Studies in classification, data analysis, and knowledge organization, Dec 31, 2022
During the first two waves of Covid-19 pandemic, territorial healthcare systems have been severel... more During the first two waves of Covid-19 pandemic, territorial healthcare systems have been severely stressed in many countries. The availability (and complexity) of data requires proper comparisons for understanding differences in performance of health services. We apply a three-steps approach to compare the performance of Italian healthcare system at territorial level (NUTS 2 regions), considering daily time series regarding both intensive care units and ordinary hospitalizations of Covid-19 patients. Changes between the two waves at a regional level emerge from the main results, allowing to map the pressure on territorial health services.
Frontiers in Applied Mathematics and Statistics, Oct 3, 2023
Introduction: Modern FinTech tools (e.g., instant payments, blockchain, roboadvisor) represent th... more Introduction: Modern FinTech tools (e.g., instant payments, blockchain, roboadvisor) represent the new frontier of digital finance. Consequently, the evaluation of the knowledge level of the population about these topics is a crucial concern. In this context, several exogenous factors may influence individual di erences in financial literacy. In particular, the territorial characteristics can have an impact on FinTech. In this work, we investigate individual heterogeneity in subjective financial knowledge in Italy, specifically focusing on modern FinTech tools, and exploring the di erences at the individual and regional levels. Methods: A sample of Italian individuals from di erent Italian regions was involved. A multilevel IRT model is performed to evaluate the level of FinTech individual knowledge and the di erences according to Italian regions to account for the hierarchical structure of the data. Results: Results reported a weak regional e ect, revealing that heterogeneity in financial knowledge can be mainly attributed to individual characteristics. At the individual level, age, economic condition, knowledge of traditional financial objects and numeracy showed a significant e ect. In addition, a scientific field of study and work have an impact on respondents' knowledge level. Discussion: What is shown and discussed in this contribution can inspire policymakers' actions to increase financial literacy in the population. In particular, the obtained results imply that policymakers should improve the population's awareness of less popular FinTech tools and foster individuals' literacy about numbers and traditional financial tools, which proved to have a great influence in explaining FinTech knowledge di erences.
During the waves of the COVID-19 pandemic, both national and/or territorial healthcare systems ha... more 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 res...
The men’s football transfer market represents a complex phenomenon requiring suitable methods fo... more The men’s football transfer market represents a complex phenomenon requiring suitable methods for an in-depth study. Network Analysis may be employed to measure the key elements of the transfer market through network indicators, such as degree centrality, hub and authority scores, and betweenness centrality. Furthermore, community detection methods can be proposed to unveil unobservable patterns of the football market, even considering auxiliary variables such as the type of transfer, the age or the role of the player, and the agents involved in the transfer flow. These methodologies are applied to the flows of player transfers generated by the 20 teams of the Italian first division (Serie A). These flows include teams from all over the world. We consider the summer market session of 2019, at the beginning of the season 2019-2020. Results also help to better understand some peculiarities of the Italian football transfer market in terms of the different approaches of the elite teams...
Summary statistics of football matches such as final score, possession and percentage of complete... more Summary statistics of football matches such as final score, possession and percentage of completed passes are not satisfyingly informative about style of play seen on the pitch. In this sense, networks and graphs are able to quantify how teams play differently from each others. We study the distribution of triad census, i.e., the distribution of local structures in networks and we show how it is possible to characterize passing networks of football teams. We describe the triadic structure and analyse its distribution under some specific probabilistic assumptions, introducing, in this context, some tests to verify the presence of specific triadic patterns in football data. We firstly run an omnibus test against random structure to asses whether observed triadic distribution deviates from randomness. Then, we redesign the Dirichlet-Multinomial test to recognize different triadic behaviours after choosing some reference patterns. The proposed tests are applied to a real dataset regardi...
Prediction of soccer clubs’ league rankings by machine learning methods: The case of Turkish Super League
Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology
The aim of this research is to predict league rankings through various machine learning models us... more The aim of this research is to predict league rankings through various machine learning models using technical and physical parameters. This study followed a longitudinal observational analytical design. The SENTIO Sports optical tracking system was used to measure the physical demands and technical practices of the players in all matches. Then, the data regarding the last three seasons of the Turkish Super League (2015–2016, 2016−2017, and 2017−2018), was collected. In this research, league rankings were estimated using three machine learning methods: Artificial Neural Networks (ANN), Radial Basis Function (RBFN), Multiple Linear Regression (MLR) with technical and physical parameters of all seasons. Performances were evaluated through R2, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Prediction results of the models are the following: ANN Model; R2 = 0.60, RMSE = 3.7855 and MAE = 2.9139, RBFN Model; R2 = 0.26, MAE = 3.6292 and RMSE = 4.5168, MLR Model; R2 = 0.46, M...
