Are AFL Scores Like Snowflakes?
/Last weekend while following the progress of a game I was struck by an odd thought: have two games ever had the same scoreline at the end of every quarter?
Read More2014 Margin Predictor Team-by-Team Performance (R1 to R12) : Part II
/In the previous blog we looked in some detail at the performance of each of the MatterOfStats Margin Predictors in terms of how well they've done in predicting the final margins in games involving a particular team as the home or as the away team.
Today I want to provide, initially, a team-based summary of that same analysis.
Read More2014 Margin Predictor Team-by-Team Performance (R1 to R12)
/I wondered then how C_Marg's team-by-team performances might compare with the other MatterOfStats Margin Predictors.
Read MoreThe Responsiveness of Bookmaker Prices To Winning and Losing
/In this blog I'm seeking to answer a single question: how are a team's subsequent head-to-head bookmaker prices affected by the returns they've provided to head-to-head wagering on them in recent weeks? More succinctly, how much less can you expect to make wagering on recent winners and how much more on recent losers?
Read MoreHow Different is C_Prob, Really?
/In the previous blog we looked at MatterOfStats' new Margin Predictor, C_Marg, and quantified just how different it was from each of the other Margin Predictors. Today I'm going to do the same thing for another of the predictors that's based on the ChiPS Team Rating System, C_Prob.
Read MoreSources of Surprisal : 2006 to 2014 Round 6
/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 MoreMaking History with VFL/AFL Final Scores
/If 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 MorePredicting the Lead at Every Change Part II: Quantile Regression Splines
/In 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 MorePresentation to the Sydney Users of R Forum (SURF) - 2014
/A 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.
Attaching Probabilities to Game Margins: An Application of Quantile Regression
/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 MoreMore Ways to Derive Probability and Margin Predictions From Head-to-Head Prices
/A 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 MoreAssessing Probability Forecasts: Beyond Brier and Log Probability Scores
/Einstein 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 MoreModelling Miscalibration
/If 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 MoreDo Favourites Kick Straighter Than Underdogs?
/We 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 MoreCreating Margin Predictions From Head-to-Head Prices: A Summary
/As 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 MorePythagorean Expectation for the VFL/AFL and the NRL
/I'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 MoreTeam Scoring Shots and Conversion Rates
/In 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 MoreExplaining Variability in Game Margins
/Some 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?
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