-match [CK]. Random forest, with considerably lower errors (130 vs. 316 U·L), found deceleration, acceleration, impacts >3g, and sprint distance to be the strongest predictors. Pre-match [CK] accounted for 11% of post-match [CK], and considerable interindividual and intraindividual variability existed in the data. Creatine kinase, an indicator of muscle damage, was considerably elevated as a result of AFL competition. Parametric and machine-learning analysis techniques found several indices of physical load associated with muscle damage during competition, with impacts >3g and high-intensity running variables as the strongest predictors. [CK] may be used as a global measure of muscle damage in field team sports such as AF, yet with some caution given cost, invasiveness, and inherent variability. Quantifying physical load and the responses to that load can guide athlete management decision-making and is best undertaken within a suite of practical, sport-specific measures, where data are interpreted individually and with an understanding of the limitations.

Wearables for Integrative Performance and Tactic Analyses: Opportunities, Challenges, and Future Directions

Micro-electromechanical systems (MEMS) have reduced drastically in size, cost, and power consumption, while improving accuracy. The combination of different sensor technologies is considered a promising step in the monitoring of athletes. Those "wearables" enable the capturing of relevant physiological and tactical information in individual and team sports and thus replacing subjective, time-consuming and qualitative methods with objective, quantitative ones. Prior studies mainly comprised sports categories such as: targeting sports, batting and fielding games as well as net and wall games, focusing on the detection of individual, non-locomotive movements. The increasing capabilities of wearables allow for more complex and integrative analysis expanding research into the last category: invasion sports. Such holistic approaches allow the derivation of metrics, estimation of physical conditions and the analysis of team strategic behavior, accompanied by integrative knowledge gains in technical, tactical, physical, and mental aspects of a sport. However, prior and current researchers find the precise measurement of the actual movement within highly dynamic and non-linear movement difficult. Thus, the present article showcases an overview of the environments in which the wearables are employed. It elaborates their use in individual as well as team-related performance analyses with a special focus on reliability and validity, challenges, and future directions.

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 S-RPE 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 S-RPE 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.

Visualizing the Complexity of the Athlete-Monitoring Cycle Through Principal-Component Analysis

PURPOSE

To discuss the use of principal-component analysis (PCA) as a dimension-reduction and visualization tool to assist in decision making and communication when analyzing complex multivariate data sets associated with the training of athletes.

CONCLUSIONS

Using PCA, it is possible to transform a data matrix into a set of orthogonal composite variables called principal components (PCs), with each PC being a linear weighted combination of the observed variables and with all PCs uncorrelated to each other. The benefit of transforming the data using PCA is that the first few PCs generally capture the majority of the information (ie, variance) contained in the observed data, with the first PC accounting for the highest amount of variance and each subsequent PC capturing less of the total information. Consequently, through PCA, it is possible to visualize complex data sets containing multiple variables on simple 2D scatterplots without any great loss of information, thereby making it much easier to convey complex information to coaches. In the future, athlete-monitoring companies should integrate PCA into their client packages to better support practitioners trying to overcome the challenges associated with multivariate data analysis and interpretation. In the interim, the authors present here an overview of PCA and associated R code to assist practitioners working in the field to integrate PCA into their athlete-monitoring process.

Comparing subjective and objective evaluations of player performance in Australian Rules football

Player evaluation plays a fundamental role in the decision-making processes of professional sporting organisations. In the Australian Football League, both subjective and objective evaluations of player match performance are commonplace. This study aimed to identify the extent to which performance indicators can explain subjective ratings of player performance. A secondary aim was to compare subjective and objective ratings of player performance. Inside Football Player Ratings (IFPR) and Australian Football League Player Ratings were collected as subjective and objective evaluations of player performance, respectively, for each player during all 1026 matches throughout the 2013-2017 Australian Football League seasons. Nine common player performance indicators, player role classification, player age and match outcomes were also collected. Standardised linear mixed model and recursive partitioning and regression tree models were undertaken across the whole dataset, as well as separately for each of the seven player roles. The mixed model analysis produced a model associating the performance indicators with IFPR at a root mean square error of 0.98. Random effects accounting for differences between seasons and players ranged by 0.09 and 1.73 IFPR each across the five seasons and 1052 players, respectively. The recursive partitioning and regression tree model explained IFPR exactly in 35.8% of instances, and to within 1.0 IFPR point in 81.0% of instances. When analysed separately by player role, exact explanation varied from 25.2% to 41.7%, and within 1.0 IFPR point from 70.3% to 88.6%. Overall, kicks and handballs were most associated with the IFPR. This study highlights that a select few features account for a majority of the variance when explaining subjective ratings of player performance, and that these vary by player role. Australian Football League organisations should utilise both subjective and objective assessments of performance to gain a better understanding of the differences associated with subjective performance assessment.

