Unexpectedly Large Victories in NRL: Parallels With AFL

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In the previous post we saw that large or what might be called "blowout" victories in AFL games occurred at a rate much higher than the rate at which bookmakers assigned equally large handicaps to those same games, and we saw why this might be the case from a theoretical perspective on the assumption that AFL game margins were Normally distributed.

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Can We Use Head-to-Head Market Movements in the Line Market?

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Yesterday's post led to an interesting Twitter thread last evening, which included a suggestion to reanalyse the data to determine whether price movements in the Pinnacle head-to-head market might have predictive value in other markets for the same game, specifically in the line market.

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Is There a Signal in Head-to-Head Market Movements?

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Watching the TAB markets as they've shifted across the course of a week I've often wondered if there might be something predictive in those movements. If the eventual favourite's price has shortened during the week, does it win more or less often than its closing price would suggest?

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Predicting Bookmaker Head-to-Head Prices : Five Years On

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Recently, in light of the discussions about the validity of the season simulations written up over on the Simulations journal, I got to thinking about modelling the Bookmaker's price-setting behaviour and how it might be expected to respond to the outcomes of earlier games in the season. It's a topic I've investigated before, but not for a while.

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The Responsiveness of Bookmaker Prices To Winning and Losing

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In this blog I'm seeking to answer a single question: how are a team's subsequent head-to-head bookmaker prices affected by the returns they've provided to head-to-head wagering on them in recent weeks? More succinctly, how much less can you expect to make wagering on recent winners and how much more on recent losers?

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The Ten Most Surprising Things I've Learned About AFL So Far

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The last few months have been a generally reflective time for me, and with my decision to leave unchanged the core of MAFL algorithms for 2014 I've been focussing some of that reflection on the eight full seasons I've now spent analysing and predicting AFL results.

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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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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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Measuring Bookmaker Calibration Errors

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We've found ample evidence in the past to assert that the TAB Bookmaker is well-calibrated, by which I mean that teams he rates as 40% chances tend to win about 40% of the time, teams he rates as 90% chances tend to win about 90% of the time and, more generally, that teams he rates as X% chances tend to win about X% of the time.
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The Changing Nature of Home Team Probability

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The original motivation for this blog was to provide additional context for the previous blog on victory probabilities for portions of games. That blog looked at the relationship between the TAB Bookmaker's pre-game assessment of the Home team's chances and the subsequent success or otherwise of the Home team in portions - Quarters, Halfs and so on - of the game under review.
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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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Deconstructing The 2011 TAB Sportsbet Bookmaker

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To what extent can the head-to-head prices set by the TAB Sportsbet Bookmaker in 2011 be modelled using only the competing teams' MAFL MARS Ratings, their respective Venue Experiences, and the Interstate Status of the fixture?
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