Mathematical Problems in Engineering

Volume 2018, Article ID 8729158, 11 pages

https://doi.org/10.1155/2018/8729158

## Modifying Olympics Medal Table via a Stochastic Multicriteria Acceptability Analysis

School of Finance, Jiangxi University of Finance and Economics, China

Correspondence should be addressed to Jiangze Du; moc.liamtoh@ud.ezgnaij

Received 12 December 2016; Accepted 22 February 2017; Published 15 August 2018

Academic Editor: Kishin Sadarangani

Copyright © 2018 Jiangze Du. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper addresses the issue of developing a widely accepted Olympics ranking scheme based upon the Olympic Game medal table published by the International Olympic Committee, since the existing lexicographic ranking and sum ranking systems are both criticized as biases. More specifically, the lexicographic ranking system is deemed as overvaluing gold medals, while the sum ranking system fails to reveal the real value of gold medals and fails to discriminate National Olympic Committees that won equal number of medals. To start, we employ a sophisticated mathematical method based upon the incenter of a convex cone to aggregate the lexicographic ranking system. Then, we consider the fact that the preferences between the lexicographic and the sum ranking systems may not be consistent across National Olympic Committees and develop a well-designed mathematical transformation to obtain interval assessment results under typical preference. The formulation of intervals is inspired by the observation that it is extremely difficult to achieve a group consensus on the exact value of weights with respect to each ranking system, since different weight elicitation methods may produce different weight schemes. Finally, regarding the derived decision making problem involving interval-valued data, this paper utilizes the Stochastic Multicriteria Acceptability Analysis to obtain a comprehensive ranking of all National Olympic Committees. Instead of determining precise weights, this work probes the weight space to guarantee each alternative getting the most preferred one. The proposed method is illustrated by presenting a new ranking of 12 National Olympic Committees participating in the London 2012 Summer Olympic Games.

#### 1. Introduction

The Olympics medal table is a list of National Olympic Committees (NOCs) released by the International Olympic Committee (IOC), which ranks NOCs according to the number of gold medals won by single NOC. The number of silver medals is taken into account next and then the number of bronze medals. Meanwhile, total medal count with respect to each NOC is summed and shown in the Olympics medal table, which is widely accepted as the alternative criterion to sort NOCs. These two ranking mechanisms are defined as the lexicographic ranking system and the sum ranking system, respectively. However, the lexicographic ranking system is criticized as overvaluing gold medals. In particular case, the NOCs that won a large quantity of silver and bronze medals but without gold medal are ranked below the NOCs that won only one gold medal. For instance, the 2012 Summer Olympics Medal Table ranks India with zero gold medal, two silver medals, and four bronze medals behind Venezuela with only one gold medal. On the other hand, the sum ranking system takes into consideration of the total sum of the medals won by the NOCs, which inevitably fails to reveal the real value of gold medals and thus fails to discriminate NOCs that won equal number of medals. For instance, the sum ranking system at 2012 London Summer Olympics ranks Uzbekistan with one gold medal and two bronze medals and Thailand with two silver medals and one bronze medal at the same position. This is definitely full of conflict in real life.

Although the IOC publishes the quasi-official medal table during each Olympic Game, the IOC itself has not officially recognized and endorsed any ranking system. The former then-president of IOC, Jacques Rogge, says during the 2008 Beijing Summer Olympic Game:

I believe that each country will highlight what suits it best. One country will say, “Gold medal.” The other country will say, “The total tally counts.” We take no position on that.

The present study is motivated by his viewpoint and seeks to provide a comprehensive ranking system while simultaneously considering the preferences between the lexicographic and the sum ranking systems. Very few studies in literature have addressed the issue that measures the NOCs’ performance based only on the number of medals won. Sitarz [1, 2], for example, introduced two methods based on the weighted mean value and volume-based sensitivity analysis and used the concept of incenter of a convex cone to aid Olympic rankings. Cao et al. [3] aggregated both ranking systems by minimizing the difference between them and elicited exact weights associated with each system. Although this small body of research is somehow helpful to support Olympics ranking, it is crucial to understand the impact of distinct preferences between the lexicographic ranking system and the sum ranking system on constructing ranking system of NOCs. However, the extant literature has left this important and interesting research topic largely unexplored. This paper fills this gap by first aggregating the lexicographic ranking system through a sophisticated approach, next formulating intervals to assess the NOC-specific achievement under typical preference, and then applying Stochastic Multicriteria Acceptability Analysis (SMAA-2) to rank NOCs with interval input data. The formulation of interval input is motivated by the observation that in the domain of multicriteria decision making (MCDM) different weight determination methods may generate different weights even for the same problem, and it would be extremely difficult to achieve a group consensus about precise value of weights [4].

