《Forecasting the Olympic medal distribution during a pandemic: a
socio-economic machine learning model》
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作者:
Christoph Schlembach, Sascha L. Schmidt, Dominik Schreyer, Linus
Wunderlich
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最新提交年份:
2021
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英文摘要:
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 na\\\"ive 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).
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中文摘要:
预测每个国家的奥运奖牌数量与不同的利益相关者高度相关:事先,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。事后,体育从政者和管理者可以衡量他们团队的表现,评估成功的驱动因素。为了显著提高奥运奖牌预测的准确性,我们采用了机器学习,更具体地说是两阶段随机森林,因此,在2008到2016年间首次举办了三届奥运会的传统预测,在2021年的2020届东京奥运会上,我们的模型表明,美国将获得奥运奖牌榜,获得120枚奖牌,紧随其后的是中国(87)和大不列颠(74)。有趣的是,2019冠状病毒疾病的流行不会显著改变奖牌总数,因为所有国家在某种程度上都遭受了流行病(数据固有的)和有限的历史数据点(类似的疾病)。
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Machine Learning 机器学习
分类描述:Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文(有监督的,无监督的,强化学习,强盗问题,等等),包括健壮性,解释性,公平性和方法论。对于机器学习方法的应用,CS.LG也是一个合适的主要类别。
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一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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一级分类:Statistics 统计学
二级分类:Computation 计算
分类描述:Algorithms, Simulation, Visualization
算法、模拟、可视化
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一级分类:Statistics 统计学
二级分类:Machine Learning 机器学习
分类描述:Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文(监督,无监督,半监督学习,图形模型,强化学习,强盗,高维推理等)与统计或理论基础
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