Estimating Bookie Bias and Variability in Home Team Probability Assessments

This blog is another in the series of blogs about simulating the contest between bookmaker and punter (for details see the 1st blog, 2nd blog, 3rd blog, and 4th blog). In these blogs we've estimated the relative importance of the bias and variability in a bookmaker's home team probability assessments relative to the bias and variability in the punter's assessments.

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To Bet or Not to Bet?

In an earlier blog, using the 5 parameter model first discussed here, I summarised the results of simulating 100 seasons played out under each of 1,000 different parameter sets in a pair of rules that described when Kelly-staking tends to be superior to Level-staking, and vice versa. Implicitly, that blog assumed that we were going to wager, so our concern was solely with selecting the better wagering approach to adopt. But there is, of course, a third option that dare not speak its name and that is not to bet at all. In this blog I'll extend the previous analysis and derive rules for when we should Kelly-Stake, when we should Level-Stake and when we should just upstakes and leave.

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Modelling Empirical Home Team Victory Probabilities

In the two blogs so far on the topic of simulating the contest between bookmaker and punter I've not addressed one important aspect of the simulations and that is how to come up with the Home team probabilities for each simulated game.

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When Is Kelly-Staking Superior to Level-Staking?

For this blog we're going to look at the conditions under which it's preferable to Kelly-stake rather than level-stake.

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Bookie v Punter Simulations: An Introduction

For this series of blogs I've built a simulation of a very different kind. It's not designed to simulate team v team but, instead, is about simulating bookie v punter.

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