Apart from the previous studies about assessing Olympic achievements, this paper provides new research directions and more method options for ranking construction, based upon the methodology developed by Song et al. [5]. The difference is that the rationale of Song et al. [5] is to determine intervals for the lexicographic ranking system, while this study takes the advantage of a widely accepted weight elicitation method for obtaining the precise weights associated with various medals and then considers different preferences between the lexicographic and sum ranking system. Such explorations go well beyond the general ideas of building ranking and shed much-needed light on potential incentives and directions for academic, managerial, and policy-related implications. This paper contributes to the growing literature on measuring Olympic achievements in the following points:(i)We modify the Olympics medal table published by IOC through jointly considering the lexicographic and the sum ranking systems, while almost all of the extant literature is interested in dealing with the lexicographic ranking system. The weights associated with gold, silver, and bronze medals are obtained through a well-designed mathematical approach to aggregate the lexicographic ranking system.(ii)Different preferences between the lexicographic and the sum ranking systems are proposed and investigated to obtain a holistic ranking. Regarding certain preference, a sophisticated mathematical transformation is developed to support the decision maker generating interval measurement with respect to each NOC. This gives rise to an interval decision matrix for aiding ultimate ranking.(iii)SMAA-2 is applied to determine holistic ranking acceptability index for the proposed interval decision matrix, by which all NOCs could be fully ranked taking into account both the lexicographic and the sum ranking systems.

The rest of this paper proceeds as follows. Section 2 reviews some related paper in literature. Section 3 presents the mathematical formulation of the problem. Section 4 describes SMAA-2 method and the related indices. Section 5 presents the application of SMAA-2 to rank NOCs. Finally, Section 6 concludes this research and provides the directions for future research.

#### 2. Literature Review

##### 2.1. Existing Approaches to Measuring Olympic Achievements

The majority of existing approaches to measuring Olympic achievement are related to Data Envelopment Analysis (DEA), evaluating input (i.e., GDP per capita and population) and output (i.e., the number of medals) efficiency using a family of DEA models and their variants. The pioneering work in this domain is presented by Lozano et al. [6]. Lins et al. [7] take into consideration the fact that the sum of medals is constant and then develop zero-sum gains DEA model to rank NOCs in Olympics. Li et al. [8] introduce multiple sets of NOC-specific assurance regions into DEA and thus establish fair models for measuring and benchmarking the performance of NOCs. Soares De Mello et al. [9] consider that different sports should be of different importance and then propose a modified cross-evaluation DEA model with weight restrictions to generate a ranking for Athens Olympic Games. Wu et al. [10, 11] utilize DEA cross-efficiency approach to rank NOCs. Zhang et al. [12] incorporate lexicographic preference into DEA models to measure the performance of NOCs at the Olympic Games. Lei et al. [13] regard the Summer and Winter Olympic Games as a parallel system and then apply a parallel DEA approach to evaluate the efficiency of each NOC. Li et al. [14] develop a two-stage DEA model to evaluate the performance of NOCs at the 2012 London Summer Olympics.

There are also some complementary approaches that rank NOCs solely considering the number of medals, i.e., Copeland method [15], mean value and volume-based sensitivity analysis [1], the incenter of a convex cone [2], Borda multicriteria method [16], and distance-based approach [3].

