It's been quite a year for upsets in the AFL so far. One of the ways of quantifying just how surprising these results have been is to use surprisals, about which I've written previously on a number of occasions
Read MoreIf the historical game data that I have is correct, we've gone very close to witnessing history this weekend, with the Hawthorn v Fremantle final score of 137-79 coming within a kick of finishing, instead, as a 131-79 win, or as a 138-79 win. Neither of these final scores were ever recorded in the 14,373 game history of the VFL/AFL between 1897 and 2013.
Read MoreIn the previous blog I fitted four separate quantile regressions to game margins at the end of each quarter using the TAB Bookmaker probability as the sole regressor
Read MoreA few blogs back I mentioned that I was preparing a presentation for the Sydney Users of R Forum and promised to post it here once I'd delivered it.
So, here it is (it's about a 5Mb PDF).
It's based on an earlier blog from this site on The Ten Most Surprising Things I've Learned About AFL So Far.
Feedback and comments welcomed.
I first heard about quantile regression, I think, over a decade ago and, for whatever reason, could never quite understand it nor fathom a useful application for it here.
Read MoreA couple of weeks ago, in this earlier blog, I described a general framework for deriving probability predictions from a bookmaker's head-to-head prices and then, if required, generating margin predictions from those probability predictions.
Read MoreEinstein once said that "No problem can be solved from the same level of consciousness that created it". In a similar spirit - but with, regrettably and demonstrably, a mere fraction of the intellect - I find that there's something deeply satisfying about discovering that an approach to a problem you've been using can be characterised as a special case of a more general approach.
Read MoreIf you're making probability assessments one of the things you almost certainly want them to be is well-calibrated, and we know both from first-hand experience and a variety of analyses here on MatterOfStats over the years that the TAB Bookmaker is all of that.
Well he is, at least, well-calibrated as far as I can tell. His actual probability assessments aren't directly available but must, instead, be inferred from his head-to-head prices and I've come up with three ways of making this inference, using an Overround-Equalising, Risk-Equalising or an LPSO-Optimising approach.
Read MoreWe know that the TAB Bookmaker is exceptionally well-calibrated. Teams that he rates 80% chances win about 80% of the time and, more generally, teams that he rates X% chances win about X% of the time. Put another way, teams rated X% chances score more than their opponents X% of the time.
What about other scoring metrics, I wondered?
Read MoreAs I was writing up the recent post about the application of the Pythagorean Expectation approach to AFL I realised that it provided yet another method for generating a margin prediction from a probability prediction.
Read MoreI've been refamiliarising myself with the idea of Pythagorean Expectation and its application to the historical home-and-away results for the VFL/AFL
Read MoreIn response to my earlier post on the explained and unexplained portions of game margins, Friend of MatterOfStats, Michael, e-mailed me to suggest that variability in teams' points-scoring per scoring shot - or, equivalently, teams' conversion rates - might usefully be explored as a source of unexplained variability.
Read MoreSome seasons are notable for the large number of blowout victories they force us to endure - a few recent seasons come immediately to mind - while others are more memorable because of their highly competitive nature. To what extent, I've often wondered, could we attribute a season full of sizable victory margins to the fact that strong teams were more often facing weak teams, making the magnitude of the defeats predictable if still lamentable, versus instead attributing them to on-the-day or random events that were genuinely unforeseeable pre-game?
Read MoreAbout 18 months ago I investigated the statistical properties of home teams' and away teams' scoring behaviour over the period from the start of the 2006 season to the middle of the 2012 season taken as a whole. In that blog, using the VGAM package, I found that the Normal distribution provided a reasonable fit to the scores of Home teams and a much better fit to the scores of Away teams over that entire period.
Read MoreEarlier posts on the Very Simple Rating System (VSRS) and Set of Games Ratings (SOGR) included a range of attractive graphs depicting team performance within and across seasons.
But, I wondered: how do the two Systems compare in terms of the team ratings they provide and the accuracy with which game outcomes can be modelled using them, and what do any differences suggest about changes in team performance within and across seasons?
Read MoreThe Set of Games Ratings post from late December introduced a twist on Tony’s VSRS concept. For any given set of games, the SOGR approach produces a rating for each team indicating its relative scoring ability within those games. Each SOGR model is optimised for the set of games on which it was fitted (in the least squares sense).
Read MoreThe last few months have been a generally reflective time for me, and with my decision to leave unchanged the core of MAFL algorithms for 2014 I've been focussing some of that reflection on the eight full seasons I've now spent analysing and predicting AFL results.
Read MoreVisitors to the MatterOfStats site in 2014 will be reading about ChiPS team Ratings and the new Margin Predictor and Probability Predictor that are based on them, which I introduced in this previous blog. I'll not be abandoning my other team Ratings System, MARS, since its Ratings have proven to be so statistically valuable over the years as inputs to Fund algorithms and various Predictors, but I will be comparing and contrasting the MARS and the ChiPS Ratings at various times during the season.
Read MoreIn years past, the MAFL Fund, Tipping and Prediction algorithms have undergone significant revision during the off-season, partly in reaction to their poor performances but partly also because of my fascination - some might call it obsession - with the empirical testing of new-to-me analytic and modelling techniques. Whilst that's been enjoyable for me, I imagine that it's made MAFL frustrating and difficult to follow at times.
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