Developing Athlete Monitoring Systems in Team Sports: Data Analysis and Visualization

In professional team sports, the collection and analysis of athlete-monitoring data are common practice, with the aim of assessing fatigue and subsequent adaptation responses, examining performance potential, and minimizing the risk of injury and/or illness. Athlete-monitoring systems should be underpinned by appropriate data analysis and interpretation, to enable the rapid reporting of simple and scientifically valid feedback. Using the correct scientific and statistical approaches can improve the confidence of decisions made from athlete-monitoring data. However, little research has discussed and proposed an outline of the process involved in the planning, development, analysis, and interpretation of athlete-monitoring systems. This review discusses a range of methods often employed to analyze athlete-monitoring data to facilitate and inform decision-making processes. There is a wide range of analytical methods and tools that practitioners may employ in athlete-monitoring systems, as well as several factors that should be considered when collecting these data, methods of determining meaningful changes, and various data-visualization approaches. Underpinning a successful athlete-monitoring system is the ability of practitioners to communicate and present important information to coaches, ultimately resulting in enhanced athletic performance.

Business Intelligence: How Sport Scientists Can Support Organization Decision Making in Professional Sport

The application of scientific principles to inform practice has become increasingly common in professional sports, with increasing numbers of sport scientists operating in this area. The authors believe that in addition to domain-specific expertise, effective sport scientists working in professional sport should be able to develop systematic analysis frameworks to enhance performance in their organization. Although statistical analysis is critical to this process, it depends on proper data collection, integration, and storage. The purpose of this commentary is to discuss the opportunity for sport-science professionals to contribute beyond their domain-specific expertise and apply these principles in a business-intelligence function to support decision makers across the organization. The decision-support model aims to improve both the efficiency and the effectiveness of decisions and comprises 3 areas: data collection and organization, analytic models to drive insight, and interface and communication of information. In addition to developing frameworks for managing data systems, the authors suggest that sport scientists’ grounding in scientific thinking and statistics positions them to assist in the development of robust decision-making processes across the organization. Furthermore, sport scientists can audit the outcomes of decisions made by the organization. By tracking outcomes, a feedback loop can be established to identify the types of decisions that are being made well and the situations where poor decisions persist. The authors have proposed that sport scientists can contribute to the broader success of professional sporting organizations by promoting decision-support services that incorporate data collection, analysis, and communication.

Training-Monitoring Engagement: An Evidence-Based Approach in Elite Sport

PURPOSE

Poor athlete buy-in and adherence to training-monitoring systems (TMS) can be problematic in elite sport. This is a significant issue, as failure to record, interpret, and respond appropriately to negative changes in athlete well-being and training status may result in undesirable consequences such as maladaptation and/or underperformance. This study examined the perceptions of elite athletes to their TMS and their primary reasons for noncompletion.

METHODS

Nine national-team sprint athletes participated in semistructured interviews on their perceptions of their TMS. Interview data were analyzed qualitatively, based on grounded theory, and TMS adherence information was collected.

RESULTS

Thematic analysis showed that athletes reported their main reason for poor buy-in to TMS was a lack of feedback on their monitoring data from key staff. Furthermore, training modifications made in response to meaningful changes in monitoring data were sometimes perceived to be disproportionate, resulting in dishonest reporting practices.

CONCLUSIONS

Perceptions of opaque or unfair decision making on training-program modifications and insufficient feedback were the primary causes for poor athlete TMS adherence. Supporting TMS implementation with a behavioral-change model that targets problem areas could improve buy-in and enable limited resources to be appropriately directed.

Monitoring of Post-match Fatigue in Professional Soccer: Welcome to the Real World

Participation in soccer match-play leads to acute and transient subjective, biochemical, metabolic and physical disturbances in players over subsequent hours and days. Inadequate time for rest and regeneration between matches can expose players to the risk of training and competing whilst not entirely recovered. In professional soccer, contemporary competitive schedules can require teams to compete in excess of 60 matches over the course of the season with periods of fixture congestion occurring, prompting much attention from researchers and practitioners to the monitoring of fatigue and readiness to play. A comprehensive body of research has investigated post-match acute and residual fatigue responses. Yet the relevance of the research for professional soccer contexts is debatable, notably in relation to the study populations and designs employed. Monitoring can indeed be invasive, expensive, time inefficient, and difficult to perform routinely and simultaneously in a large squad of regularly competing players. Uncertainty also exists regarding the meaningfulness and interpretation of changes in fatigue response values and their functional relevance, and practical applicability in the field. The real-world need and cost-benefit of monitoring must be carefully weighed up. In relation to professional soccer contexts, this opinion paper intends to (1) debate the need for post-match fatigue monitoring; (2) critique the real-world relevance of the current research literature; (3) discuss the practical burden relating to measurement tools and protocols, and the collection, interpretation and application of data in the field; and (4) propose future research perspectives.

