Investigating the association between workload data and fitness in elite soccer
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Background The development of soccer has led to congested schedules, resulting in higher risks of overload. Therefore, monitoring the workload and fitness of players has become more important. Using fitness tests to monitor players’ fitness imposes extra burden and interferes with regular training program. If it is possible to measure players’ fitness using workload data, this will decrease the need for fitness tests. And with that it could create some space to rest or recover in the congested schedule and thereby this will decrease the chance of overload. Aim The aim of this study is to investigate if with the use of workload data it is possible to measure player’s fitness in elite soccer. Methods Workload data (distance and sRPE-TL) of every training and match and fitness data (Interval Shuttle Run Test (ISRT)) collected from one elite soccer team playing on the second highest level in Dutch soccer during the season 2018 – 2019 was used. Data was collected for three moments (T1, T2 and T3). Training efficiency index scores were calculated for workload data for every day and for ISRT data. For workload these scores were transformed to one value using the exponentially weighted movement averages for the timeframes of 1, 2, 3 and 4 weeks before ISRT. Structural equitation modelling was used to calculate overall and separate correlations over T1, T2 and T3. Results All participants were male (n=27), with mean age of 24.0 years (± 3.8 years). Completed ISRT-test were available for 100% at T1 and T3 and 88.9% at T2. Found overall correlation is almost equal between the timeframes ranging from r = 0.108 – 0.152, which can be interpreted as weak. Correlations on T1, T2 and T3 were also weak (respectively r = 0.088 – 0.341). Conclusion and key findings We conclude that it is not possible to measure players fitness with the use of distance and sRPE data. For now, it is not possible to stop using fitness tests to determine players’ fitness. It might be rewarding to use different workload metrics (e.g. acceleration/deceleration and heart-rate), or small sided games to measure players’ fitness in future studies.