Reoptimisation and the Fear of Overfitting : ChiPS 2016

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Richard McElreath, in one of the lectures from his Statistical Rethinking course on YouTube aptly and amusingly notes that (and I'm paraphrasing) models are prone to get excited by exposure to data and one of our jobs as statistical modellers is to ensure that this excitability doesn't lead to problems such as overfitting.

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An Improved VFL/AFL Team Rating System: MoSSBODS 2.0

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Earlier this year in this blog, I introduced the MoSSBODS Team Rating System, an ELO-style system that provides separate estimates of each team's offensive and defensive abilities, as well as a combined estimate formed from their sum. That blog post describes the main motivations for a MoSSBODS-like approach, which I'll not repeat here.

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Modelling the Total Score of an AFL Game

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Over 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.

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Modelling Team Scores as Weibull Distributions : Part II

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In 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.

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Introducing ChiPS

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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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Revisiting Home Ground Advantage

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This week I've been part of a Twitter conversation about Home Ground Advantage in the AFL, a trending topic because of the shift from Football Park to Adelaide Oval for the home games of Adelaide and Port Adelaide in the 2014 season.
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The Relative Importance of Class and Form in AFL

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Today's blog is motivated by a number of things, the first of which is alluded to in the title: the quantitative exploration of the contributions that teams' underlying class or skill plays in their success in a given game relative to their more recent, more ephemeral form. Is, for example, a top-rated team that's been a little out of form recently more or less likely to beat a less-credentialled team that's been in exceptional form?
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Game Margins and the Generalised Tukey Lambda Distribution

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The Normal Distribution often turns up, like the Spanish Inquisition, in places where you've no a priori reason to expect it. For example, I've shown before that bookmaker handicap-adjusted margins appear to be distributed Normally.
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The Predictability of Game Margins

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In a recent blog post I described how the results of games in 2013 have been more predictable than game results from previous seasons in the sense that the final victory margins have been, on average, closer to what you'd have expected them to be based on a reasonably constructed predictive model. In short, teams have this year won by margins closer to what an informed observer, like a Bookmaker, would have expected.
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The Predictability of 2013

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Friend of MAFL, Michael, e-mailed me earlier to ask about my claim that 2013 was on track to be the most predictable MAFL season ever, pointing out, quite correctly, that bookmaker favourites have been winning at about the same rate - perhaps even at a slightly higher rate - as they had been at the same time last year.
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Are the Victory Margins for Some Games Harder to Predict than for Others?

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It's unarguable that the winner of some games will be harder to predict than the winner of others. When genuine equal-favourites meet, for example, you've only a 50:50 chance of picking the winner, but you can give yourself a 90% chances of being right when a team with a 90% probability of victory meets a team with only a 10% chance. The nearer to equal-favouritism the two teams are, the more difficult the winner is to predict, and the further away we are from this situation the easier the game is to predict.
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Does An Extra Day's Rest Matter in the Finals?

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This week Collingwood faces Sydney having played its Semi-Final only 6 day previously while Adelaide take on Hawthorn a more luxurious 8 days after their Semi-Final encounter. The gap for Sydney has been 13 days while that for the Hawks has been 15 days. In this blog we'll assess what, if any, effect these differential gaps between games for competing finalists might have on game outcome.
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Finding Non-Linear Relationships Between AFL Variables : The MINER Package

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It's easy enough to determine whether or not one continuous variable has a linear relationship with another, and how strong that relationship is, by calculating the Pearson product-moment correlation coefficient for the two variables. A value near +1 for this coefficient indicates a strong, positive linear relationship between the variables in question, so that high values of one tend to coincide with high values of the other, and vice versa for low values; a value near -1 indicates a strong, negative linear relationship; and a value of 0 indicates a lack of any linear relationship at all. But what if we want to assess more generally if there's a relationship between two variables, linear or otherwise, and we don't know the exact form that this relationship takes? That's the purpose for which the Maximal Information Coefficient (MIC) was created, and recently made available in an R package called MINER.
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Optimising the Wager: Yet More Custom Metrics in Formulize

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As the poets Galdston, Waldman & Lind penned for the songstress Vanessa Williams: "sometimes the very thing you're looking for, is the one thing you can't see" (now try to get that song out of your head for the next few hours ...)
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