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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Who's Best To Play At Home?

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The 2015 AFL Schedule is imbalanced, as have been all AFL schedules since 1987 when the competition expanded to 14 teams,  by which I mean that not every team plays every other team at home and away during the regular season. As many have written, this is not an ideal situation since it distorts the relative opportunities of teams' playing in Finals. 

As we'll see in this blog, teams will have distinct preferences for how that imbalance is reflected in their draw.

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A Comparison of SOGR & VSRS Ratings

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Earlier posts on the Very Simple Rating System (VSRS) and Set of Games Ratings (SOGR) included a range of attractive graphs depicting team performance within and across seasons.

But, I wondered: how do the two Systems compare in terms of the team ratings they provide and the accuracy with which game outcomes can be modelled using them, and what do any differences suggest about changes in team performance within and across seasons?

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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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Estimating Team-and-Venue Specific Home Ground Advantage Using the VSRS

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In the Very Simple Rating System as I've described it so far, a single parameter, HGA, is used to adjust the expected game margin to account for the well-documented advantages of playing at home. We found that, depending on the timeframe we consider and the performance metric that we chose to optimise, the estimated size of this advantage varied generally in the 6 to 8-point range.
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Optimising the Very Simple Rating System (VSRS)

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In the previous blog, introducing the VSRS, I provided optimal values for the tuning parameters of that System, optimal in the sense that they minimised either the mean absolute or the mean squared error across the period 1999 to 2013
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Estimating Home Ground Advantage by Venue

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In the previous blog I fitted models to the game margins of each team separately, seeking to explain the margin in any game in terms of the Venue at which the game was played and three "Excess" variables summarising from the designated home team's perspective its relative Venue Experience, MARS Rating and recent form.
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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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Home Ground Advantage: Fans and Familiarity

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In AFL, playing at home is a distinct advantage, albeit perhaps a little less of an advantage than it once was. So, around this time of year, I usually spend a few days agonising over the allocation of home team status for each game in the upcoming season.
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Modelling AFL Team Scoring

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Today's blog is the first in a series that will look at statistically modelling the scoring behaviour of teams in the AFL. If you're profoundly reductionist about it, you can think about a team's footy score as being the product of the number of scoring shots it creates and the proportion of those scoring shots that it converts into goals.
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