Cursory Mention of MAFL in New Scientist (Probably)

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At the start of the year, Michael Schmidt, creator of the Eureqa application, mentioned that Justin Mullins from New Scientist was researching for a piece on Eureqa. I dropped an e-mail to Justin, received a polite reply and thought little more of it.

Turns out the final article included this paragraph:

"Today, the algorithm is called Eureqa and has thousands of users all over the world, with people using it for everything from financial forecasting to particle physics. One person even uses it to analyse the statistics of Australian rules football games."

(Various people have cut-and-pasted the full article, for example Transcurve and Kurzweilai, and you can access the original content directly via the New Scientist site if you're willing to create a free subscription.)

I can't be completely certain, but it's more likely than not that the last sentence refers to MAFL.

It'd be nice if the reference was a tad more direct - say with a name or a URL - but then again it'd be preferable if any wider awareness of MAFL's existence came at a time when the Funds were making rather than losing money. So, swings and roundabouts ... 

Explaining More of the Variability in the Victory Margin of Finals

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This morning while out walking I got to wondering about two of the results from the latest post on the Wagers & Tips blog. First that teams from higher on the ladder have won 20 of the 22 Semi Finals between 2000 and 2010, and second that the TAB bookmaker has installed the winning team as favourite in only 64% of these contests. Putting those two facts together it's apparent that, in Semi Finals at least, the bookmaker's often favoured the team that finished lower on the ladder, and these teams have rarely won.
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MARS Ratings : How Important Are Teams' Initial Ratings?

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It's been a few years since I chose the key parameters for the MARS Ratings System, which I selected on the basis that they maximised the predictive accuracy of the resulting System. One of those parameters - the percentage carryover of team Ratings from one season to the next - determines each team's initial MARS Rating for the season.
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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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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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