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Francesco Porro

Publications and source records attributed to Francesco Porro.

2 recordsLinked to original sources

Identifying the determinants of soccer coach turnover by competing risks models: a case study on Italian League Serie A

This paper investigates the determinants of head coach turnover in the Italian Serie A over the period 2010-2019 by explicitly accounting for multiple exit mechanisms within a competing risks framework. Four mutually exclusive exit types are considered: end of contract, mutual consent, resignation, and sacking. To identify robust predictors of turnover, we combine classical cause-specific Cox models with a variable selection procedure based on forward selection, LASSO regression, and Random Survival Forests. The results show that performance-related variables are the primary drivers of coach turnover, while personal characteristics play a limited role once performance is controlled for. Short-term performance indicators, such as points per match until turnover, strongly influence the risk of dismissal. Conversely, cumulative poor performance substantially increases dismissal risk: a higher proportion of losses over a coach's tenure raises the hazard of sacking by more than four times. Season timing also plays a crucial role, as sackings and mutual-consent terminations are significantly more likely to occur during the season, whereas contract expirations concentrate at the end of the season. Overall, the findings highlight that clubs differentiate between short-term performance shocks and longer-term performance trajectories when deciding how and when to replace a head coach. By distinguishing among exit mechanisms and adopting a conservative variable selection strategy, the study provides a robust and nuanced assessment of managerial turnover dynamics in the most important Italian professional football league.

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Testing for a common subspace in compositional datasets with structural zeros

In this paper, we consider the problem of testing for a common principal subspace in compositional datasets with structural zeros. In particular, we address the problem in the general setting in which two groups are compared, each characterized by its own pattern of structural zeros. This situation prevents the direct use of standard logratio-based principal subspace comparison methods, since the two groups cannot be represented in a common logratio coordinate system in the usual way. We thus define a test for the presence of a common subspace that is fully compatible with the Aitchison geometry and logratio analysis. The proposed construction allows the principal subspaces associated with the two groups to be compared despite the different patterns of structural zeros. Under logratio-normality, we derive an analytical approximation to the null distribution of the test statistic. Alongside this parametric version, we also introduce a nonparametric bootstrap procedure that does not rely on distributional assumptions. The finite-sample behaviour of the test is investigated through simulations. The method is finally illustrated on a reproducible microbiome dataset available through Bioconductor.

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