Understanding Bookmakers’ Odds Using R

Kevin Feasel

2016-08-11

R

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

Deploying An R Service To Azure Kubernetes Service

Hong Ooi shows us how we can use Azure Container Registry and Azure Kubernetes Service to deploy an R model via Plumber: If you run this code, you should see a lot of output indicating that R is downloading, compiling and installing randomForest, and finally that the image is being pushed to Azure. (You will […]

Read More

Road Construction Incentive Contracts And R

Sebastian Kranz promotes an interesting RTutor project: Patrick Bajari and Gregory Lewis have collected a detailed sample of 466 road construction projects in Minnesota to study this question in their very interesting article Moral Hazard, Incentive Contracts and Risk: Evidence from Procurement in the Review of Economic Studies, 2014.They estimate a structural econometric model and find that […]

Read More

Categories

August 2016
MTWTFSS
« Jul Sep »
1234567
891011121314
15161718192021
22232425262728
293031