The Relationships Between Internal and External Measures of Training Load and Intensity in Team Sports: A Meta-Analysis

BACKGROUND

The associations between internal and external measures of training load and intensity are important in understanding the training process and the validity of specific internal measures.

OBJECTIVES

We aimed to provide meta-analytic estimates of the relationships, as determined by a correlation coefficient, between internal and external measures of load and intensity during team-sport training and competition. A further aim was to examine the moderating effects of training mode on these relationships.

METHODS

We searched six electronic databases (Scopus, Web of Science, PubMed, MEDLINE, SPORTDiscus, CINAHL) for original research articles published up to September 2017. A Boolean search phrase was created to include search terms relevant to team-sport athletes (population; 37 keywords), internal load (dependent variable; 35 keywords), and external load (independent variable; 81 keywords). Articles were considered for meta-analysis when a correlation coefficient describing the association between at least one internal and one external measure of session load or intensity, measured in the time or frequency domain, was obtained from team-sport athletes during normal training or match-play (i.e., unstructured observational study). The final data sample included 122 estimates from 13 independent studies describing 15 unique relationships between three internal and nine external measures of load and intensity. This sample included 295 athletes and 10,418 individual session observations. Internal measures were session ratings of perceived exertion (sRPE), sRPE training load (sRPE-TL), and heart-rate-derived training impulse (TRIMP). External measures were total distance (TD), the distance covered at high and very high speeds (HSRD ≥ 13.1-15.0 km h^-1 and VHSRD ≥ 16.9-19.8 km h^-1, respectively), accelerometer load (AL), and the number of sustained impacts (Impacts > 2-5 G). Distinct training modes were identified as either mixed (reference condition), skills, metabolic, or neuromuscular. Separate random effects meta-analyses were conducted for each dataset (n = 15) to determine the pooled relationships between internal and external measures of load and intensity. The moderating effects of training mode were examined using random-effects meta-regression for datasets with at least ten estimates (n = 4). Magnitude-based inferences were used to interpret analyses outcomes.

RESULTS

During all training modes combined, the external load relationships for sRPE-TL were possibly very large with TD [r = 0.79; 90% confidence interval (CI) 0.74 to 0.83], possibly large with AL (r = 0.63; 90% CI 0.54 to 0.70) and Impacts (r = 0.57; 90% CI 0.47 to 0.64), and likely moderate with HSRD (r = 0.47; 90% CI 0.32 to 0.59). The relationship between TRIMP and AL was possibly large (r = 0.54; 90% CI 0.40 to 0.66). All other relationships were unclear or not possible to infer (r range 0.17-0.74, n = 10 datasets). Between-estimate heterogeneity [standard deviations (SDs) representing unexplained variation; τ] in the pooled internal-external relationships were trivial to extremely large for sRPE (τ range = 0.00-0.47), small to large for sRPE-TL (τ range = 0.07-0.31), and trivial to moderate for TRIMP (τ range= 0.00-0.17). The internal-external load relationships during mixed training were possibly very large for sRPE-TL with TD (r = 0.82; 90% CI 0.75 to 0.87) and AL (r = 0.81; 90% CI 0.74 to 0.86), and TRIMP with AL (r = 0.72; 90% CI 0.55 to 0.84), and possibly large for sRPE-TL with HSRD (r = 0.65; 90% CI 0.44 to 0.80). A reduction in these correlation magnitudes was evident for all other training modes (range of the change in r when compared with mixed training - 0.08 to - 0.58), with these differences being unclear to possibly large. Training mode explained 24-100% of the between-estimate variance in the internal-external load relationships.

CONCLUSION

Measures of internal load derived from perceived exertion and heart rate show consistently positive associations with running- and accelerometer-derived external loads and intensity during team-sport training and competition, but the magnitude and uncertainty of these relationships are measure and training mode dependent.