On the use of passing network indicators to predict football outcomes
Knowledge-Based Systems, 2021
Abstract Summary statistics for football matches, such as ball possession and percentage of compl... more Abstract Summary statistics for football matches, such as ball possession and percentage of completed passes, are not always satisfyingly informative about team strategies seen on the pitch. Passing networks and their structural features can be used to evaluate the style of play in terms of passing behavior, analyzing and quantifying interactions among players. The aim of the present paper is to show how information retrieved from passing networks can have a significant impact on the match outcome. At a descriptive level, we provide useful graphic visualizations to compare teams and their individual level of connection. Therefore, we directly compute and discuss network properties, such as centralization, clustering and cliques, from a football perspective. Then, we model the probability of winning the game through four competitive machine learning models including network-based indicators as explanatory variables with a set of in-field variables. The real dataset for application includes 96 matches in the Group Stage of the 2016–2017 UEFA Champions League, involving the 32 best European teams. This approach shows that some network-based variables, such as diameter and betweenness centralization, can be related to the level of offensive actions and finalizations for a team. Furthermore, we show that such variables help improve all considered models in terms of explanatory power, compared to those presenting only in-field regressors. Among the presented models, binomial logistic regression shows the best results according to a set of performance indicators.
The present paper explores the application of bootstrap methods in testing for serial dependence ... more The present paper explores the application of bootstrap methods in testing for serial dependence in observed driven Integer-AutoRegressive (models) considering Poisson arrivals (P-INAR). To this end, a new semiparametric and restricted bootstrap algorithm is developed to ameliorate the performance of the score-based test statistic, especially when the time series present small or moderately small lengths. The performance of the proposed bootstrap test, in terms of empirical size and power, is investigated through a simulation study even considering deviation from Poisson assumptions for innovations, i.e., overdispersion and underdispersion. Under non-Poisson innovations, the semiparametric bootstrap seems to “restore” inference, while the asymptotic test usually fails. Finally, the usefulness of this approach is shown via three empirical applications.
Southern Italian Peripheries at the Ballot Box. Continuity and Innovation in Electoral Behaviours in Three Southern Italian Cities during Post-Crisis Decade (2008–2019)
The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models
Passes are undoubtedly the more frequent events in football and other team sports. Passing networ... more 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...
The so called "Startup Act" (Decree Law 179/2012, converted into Law 221/2012), has int... more 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 impleme...
Recently, the interest of the academic literature on sports statistics has increased enormously. ... more 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 per...
Recently, the interest of the academic literature on sports statistics has increased enormously. ... more 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 per...
Studies in classification, data analysis, and knowledge organization, Dec 31, 2022
During the first two waves of Covid-19 pandemic, territorial healthcare systems have been severel... more During the first two waves of Covid-19 pandemic, territorial healthcare systems have been severely stressed in many countries. The availability (and complexity) of data requires proper comparisons for understanding differences in performance of health services. We apply a three-steps approach to compare the performance of Italian healthcare system at territorial level (NUTS 2 regions), considering daily time series regarding both intensive care units and ordinary hospitalizations of Covid-19 patients. Changes between the two waves at a regional level emerge from the main results, allowing to map the pressure on territorial health services.
Frontiers in Applied Mathematics and Statistics, Oct 3, 2023
Introduction: Modern FinTech tools (e.g., instant payments, blockchain, roboadvisor) represent th... more Introduction: Modern FinTech tools (e.g., instant payments, blockchain, roboadvisor) represent the new frontier of digital finance. Consequently, the evaluation of the knowledge level of the population about these topics is a crucial concern. In this context, several exogenous factors may influence individual di erences in financial literacy. In particular, the territorial characteristics can have an impact on FinTech. In this work, we investigate individual heterogeneity in subjective financial knowledge in Italy, specifically focusing on modern FinTech tools, and exploring the di erences at the individual and regional levels. Methods: A sample of Italian individuals from di erent Italian regions was involved. A multilevel IRT model is performed to evaluate the level of FinTech individual knowledge and the di erences according to Italian regions to account for the hierarchical structure of the data. Results: Results reported a weak regional e ect, revealing that heterogeneity in financial knowledge can be mainly attributed to individual characteristics. At the individual level, age, economic condition, knowledge of traditional financial objects and numeracy showed a significant e ect. In addition, a scientific field of study and work have an impact on respondents' knowledge level. Discussion: What is shown and discussed in this contribution can inspire policymakers' actions to increase financial literacy in the population. In particular, the obtained results imply that policymakers should improve the population's awareness of less popular FinTech tools and foster individuals' literacy about numbers and traditional financial tools, which proved to have a great influence in explaining FinTech knowledge di erences.
During the waves of the COVID-19 pandemic, both national and/or territorial healthcare systems ha... more 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 res...
