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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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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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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Super Smart is Taking Heed of Bookies

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Across a series of blogs now we've explored the Super Smart Model (SSM) and investigated its ability to predict victory margins. In this blog we'll look more closely at which variables most influence SSM's forecasts.
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Trialling The Super Smart Model

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The best way to trial a potential Fund algorithm, I'm beginning to appreciate, is to publish each week the forecasts that it makes. This forces me to work through the mechanics of how it would be used in practice and, importantly, to set down what restrictions should be applied to its wagering - for example should it, like most of the current Funds, only bet on Home Teams, and in which round of the season should it start wagering.
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Predicting Head-to-Head Market Prices

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In earlier blogs I've claimed that there's not much additional information in bookie prices that's useful for predicting victory margins than what can be derived from a statistical analysis of recent results and an understanding of game venues.
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What Do Bookies Know That We Don't?

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Bookies, I think MAFL has comprehensively shown, know a lot about football, but just how much more do they know than what you or I might glean from a careful review of each team's recent results and some other fairly basic knowledge about the venues at which games are played?
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