On Choosing Strong Classifiers for Predicting Line Betting Results

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The themes in this blog have been bouncing around in my thoughts - in virtual and in unpublished blog form - for quite a while now.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.)

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Team Scores - Statistical Distribution and Dependence

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In the most recent post on the Simulations blog I assumed that Home Team and Away Team scores were independently and Normally distributed (about their conditional means). I'll investigate both these assumptions in this blog.
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The 2011 Performance of the MARS, Colley and Massey Ratings Systems

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I was curious - and that's rarely a portent of lazy evenings - as to which of the three ratings systems we've been tracking since Round 14 is best, so I set about finding out.
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Predicting the Home Team's Final Margin: A Competition Amongst Predictive Algorithms

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With fewer than half-a-dozen home-and-away rounds to be played, it's time I was posting to the Simulations blog, but this year I wanted to see if I could find a better algorithm than OLS for predicting the margins of victory for each of the remaining games.
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Picking Winners - A Deeper Dive

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Last blog I identified a banker's dozen of algorithms that I thought were worthy of further consideration for Fund honours next season.

Experience has taught me that, behind the attractive veneer of some models with impressive historical ROIs often lurk troubling pathologies. One form of that pathology is exhibited by models with returns that come mostly from a handful of bets, one or two of them especially fortuitous. Another manifests as a 'bet large, bet often' approach that would subject any human on the business end of such wagering to the punting equivalent of a ride on The Big Dipper that's just as likely to end with you 100 metres above the ground as 200 metres below it. The question to be answered in this blog then is: do any of the 11 algorithms I've identified this time show any such characteristics?
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Can We Do Better Than The Binary Logit?

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To say that there's a 'bit in this blog' is like declaring the 100 year war 'a bit of a skirmish'.

I'll start by broadly explaining what I've done. In a previous blog I constructed 12 models, each attempting to predict the winner of an AFL game. The 12 models varied in two ways, firstly in terms of how the winning team was described ...
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