Using Portable Force Plates to Assess Vertical Jump Performance: A Metrological Appraisal

The purpose of this study was to verify the metrological properties of portable force plates that are used to assess countermovement jump performance. While 88 participants (38 males, 50 females) were included in the agreement analyses, 84 participants (37 males and 47 females) completed the reliability part of the study. This randomized crossover design suggests that portable force plates could be used interchangeably with a reference system. Indeed, the differences between both devices were all considered trivial (effect size (ES) < 0.20), and the mean bias was never greater than 3.41% in comparison to the reference system. In addition, the absolute and relative reliability parameters were found to be acceptable for clinical use, even when used on different floor surfaces. However, it was found that the ratio between flight time and contraction time (FTCT) showed questionable reliability when tests were conducted on different surfaces (intraclass correlation coefficient = 0.49; coefficient of variation = 26.72%). Therefore, practitioners should be careful when installing the portable device on different floor surfaces in order to optimize the reliability and the ability to detect real change in the context of a countermovement jump monitoring process.

The Same Story or a Unique Novel? Within-Participant Principal-Component Analysis of Measures of Training Load in Professional Rugby Union Skills Training

Purpose: To identify which combination metrics of external and internal training load (TL) capture similar or unique information for individual professional players during skills training in rugby union using principal-component (PC) analysis. Methods: TL data were collected from 21 male professional rugby union players across a competitive season. This included PlayerLoad™, total distance, and individualized high-speed distance (>61% maximal velocity; all external TL) obtained from a microtechnology device (OptimEye X4; Catapult Innovations, Melbourne, Australia) that was worn by each player and the session rating of perceived exertion (RPE) (internal TL). PC analysis was conducted on each individual to extract the underlying combinations of the 4 TL measures that best describe the total information (variance) provided by the measures. TL measures with PC loadings (PCL) above 0.7 were deemed to possess well-defined relationships with the extracted PC. Results: The findings show that from the 4 TL measures, the majority of an individual’s TL information (first PC: 55–70%) during skills training can be explained by session RPE (PCL: 0.72–0.95), total distance (PCL: 0.86–0.98), or PlayerLoad (PCL: 0.71–0.98). High-speed distance was the only variable to relate to the second PC (PCL: 0.72–1.00), which captured additional TL information (+19–28%). Conclusions: Findings suggest that practitioners could quantify the TL of rugby union skills training with one of PlayerLoad, total distance, or session RPE plus high-speed distance while limiting omitted information of the TL imposed during professional rugby union skills training.

The Individual and Combined Effects of Multiple Factors on the Risk of Soft Tissue Non-contact Injuries in Elite Team Sport Athletes

Aim: Relationships between athlete monitoring-derived variables and injury risk have been investigated predominantly in isolation. The aim of this study was to evaluate the individual and combined effects of multiple factors on the risk of soft-tissue non-contact injuries in elite team sport athletes. Methods: Fifty-five elite Australian footballers were prospectively monitored over two consecutive seasons. Internal and external training load was quantified using the session rating of perceived exertion and GPS/accelerometry, respectively. Cumulative load and acute-to-chronic workload ratios were derived using rolling averages and exponentially weighted moving averages. History of injuries in the current and previous seasons was recorded along with professional experience, weekly musculoskeletal screening, and subjective wellness scores for individual athletes. Individual and combined effects of these variables on injury risk were evaluated with generalized linear mixed models. Results: High cumulative loads and acute-to-chronic workload ratios were associated with increased risk of injuries. The effects for measures derived using exponentially weighted moving averages were greater than those for rolling averages. History of a recent injury, long-term experience at professional level, and substantial reductions in a selection of musculoskeletal screening and subjective wellness scores were associated with increased risk. The effects of high cumulative loads were underestimated by ~20% before adjusting for previous injuries, whereas the effects of high acute-to-chronic workload ratios were overestimated by 10-15%. Injury-prone players, identified via player identity in the mixed model, were at > 5 times higher risk of injuries compared to robust players (hazard ratio 5.4, 90% confidence limits 3.6-12) despite adjusting for training load and previous injuries. Combinations of multiple risk factors were associated with extremely large increases in risk; for example, a hazard ratio of 22 (9.7-52) was observed for the combination of high acute load, recent history of a leg injury, and a substantial reduction in the adductor squeeze test score. Conclusion: On the basis of our findings with an elite team of Australian footballers, the information from athlete monitoring practices in team sports should be interpreted collectively and used as a part of the injury prevention decision-making process along with consideration of individual differences in risk.

Putting the "I" Back in Team

There is a common expression in sports that "there is no 'I' in team." However, collectively, there is actually a very important "I" in sport teams-the individual athlete/player. Each player has his or her own unique characteristics including physical, physiological, and psychological traits. Due to these unique characteristics, each player requires individual provision-whether it be an injury risk profile and targeted prevention strategy or treatment/rehabilitation for injury, dietary regimen, recovery, or psychological intervention. The aim of this commentary is to highlight how 4 high-performance teams from various professional football codes are analyzing individual player data.

