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

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 p...

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Bibliographic Details
Published inApplied economics Vol. 55; no. 52; pp. 6187 - 6204
Main Authors Cabras, Stefano, Delogu, Marco, Tena, J.D.
Format Journal Article
LanguageEnglish
Published London Routledge 08.11.2023
Taylor & Francis Ltd
Subjects
causal analysis
Deep learning
Distortion
Economic theory
Football
Games
Group performance
Learning
Multitasking
Productivity
Professional soccer
sports economics
Teams
Tournaments
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Summary: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.
ISSN:0003-6846
1466-4283
DOI:10.1080/00036846.2022.2141462