Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Tengfei Fan,Xintong Fan,Sifeng Wang,Zhichen Hu,Shengli Tian

doi:10.1109/access.2021.3114590

Abstract

With the widely-adopted idea of health and longevity, sports have been becoming one of the most popular entertainment ways of the public. For the majority of sports, players need to know about their concrete physical conditions in a real time manner so as to pursue a good sport score or ranking in a competition or a race. Generally, we can achieve the above goal through analyzing and evaluating the daily training scores of each player. However, there are often multiple physical trainings for players and various correlations are existent among them, which significantly decrease the fairness and trust of player training score evaluation and ranking since traditional multi-dimensional data integration solutions are often based on a strong hypothesis, i.e., the involved multiple dimensions are independent with each other. In view of this shortcoming, we introduce the Mahalanobis Distance into the multi-dimensional player training score evaluation and further propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c orrelation-aware <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p layer training score <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e valuation method with trust (abbreviated as CPE MD ) based on <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M ahalanobis <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D istance. As Mahalanobis Distance can eliminate the hidden linear correlations among the involved multiple dimensions, we can guarantee the fairness and trust of Mahalanobis Distance-based player training score evaluation and ranking results. At last, we use a case study to show the feasibility of CPE MD in this paper.

Highlights

With the continuous progress of social reformation and economic developments, people’s living conditions have gained considerable improvements [1]–[3]
(3) A case study and a set of experiments are provided to demonstrate the concrete processes of CPEMD method, through which we show the feasibility of our proposal in eliminating the linear correlation relationships among the multiple sport training scores of players
As Mahalanobis Distance can eliminate the hidden linear correlations among the multiple dimensions involved in the player training score evaluation process, we can guarantee the fairness and trust of Mahalanobis Distance-based player training score evaluation and ranking results outputted by CPEMD method

Summary

INTRODUCTION

With the continuous progress of social reformation and economic developments, people’s living conditions have gained considerable improvements [1]–[3]. A taller man often player better than a shorter man in terms of volleyball and basketball due to the body height advantages of the former man In this situation, it is inappropriate to evaluate the comprehensive score of a player by calculating the sum of his or her engaged multiple sport items directly, since the hidden correlations among different sport training scores may decrease the fairness and trust. (3) A case study and a set of experiments are provided to demonstrate the concrete processes of CPEMD method, through which we show the feasibility of our proposal in eliminating the linear correlation relationships among the multiple sport training scores of players.

RELATED WORK

SOLUTION

CASE STUDY

EVALUATIONS

CONCLUSION