More than a Metric: How Training Load is Used in Elite Sport for Athlete Management.

Training load monitoring is a core aspect of modern-day sport science practice. Collecting, cleaning, analysing, interpreting, and disseminating load data is usually undertaken with a view to improve player performance and/or manage injury risk. To target these outcomes, practitioners attempt to optimise load at different stages throughout the training process, like adjusting individual sessions, planning day-to-day, periodising the season, and managing athletes with a long-term view. With greater investment in training load monitoring comes greater expectations, as stakeholders count on practitioners to transform data into informed, meaningful decisions. In this editorial we highlight how training load monitoring has many potential applications and cannot be simply reduced to one metric and/or calculation. With experience across a variety of sporting backgrounds, this editorial details the challenges and contextual factors that must be considered when interpreting such data. It further demonstrates the need for those working with athletes to develop strong communication channels with all stakeholders in the decision-making process. Importantly, this editorial highlights the complexity associated with using training load for managing injury risk and explores the potential for framing training load with a performance and training progression mindset.

"To Tech or Not to Tech?" A Critical Decision-Making Framework for Implementing Technology in Sport.

The current technological age has created exponential growth in the availability of technology and data in every industry, including sport. It is tempting to get caught up in the excitement of purchasing and implementing technology, but technology has a potential dark side that warrants consideration. Before investing in technology, it is imperative to consider the potential roadblocks, including its limitations and the contextual challenges that compromise implementation in a specific environment. A thoughtful approach is therefore necessary when deciding whether to implement any given technology into practice. In this article, we review the vision and pitfalls behind technology's potential in sport science and medicine applications and then present a critical decision-making framework of 4 simple questions to help practitioners decide whether to purchase and implement a given technology.

Are Elite Soccer Teams' Preseason Training Sessions Associated With Fewer In-Season Injuries? A 15-Year Analysis From the Union of European Football Associations (UEFA) Elite Club Injury Study.

BACKGROUND: Preseason training develops players' physical capacities and prepares them for the demands of the competitive season. In rugby, Australian football, and American football, preseason training may protect elite players against in-season injury. However, no study has evaluated this relationship at the team level in elite soccer. PURPOSE/HYPOTHESIS: The aim of this study was to investigate whether the number of preseason training sessions completed by elite soccer teams was associated with team injury rates and player availability during the competitive season. It was hypothesized that elite soccer teams who participate in more preseason training will sustain fewer injuries during the competitive season. STUDY DESIGN: Descriptive epidemiology study. METHODS: We used the Union of European Football Associations (UEFA) injury dataset to analyze 44 teams for up to 15 seasons (total, 244 team-seasons). Separate linear regression models examined the association between the number of team preseason training sessions and 5 in-season injury measures. Injury-related problems per team were quantified by totals of the following: (1) injury burden, (2) severe injury incidence, (3) training attendance, (4) match availability, and (5) injury incidence. RESULTS: Teams averaged 30 preseason training sessions (range, 10-51). A greater number of preseason training sessions was associated with less injury load during the competitive season in 4 out of 5 injury-related measures. Our linear regression models revealed that for every 10 additional preseason training sessions that the team performed, the in-season injury burden was 22 layoff days lower per 1000 hours (P = .002), the severe injury incidence was 0.18 severe injuries lower per 1000 hours (P = .015), the training attendance was 1.4 percentage points greater (P = .014), and the match availability was 1.0 percentage points greater (P = .042). As model fits were relatively low (adjusted R2 = 1.3%-3.2%), several factors that contribute to in-season injury outcomes were unaccounted for. CONCLUSION: Teams that performed a greater number of preseason training sessions had "healthier" in-season periods. Many other factors also contribute to in-season injury rates. Understanding the benefit of preseason training on in-season injury patterns may inform sport teams' planning and preparation.

Close encounters of the US kind: illness and injury among US athletes at the PyeongChang 2018 Winter Olympic Games.