The men’s football transfer market represents a complex phenomenon requiring suitable methods fo... more The men’s football transfer market represents a complex phenomenon requiring suitable methods for an in-depth study. Network Analysis may be employed to measure the key elements of the transfer market through network indicators, such as degree centrality, hub and authority scores, and betweenness centrality. Furthermore, community detection methods can be proposed to unveil unobservable patterns of the football market, even considering auxiliary variables such as the type of transfer, the age or the role of the player, and the agents involved in the transfer flow. These methodologies are applied to the flows of player transfers generated by the 20 teams of the Italian first division (Serie A). These flows include teams from all over the world. We consider the summer market session of 2019, at the beginning of the season 2019-2020. Results also help to better understand some peculiarities of the Italian football transfer market in terms of the different approaches of the elite teams...
Summary statistics of football matches such as final score, possession and percentage of complete... more Summary statistics of football matches such as final score, possession and percentage of completed passes are not satisfyingly informative about style of play seen on the pitch. In this sense, networks and graphs are able to quantify how teams play differently from each others. We study the distribution of triad census, i.e., the distribution of local structures in networks and we show how it is possible to characterize passing networks of football teams. We describe the triadic structure and analyse its distribution under some specific probabilistic assumptions, introducing, in this context, some tests to verify the presence of specific triadic patterns in football data. We firstly run an omnibus test against random structure to asses whether observed triadic distribution deviates from randomness. Then, we redesign the Dirichlet-Multinomial test to recognize different triadic behaviours after choosing some reference patterns. The proposed tests are applied to a real dataset regardi...
Prediction of soccer clubs’ league rankings by machine learning methods: The case of Turkish Super League
Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology
The aim of this research is to predict league rankings through various machine learning models us... more The aim of this research is to predict league rankings through various machine learning models using technical and physical parameters. This study followed a longitudinal observational analytical design. The SENTIO Sports optical tracking system was used to measure the physical demands and technical practices of the players in all matches. Then, the data regarding the last three seasons of the Turkish Super League (2015–2016, 2016−2017, and 2017−2018), was collected. In this research, league rankings were estimated using three machine learning methods: Artificial Neural Networks (ANN), Radial Basis Function (RBFN), Multiple Linear Regression (MLR) with technical and physical parameters of all seasons. Performances were evaluated through R2, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Prediction results of the models are the following: ANN Model; R2 = 0.60, RMSE = 3.7855 and MAE = 2.9139, RBFN Model; R2 = 0.26, MAE = 3.6292 and RMSE = 4.5168, MLR Model; R2 = 0.46, M...
On the use of passing network indicators to predict football outcomes
Knowledge-Based Systems, 2021
Abstract Summary statistics for football matches, such as ball possession and percentage of compl... more Abstract Summary statistics for football matches, such as ball possession and percentage of completed passes, are not always satisfyingly informative about team strategies seen on the pitch. Passing networks and their structural features can be used to evaluate the style of play in terms of passing behavior, analyzing and quantifying interactions among players. The aim of the present paper is to show how information retrieved from passing networks can have a significant impact on the match outcome. At a descriptive level, we provide useful graphic visualizations to compare teams and their individual level of connection. Therefore, we directly compute and discuss network properties, such as centralization, clustering and cliques, from a football perspective. Then, we model the probability of winning the game through four competitive machine learning models including network-based indicators as explanatory variables with a set of in-field variables. The real dataset for application includes 96 matches in the Group Stage of the 2016–2017 UEFA Champions League, involving the 32 best European teams. This approach shows that some network-based variables, such as diameter and betweenness centralization, can be related to the level of offensive actions and finalizations for a team. Furthermore, we show that such variables help improve all considered models in terms of explanatory power, compared to those presenting only in-field regressors. Among the presented models, binomial logistic regression shows the best results according to a set of performance indicators.
The present paper explores the application of bootstrap methods in testing for serial dependence ... more The present paper explores the application of bootstrap methods in testing for serial dependence in observed driven Integer-AutoRegressive (models) considering Poisson arrivals (P-INAR). To this end, a new semiparametric and restricted bootstrap algorithm is developed to ameliorate the performance of the score-based test statistic, especially when the time series present small or moderately small lengths. The performance of the proposed bootstrap test, in terms of empirical size and power, is investigated through a simulation study even considering deviation from Poisson assumptions for innovations, i.e., overdispersion and underdispersion. Under non-Poisson innovations, the semiparametric bootstrap seems to “restore” inference, while the asymptotic test usually fails. Finally, the usefulness of this approach is shown via three empirical applications.
Southern Italian Peripheries at the Ballot Box. Continuity and Innovation in Electoral Behaviours in Three Southern Italian Cities during Post-Crisis Decade (2008–2019)
The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models
Passes are undoubtedly the more frequent events in football and other team sports. Passing networ... more 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...
The so called "Startup Act" (Decree Law 179/2012, converted into Law 221/2012), has int... more 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 impleme...
Recently, the interest of the academic literature on sports statistics has increased enormously. ... more 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 per...
Recently, the interest of the academic literature on sports statistics has increased enormously. ... more 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 per...
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Papers by Lucio Palazzo