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

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  • Stefano Cabras
  • Marco Delogu
  • J.D. Tena

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.

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  • Stefano Cabras & Marco Delogu & J.D. Tena, 2023. "Forced to play too many matches? A deep-learning assessment of crowded schedule," Applied Economics, Taylor & Francis Journals, vol. 55(52), pages 6187-6204, November.
    • Handle: RePEc:taf:applec:v:55:y:2023:i:52:p:6187-6204
    DOI: 10.1080/00036846.2022.2141462
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    1. Babatunde Buraimo & Jing Guan & J.D. Tena, 2023. "Does a super league tournament harm domestic leagues? Evidence from basketball’s Euroleague," Working Papers 202306, University of Liverpool, Department of Economics.

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