What's Easier - Predicting the Home or the Away Team Score?

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Consider the following scenario. You're offered a bet in which you can choose to predict the final score of the Home or of the Away team and your adversary is then required to predict the final score of the other team.
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Setting an Initial Rating for GWS

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Last season I set Gold Coast's initial MARS Rating to the all-team average of 1,000 and they reeled off 70 point or greater losses in each of their first three outings, making a mockery of that Rating. Keen to avoid repeating the mistake with GWS this year, I've been mulling over my analytic options.
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Specialist Margin Prediction: Epsilon Insensitive Loss Functions

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In the last blog we looked at Margin Prediction using what I called "bathtub" loss functions. For the current blog I've extended the range of loss functions to include what are called epsilon-insensitive loss functions, which are similar to the "bathtub" loss functions except that they don't treat absolute errors of size greater than M points equally.
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Specialist Margin Prediction: "Bathtub" Loss Functions

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We know that we can build quite simple, non-linear models to predict the margin of AFL games that will, on average, be within about 30 points of the actual result. So, if you found a bet type for which general margin prediction accuracy was important - where every point of error contributed to your less - then this would be your model. This year we'll be moving into margin betting though, where the goal is to predict within X points of the actual result and being in error by X+1 points is no different from being wrong by X+100 points. In that environment, our all-purpose model might not be the right choice. In this blog I'll be describing a process for creating margin predicting models that specialise in predicting within X points of the final outcome.
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A Well-Calibrated Model

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It's nice to come up with a new twist on an old idea. This year, in reviewing the relative advantages and disadvantages conferred on each team by the draw, I want to do it a little differently. Specifically, I want to estimate these effects by measuring the proportion of games that I expect each team will win given their actual draw compared to the proportion I'd expect them to win if they played every team twice (yes, that hoary old chestnut in a different guise - that isn't the 'new' bit).
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Measures of Game Competitiveness

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All this analysis of victory margins, and a query from Dan about a recent blog post, has had me wondering about victory margin as a measure of the competitiveness of games. Within a given era - say 10 years or so - during which the average points scored per game won't vary by too much, victory margin seems to be a reasonable proxy for competitiveness, but if you want to consider a broader swathe of AFL history, it strikes me as being deficient.
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Margins of Victory Across the Seasons

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This year MAFL Investors will be taking on the TAB bookmaker in a new arena by attempting to pick the final victory margin for each game within a 10-point range. Having not wagered in this market I've no bedrock of intuitions - nor misconceptions - about it yet; I thought I'd start with a little historical analysis.
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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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