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 MoreThis 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 MoreSeasons rarely pan out as you expect and team strengths wax and wane over the duration, so it's not entirely surprising that an assessment of the difficulty of a team's draw will differ in retrospect compared to an assessment made in prospect.
Read MoreEach week the TAB Bookmaker forms opinions about the likely outcome of upcoming AFL matches and conveys those opinions to bettors in the form of market prices. If we assume that these opinions are unbiased reflections of the true likely outcomes, how might the competition ladder have differed from what we have now?
Read MoreLast weekend, thinking back over the season so far, I couldn't recall a game where a team had come from a long way behind in the final term.
Read MoreAt the distinct risk of diving yet deeper into what was already a fairly esoteric topic, I'm going to return in this blog to the notion of entropy as it applies to VFL/AFL scoring, which I considered at some length in a previous blog. Consider yourself duly warned - this post is probably only for those of you who truly enjoyed that earlier blog.
Read MoreIt's been a while since I've written about a proposition bet.
Read MoreMatterOfStats Founding Fellow, Greg, posed an interesting question to me last week: in which quarter is Home Ground Advantage (HGA) greatest?
Read MoreOver the eight seasons from 2006 to 2013 an average AFL game produced about 185 points with a standard deviation of around 33 points. In about one quarter of the games the two teams between them could only muster about 165 points while in another one quarter they racked up 207 points or more.
Read MoreIn a previous post I discussed the possibility of modelling AFL team scores as Weibull distributions, finding that there was no compelling empirical or other reason to discount the idea and promising to conduct further analyses to more directly assess the Weibull distribution's suitability for the task.
Read MoreA recent paper on arxiv provided a statistical motivation for that interpretation of the Pythagorean Expectation formula by showing that it can be derived if we consider the two teams' scores in a contest to be distributed as independent Weibull variables under certain assumptions.
Read MoreFor today's post I'm also going to find eras by employing that same package, this time using as the metric for each season the fitted optimal Pythagorean Expectation Exponent.
Read MoreMost 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 MoreI'll be using that same dataset today to investigate the extent to which there is a correlational structure in the quarter-to-quarter scoring of the teams in a football contest.
Read MoreLast 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 MoreIn 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 MoreI wondered then how C_Marg's team-by-team performances might compare with the other MatterOfStats Margin Predictors.
Read MoreIn 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 MoreIn 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.
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