1,720,974 research outputs found
Discussion of “The class of cub models: statistical foundations, inferential issues and empirical evidence” by Domenico Piccolo and Rosaria Simone
No full textIn this paper we discuss the article “The class of CUB models: statistical foundations, inferential issues and empirical evidence” by Domenico Piccolo e Rosaria Simone. The main considered topic is the decision process that, according to the CUB paradigm, leads the respondent to express a rating about a latent trait on a given ordinal scale. We discuss a generalized formalization of this decision process, comprising the basic CUB model, some already existing extensions and possible future developments
Analyzing and modelling rating data for sensory analysis in food industry
No full textConsumers’ and experts’ preferences and perceptions of the sensory attributes of products are very important for manufacturers in the food industry, in order to avoid market disappointment and improve food quality. Indeed, appropriate sensory analyses combined with proper statistical methods allow to segment market, obtain positioning of products (brands, organizations, etc.) and identify the market acceptability.
This finally has a great impact upon food quality and ndustrial competitiveness. In this paper, we use CUB models to analyze sensory data coming from a survey on the Italian espresso
Book of short papers, IES 2025 - Innovation & Society: Statistics and Data Science for Evaluation and Quality
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Reporting of clustering techniques in sports sciences: a scoping review
No full textMultivariate statistical methods are among the most used ones in sports sciences with clustering methods emerging as prominent unsupervised learning techniques. This study presents a scoping review of original articles utilizing clustering techniques in sports sciences, following the PRISMASCR guidelines. A comprehensive search across various databases using the boolean "AND" combination of "clustering" and "sport" yielded 278 articles. Notably, 86.7% of these articles were published within the last 14 years, with a predominant focus (66.2%) on sports performance analysis. The majority of studies included professional athletes (56.4%), with football/soccer, basketball, and tennis being the most commonly studied sports, representing 12.2%, 7.5%, and 2.2% of the selected articles, respectively. Hierarchical clustering was the most frequently used method (31.6%), followed by the k-means algorithm for partitional clustering. However, the clustering method was not reported in 26.6% of the articles, and 55.0% did not specify the criterion used for determining the optimal number of clusters. Moreover, more than 85% of the articles lacked computational details related to data reproducibility. These findings underscore the urgent need for substantial improvement in reporting practices regarding the methodology, algorithms, criteria for cluster identification, and software usage in sports science literature
A SURVIVAL ANALYSIS STUDY TO DISCOVER WHICH SKILLS DETERMINE A HIGHER SCORING IN BASKETBALL
No full textOver the years data analytics for sports has developed consistently. Survival analysis is a method that allows to study the occurrence of a particular event during a period of follow-up. This work aims studying the main achievements associated to the probability of reaching a certain amount of points during a NBA season segment.
A Stepwise Cox regression model and a Lasso Cox regression were used to select the most important variables. Two settings were examined, with 20% and 50% censoring. Results showed that attempting more shots, gaining more achievements (double doubles) and having been selected for the All-Star game increase the probability of success, i.e. exceeding the given threshold of points. Moreover, a higher number of steals seems to decrease the probability of reaching a certain amount of points. Thus, players more involved in this fundamental are penalized in terms of scored points
Modelling perceived variety in a choice process with nonlinear CUB
No full textIn consumer research, marketing, public policy and other fields, individuals’ choice depends on the number of possible alternatives. In addition, according to the literature, the choice satisfaction is influenced not only by the number of options but also by the perceived variety. The aim of the present study is to apply a novel statistical approach to model perceived variety, in order to better understand the perceptions of individuals about the variety of the possible choice options. We resort to the class of CUB (Combination of Uniform and Binomial random variables) models, in particular to the Nonlinear extension of CUB, in order to (i) provide a measure for perceived variety, (ii) add a measure of uncertainty, (iii) give insights on the state of mind of respondents toward the response scale. The application of the Nonlinear CUB to real data previously published shows interesting results
Markov Switching Modelling of Shooting Performance Variability and Teammate Interactions in Basketball
No full textIn basketball, measures of individual player performance provide critical guidance for a broad spectrum of decisions related to training and game strategy. However, most studies on this topic focus on performance level measurement, neglecting other important factors, such as performance variability. Here we model shooting performance variability by using Markov switching models, assuming the existence of two alternating performance regimes related to the positive or negative synergies that specific combinations of players may create on the court. The main goal of this analysis is to investigate the relationships between each player's performance variability and team line-up composition by assuming shot-varying transition probabilities between regimes. Relationships between pairs of players are then visualized in a network graph, highlighting positive and negative interactions between teammates. On the basis of these interactions, we build a score for the line-ups, which we show correlates with the line-up's shooting performance. This confirms that interactions between teammates detected by the Markov switching model directly affect team performance, which is information that would be enormously useful to coaches when deciding which players should play together
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