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AFL football is the most popular sport to bet on in Australia, which means the markets are razor sharp and finding an edge is extremely difficult.
Luckily for us, AFL analysts Daniel and Stephen know first-hand how the bookies manage their markets and have set about building their own more in-depth model to beat them.
Last year we had Daniel on the Betting 360 podcast to take us through the background and what they do. With the JLT pre-season series kicking off this week, it’s an opportune time to look back at the chat we had.
Daniel: Stephen and I met at university back when we were 18. We were both studying actuarial studies and finance. We both got a job just as casual employees at a bookmaker and so that started giving us an insight in to the betting industry and how it all works behind the scenes.
While I was working there we started playing around with scraping AFL data and trying to model games based on historic data.
From there we graduated university and both spent about two years working in finance, and during that time we were developing the betting side of things, just in our spare time after work. We kind of got more and more detailed and confident at it, and at the end of 2014 we both decided to give the betting a crack full time.
AFL is definitely our main sport, and over the course of many years we’ve developed the model. It started out as a primitive team-based statistics model to where we are today. We’ve got it down to a pretty fine art where it’s quite a complex model that’s largely based on player ratings.
Basics Of The Model
In terms of how we rate players, we won’t really give too much away obviously, but we rate every player in the competition, including rookies. It’s all done mathematically based on statistics so it’s not really opinion based – it’s all backed up by data. Coming from the actuarial background it’s kind of in our nature.
We look at the significant statistics for players in different positions. So it’s not as simple as having one equation to rate all players in the competition. We actually have six different player rating formulas for each of the main positions, being key defenders, general defenders, midfielders, rucks, key Forwards and general forwards. So that’s the high level of how it works.
A player’s rating will change every time they play a game. The speed at which it changes will differ depending on what level of their career they’re at. So for established players who have played one or two or three hundred games we’re not going to adjust their rating heavily based on one additional game’s data coming in. We’re still going to put some pretty significant weight to how they’ve played in the past two years, which is where we mainly look.
And then on the other end of the spectrum, if you have young player who’s played, say 10 games last year, and then he starts playing this year and he’s playing at a level much higher than the prior year, we’ll be updating his rating much more aggressively. Because you see with young players they can really develop quite rapidly year to year in their first five seasons generally.
Another thing we do with the younger players is we actually predict expected improvement based on another historical data analysis we did. The data showed that first year players generally improved in their second year, and improve again in their third year up until around their fifth year. We make an allowance for that, so for young teams with a lot of players returning for their third and fourth seasons, we’ll actually anticipate that team to have some improvement at the start of the year.
On the other side of things, if a team has a lot of older players we generally won’t expect them to improve as much as teams with those younger players who have more upside to potential improvement with their rating.
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