AIM: To describe injury and illness incidence during the 2018 Winter Olympic Games (WOG) by Team USA. METHODS: A descriptive observational study. We used registered Team USA Olympic athletes' electronic medical records to review preparticipation health histories and medical encounters immediately prior to and throughout the 2018 WOG. Medical encounters were defined as all medical services provided by a healthcare provider, including evaluation, treatment and prophylactic services. All medical conditions were described according to International Olympic Committee injury and illness reporting criteria. RESULTS: Team USA included 134 men and 108 women, aged 18-39 years, who represented 17 sport federations. The 47 Team USA medical staff documented 1744 medical encounters on 242 registered athletes (7.2 medical encounters per athlete). Forty-seven illnesses (194.2/1000 athletes) and 32 time loss injuries (132.2/1000 athletes) were recorded during the Games. CONCLUSIONS: An injury surveillance programme consisting of an electronic preparticipation health history and surveillance of medical encounters during the WOG was used to describe the health status of Team USA. We noted limitations to the surveillance process that can be addressed at future events.

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.

Getting the most out of intensive longitudinal data: a methodological review of workload-injury studies.

OBJECTIVES: To systematically identify and qualitatively review the statistical approaches used in prospective cohort studies of team sports that reported intensive longitudinal data (ILD) (>20 observations per athlete) and examined the relationship between athletic workloads and injuries. Since longitudinal research can be improved by aligning the (1) theoretical model, (2) temporal design and (3) statistical approach, we reviewed the statistical approaches used in these studies to evaluate how closely they aligned these three components. DESIGN: Methodological review. METHODS: After finding 6 systematic reviews and 1 consensus statement in our systematic search, we extracted 34 original prospective cohort studies of team sports that reported ILD (>20 observations per athlete) and examined the relationship between athletic workloads and injuries. Using Professor Linda Collins' three-part framework of aligning the theoretical model, temporal design and statistical approach, we qualitatively assessed how well the statistical approaches aligned with the intensive longitudinal nature of the data, and with the underlying theoretical model. Finally, we discussed the implications of each statistical approach and provide recommendations for future research. RESULTS: Statistical methods such as correlations, t-tests and simple linear/logistic regression were commonly used. However, these methods did not adequately address the (1) themes of theoretical models underlying workloads and injury, nor the (2) temporal design challenges (ILD). Although time-to-event analyses (eg, Cox proportional hazards and frailty models) and multilevel modelling are better-suited for ILD, these were used in fewer than a 10% of the studies (n=3). CONCLUSIONS: Rapidly accelerating availability of ILD is the norm in many fields of healthcare delivery and thus health research. These data present an opportunity to better address research questions, especially when appropriate statistical analyses are chosen.

Does player unavailability affect football teams' match physical outputs? A two-season study of the UEFA champions league.

OBJECTIVES: Player unavailability negatively affects team performance in elite football. However, whether player unavailability and its concomitant performance decrement is mediated by any changes in teams' match physical outputs is unknown. We examined whether the number of players injured (i.e. unavailable for match selection) was associated with any changes in teams' physical outputs. DESIGN: Prospective cohort study. METHODS: Between-team variation was calculated by correlating average team availability with average physical outputs. Within-team variation was quantified using linear mixed modelling, using physical outputs - total distance, sprint count (efforts over 20km/h), and percent of distance covered at high speeds (>14km/h) - as outcome variables, and player unavailability as the independent variable of interest. To control for other factors that may influence match physical outputs, stage (group stage/knockout), venue (home/away), score differential, ball possession (%), team ranking (UEFA Club Coefficient), and average team age were all included as covariates. RESULTS: Teams' average player unavailability was positively associated with the average number of sprints they performed in matches across two seasons. Multilevel models similarly demonstrated that having 4 unavailable players was associated with 20.8 more sprints during matches in 2015/2016, and with an estimated 0.60-0.77% increase in the proportion of total distance run above 14km/h in both seasons. Player unavailability had a possibly positive and likely positive association with total match distances in the two respective seasons. CONCLUSIONS: Having more players injured and unavailable for match selection was associated with an increase in teams' match physical outputs.