Multiobjective centralized DEA approach to Tokyo 2020 Olympic Games

Abstract

There exist two types of Data Envelopment Analysis (DEA) approaches to the Olympic Games: conventional and fixed-sum outputs (FSO). The approach proposed in this paper belongs to the latter category as it takes into account the total number de medals of each type awarded. Imposing these constraints requires a centralized DEA perspective that projects all the countries simultaneously. In this paper, a multiobjective FSO approach is proposed, and the Weighted Tchebychef solution method is employed. This approach aims to set all output targets as close as possible to their ideal values. In order to choose between the alternative optima, a secondary goal has been considered that minimizes the sum of absolute changes in the number of medals, which also renders the computed targets to be as close to the observed values as possible. These targets represent the output levels that could be expected if all countries performed at their best level. For certain countries, the targets are higher than the actual number of medals won while, for other countries, these targets may be lower. The proposed approach has been applied to the results of the Tokyo 2020 Olympic Games and compared with both FSO and non-FSO DEA methods.

Achievement Prediction and Performance Assessment System for Nations in the Asian Games

“…Additionally, Li et al [16] extended GEEF to a two-stage network DEA configuration similar to Li et al [24]. Lozano and Villa [18] pointed out that the ZSG and GEEF methods did not consider the completeness of output indicators for the Olympic Games or calculate output targets. Instead, these approaches solely calculated efficiency scores and rankings.…”