Informational aspects of boxing development process in Russian Federation


Dr.Hab., Associate Professor A.A. Polozov1
Honored Master of Sports K.B. Tszu2
PhD E.G. Shurmanov1
1Ural Federal University named after First President of Russia B.N. Yeltsin, Yekaterinburg
2Academy of Boxing named after K.B. Tszu, Yekaterinburg

Objective of the study was to scientifically substantiate the use of automated rating and refereeing systems in boxing.
Methods and structure of the study. To develop the author's rating computation algorithm in boxing, we made a comparative assessment of the approaches used.
Study results and conclusions. The best way to implement the proposed rating computation algorithm is to accumulate data over time by the ascension and degradation periods in an athletic career, which will enable us to determine the highest skill level and allocate the most talented boxers. The analysis of changes in this indicator when working with different coaches will help rank coaches and their training methodologies. The establishment of an advanced data environment creates an advantage for the national boxing school.
The automated refereeing project based on the developed neural network suggests that, instead of judges, a bout will be watched through 3 video cameras: 4 in the corners of the ring and 1 on the body of a referee. A specially trained neural network will decide whether to count a strike or not. It will be activated at the moment of impact. To understand when this moment arrives, a special sensor should be chosen and given to a boxer to hold in both hands. The solution to this problem will help decide the winner not by the number of strikes but by the total strike weight.
The authors conclude that the employment of informational resources to create Dig Data in boxing with the most adequate rating computation for comparing the expected and actual results; the use of neural networks to solve the current problem in boxing - refereeing - are the priority areas in the boxing development process.

Keywords: boxing, rating, IT, neural network.


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