Australia Loves an Underdog and it’s Losing us Money …
Sport bookmakers calculate the odds a team has in winning and then offers worse odds to its customers to make a profit.
Seems simple, right?
Not quite, bookmakers are in the business of making money and the best way to do that is to ‘balance the book.’ That is making sure the amount wagered on each team is roughly equal so that the bookmaker makes a profit regardless of the outcome of the sporting event. Now if the book is ‘unbalanced,’ i.e. more money has been placed on one team, the bookmaker will try to balance the book again by adjusting the odds to entice punters to bet on the less popular team.
Now us Aussies love our sport and gambling, but we also love an underdog. But what if too many of us are betting on the underdogs, forcing the bookmakers to drive down the odds and therefore reducing our winnings and increasing our losses. This is what we are going to explore. But first, some definitions:
· Bookmaker’s/Profit Margin — The difference between the odds the customer is offered to bet at and the true probability of the outcome.
· Implied Odds — The percentage each team has in winning as given by the odds set by the bookmaker.
· Fair Odds — The actual percentage each team as in winning which is calculated by removing the bookmaker’s margin from the implied odds.
· Expected Return — The profit a punter can expect when placing a bet, note that this will always be negative due to the bookmaker’s margin.
Example: Tossing a coin has 50% chance (fair odds) of being heads/tails. A bookmaker sets the odds for heads and tails to be $1.90 each way. The $1.90 odds imply that there is a 52.63% chance (implied odds) of being head/tails. The total difference between the fair odds and implied odds is 2.63 + 2.63 = 5.26%(Bookmaker’s margin). Now the expected return per dollar is 50% * $1.90 - $1 = $-0.05.
To analyse the Australian betting market, I have used the Australian Football League (AFL) and National Rugby League (NRL) games since the 2009 season (4637 games). For each game, and for the favourite and underdog team, I have calculated the expected return and the difference between this, and the actual amount won/lost depending on the actual result of the game. The following table represents what happens if a punter placed one dollar on all the favourites or all the underdogs:
Now we would expect that if the odds were truly balanced, we would lose the same amount of money on both the favourite and underdog team.
However, we can see that a punter would have lost double the amount of money than expected by betting on the underdog!
Now we can use statistical tests to help us draw inferences from this data and conclude whether the differences observed above is due to chance. A t-test concludes that there is a 1% likelihood that the difference in expected return and actual return was due to natural randomness.
Therefore, we accept our explanation that more Australians are betting on the underdog team, forcing the bookmakers to drop the odds and consequently reducing the pay out.
To highlight this discovery, we can compare the Australian betting market with that in the United States. To represent the United States betting market, I have used all NBA games since the 2008–09 season and all NFL games since the 2006 season which accumulates to a total of 17,104 games. Same as the previous table, the following table represents the return if a punter placed one dollar on all the favourites or all the underdogs:
The results highlight that compared to Australia, the percentage change between the expected return and actual return isn’t nearly as significant and can be explained as natural variation. This was also the conclusion of the t-test.
So, what have we learnt? Bookmakers adjust their odds in reaction to betting behaviour. Generally, the best bet is the least popular bet. The habit of Australians to support the underdog is causing the underdog team to be overvalued.
This is an example of how betting odds are influenced by irrational punters. This is a key concept that professional gamblers aim to exploit; that is, they identify games that have been influenced enough by irrational betting activity to become profitable.
For my code and a more detailed explanation of the t-tests conducted please see this GitHub Link: https://github.com/jama001/Australia-Underdog-Betting
My name is Jamie Ferreira and I am an aspiring data scientist majoring in Data Science and Business Analytics at the University of Sydney. Feel free to leave any comments below and connect with me on LinkedIn: https://www.linkedin.com/in/jamie-ferreira-942374186/