##### 2.2. Stochastic Multicriteria Acceptability Analysis (SMAA)

Initiated by Lahdelma et al. [17], SMAA denotes a methodology that intends to support multiperson, multicriteria decision making problem, in which extremely limited or even no weight knowledge is known, and the values of criteria are unknown as well. SMAA does not require any expert to precisely describe the input information and proposes three meaningful and useful indices, namely, acceptability index, central weight, and confidence factor. Lahdelma and Salminen [4] provide an extension of SMAA through taking all ranks into account and present a comprehensive SMAA-2 analysis to assess alternatives. Regarding the decision making problems including ordinal criteria knowledge, Lahdelma et al. (2003) provide a novel SMAA-O approach. Durbach [18] integrates SMAA and achievement functions to develop a SMAA-A model with respect to discrete-choice decision, which considers the significance of decision makers’ aspirations. Lahdelma and Salminen [19] propose cross confidence factor concept on the basis of computing the alternatives’ confidence factors using other’s central weights. Lahdelma and Salminen [20] integrate SMAA-2 and DEA to assess alternatives in the multicriteria framework. Lahdelma and Salminen [21] combine SMAA and prospect theory to develop a SMAA-P method. Lahdelma et al. [22, 23], respectively, develop and compare simulation technique and multivariate Gaussian distribution method for treating the uncertainty and dependency information with respect to the SMAA-2 MCDM problem. Tervonen and Lahdelma [24] provide efficient approaches to perform the computations by Monte Carlo simulation, conduct complexity analysis, and assess the accuracy of proposed algorithms. Corrente et al. [25] combine SMAA and PROMETHEE to investigate the parameters compatibility together with decision makers’ preference information. Angilella et al. [26] and Angilella et al. [27], respectively, integrate SMAA and Choquet integral preference model to achieve robust recommendations and robust ordinal regression. Durbach and Calder [28] explore the conditions under which decision makers are unable or unwilling to provide exact information in SMAA.

SMAA has been efficiently applied in the domain of decision making, i.e., facility location [29], forest planning [30], elevator planning [31], descriptive multiattribute choice model [32], estimation of a satisficing model of choice [33], DEA cross-efficiency aggregation [34], mutual fund performance assessment [35], project portfolio optimization [36], and energy performance evaluation [5].

To the best of our knowledge, almost all existing studies have ignored the fact that different NOCs may have different preferences between the lexicographic and the sum ranking systems. Even under typical preference, it is significantly difficult to achieve a group consensus on the exact weights with respect to each ranking system. Therefore, this paper pioneers the adventure to formulate intervals to represent the NOC-specific performances and then apply SMAA-2 to holistically rank NOCs in the presence of interval input data.

#### 3. Problem Formulation

##### 3.1. Aggregating the Lexicographic System

Regarding the lexicographic system, this section determines a system of points with respect to various medals. This is in line with the work performed by Sitarz [2]. The conditions considered in this paper are presented as follows:(i)Gold medal should be assigned more points than silver medal, while silver medal should be given more points than bronze medal [2](ii)The difference between a gold medal and a silver medal is larger than that between a silver medal and a bronze medal [9, 37, 38]

These conditions definitely make sense in real life and could be mathematically expressed by a convex cone as follows:where represents the point for gold medal, denotes the point for silver medal, and is the point for bronze medal.

Inspired by the observations that MCDM and statistical problems usually use the mean value to support decision making [2], it is reasonable to determine the incenter of the set , which is defined by Henrion and Seeger [39] as the optimal solution to the following optimization problem:where indicates the unit sphere, represents the boundary of set , and* dist* means the distance in the Euclidean space. Details on theory justification and mathematical properties about the incenter of convex cone can be found in Henrion and Seeger [39, 40].

Following the numerical methods developed by Henrion and Seeger [40], the incenter of the set is obtained as follows:where the multiplier is a parameter and does not impact ranking. Therefore, the points associated with gold, silver, and bronze medals are determined as , and 1, respectively. The lexicographic ranking system is then aggregated by calculating the weighted sum of the number of medals and the corresponding points.

##### 3.2. Formulation

Based upon the results derived from aggregating the lexicographic system, we modify the Olympics medal table as shown in Table 1, where , are exact values and have been normalized to eliminate the effect of magnitude of data. Therefore, the evaluation results for each NOC are calculated by weighted sum of the ranking system measures; that is,where are the weights of ranking system associated with NOC , and .