This study examines differences in weekly load between the first (FT) and the under 19 team (U19) within a professional football setting. Data were collected in 11 FT and 9 U19 players (2016-2017 season). FT data was divided into weeks with (FT-M1) or without (FT-M0) a mid-week match. Indicators were total distance (TD) and TD at 12-15, 15-20, 20-25 and >25 km . h -1 and were analysed as external load (m), intensity (m . min -1 ) and load monotony (a.u.). TD-based load was higher for U19 compared to FT-M0 (very likely moderate) and FT-M1 (likely large). Differences at higher velocities were substantially less (trivial to possibly small), with TD >25 km . h -1 being lower than FT-M0 (very likely moderate) and FT-M1 (likely small). All intensity indicators were lower for U19 (likely small to almost certainly large). Load monotony was higher compared to FT-M1 (possibly small to almost certainly very large). Compared to FT-M0, monotony was higher for TD (possibly very large) and TD >25 km . h -1 (possibly moderate) but lower for TD 12-15 (possibly small) and 15-20 km . h -1 (likely moderate). So, despite higher weekly external loads at low velocity for elite youth players, external intensity and load variation increases when these players may transition to professional football.

Load monitoring is considered important to manage the physical training process in team sports such as Association Football. Previous studies have described the load monitoring practices of elite English football clubs and clubs with an established sports-science department. An examination of a broader international sample is currently not available. In addition, previous research has suggested factors that may improve the implementation of load monitoring practices, such as a strong club belief on the benefit of evidence-based practice (EBP) and high club financial resources. However, no study has examined yet the actual impact of these factors on the monitoring practices. Therefore, this study aims (1) to provide an overview of load monitoring practices in European elite football and (2) to provide insight into the differences in implementation between clubs by examining the impact of the club beliefs on the benefit of EBP and the club financial resources. An online survey, consisting of multiple choice and Likert scale questions, was distributed among sports-science and sports-medicine staff (n = 99, 50% response rate). Information was asked about the types of data collected, collection purposes, analysis methods, and staff involvement. The results indicated that external load data (e.g., global navigation satellite system, accelerometer…) was collected the most whilst respondents also indicated to collect internal load (e.g., heart rate, rating of perceived exertion…) and training outcome data (e.g., aerobic fitness, neuromuscular fatigue…) for multiple purposes. Considerable diversity in data analysis was observed suggesting that analysis is often limited to reporting the gathered data. Sports-science staff were responsible for data collection and analysis. Other staff were involved in data discussion to share decision-making. These practices were positively impacted by a stronger club belief on the benefit of EBP and greater financial resources. Creating an organizational culture, characterized by a strong belief on the benefit of EBP, is important to increase the impact of load monitoring. However, the actual potential may still be largely determined by financial resources. High-level clubs could therefore play a leading role in generating and sharing knowledge to improve training practices and player health.

Purpose: To examine the utility of a standardized small-sided game (SSG) for monitoring within-player changes in mean exercise heart rate (HRex) when compared with a submaximal interval shuttle-run test (ISRT). Methods: Thirty-six elite youth football players (17 [1] y) took part in 6 test sessions across an in-season period (every 4 wk). Sessions consisted of the ISRT (20-m shuttles, 30″:15″ work:rest ratio, 70% maximal ISRT) followed by an SSG (7v7, 80 × 56 m, 6 min). HRex was collected during both protocols, with SSG external load measured as high-speed running distance (>19.8 km·h–1) and acceleration distance (>2 m·s−2). Data were analyzed using linear mixed-effect models. Results: Controlling for SSG external load improved the model fit describing the SSG–ISRT HRex relationship (χ2 = 12.6, P = .002). When SSG high-speed running distance and SSG acceleration distance were held constant, a 1% point change in SSG HRex was associated with a 0.5% point change in ISRT HRex (90% CI: 0.4 to 0.6). Inversely, when SSG HRex was held constant, the effects of a 100-m change in SSG high-speed running distance and a 21-m change in SSG acceleration distance on ISRT HRex were −1.0% (−1.5 to −0.4) and −0.6% points (−1.1 to 0.0), respectively. Conclusions: An SSG can be used to track within-player changes in HRex for monitoring physiological state. Given the uncertainty in estimates, we advise to only give meaning to changes in SSG HRex >2% points. Additionally, we highlight the importance of considering external load when monitoring SSG HRex.

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.

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.