FORCED TO PLAY TOO MANY MATCHES? A DEEP-LEARNING ASSESSMENT OF CROWDED SCHEDULE

Cabras, Stefano, Delogu, Marco and Tena Horrillo, Juan De Dios ORCID: 0000-0001-8281-2886 (2022) FORCED TO PLAY TOO MANY MATCHES? A DEEP-LEARNING ASSESSMENT OF CROWDED SCHEDULE. Applied Economics, 55 (52). pp. 6187-6204.

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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.

Item Type:	Article
Uncontrolled Keywords:	Multitasking, causal analysis, deep learning, sports economics
Depositing User:	Symplectic Admin
Date Deposited:	10 Nov 2022 11:18
Last Modified:	23 Sep 2023 17:57
DOI:	10.1080/00036846.2022.2141462
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3166114