Current Teams' All-Time MARS Rankings

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I've looked previously at the best and worst AFL teams of all time and, whilst none of the current crop of teams is vying for either of those honours as at the end of Round 11 in the 2013 season, two (GWS and Melbourne) are in the 30 lowest-rated teams ever and one (Hawthorn) is in the 50 highest-rated teams ever.
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Do Bookies Undervalue Team Performance Metrics?

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In 2003 Michael Lewis' Moneyball was published, in which he related the story of Billy Beane, Oakland A's General Manager, and his discovery that the market for baseball players mispriced particular skills. Some skills that could be shown, statistically, as being associated with greater team success weren't recognised as valuable (for example, getting on base, as measured by On-Base Percentage), while other skills were over-valued because of an historical belief that they were related to success (for example, batting in runs, as measured by RBI).
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Really Simple Proves Remarkably Effective

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The Really Simple Margin Predictors (RSMPs), which were purpose-built for season 2013, have shown themselves to be particularly accurate at forecasting game margins. So much so, in fact, that they're currently atop the MAFL Leaderboard, ahead of the more directly Bookmaker-derived Predictors like Bookie_3 that have excelled in previous years.
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Measuring the Surprise in a Season's Results

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In the previous blog we looked at the average level of surprisals generated by teams and by team pairings across all of VFL/AFL history and during the most-recent seasons. Today, as promised in that blog, I'm going to analyse surprisals using the same general methodology, but by season.
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The Value of MARS Ratings Points Across Time

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As we look at MARS Ratings across the expanse of VFL/AFL history, one thing we might want to consider is whether the value of a Rating Point has remained stable across those 13 decades. I've assumed, implicitly, that this is the case in the interpretations I've made of recent analyses, for example about the game's greatest upsets, but there's no a priori reason for this to be the case.
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Which Teams Fare Better as Favourites?

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In this blog, the next is a series in which I've been exploring the all-time MARS Ratings I created for every game from the start of 1897 to the end of the 2012 season, I'll be looking at how well each team has performed depending on the relative strength of its opponent, as measured by their MARS Rating. So, for example, we'll consider how well Collingwood tends to do when playing a team it is assessed as being much stronger than, a little stronger than, about as capable as, and so on.
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The Greatest Upset Victories in VFL/AFL History (1897-2012)

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The Suns' victory over the Saints in the first round of the 2013 season was heralded as an "upset win" for the Suns and one of the greatest in their short history. Undoubtedly their win was unexpected, but even the bookmakers rated the Suns as only $3.75 chances, so it was hardly a Halley's comet-like occurrence.
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Team MARS Ratings Performance By Decade and Overall: 1897 to 2012

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In the previous blog on the topic of all-time MARS Ratings I explained the process I used to derive team Ratings across history and then identified those teams that had achieved the highest (Essendon) and lowest (Fitzroy) MARS Ratings ever. We know then which teams have burned brightest - and which flickered dimmest - across VFL/AFL history. In this blog I want to explore more extended bursts of talent or apparent lack of it.
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And the Best and Worst VFL/AFL Teams of All-Time Are ...

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I've promised for a while now to create MARS Ratings for the entire history of the VFL/AFL and with some time away from home over Easter, I'm now able to deliver on that promise.
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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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Clustering Your Way To Line Betting Success : Building a Predictive Model

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In the previous blog I used a clustering algorithm - Partitioning Around Medoids (PAM) as it happens - to group games that were similar in terms of pre-game TAB Bookmaker odds, the teams' MARS Ratings, and whether or not the game was an Interstate clash. There it turned out that, even though I'd clustered using only pre-game data, the resulting clusters were highly differentiated with respect to the line betting success rates of the Home teams in each cluster.
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Clustering Your Way To Line Betting Success

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For today's blog I'll be creating a game clustering that uses as input only the information that we might reasonably know pre-game - for example, the pre-game team MARS Ratings, Bookmaker prices (or some metric derived from them), and information about the game venue.
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Incorporating the Draw in Overround Calculations

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At university, studying undergraduate Economics - which, granted, was a while ago - I particularly disliked theories premised on simplifying assumptions, which were introduced with an implicit promise, rarely honoured, to relax these assumptions later and nudge the theory a little closer to observed reality.
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