The Ideal Competition: How Many Blowouts and Upsets?

Working on a few recent posts here on the Statistical Analysis journal has made me think a lot more about blowouts (games won by a large margin) and upsets (games won by the team less-favoured to win pre-game), and realise how inter-related is their prevalence.

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Why There'll Always Be More Blowouts Than We Expect

Last night I was thinking about the results we found in the previous blog post about upsets and mismatches and wondered if the historical pattern of expected game margins was borne out in the actual results. On analysing the data I found that there were a lot more victories of 10 Scoring Shots or more in magnitude than MoSSBODS had predicted. In most seasons, at least one-third of the games finished with a victory margin equivalent to 10 Scoring Shots or more, which was usually two or three times as many as MoSSBODS had predicted.

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What's Different About Finals?

Finals, by their nature, tend to pit more-evenly matched teams against one another, on average, than do games from the home-and-away season. It seems reasonable, therefore, to hypothesise that margins will tend to be smaller in Finals than in the home-and-away season, but what other changes in scoring behaviour might we expect to see?

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Ensemble Encore

The 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 Predictorshere 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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More Ways to Derive Probability and Margin Predictions From Head-to-Head Prices

A couple of weeks ago, in this earlier blog, I described a general framework for deriving probability predictions from a bookmaker's head-to-head prices and then, if required, generating margin predictions from those probability predictions.

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Creating Margin Predictions From Head-to-Head Prices: A Summary

As I was writing up the recent post about the application of the Pythagorean Expectation approach to AFL I realised that it provided yet another method for generating a margin prediction from a probability prediction.

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The Dynamics of ChiPS Ratings: 2000 to 2013

Visitors to the MatterOfStats site in 2014 will be reading about ChiPS team Ratings and the new Margin Predictor and Probability Predictor that are based on them, which I introduced in this previous blog. I'll not be abandoning my other team Ratings System, MARS, since its Ratings have proven to be so statistically valuable over the years as inputs to Fund algorithms and various Predictors, but I will be comparing and contrasting the MARS and the ChiPS Ratings at various times during the season.

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Introducing ChiPS

In years past, the MAFL Fund, Tipping and Prediction algorithms have undergone significant revision during the off-season, partly in reaction to their poor performances but partly also because of my fascination - some might call it obsession - with the empirical testing of new-to-me analytic and modelling techniques. Whilst that's been enjoyable for me, I imagine that it's made MAFL frustrating and difficult to follow at times.

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Set of Games Ratings: A Comparison With VSRS

A few weeks back, Tony introduced the Very Simple Rating System (VSRS). It’s an ELO-style rating system applied to the teams in the AFL, designed so that the difference in the ratings between any pair of teams plus some home ground advantage (HGA) can be interpreted as the expected difference in scores for a game involving those two teams played at a neutral venue. Tony's explored a number of variants of the basic VSRS approach across a number of blogs, but I'll be focussing here on the version he created in that first blog.

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