Serra do Curral   

Professor,  Departament  of  Computer  Science

Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

Research Areas :  Spatial Statistics and Machine Learning

I am a Professor in the Department of Computer Science, Universidade Federal de Minas Gerais (UFMG) located in Belo Horizonte, Brazil. I received my Ph.D. in Statistics in 1994 from the University of Washington, Seattle, USA, having Peter Guttorp as advisor. From 1994 to 2011, I worked in the Department of Statistics, UFMG. I moved to my current position in September 2011. I prefer to work in applied problems where the stochastic nature of the problem requires probabilistic and statistical modeling. My main application areas have been epidemiology social issues, actuarial risk, and demographic analysis. Due to my recent move to the computer science department, I have been adding new application areas to my work. This interdisciplinary work in turn shape my methodological research in statistics and machine learning. 

My current research is focused on the development of new algorithms and statistical methods to analyse spatial and space-time data. I am primarily concerned with the spatial analysis of risk appearing in many fields such as epidemiological surveillance, geosensor networks, enviornmental problems, marketing, spatially variable risk spatially variable, among many others. The computer revolution, still going on, have made it possible to manage huge space-time databases. The extraction of interesting patterns from these massive databases create new, challenging, and interesting problems that require creative algorithmic, statistical and probabilistic solutions.

Posted on February 06, 2014 by Renato  

Luck is hard to beat: The Difficulty of Sports Prediction


Recent work with Raquel Aoki and Pedro Vaz de Melo. First, watch the video.


Predicting the outcome of sports events is a hard task. We quantify this difficulty with a coefficient that measures the distance between the observed final results of sports leagues and idealized perfectly balanced competitions in terms of skill. This indicates the relative presence of luck and skill. We collected and analyzed all games from 198 sports leagues comprising 1503 seasons from 84 countries of 4 different sports: basketball, soccer, volleyball and handball. We measured the competitiveness by countries and sports. We also identify in each season which teams, if removed from its league, result in a completely random tournament. Surprisingly, not many of them are needed. As another contribution of this paper, we propose a probabilistic graphical model to learn about the teams’ skills and to decompose the relative weights of luck and skill in each game. We break down the skill component into factors associated with the teams’ characteristics. The model also allows to estimate as 0.36 the probability that an underdog team wins in the NBA league, with a home advantage adding 0.09 to this probability. As shown in the first part of the paper, luck is substantially present even in the most competitive championships, which partially explains why sophisticated and complex feature-based models hardly beat simple models in the task of forecasting sports’ outcomes.

Posted on July 14, 2017 by Renato