Future Technology

Sports Prediction Software Will Revolutionize The Industry

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PHOTOGRAPH: Pixabay |

There is a science to sports rankings, which might get easier with a newly-developed sports prediction software. The use of such technology is ideal especially in team sports, because performance can be measured, and performance declines and improvements can be trended. This bodes well overall because it provides a large amount of data that can be fed, which are in turn used to determine what factors affect the equation.

What Was the Information Used?

With the use of different data that is available, Digital Journal has noted that a group of Mexican researchers has collaborated information on national sports teams. The specific sports included are tennis, chess, golf, poker, and football. Scientists specifically looked at the performances of the players and the trends of their corresponding careers. The corresponding data have been used to explore universal features that in turn help in the creation of hierarchies.

What Was The Point Of The Study?

Dr. Jose Morales, who works at the National Autonomous University of Mexico, determined a model that can predict changes in rank over the course of an athlete’s career. Within the study, the researchers set out to predict statistical regularities that could help explain how competition shapes the position of players and teams. Furthermore, they focused on how these hierarchies evolved over time, both for the players and the overall team. One area of focus was how similar sports are to the classification structures found within the distribution of wealth.

What Were The Results Of The Research?

Scoop It reported that the outcome of the study was interesting and incredibly useful. “Our results support the notion that hierarchical phenomena may be driven by the same underlying mechanisms of rank formation, regardless of the nature of their components,” a summary of the study read. “Moreover, such regularities can in principle be used to predict lifetimes of rank occupancy, thus increasing our ability to forecast stratification in the presence of competition.”

What Does It Mean?

The study found that the results yielded more or less within the same direction regardless of the player’s individual characteristics. Instead, competition in sports is structured over a series of rules, whether simple or complicated.

The paper, entitled “Generic temporal features of performance rankings in sports and games,” was published in the journal “EPJ Data Science.” It was worked on by Morales, Sergio Sánchez, Jorge Flores, Carlos Pineda, Carlos Gershenson, Germinal Cocho, Jerónimo Zizumbo, Rosalío F Rodríguez and Gerardo Iñiguez.

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