A Proposition Bet on the Score
/It's been a while since I've written about a proposition bet.
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.
Read MoreIt'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 MoreIf 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 MoreIn 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 MoreA 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.
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 MoreMAFL is a website for ...