Do Olympic Hosts Have a “Home-Field” Advantage?

My wife questioned in passing yesterday whether summer Olympic hosts have a home-field advantage; that is, do the hosts generally win more medals in their hosting year than in their non-hosting years?

That a home-field advantage exists in many team sports is generally not disputed—see for example this excellent blog post by the Freakonomics team. But is this true for (generally) individual sports like the Olympics? Most of us Brits recall our amazing—and quite unusual—3rd place finish when we hosted the event in 2012, so anecdotally I can understand why suspicion of a home-field advantage exists.

But is it real? I am quite sure there is an answer to this question on the web somewhere, but I wanted to take this opportunity to try and find an answer myself. Basically, I saw this as an excuse to learn some web-scraping techniques using R statistics.

The Data

Wikipedia holds unique pages for each Summer Olympic games. On these pages are medal tables tallying the number of Gold, Silver, and Bronze each competing nation won that year, as well as the Total Medals. So, I wrote some code in R that visits each of these pages in turn, finds the relevant html table containing the medal counts, and extracts it into my work-space. I only looked at post-2nd-world-war games.

My idea was to plot all the medals won for each host nation for all years they have appeared at the games. I was interested in whether the total number of medals that the host won in their host-year was more than their average (mean) across all the games the host had appeared. If there is some sort of home-field advantage, generally we would expect their host-year to be one of their better years, certainly above their average Olympic performance.

The Results

Below is a plot of the results. The header of each plot shows who the host was that year, and the data in each plot shows the total number of medals won by the host in all of the games they have appeared in. To help interpretation of the results, for each plot, the vertical blue line shows the year that nation hosted the games, and the horizontal red line shows that nation’s mean performance across all their games.



I would take this data as providing some evidence that nations generally perform better when they are hosting the games. 11 out of 16 nations had their best year the year they hosted the games. All nations performed above average the year they hosted the games (although maybe Canada, 1976, just missed out).

The Real Conclusion (And the Code)

Coding in R is fun, and I look for any excuse to work on new projects. This is my first attempt at doing web scraping, and it wasn’t as painful as I thought it would be. Below is the code, relying a lot on the rvest R package which I highly recommend; check out this nice introduction to using it.

The code I wrote is below. It’s certainly not optimal, and likely full of errors, but I hope someone finds it of use. Although I tried to automate every aspect of the analysis, some aspects had to be manually altered (for example to match “Soviet Union” data with “Russia” data).


# clear workspace
rm(list = ls())

# set working directory
setwd("D:/Work/Blog_YouTube code/Blog/Olympic Medals")

# load relevant packages

# suppress warnings
options(warn = -1)

### get a list of all of the host nations

# set the url and extract html elements
host_url <- ""
temp <- host_url %>%
  html %>%

# extract the relevant table
hosts <- data.frame(html_table(temp[1]))

# remove the years that the Olympics were not held
hosts <- hosts[!grepl("not held", hosts$Host.City..Country), ]

# remove the cities from the host column
countries <- hosts$Host.City..Country
countries <- gsub(".*,", "", countries)
hosts$Host.City..Country <- countries

# remove the Olympics that are ongoing (or are yet to occur) and generally
# tidy the table up. Also, only select post-1948 games.
hosts <- hosts %>%
  select(-Olympiad) %>%
  select(year = Year, host = Host.City..Country) %>%
  filter(year < 2016 & year > 1948)

# remove white space from the names
hosts$host <- gsub(" ", "", hosts$host, fixed = TRUE)

# change host England to Great Britain. 
# change SouthKorea to South Korea
# change USSR to Russia
hosts$host <- gsub("England", "Great Britain", hosts$host, fixed = TRUE)
hosts$host <- gsub("SouthKorea", "South Korea", hosts$host, fixed = TRUE)
hosts$host <- gsub("USSR", "Russia", hosts$host, fixed = TRUE)

### get the medal tables for each year and store them in one list

# get a vector of all years
years <- hosts$year

# create a list to store the medal tables
medal_tables <- list()

# loop over each year and retrieve the data from Wikipedia
for(i in 1:length(years)){
  # what is the current year?
  curr_year <- years[i]
  # construct the relevant URL to the Wikipedia page
  url <- paste("", curr_year, 
               "_Summer_Olympics_medal_table", sep = "")
  # retrieve the data from this page
  temp <- url %>%
    html %>%
  # find the html table's position. The medal table is in a "sortable" Wiki 
  # table, so we search for this term and return its position in the list
  position <- grep("sortable", temp)
  # get the medal table. Add a new column storing the year
  medals <- data.frame(html_table(temp[position], fill = TRUE))
  medals <- medals %>%
    mutate(Year = curr_year)
  # change the names of the "Nation" column, as this is not consistent between
  # games tables
  colnames(medals)[2] <- "Nation"
  # remove the weird symbols from the html file (Â)
  nations <- medals$Nation
  nations <- gsub("[^\\x{00}-\\x{7f}]", "", nations, perl = TRUE)
  # we need to change "Soviet Union" to USSR for consistency
  nations <- gsub("Soviet Union(URS)", "Russia(RUS)", nations, fixed = TRUE)
  # also change West & East Germany to "Germany"
  nations <- gsub("East Germany(GDR)", "Germany(GER)", nations, fixed = TRUE)
  nations <- gsub("West Germany(FRG)", "Germany(GER)", nations, fixed = TRUE)
  medals$Nation <- nations

  # save the medal table and move to the next games
  medal_tables[[i]] <- medals


### loop over each host, then find how many medals they won in each games and
### store it in data frame

# initialise the data frame
final_data <- data.frame(hosts)
final_data[, as.character(years)] <- 0

for(i in 1:length(hosts$host)){
  # get the current host
  curr_host <- hosts$host[i]

  # loop over all years, find the number of medals won by the current host, 
  # and store it in final_data frame
  for(j in 1:length(years)){
    # what is the current year?
    curr_year <- years[j]
    # get the medal table for the current year
    curr_medals <- medal_tables[[j]]
    # get the row for the current host if it is present
    curr_medals <- curr_medals %>%
      filter(str_detect(Nation, curr_host))
    # collate the number of medals won if there is data
    if(nrow(curr_medals) > 0){
      final_data[i, j + 2] <- sum(curr_medals$Total)
    } else
      final_data[i, j + 2] <- 0
  } # end of each year loop
} # end of each host loop

### now do some plotting
pdf("medals.pdf", width = 12, height = 12)

# change the layout of the plotting window
par(mfrow = c(4, 4))

# loop over each hosting nation
for(i in 1:nrow(final_data)){
  # get the current host's data for all years
  host_data <- as.numeric(final_data[i, 3:ncol(final_data)])
  # what is their mean number of medals won?
  host_mean <- mean(host_data)
  # plot the data!
  plot(years, host_data, xlab = "Year", ylab = "Number of Medals", pch = 19, 
       type = "b", lwd = 2, 
       main = paste(hosts$host[i], "–", years[i], sep = ""))
  abline(v = final_data$year[i], lty = "dashed", col = "blue", lwd = 1.5)
  abline(h = host_mean, lty = "dashed", col = "red", lwd = 1.5)


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