论文标题
在大流行期间预测奥林匹克奖牌分配：一种社会经济的机器学习模型
Forecasting the Olympic medal distribution during a pandemic: a socio-economic machine learning model
论文作者
论文摘要
预测每个国家的奥林匹克奖牌数量与不同的利益相关者高度相关：事前，体育博彩公司可以确定赔率，而赞助商和媒体公司可以将其资源分配给有前途的团队。前职位，体育政客和经理可以基准对其团队的表现进行基准测试，并评估成功的驱动力。为了显着提高奥林匹克奖章的预测准确性，我们采用机器学习，更具体地说是两阶段的随机森林，因此首次在2008年至2016年间举行的三场奥运会上的传统幼稚预测超过了传统的幼稚预测。关于2021年东京2020年奥运会的2020年奥运会，我们的模型表明，美国将领先奥运会奖牌，获得120枚奖牌，其次是中国（87）和英国（74）。有趣的是，我们预测，由于所有国家在某种程度上遭受了大流行（数据固有）和有限的可比较疾病（模型固有）的历史数据点（固有的模型），因此当前的Covid-19大流行将不会显着改变奖牌数量。
Forecasting the number of Olympic medals for each nation is highly relevant for different stakeholders: Ex ante, sports betting companies can determine the odds while sponsors and media companies can allocate their resources to promising teams. Ex post, sports politicians and managers can benchmark the performance of their teams and evaluate the drivers of success. To significantly increase the Olympic medal forecasting accuracy, we apply machine learning, more specifically a two-staged Random Forest, thus outperforming more traditional naïve forecast for three previous Olympics held between 2008 and 2016 for the first time. Regarding the Tokyo 2020 Games in 2021, our model suggests that the United States will lead the Olympic medal table, winning 120 medals, followed by China (87) and Great Britain (74). Intriguingly, we predict that the current COVID-19 pandemic will not significantly alter the medal count as all countries suffer from the pandemic to some extent (data inherent) and limited historical data points on comparable diseases (model inherent).
