Projecting the Favourite's Final Margin

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

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

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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Setting a Target for Mean Absolute Prediction Error

Until today I've never known why an MAPE of 30 was a sensible target, other than that this seemed to be roughly the figure that the TAB Sportsbet bookmaker achieved each year.

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