The 2011 Performance of the MARS, Colley and Massey Ratings Systems

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I was curious - and that's rarely a portent of lazy evenings - as to which of the three ratings systems we've been tracking since Round 14 is best, so I set about finding out.
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A First Look At Surprisals for 2011

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We first discussed surprisals back in 2009 (if you perform a site search using the term "surprisals" you'll be linked to a couple of PDFs as well as to a handful of blog posts on the topic) as a method for quantifying the surprise associated with the outcome of a football game.
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Predicting the Home Team's Final Margin: A Competition Amongst Predictive Algorithms

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With fewer than half-a-dozen home-and-away rounds to be played, it's time I was posting to the Simulations blog, but this year I wanted to see if I could find a better algorithm than OLS for predicting the margins of victory for each of the remaining games.
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Projecting the Favourite's Final Margin

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In a couple of earlier blogs I created binary logit models to predict the probability that the favourite would win given a specified lead at a quarter break and the bookmaker's assessed pre-game probability for the favourite. These models allow you to determine what a fair in-running price would be for the favourite. You might instead want to know what the favourite's projected victory margin is given the same input data, so in this blog I'll be providing some simple linear regressions that provide this information.
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An Empirical Review of the Favourite In-Running Model

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In the previous blog we reviewed a series of binary logits that modelled a favourite's probability of victory given its pre-game bookmaker-assessed head-to-head probability and its lead at the end of a particular quarter. There I provided just a single indication of the quality of those models: the accuracy with which they correctly predicted the final result of the game. That's a crude and very broad measure. In this blog we'll take a closer look at the empirical model fits to investigate their performance in games with different leads and probabilities.
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Hanging Onto a Favourite: Assessing a Favourite's In-Running Chances of Victory

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Over the weekend I was paying particular attention to the in-running odds being offered on various games and remain convinced that punters overestimate the probability of the favourite ultimately winning, especially when the favourite trails.
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Line Fund Profitability and Probability Scores

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Over on the Simulations blog as part of a more general investigation into the dynamics of the contest between punter and bookmaker in head-to-head wagering I've looked at the relationship between the probability score attained by the Head-to-Head Fund in each season and its profitability. What I found, among other things, was that the Fund's profitability was related not to the absolute probability score of the Fund algorithm, but to its probability score relative to the bookmaker's.
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The Drivers of Overround

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What features of a contest, I wondered this week, led to it having a larger or smaller overround than an average game? In which games might the bookie be able to grab another quarter or half a percent, and in which might he be forced to round down the overround?
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Predicting a Team's Winning Percentage for the Season

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In recent blogs where I've been posting about a win production function the goal has been to fit a team's season-long winning percentage as a function of its scoring statistics for that same season. What if, instead, our goal was to predict a team's winning percentage at the start of a season, using only scoring statistics from previous seasons?
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Underachieving and Overachieving Teams

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A couple of blogs back I described some win production functions, which relate a team's winning percentage in the home-and-away season to characteristics of its scoring during that season, in particular to its rate of scoring shot production and its conversion of those scoring shots relative to its opponents'.
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What 1% of Overround Worth?

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Over on the Simulations blog I've been investigating how the returns to Kelly-staking and Level-staking respond to different levels of variability and bias in the bookmaker's team probability assessments, and to different levels of overround in that bookmaker's market prices. In this blog I'll investigate, using a purely mathematical approach, how a punter's expected return varies as the overround varies, depending on the size of the bias in the bookmaker's probability assessment and in the true probability of the team being wagered on.
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Applying the Win Production Functions to 2009 to 2011

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In the previous blog I came up with win production functions for the AFL - ways of estimating a team's winning percentage on the basis of the difference between the scoring shots it produces and those it allows its opponents to create, and the difference between the rate at which it converts those scoring shots and the rate at which its opponents convert them.
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Win Production Functions for AFL Teams - 1897 to 2010

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Right now I'm reading Wayne L Winston's Mathletics, a book about the use of fairly simple mathematics and sports statistics to gain insights into the results of American sports. Inspired by this book, in particular by a piece on Pythagorean Expectation which relates the season-long winning percentage of a baseball team to the total runs that it's scored and allowed, I wondered if an AFL team's win percentage could be similarly predicted by a handful of summary statistics about its own and its opponents' scoring.
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A Little Behind in the Scoring

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We've not had a proposition bet for a while, so here's a new one for you. We're going to pick a large number of games at random and, based on the half-time score in each, I'll pledge to bet on the team that's scored the greater number of behinds whether they be the raging favourite or the deserving underdog. It both teams have scored the same number of behinds at the main break or if the game ends in a draw, the bet is a push and neither of us need reach into our pockets. Otherwise, I collect if the team that had scored the greater number of behinds at the half goes on win, and you win if the team that had scored the lesser number of behinds at the half goes on win. Simple.
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Tipping Without Market Price Information

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In a previous blog I looked at the notion of momentum and found that Richmond, St Kilda, Melbourne and Geelong all seemed to be "momentum" teams in that their likelihood of winning a game seemed to be disproportionately affected by whether they'd won or lost their previous match.
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A Friendly Wager on the Margin

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You're watching the footy with a mate who leans over and says he reckons the Cats will win by 15 points. How much leeway should you give him to make it a fair even money bet? Surprisingly - to me anyway - the answer is 24 points either way. So, if the Cats were to record any result between a loss by 9 points and a win by 39 points you should pay out.
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