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

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Taylor and Francis Group
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Do important upcoming or recent scheduled tasks a!ect the current productivity of working teams? How is the impact (if any) modi"ed according to team size or by external conditions faced by workers? We study this issue using association football data where team performance is clearly de"ned and publicly observed before and after completing di!erent activities (football matches). UEFA Champions League (CL) games a!ect European domestic league matches in a quasi-random fashion. We estimate this e!ect using a deep learning model. This approach is instrumental in estimating performance under ‘what if’ situations required in a causal analysis. We "nd that dispersion of attention and e!ort to di!erent tournaments signi"cantly 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.
Multitasking, Causal Analysis, Deep learning, Sports economics
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Cabras, S., Delogu, M., & Tena, J. D. (2022). Forced to play too many matches? A deep-learning assessment of crowded schedule. En Applied Economics, 55 (52), pp. 6187-6204.