I have some big updates to the ExpScore and Goal-Kicking Accuracy maps planned for the next week or so. I hopefully launch this along with a few new visualisations and forms of analysis that I think will allow us to analyse and dissect individual games, teams and players in a way that has never been done before in the AFL. Keep your eyes peeled for that. Subscribe to the blog or follow me on Twitter to make sure you don’t miss out.
Unfortunately, as my free time has been going into this, I don’t have much in the way of predictions and previews for this week.
I’ll post up my usual prediction graphic, as well as any possible value bets,1 but outside of this it will be your responsibility to research the ins-and-outs and understand what the FFSS system currently predicts and what it does not.
The graphic above highlights all of FFSS’s probability estimates for the week. The prices next to each team are the minimum suggested price to take if you were to bet on them to win the match, assuming that FFSS predictions are perfect. In reality this is, of course, very likely not so.
The graphic opposite shows the FFSS team ratings at the start of the round. These, along with a concession for Home Ground Advantage are the direct inputs for the final probability estimates.
Fremantle v Geelong
Bet 1 Unit on Fremantle @ $5.30 – Luxbet
Adelaide v Collingwood
Bet 4 Units on Adelaide @ $1.18 – Sportsbet
Carlton v West Coast
Bet 1 Unit on Carlton @ $5.80 – Unibet
St Kilda v Melbourne
Bet 2 Units on St Kilda @ 2.10 – Bet365, William Hill, Centrebet
- I have translated my calculated probabilities into inferred match odds and compared these to the current prices offered by some of the bigger bookmakers around the country, highlighting any major discrepancies. The reason I have done this is not to recommend or even advocate having a bet on any particular team (although I will certainly talk about “good bets” and “value”). But it is rather used as a way to explore the strengths and weaknesses of the model in greater detail.
Bookie prices can be seen as a general “public consensus” about what the true probabilities of a team winning a match are. When the model differs greatly from the public view it is good to know why. Is it seeing something else that the public are not valuing? Or, is it missing something entirely that others are taking into account? If it’s the latter, then there is clear improvement that can be made, if the former, then I guess we’re on to a winner. All betting amounts will be discussed as unit bets assuming you have 100 units to play with as your full bankroll. For example if you have $1000 that you’re willing to lose over the year if worst comes to worst, then 1 unit is $10. A higher unit bet shows more confidence in the models assessment and the value to be made.