No abstract

Recently, the interest of the academic literature on sports statistics has increased enormously. In such a framework, two of the most significant challenges are developing a model able to beat the existing approaches and, within a betting market framework, guarantee superior returns than the set of competing specifications considered. This contribution attempts to achieve both these results, in the context of male tennis. In tennis, several approaches to predict the winner are available, among which the regression-based, point-based and paired comparison of the competitors’ abilities play a significant role. Contrary to the existing approaches, this contribution employs artificial neural networks (ANNs) to forecast the probability of winning in tennis matches, starting from all the variables used in a large selection of the previous methods. From an out-of-sample perspective, the implemented ANN model outperforms four out of five competing models, independently of the considered period. For what concerns the betting perspective, we propose four different strategies. The resulting returns on investment obtained from the ANN appear to be more broad and robust than those obtained from the best competing model, irrespective of the betting strategy adopted.

The so called "Startup Act" (Decree Law 179/2012, converted into Law 221/2012), has introduced in Italy the notion of innovative companies with a high technological value, denoted as the innovative startups. Among them, the Italian government includes the category of SIAVS ("Startup Innovative A Vocazione Sociale"), which represents a relatively new field of interest in both scientific and normative perspective. A social startup must satisfy the same requirement of other innovative startups, usually operating in sectors such as social assistance, education, health, social tourism and culture which can have a direct (social) impact on collective well-being. Furthermore, they must produce specific reporting of the produced social impact, enjoying also some tax benefits. In 2020 more than 200 SIAVS are registered in Italy, more than doubled with respect to 2015. This work is concerned with the empirical analysis of innovative companies focused in funding and implementing solutions to social, cultural, or environmental issues. Specifically, the aim of the paper is to investigate what are the relevant factors for the arise of SIAVS in Italy. The response variable is based on the number of active social startups in Italian provinces while the set of explanatory variables is composed by economic and demographic indicators at the provincial level. Generalized linear models (GLM) for discrete outcomes are applied and compared, even taking into account the zero-inflated issue arising due to the distribution of these particular data.

During the waves of the COVID-19 pandemic, both national and/or territorial healthcare systems have been severely stressed in many countries. The availability (and complexity) of data requires proper comparisons for understanding differences in the performance of health services. With this aim, we propose a methodological approach to compare the performance of the Italian healthcare system at the territorial level, i.e., considering NUTS 2 regions. Our approach consists of three steps: the choice of a distance measure between available time series, the application of weighted multidimensional scaling (wMDS) based on this distance, and, finally, a cluster analysis on the MDS coordinates. We separately consider daily time series regarding the deceased, intensive care units, and ordinary hospitalizations of patients affected by COVID-19. The proposed procedure identifies four clusters apart from two outlier regions. Changes between the waves at a regional level emerge from the main results, allowing the pressure on territorial health services to be mapped between 2020 and 2022.

Passes are undoubtedly the more frequent events in football and other team sports. Passing networks and their structural features can be useful to evaluate the style of play in terms of passing behavior, analyzing and quantifying interactions among players. The present paper aims to show how information retrieved from passing networks can have a relevant impact on predicting the match outcome. In particular, we focus on modeling both the scored goals by two competing teams and the goal difference between them. With this purpose, we fit these outcomes using Bayesian hierarchical models, including both in-match and network-based covariates to cover many aspects of the offensive actions on the pitch. Furthermore, we review and compare different approaches to include covariates in modeling football outcomes. The presented methodology is applied to a real dataset containing information on 125 matches of the 2016–2017 UEFA Champions League, involving 32 among the best European teams. From our results, shots on target, corners, and such passing network indicators are the main determinants of the considered football outcomes.