Data envelopment analysis (DEA) is a useful tool for measuring the relative efficiencies of participating nations in the Olympic Games. DEA models with restricted multipliers have been used to refine efficiency evaluations by imposing additional information. Existing DEA models for evaluating Olympic medals do not focus on multiplier restrictions regarding input. To fill this research gap, this study incorporates a data fitting technique of medal prediction using ordinary least squares regression in input multiplier restrictions of the conventional DEA model. We show that the efficiency of the proposed model can be decomposed into the achievement ratio of substantial medal total and the unit value index of medals. Such decompositions can be used to analyze the effectiveness of host nations and athlete development initiatives. For an illustrative empirical application, we examine the target that the Brazilian Olympic Committee (BOC) set for the 2016 Summer Olympic Games (Rio 2016). Our results explain the extremely high feasibility of Brazil’s target of being in the top 10 medals table in Rio 2016.

数据包络分析 (DEA) 是衡量奥运会参赛国相对效率的有用工具。具有受限乘数的 DEA 模型已被用于通过强加附加信息来改进效率评估。现有的用于评估奥运奖牌的 DEA 模型不关注有关输入的乘数限制。为了填补这一研究空白，本研究在传统 DEA 模型的输入乘数限制中结合了使用普通最小二乘回归的奖牌预测数据拟合技术。我们表明，所提出模型的效率可以分解为奖牌总数的实现率和奖牌的单位价值指数。这种分解可用于分析东道国和运动员发展计划的有效性。为了说明性的实证应用，我们研究了巴西奥委会 (BOC) 为 2016 年夏季奥运会（里约 2016）设定的目标。我们的结果解释了巴西进入 2016 年里约奥运会奖牌榜前 10 名的目标具有极高的可行性。