Shortcomings of applying data science to improve professional football performance: Takeaways from a pilot intervention study

Mat Herold*, Matthias Kempe, Ludwig Ruf, Luis Guevara, Tim Meyer

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

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Positional tracking data allows football practitioners to derive features that describe patterns of player behavior and quantify performance. Existing research using tracking data has mostly focused on what occurred on the pitch, such as the determinants of effective passing. There have yet to be studies attempting to use findings from data science to improve performance. Therefore, 24 professional players (mean age = 21.6 years, SD = 5.7) were divided into a control team and an intervention team which competed against each other in a pre-test match. Metrics were gathered via notational analysis (number of passes, penalty box entries, shots on goal), and positional tracking data including pass length, pass velocity, defensive disruption (D-Def), and the number of outplayed opponents (NOO). D-Def and NOO were used to extract video clips from the pre-test that were shown to the intervention team as a teaching tool for 2 weeks prior to the post-test match. The results in the post-test showed no significant improvements from the pre-test between the Intervention Team and the Control Team for D-Def (F = 1.100, p = 0.308, η2 = 0.058) or NOO (F = 0.347, p = 0.563, η2 = 0.019). However, the Intervention Team made greater numerical increases for number of passes, penalty box entries, and shots on goal in the post-test match. Despite a positive tendency from the intervention, results indicate the transfer of knowledge from data science to performance was lacking. Future studies should aim to include coaches' input and use the metrics to design training exercises that encourage the desired behavior.

Originele taal-2English
Aantal pagina's8
TijdschriftFrontiers in Sports and Active Living
StatusPublished - 12-okt.-2022

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