ECSS Paris 2023: CP-AP13
INTRODUCTION: With the increasing professionalisation and rising physical demands in women`s football, the need for accurate load management is evident (1). Athlete monitoring, through the systematic collection, analysis, and application of workload data alongside athletes’ training responses and wellbeing, is a well-established approach for optimising performance and mitigating injury risk (2) . With occurring recommendations on monitoring methods in women’s football, it is important to characterise potential gaps with real-life practices (3,4). However, little is known about current practices and key stakeholder’s perceptions in women`s football. Therefore, the present study aims to examine monitoring practices and perceptions among (i) players and (ii) coaching and performance staff. METHODS: A descriptive cross-sectional survey design was used. All cubs from the Norwegian women’s premier league were invited to complete a role-specific online survey on their data collection methods, data analysis, implementation and evaluation of their current monitoring practice. Eight clubs participated, eliciting responses from six head coaches, eight practitioners, and 113 players. RESULTS: Five clubs (62.5%) reported systematic monitoring. All five included subjective measures, four collected external loads, while one club measured counter movement jump-test. On perceived individualisation of training load (scale 1–7), monitoring players and staff reported median scores of 5 and 6, respectively, whereas non-monitoring players and staff reported median scores of 4 and 2. The most frequently reported challenges were “players not responding” for data collection, “time constraints” for data analysis, and “lack of equipment” and “tactical considerations” for decision on load adjustment. CONCLUSION: In this cohort, athlete monitoring is widely accepted but implemented heterogeneously and constrained by available resources. Findings on perceived individualisation generate the hypothesis that implementing monitoring may support perceived individualisation, with visible use of data and good communication acting as key facilitators. Future research should further investigate the acute and chronic effects of implementing monitoring, clarify the practical value of objective readiness measures, and identify which monitoring methods and variables that support coaches when making decisions on training load adjustments. References: 1 FIFA, 2020. 2 Gabbett et al., 2017. https://doi.org/10.1136/bjsports-2016-097298 3 Beato et al., 2024. https://doi.org/10.1123/ijspp.2023-0405 4 Coutts, 2017. https://doi.org/10.1123/ijspp.2017-0455
Read CV Miriam BybergECSS Paris 2023: CP-AP13
INTRODUCTION: Duty factor (DF) is a simple marker of movement strategy, defined as the ratio of ground contact time to stride time during running.1 Athletes who adopt aerial-based running patterns (low DF) receive increased biomechanical loads on the foot, ankle, and calf muscles whereas terrestrial-based runners (high DF) place increased load on knees and hips. Given football players typically run 9-11 km in matches, including 111-255 m of sprinting,2 there is a need to better understand loading patterns regarding implications for optimal training regimens. The aim of this study was twofold: (1) to create a method that practitioners could implement easily into training routines and (2) to categorise running styles remotely during the maximum velocity phase of a controlled sprint. METHODS: 53 football players across the Women’s Pro Game Academy League performed controlled sprints as part of their normal training routine across the 2024-25 season (161 observations total; 3.0 ± 1.5 per player). As minimal disruption to training was prioritised, a method was needed to automatically detect the maximum velocity phase while considering different routines in place at each club (e.g., 30 m or 40 m sprints, repeated sprints). An algorithm (RStudio, Posit Software, Boston, MA) was created to automatically detect the start and end times of the maximum velocity phase of the controlled sprints from 10-Hz acceleration, velocity, and distance data from a foot-mounted inertial measurement unit (Playermaker, London, UK), already in use in training by clubs. These times were manually entered into the Playermaker dashboard, which allowed the mean contact time and stride time to be calculated for the entered bout. Contact time and stride time were adjusted using a prior validation study comparing the F-IMU system with an instrumented treadmill and used to calculate DF. DF values were used to identify two separate groups (high and low DF) and to compare between playing positions. RESULTS: Results showed low DF (0.290 ± 0.016) and high DF (0.330 ± 0.015) groups achieved similar maximal sprint speeds (6.59 ± 0.41 m·s-1 and 6.61 ± 0.48 m·s-1, respectively), indicating that DF reflects movement strategy and leg stiffness rather than sprinting ability. Playing position itself did not meaningfully influence DF, with substantial variation in sprinting styles existing within positions (e.g., central [0.258-0.351] vs wide [0.268-0.330] defenders). CONCLUSION: This study provided a novel proof of concept for assessing running styles across several Women’s Pro Game Academies during the maximum velocity phase of a controlled sprint. DF can help contextualise sprint exposure beyond velocity metrics when individualising training. In team sports where there is limited time, this offers one method of monitoring running styles with minimal burden to practitioners. 1. Hanley et al. (2022). Frontiers in Sports and Active Living, 4:939676 2. Savolainen et al. (2023). Biology of Sport, 40(4):1187–1195
Read CV Claire ChaissonECSS Paris 2023: CP-AP13
INTRODUCTION: Sleep is essential for overall health, athletic performance, and recovery due to its physiological and psychological restorative functions. Football competitions involve frequent contextual variations—such as home versus away matches, day versus night games, and differing match outcomes—which may influence athletes’ sleep patterns.This study examined the effects of match-related contextual factors, including match location, match timing, and match outcomes, on sleep duration and sleep quality in professional adult female football players. METHODS: An observational repeated-measures design was employed. Sleep diaries were used to record daily sleep duration and quality across 22 league matches (home-and-away format, one match per week). Match location (home/away), match timing (day/night), and match outcome (win/draw/loss) were documented for each game. Thirty-three professional adult female football players participated (mean age 24.42 ± 3.61 years; height 169.08 ± 5.68 cm; weight 61.21 ± 6.58 kg). All data are presented as mean ± standard deviation (SD). Sleep data were analysed using two approaches: independent-samples t-tests compared sleep differences between home versus away and day versus night matches, while one-way ANOVA examined the effects of match outcomes on sleep variables. When significant differences were identified, post hoc analyses were conducted using Tukey and Bonferroni corrections. A linear mixed-effects model with individual random intercepts was constructed to test the fixed effects of match-related contextual factors. Statistical significance was set at P ≤ 0.05. All analyses were performed using IBM SPSS Statistics 20. RESULTS: The league spanned from March to September and included 22 matches (13 wins, 5 draws, 4 losses, 13 day matches, and 11 night matches). Sleep duration was significantly longer following home matches than away matches (7.52 ± 0.89 vs. 7.30 ± 0.71 hours; P < 0.05). Following day matches, both sleep duration and sleep quality were higher than after night matches sleep duration(7.70 ± 0.60 vs. 7.11 ± 1.25 hours); sleep quality score(7.66 ± 0.92 vs. 7.11 ± 1.25; P < 0.05). Sleep quality was lower after draws compared with both wins and losses (7.05 ± 1.41 vs. 7.53 ± 0.95 vs. 7.51 ± 1.01; P < 0.05). Linear mixed-effects modelling indicated that sleep duration was primarily influenced by match outcomes (F = 4.45, P < 0.05) and match timing (F = 97.51, P < 0.05). Sleep quality was significantly affected by match outcomes (F = 9.29, P < 0.05) and match timing (F = 24.32, P < 0.05), with a significant interaction between outcome and timing (F = 7.98, P < 0.05). CONCLUSION: Night matches and unfavourable match outcomes were associated with significantly reduced sleep duration and quality in professional female football players. Post-match recovery strategies should therefore be tailored according to match timing and outcomes to optimise sleep management.
Read CV Nan LiECSS Paris 2023: CP-AP13