Lead Changes as a Measure of Game Competitiveness

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The final victory margin is one measure of how close a contest was, but it can sometimes mislead when the team that's in front midway through the final term piles on a slew of late goals against a progressively more demoralised opponent, improving its percentage in so doing, but also erasing any trace of the fact that the game might have been a close-run thing throughout the first three-and-a-half or more quarters.
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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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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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Does Crowd Size Affect Game Outcomes?

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Based on empirical evidence we know that there is a home ground advantage in AFL which, in part, might be attributable to the pro-Home team leanings amongst the majority of the crowd. In this blog I want to explore a slightly different question about the effects of the crowd: specifically, does the size of the crowd matter too?
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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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Estimating Fair Head-to-Head Prices : Part I

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You'll recall that the total overround embedded in the head-to-head market, ignoring the possibility of a draw, is calculated by summing the reciprocal of the head-to-head prices for each team. So, for example, if the head-to-head prices for a game were $1.20 / $4.60, the overround would be 1/1.2 + 1/4.6, which is 105.1%. Some subtract 1 from this figure and would report this overround as 5.1%.
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1897 to 2011 : Winners v Losers - Leads, Scoring Shots and Conversion

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In the previous blog, among other things we analysed which quarter winning teams win. We might also ask about winnng teams, in what proportion of games do they trail at the end of a particular quarter, and how has this proportion tracked over the seasons.
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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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The Increased Importance of Predicting Away Team Scores

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In an earlier blog we found that the score of the Home team carried more information about the final game margin than did the score of the Away team. One way of interpreting this fact is that, given the choice between improving your prediction of the Home team score or your prediction of the Away team score, you should opt for the former if your goal is to predict the final game margin. While that's true, it turns out that it's less true now than it once was.
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Finding Non-Linear Relationships Between AFL Variables : Alternative Measures to MIC

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The Maximal Information Coefficient (MIC) that we explored in the previous blog is not the only non-linear measure of the pairwise relationship between any two continuous variables.
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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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