So far this season, eight teams have lost after generating more scoring shots than their opponents and three more have been defeated despite matching their opponent's scoring shot production, which means that the outcome of over 15% of games might this year have been reversed had the losing team kicked straighter.
Read MoreI'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 MoreThis 2015 season, seven rounds in, has felt like one where leads of any size have been less comfortable.
Read MoreIn the comments section of the previous blog, LT pointed out that Bookmakers seem to be doing a better job this year predicting the sum of the Home Team and Away Team scores than predicting the difference between them.
Read MoreLately 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 MoreI've raised an eyebrow or two more than once when I've seen the TAB bookmaker post two markets with the same head-to-head prices but different line market handicaps priced at even-money.
Read MoreThe 2015 AFL Schedule is imbalanced, as have been all AFL schedules since 1987 when the competition expanded to 14 teams, by which I mean that not every team plays every other team at home and away during the regular season. As many have written, this is not an ideal situation since it distorts the relative opportunities of teams' playing in Finals.
As we'll see in this blog, teams will have distinct preferences for how that imbalance is reflected in their draw.
Read MoreIf the Dogs make this year's Grand Final they'll also make history as the lowest MARS Rated team after 5 rounds to do so in the modern era.
Read MoreIt's been a difficult season for tipping game margins, by which I mean that Mean Absolute Errors (MAEs) for most of the forecasters I follow have been elevated relative to last year.
Read MoreFor the weaker team in any contest which rewards only victory (or at least does so disproportionately to the rewards for proximity to victory), variability can be an advantage.
Read MoreMonash University has been running AFL tipping competitions for over 20 years and this year is offering three, all of which are open to the public.
Read MoreAccurate kicking, obviously, contributes to a team's success. But, just how much does it contribute, what are some of the sources of its variability, and just how predictable is it?
Read MoreDiscussions about the final finishing order of the 18 AFL teams are popular at the moment. In the past few weeks alone I've had an e-mail request for my latest prediction of the final ordering (which I don't have), a request to make regular updates during the season, a link to my earlier post on the teams' 2015 schedule strength turning up in a thread on the bigfooty site about the whole who-finishes-where debate, and a Twitter conversation about just how difficult it is, probabilistically speaking, to assign the correct ladder position to all 18 teams.
Read MoreThe 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 MoreFans the world over, the literature shows, like a little uncertainty in their sports. AFL fans are no different, as I recounted in a 2012 blog entitled Do Fans Really Want Close Games? in which I described regressions showing that crowds were larger at games where the level of expected surprisal or 'entropy' was higher.
Read MoreI'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 MoreThe themes in this blog have been bouncing around in my thoughts - in virtual and in unpublished blog form - for quite a while now. My formal qualifications are as an Econometrician but many of the models that I find myself using in MoS come from the more recent (though still surprisingly old) Machine Learning (ML) discipline, which I'd characterise as being more concerned with the predictive ability of a model than with its theoretical pedigree. (Breiman wrote a wonderful piece on this topic, entitled Statistical Modelling: The Two Cultures, back in 2005.)
Read MoreSimple question: which of MoS' 17 Margin Predictors has been best-performed over the past two seasons?
Read MoreI've addressed the topic of fitting a team's winning rate as a function of its scoring behaviour before on MoS, in discussions about Win Production Functions generally and in posts about Pythagorean Expectation specifically.
Read MoreThe AFL draw remains imbalanced, with teams meeting only five of their 17 potential opponents twice, and meeting the other 12 teams only once.
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