A Few More Simulations: Losing With More Scoring Shots and Playing a Draw

The 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.

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The Importance of Goal-Kicking Accuracy

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.

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Are Some Games Harder to Predict Than Others?

If you've ever had to enter tips for an office competition where the the sole objective was to predict the winner of each game, you'll intuitively recognise that the winners of some games are inherently harder to predict than others.

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SuperMargin Implications? Yes, They Are Atrocious.

In a recent blog I developed an empirical model of AFL scoring in which I assumed that the Scoring Shots generated by Home and Away teams could be modelled by a bivariate Negative Binomial and that the conversion of these shots into Goals could be modelled by Beta Binomials.

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Why 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).

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Why 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.

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