The 2016 AFL Draw: Difficulty and Distortion

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The 2016 AFL Draw, released in late October, once again sees teams playing only 22 of the 34 games required for an all-plays-all, home-and-away competition. In determining which 12 games - 6 at home and 6 away - a given team will miss, the League has in the interests of what it calls "on-field equity" applied a 'weighted rule', which is a mechanism for reducing the average disparity in ability between opponents, using the final ladder positions of 2015 as the measure of that ability.

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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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Predicting the Final Ladder

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Discussions about the final finishing order of the 18 AFL teams are popular at the moment. In the past few weeks alone I've had an e-mail request for my latest prediction of the final ordering (which I don't have), a request to make regular updates during the season, a link to my earlier post on the teams' 2015 schedule strength turning up in a thread on the bigfooty site about the whole who-finishes-where debate, and a Twitter conversation about just how difficult it is, probabilistically speaking, to assign the correct ladder position to all 18 teams. 

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AFL Crowds and Optimal Uncertainty

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Fans the world over, the literature shows, like a little uncertainty in their sports. AFL fans are no different, as I recounted in a 2012 blog entitled Do Fans Really Want Close Games? in which I described regressions showing that crowds were larger at games where the level of expected surprisal or 'entropy' was higher.

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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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Game Statistics and the Dream Team

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Today, a new voice comes to MAFL. It's Andrew's. He's the guy that uncovered the treasure-trove of game statistics on the AFL website and he's kindly contributed a blog taking you through some of the analyses he's performed on that data. Let me be the first to say "welcome mate". I have lurked on the sidelines of MAFL and Matter of Stats for a couple of years and enjoyed many conversations with Tony about his blogs. I found myself infected with curiosity and so, with gratitude to Tony, here's my newbie blog post.
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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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Building Simple Margin Predictors

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Having a new - and, it seems, generally superior - way to calculate Bookmaker Implicit Probabilities is like having a new toy to play with. Most recently I've been using it to create a family of simple Margin Predictors, each optimised in a different way.
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Bookmaker Implicit Probabilities: Empirical Value of the Risk-Equalising Approach

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A few blogs back I developed the idea that bookmakers might embed overround in each team's price not equally but instead such that the resulting head-to-head market prices provide insurance for a fixed (in percentage point terms) calibration error of equivalent size for both teams. Since then I've made only passing comment about the empirical superiority of this approach (which I've called the Risk-Equalising Approach) relative to the previous approach (which I've called the Overround-Equalising Approach).
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How Many Quarters Will the Home Team Win?

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In this last of a series of posts on creating estimates for teams' chances of winning portions of an AFL game I'll be comparing a statistical model of the Home Team's probability of winning 0, 1, 2, 3 or all 4 quarters with the heuristically-derived model used in the most-recent post.
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How Many Quarters Will the Favourite Win?

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Over the past few blogs I've been investigating the relationship between the result of each quarter of an AFL game and the pre-game head-to-head prices set for that same game. In the most recent blog I came up with an equation that allows us to estimate the probability that a team will win a quarter (p) using as input only that team's pre-game Implicit Victory Probability (V), which we can derive from the pre-game head-to-head prices as the ratio of the team's opponent's price divided by the sum of the two teams' prices.
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Deriving the Relationship Between Quarter-by-Quarter and Game Victory Probabilities

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In an earlier blog we estimated empirical relationships between Home Teams' success rate in each Quarter of the game and their Implicit Probability of Victory, as reflected in the TAB Bookmaker's pre-game prices. It turned out that this relationship appeared to be quite similar for all four Quarters, with the possible exception of the 3rd. We also showed that there was a near one-to-one relationship between the Home Team's Implicit Probability and its actual Victory Probability - in other words, that the TAB Bookmaker's forecasts were well-calibrated. Together, these results imply an empirical relationship between the Home Team's likelihood of winning a Quarter and its likelihood of winning an entire Game. In this blog I'm going to draw on a little probability theory to see if I can derive that relationship theoretically, largely from first principles.
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