(A) Distribution of the normalized player passing accuracy. We normalize the passing accuracy of each player that passed the ball at least 5 times during the match by the mean Study could help employees work together on team projects and standard deviation for the player's position. The mean (standard deviation) passing accuracy is 60.8 (15.7) for goalkeepers, 78.1 (10.1) for defenders , 75.6 (10.6) for midfielders, and 64.9 (12.8) for forwards. (B) Distribution of player shooting accuracy. We include only those players that shot the ball at least twice in a match. (C) Distribution of player performances. We define player performance as the normalized logarithm of the flow centrality (see text). We only include those players that passed the ball at least 5 times in a match. (D) Distribution of the normalized logarithm of the flow centrality for the passes (arcs) between players Researchers Use Science to Identify Soccer StarsSerá que Dunga aceitaria os critérios científicos para escolha da seleção!!!' E você aceitaria esses critérios na formação da sua equipe de pesquisa??
New research statistically identifies true soccer stars based on objective measures.
Until now, rating the world's best soccer players was often based on a fan's personal sense of the game. But researchers at Northwestern University in Evanston, Ill., recently developed a computer program that, for the first time, measures player success based on objective assessments of performance instead of opinion.
Luis Amaral, an associate professor of chemical and biological engineering at Northwestern, says that while basketball and baseball offer a wealth of statistical data to gauge the performance of individual players--such as runs batted in, strikeouts, steals and rebounds--this isn't the case with soccer.
So he and a team of researchers used their computational skills to write software that pulled play-by-play statistical information from the 2008 Euro Cup website.
Their results closely matched the opinions of sports reporters who covered the matches as well as the team of experts, coaches and managers that subjectively chose players for the "best of" tournament teams.
Amaral says this kind of analysis, published today in a Public Library of Science journal, PLoS ONE, can be used outside of the world of soccer. For example, companies could use the method to measure the performance of employees working together on a team project.
The National Science Foundation's Science of Science & Innovation Program, in the Social, Behavioral and Economic Sciences directorate, partially funded the research. The project is the outgrowth of an effort to develop transparent, statistically-sound methods to describe the productivity of researchers and institutions, and the impact of their work.
Until now, rating the world's best soccer players was often based on a fan's personal sense of the game. But researchers at Northwestern University in Evanston, Ill., recently developed a computer program that, for the first time, measures player success based on objective assessments of performance instead of opinion.
Luis Amaral, an associate professor of chemical and biological engineering at Northwestern, says that while basketball and baseball offer a wealth of statistical data to gauge the performance of individual players--such as runs batted in, strikeouts, steals and rebounds--this isn't the case with soccer.
So he and a team of researchers used their computational skills to write software that pulled play-by-play statistical information from the 2008 Euro Cup website.
Their results closely matched the opinions of sports reporters who covered the matches as well as the team of experts, coaches and managers that subjectively chose players for the "best of" tournament teams.
Amaral says this kind of analysis, published today in a Public Library of Science journal, PLoS ONE, can be used outside of the world of soccer. For example, companies could use the method to measure the performance of employees working together on a team project.
The National Science Foundation's Science of Science & Innovation Program, in the Social, Behavioral and Economic Sciences directorate, partially funded the research. The project is the outgrowth of an effort to develop transparent, statistically-sound methods to describe the productivity of researchers and institutions, and the impact of their work.
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