Inspired by this post about the recent World Cup I thought I'd have a look at something similar for the Grand Slam tennis tournaments. The result is the graph above.

The graph shows the average return per match you'd get if you were to place a $1 bet on each match in a round, and if you did that consistently on either the underdog or the favourite. It's based on data for 2007 onwards, noting that I've aggregated quarterfinals, semifinals and finals because of the low number of observations you'd get otherwise.

The data is from http://www.tennis-data.co.uk/ and the plot is made using R (ggplot). The original datasets contain odds from numerous betting firms that differ by tournament and year, so I've taken the mean odds across whatever was available for each match.

So, should you bet on the favourite or the underdog? Well, neither really, unless we're talking about the men's quarter finals and beyond at Roland Garros, The men's fourth round at the US open, the women's final at the AO and Wimbledon, and the women's fourth round at Roland Garros and the US Open.

This should not be taken as financial advice or sound strategy in any way, shape or form, so my last advice is just to watch and enjoy the matches without stressing over losing money!

This data and your assessment is kind of the bookies dream. In a world where no choice is the profitable choice for the gambler then the house wins. A chart like this might be worth showing a sportsbook if you’re trying to pitch a more effective odds generator

Looks like a bunch of resistors heh

Question: why not use parametric statistics and compare means and 95% confidence intervals?

In school, we were warned to avoid non-parametric statistics given their underlying assumptions, but were never told WHY it was bad to do so.