In today's post I'll review the performance of all the teams that have been assessed as favourites by the TAB in games played during the period 2006 to the end of Round 17 in 2015, excluding only those games where the TAB bookmaker installed equal-favourites.
Read MoreRecently, I noted, somewhat in passing in this piece on close game and blowouts, the decline in overall team scoring, a topic that's receiving not a little attention within the football community at the moment, fuelled partly by some recent low-scoring games, in particular the Dees v Lions encounter.
Read MoreIn the last blog on this part of the site I introduced the MoSSBOD Team Rating System, the defining characteristics of which were that it Rated teams based on Scoring Shot Production and Concession and that it provided both a Defensive and an Offensive Rating for all teams.
Today I want to explore the history of those Ratings across the last decade to see what MoSSBOD has to say about the strongest and weakest Offensive and Defensive teams across that period.
Read MoreThree distinct pieces from three different Friends of MoS were the direct ingredients for this fresh (to MoS, anyway) take on team ratings
Read MoreIn recent blogs we've being exploring a range of topics related to team scoring, all of them based on a model I created in a series of blogs
Read MoreThe last few blogs here on the Statistical Analyses part of the website have used a model of team scoring that I fitted late last year to explore features of game scores and outcomes that we might expect to observe if that model is a reasonable approximation of reality.
Read MoreSo 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).
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