Understanding Bookmakers’ Odds Using R

Kevin Feasel



Andrew Collier looks at odds, vigs, and other bookmaking concepts through the lens of the R programming language:

The house edge is 2.70%. On average a gambler would lose 2.7% of his stake per game. Of course, on any one game he would either win or lose, but this is the long term expectation. Another way of looking at this is to say that the Return To Player (RTP) is 97.3%, which means that on average a gambler would get back 97.3% of his stake on every game.

Below are the results of a simulation of 100 gamblers betting on even numbers. Each starts with an initial capital of 100. The red line represents the average for the cohort. After 1000 games two gamblers have lost all of their money. Of the remaining 98 players, only 24 have made money while the rest have lost some portion of their initial capital.

This is a very interesting article if you’re interested in basic statistics.  13-year-old Onion article of note.

Related Posts

Reasons For Using Docker With R

Jeroen Ooms gives us a few reasons why we might want to containerize our R-based products: The flagship of the OpenCPU system is the OpenCPU server: a mature and powerful Linux stack for embedding R in systems and applications. Because OpenCPU is completely open source we can build and ship on DockerHub. A ready-to-go linux server […]

Read More

Linear Discriminant Analysis

Jake Hoare explains Linear Discriminant Analysis: Linear Discriminant Analysis takes a data set of cases (also known as observations) as input. For each case, you need to have a categorical variable to define the class and several predictor variables (which are numeric). We often visualize this input data as a matrix, such as shown below, with each case being a row and each variable a column. In this […]

Read More


August 2016
« Jul Sep »