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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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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