Forecasting the the Olympic medal distribution - A socioeconomic machine learning model

被引：19

作者：

Schlembach, Christoph ^{[1
]}

Schmidt, Sascha L. ^{[1
,3
,4
]}

Schreyer, Dominik ^{[1
]}

Wunderlich, Linus ^{[2
]}

机构：

[1] WHU Otto Beisheim Sch Management, Ctr Sports & Management, Campus Dusseldorf,Erkrather Str 224a, D-40233 Dusseldorf, Germany

[2] Queen Mary Univ London, Sch Math Sci, Mile End Rd, London E1 4NS, England

[3] CREMA Ctr Res Econ Management & Arts, Sudstr 11, CH-8008 Zurich, Switzerland

[4] LISH Lab Innovat Sci Harvard, 175 N Harvard St,Suite 1350, Boston, MA 02134 USA

来源：

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE | 2022年 / 175卷

关键词：

Olympic games; Medals; Sports; Forecasting; Machine learning; random forest; SPORTS PERFORMANCE; ECONOMIC-FACTORS; SUMMER OLYMPICS; SUCCESS; GAMES; PARTICIPATION; DETERMINANTS; COMPETITION; PREDICTION; ALGORITHM;

D O I：

10.1016/j.techfore.2021.121314

中图分类号：

F [经济];

学科分类号：

02 ;

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. We apply machine learning, more specifically a two-staged Random Forest, to a dataset containing socioeconomic variables of 206 countries (1991-2020). For the first time, we outperform the more traditional naive forecast for four consecutive Olympics between 2008 and 2020.

引用

页数：15

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