Mapping Expected Margins to Probabililties
/I'm a sucker for a colourful chart, and today's is based on simulations using an earlier model of Home and Away team scoring, constrained by bookmaker-based empirical realities.
Read MoreEstimating Game Margin Variability: Empirical Challenges
/Lately I've been thinking a lot and writing a little - a mix that experience has taught me is nearer optimal - about the variability of game margins around their expected values.
Read MoreEnsemble Encore
/The idea of ensemble learning and prediction intrigues me, which, I suppose, is why I've written about it so often here on MoS, for example here in introducing the Really Simple Margin Predictors, here in a more theoretical context, and, much earlier, here about creating an ensemble from different Head-to-Head predictors. The basic concept, which is that a combination of forecasters can outperform any single one of them, seems plausible yet remarkable. By taking nothing more than what we already have - a set of forecasts - we're somehow able to conjure empirical evidence for the cliche that "none of us is better than all of us" (at least some of the time).
Read MoreThe Effects of Narrow Wins and Losses On Subsequent Performance
/I've often heard it asserted after a team's close loss that it will "bounce back harder next week". With a little work, that's a testable claim.
Read MoreWhy AFL Handicap-Adjusted Margins Are Normal : Part II
/In the previous blog on this topic I posited that the Scoring Shot production of a team could be modelled as a Poisson random variable with some predetermined mean, and that the conversion of these Scoring Shots into Goals could be modelled as a BetaBinomial with fixed conversion probability and theta (a spread parameter).
Read MoreWhy AFL Handicap-Adjusted Game Margins Are Normal
/This week, thanks to Amazon, who replaced my unreadable Kindle copy of David W Miller's Fitting Frequency Distributions: Philosophy and Practice with a dead-tree version that could easily be used as a weapon such is its heft (and assuming you had the strength to wield it), I've been reminded of the importance of motivating my distributional choices with a plausible narrative. It's not good enough, he contends, to find that, say, a Gamma Distribution fits your data set really well, you should be able to explain why it's an appropriate choice from first principles.
Read MoreHow Many Eras of VFL/AFL Football Have There Been?
/Most sporting codes with a history of any significant length will eventually be described in terms of having passed through a number of eras, one or both ends of which are usually defined by some relatively obvious characteristic that forms the basis of the discussion.
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 MoreAttaching 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 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 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?
Read MoreA Comparison of SOGR & VSRS Ratings
/Earlier 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 Ten Most Surprising Things I've Learned About AFL So Far
/The 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 MoreSet of Games Ratings: A Comparison With VSRS
/A few weeks back, Tony introduced the Very Simple Rating System (VSRS). It’s an ELO-style rating system applied to the teams in the AFL, designed so that the difference in the ratings between any pair of teams plus some home ground advantage (HGA) can be interpreted as the expected difference in scores for a game involving those two teams played at a neutral venue. Tony's explored a number of variants of the basic VSRS approach across a number of blogs, but I'll be focussing here on the version he created in that first blog.
Read MoreEstimating Team-and-Venue Specific Home Ground Advantage Using the VSRS
/In the Very Simple Rating System as I've described it so far, a single parameter, HGA, is used to adjust the expected game margin to account for the well-documented advantages of playing at home. We found that, depending on the timeframe we consider and the performance metric that we chose to optimise, the estimated size of this advantage varied generally in the 6 to 8-point range.
Read MoreOptimising the Very Simple Rating System (VSRS)
/In the previous blog, introducing the VSRS, I provided optimal values for the tuning parameters of that System, optimal in the sense that they minimised either the mean absolute or the mean squared error across the period 1999 to 2013
Read MoreWhat's More Important: Who You Play or Where You Play Them?
/The benefits of playing at home have been extensively investigated both here on MAFL for Australian Rules football and more generally within the sports prediction community for this and other sports. Put simply, teams that play at home win more often and score more points than you'd otherwise expect them to after adjusting for the quality of the opponents they face.
Read MoreReally Simple Margin Predictors : 2013 Review
/MAFL's two new Margin Predictors for 2013, RSMP_Simple and RSMP_Weighted, finished the season ranked 1 and 2 with mean absolute prediction errors (MAPEs) under 27 points per game. Historically, I've considered any Predictor I've created as doing exceptionally well if it's achieved a MAPE of 30 points per game or less in post-sample, live competition. An MAPE of 27 is in a whole other league.
Read More
