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- Title
A regularized hidden Markov model for analyzing the 'hot shoe' in football.
- Authors
Ötting, Marius; Andreas, Groll
- Abstract
We propose a penalized likelihood approach in hidden Markov models (HMMs) to perform automated variable selection. To account for a potential large number of covariates, which also may be substantially correlated, we consider the elastic net penalty containing LASSO and ridge as special cases. By quadratically approximating the non-differentiable penalty, we ensure that the likelihood can be maximized numerically. The feasibility of our approach is assessed in simulation experiments. As a case study, we examine the 'hot hand' effect, whose existence is highly debated in different fields, such as psychology and economics. In the present work, we investigate a potential 'hot shoe' effect for the performance of penalty takers in (association) football, where the (latent) states of the HMM serve for the underlying form of a player.
- Subjects
SOCCER players; PENALTY kicks (Soccer); HIDDEN Markov models
- Publication
Statistical Modelling: An International Journal, 2022, Vol 22, Issue 6, p546
- ISSN
1471-082X
- Publication type
Article
- DOI
10.1177/1471082X211008014