Monitoring Rugby Players for Fitness and Fatigue: What Do Coaches Want?

Purpose: The advantages of monitoring players in a team are well documented. However, barriers associated with lack of resources and time prevent teams from implementing systematic monitoring programs. This study aimed to identify (1) the methods rugby teams use to monitor the training load and associated response to the training load and (2) prerequisites of a monitoring protocol that are scientifically suitable and practically applicable for monitoring fitness and fatigue of rugby players. Methods: Coaches and support staff working with varying levels of rugby union were invited to complete an online questionnaire. Results: Of the 55 respondents, 96% indicated that although they regarded monitoring the training load and training-load response as important, there is no monitoring protocol that is cost-effective, time efficient, and nonaversive to the players. Respondents measured several variables when monitoring and incorporated more subjective than objective measures. Respondents (41%) indicated that they would like a protocol that is time efficient (5–10 min) and provides immediate feedback on players who identify as fatigued (50%). For coaches to have confidence in the information provided by the protocol, it needs to meet basic clinimetric principles of reliability and validity. The technical and biological error in the measurement needs to be known so that meaningful changes in fatigue and fitness can be distinguished from natural variations in the measurements. Conclusions: Prerequisites of an ideal monitoring protocol for rugby players were identified. It follows that a monitoring protocol that fulfills these prerequisites should satisfy both scientific principles and the coach’s demands.

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

A comprehensive monitoring of fitness, fatigue, and performance is crucial for understanding an athlete's individual responses to training to optimize the scheduling of training and recovery strategies. Resting and exercise-related heart rate measures have received growing interest in recent decades and are considered potentially useful within multivariate response monitoring, as they provide non-invasive and time-efficient insights into the status of the autonomic nervous system (ANS) and aerobic fitness. In team sports, the practical implementation of athlete monitoring systems poses a particular challenge due to the complex and multidimensional structure of game demands and player and team performance, as well as logistic reasons, such as the typically large number of players and busy training and competition schedules. In this regard, exercise-related heart rate measures are likely the most applicable markers, as they can be routinely assessed during warm-ups using short (3–5 min) submaximal exercise protocols for an entire squad with common chest strap-based team monitoring devices. However, a comprehensive and meaningful monitoring of the training process requires the accurate separation of various types of responses, such as strain, recovery, and adaptation, which may all affect heart rate measures. Therefore, additional information on the training context (such as the training phase, training load, and intensity distribution) combined with multivariate analysis, which includes markers of (perceived) wellness and fatigue, should be considered when interpreting changes in heart rate indices. The aim of this article is to outline current limitations of heart rate monitoring, discuss methodological considerations of univariate and multivariate approaches, illustrate the influence of different analytical concepts on assessing meaningful changes in heart rate responses, and provide case examples for contextualizing heart rate measures using simple heuristics. To overcome current knowledge deficits and methodological inconsistencies, future investigations should systematically evaluate the validity and usefulness of the various approaches available to guide and improve the implementation of decision-support systems in (team) sports practice.

Normal Variability of Weekly Musculoskeletal Screening Scores and the Influence of Training Load across an Australian Football League Season

Aim: The sit and reach test (S&R), dorsiflexion lunge test (DLT), and adductor squeeze test (AST) are commonly used in weekly musculoskeletal screening for athlete monitoring and injury prevention purposes. The aim of this study was to determine the normal week to week variability of the test scores, individual differences in variability, and the effects of training load on the scores. Methods: Forty-four elite Australian rules footballers from one club completed the weekly screening tests on day 2 or 3 post-main training (pre-season) or post-match (in-season) over a 10 month season. Ratings of perceived exertion and session duration for all training sessions were used to derive various measures of training load via both simple summations and exponentially weighted moving averages. Data were analyzed via linear and quadratic mixed modeling and interpreted using magnitude-based inference. Results: Substantial small to moderate variability was found for the tests at both season phases; for example over the in-season, the normal variability ±90% confidence limits were as follows: S&R ±1.01 cm, ±0.12; DLT ±0.48 cm, ±0.06; AST ±7.4%, ±0.6%. Small individual differences in variability existed for the S&R and AST (factor standard deviations between 1.31 and 1.66). All measures of training load had trivial effects on the screening scores. Conclusion: A change in a test score larger than the normal variability is required to be considered a true change. Athlete monitoring and flagging systems need to account for the individual differences in variability. The tests are not sensitive to internal training load when conducted 2 or 3 days post-training or post-match, and the scores should be interpreted cautiously when used as measures of recovery.