Forced to play too many matches? A deep-learning assessment of crowded schedule

Stefano Cabras, Marco Delogu and Juan de Dios Tena

Applied Economics, 2023, vol. 55, issue 52, 6187-6204

Abstract: Do important upcoming or recent scheduled tasks affect the current productivity of working teams? How is the impact (if any) modified according to team size or by external conditions faced by workers? We study this issue using association football data where team performance is clearly defined and publicly observed before and after completing different activities (football matches). UEFA Champions League (CL) games affect European domestic league matches in a quasi-random fashion. We estimate this effect using a deep learning model. This approach is instrumental in estimating performance under ‘what if’ situations required in a causal analysis. We find that dispersion of attention and effort to different tournaments significantly worsens domestic performance before/after playing the CL match. However, the size of the impact is higher in the latter case. Our results suggest that this distortion is higher for small teams and that, compared to home teams, away teams react more conservatively by increasing their probability of drawing.

Date: 2023
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Working Paper: Forced to Play Too Many Matches? A DeepLearning Assessment of Crowded Schedule (2021)
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DOI: 10.1080/00036846.2022.2141462

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