Analyzing Volleyball Data on a Compositional Regression Model Approach: An Application to the Brazilian Men's Volleyball Super League 2011/2012 Data

Volleyball has become a competitive sport with high physical and technical performance. Matches results are based on the players and teams'skills as technical and tactical strategies to succeed in a championship. At this point, some studies are carried out on the performance analysis of different match elements, contributing to the development of this sport. In this paper, we proposed a new approach to analyze volleyball data. The study is based on the compositional data methodology modeling in regression model. The parameters are obtained through the maximum likelihood. We performed a simulation study to evaluate the estimation procedure in compositional regression model and we illustrated the proposed methodology considering real data set of volleyball.Comment: 12 page

Modeling Compositional Regression with uncorrelated and correlated errors: a Bayesian approach

Compositional data consist of known compositions vectors whose components are positive and defined in the interval (0,1) representing proportions or fractions of a "whole". The sum of these components must be equal to one. Compositional data is present in different knowledge areas, as in geology, economy, medicine among many others. In this paper, we introduce a Bayesian analysis for compositional regression applying additive log-ratio (ALR) transformation and assuming uncorrelated and correlated errors. The Bayesian inference procedure based on Markov Chain Monte Carlo Methods (MCMC). The methodology is illustrated on an artificial and a real data set of volleyball