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 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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Game Statistics and Game Outcomes

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My first Matter of Stats blog looked at how game statistics, averaged across an entire season for each team, are predictive of key season outcomes like ladder position, competition points and MARS Ratings. This post summarises similar analyses, but here performed on a per-game basis
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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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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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Characterising AFL Seasons

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I can think of a number of ways that an AFL season might be characterised but for today's blog I'm going to call on a modelling approach that I used back in 2010, which is based on Brownian motion and which was inspired by a JASA paper from Hal S Stern.
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Do Fans Really Want Close Games?

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The AFL Draft is predicated on a belief that the equalisation of talent across teams across time is somehow good for the sport. It seems a reasonable premise, but how might we test it? Well, if it's true, one of the ways it should manifest is in attendance figures. Put simply: do games where the result is more uncertain draw larger crowds?
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2012 - Recent History of MARS Ratings and Ladder Positions

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This year we finished the home-and-away season with 11 teams carrying MARS Ratings of over 1,000, hinting at the competitiveness we saw for positions in the Finals. MARS Ratings are zero-sum though, so a large crop of highly-rated teams necessitates a smaller crop of lowly-rated ones
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Team Scores - Statistical Distribution and Dependence

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In the most recent post on the Simulations blog I assumed that Home Team and Away Team scores were independently and Normally distributed (about their conditional means). I'll investigate both these assumptions in this blog.
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Predicting the Final SuperMargin Bucket In-Running

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On Friday night, while watching the progress of the Saints v Freo game knowing that Investors has a SuperMargin wager on the Saints to win by 20-29, I was wondering how to react to the changes in the scoreline as the game progressed. Should I want the Saints to lead early? By a little? By a lot? By about 5 points at Quarter Time and 10 points at Half Time?
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Predicting the Final Margin In-Running (and Does Momentum Exist)?

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Just a short post tonight while we wait for the serious footy to begin. For this blog I've again called upon the services of Formulize, this time to find for me equations that predict the final victory margin for the Home team (which might be negative or zero) purely as a function of the scores at the various quarter breaks.
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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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What's Easier - Predicting the Home or the Away Team Score?

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Consider the following scenario. You're offered a bet in which you can choose to predict the final score of the Home or of the Away team and your adversary is then required to predict the final score of the other team.
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Setting an Initial Rating for GWS

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Last season I set Gold Coast's initial MARS Rating to the all-team average of 1,000 and they reeled off 70 point or greater losses in each of their first three outings, making a mockery of that Rating. Keen to avoid repeating the mistake with GWS this year, I've been mulling over my analytic options.
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A Well-Calibrated Model

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It's nice to come up with a new twist on an old idea. This year, in reviewing the relative advantages and disadvantages conferred on each team by the draw, I want to do it a little differently. Specifically, I want to estimate these effects by measuring the proportion of games that I expect each team will win given their actual draw compared to the proportion I'd expect them to win if they played every team twice (yes, that hoary old chestnut in a different guise - that isn't the 'new' bit).
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