![]() We do not need to scale our features because the sensitivity of our target variables will be incorporated into the coefficients of the features. Since we are using a linear regression model, we decided to limit our training data to those records beginning with the Barcelona 1992 Olympic Games, since the era prior to the collapse of the Soviet Union might be significantly different. The first step is dummy encoding the nations, which are categorical, converting them into numerical data. Now that we have identified several features ( athletes, events, athletes per event, summer games, outlier nations, host nation) to include in predicting the medal count of a nation at an individual Olympic Games, our data needs to be prepared for a multilinear regression model. Image: Author The Olympic Medal Table Regression Model ![]() The home-field advantage of host nation teams at the Olympic Games. ![]() Since we realize that there were major geopolitical shifts and Olympic organizational changes, let’s limit the training set of our regression model begin with the Barcelona 1992 Olympic Games. This also coincided with the biennial staggering of the Summer and Winter Olympics. It is notable that Russia competed as EUN and now as RUS after the collapse of the Soviet Union (URS). These back-to-back Olympic Games demonstrate the influence that these global superpowers wielded in shaping the outcome of the Olympic medal table. In retaliation, the USSR led 14 nations in boycotting the 1984 Olympic Games held in Los Angeles. The USA led 66 nations in boycotting the 1980 Olympic Games, which were hosted by Moscow during the Cold War. After the USA dominated the 1904 Olympic Games, in which only 12 nations competed, the 1980 Russia and 1984 USA teams collected the most medals during the modern era. 15 of the top 20 Olympic teams came from the USA and Russia. We can group our dataset by nation and year, sorting by total medal count, to find the top performing Olympic teams of all-time. Let’s try to visualize our dataset by aggregating individual athletic records by nation for each Olympic Games. ![]() Our dataset features details about each Olympic Games ( year, season, host city), the physical traits of each athlete ( gender, age, weight, height), team identification ( National Olympic Committee) and the outcome for each athlete and event ( sports, events, medals). Randi Griffin posted a complete Kaggle dataset containing the records of each athlete and event from the Athens 1896 Olympic Games through the Rio 2016 Olympic Games.⁵ With 271,116 records and 15 columns, let’s build our own machine learning regression model to predict the medal table of the Tokyo 2020 Olympic Games, which we can train using the historic Olympic record! The Olympic Games ![]() The national medal table is a common metric for quantifying the overall performance of each country, aggregating the number of gold, silver, bronze and total medals collected by the individual athletes of each national team.ĭaniel Johnson, an economics professor from Colorado College, used socio-economic data to predict national Olympic performance from 2000 to 2008.² His model predicted the total medal count of each country at the Beijing 2008 Olympics with 94% accuracy, relying on per-capita income, population, political structure, climate, home-field advantage and geographic proximity.Ī model of the Sochi 2014 Olympics employed economic trade information, namely the total value of national exports, as well as geographic data, such as land area and latitude.³ Subsequently, the Rio 2016 Olympics was modeled with similar national information, including comparative levels of national wealth along with historic performance in previous Olympic Games.⁴ Next year on July 24th, 2020, an expected 11,091 athletes from 206 nations will gather in Tokyo, Japan to celebrate at the opening ceremony of the Games of the XXXII Olympiad.¹ They will compete for gold, silver and bronze medals in 339 events across 33 sports, honoring the long-standing tradition of the modern Olympic Games, which began in Athens, Greece in 1896.Īs with many international sporting mega-events, professional forecasters and enthusiastic fans enjoy predicting the outcome of the Olympic Games. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |