In phillc73/abettor: R bindings to Betfair API-NG

How to extract runner data to a dataframe with abettor

Purpose

The purpose of this document is to provide an example of how to extract runner data from the Betfair betting exchange to a dataframe using their API-NG product, the abettor R package and a simple R script. This runner data can then be used to place bets using the API.

This assumes that you have read the vignette on placing a simple bet. To avoid duplication, this vignette does not cover: logging in getting an Application Key

Scope

This tutorial will outline a workflow for extracting runner data for a horse racing market.

It is assumed that the bettor wants the runner data for the race given:

• Race Course Name
• Race Start Time

We use this information to obtained the market ID from the Betfair API. Runner data is then obtained for each horse in the race.

Tutorial

Prior reading

This tutorial does not assumes you have read the initial tutorial which contains information on obtaining and Application Key and logging in.

Load Required Packages

Only two packages or libraries are required. All packages obviously must be installed first.

# Requires a minimum of version 1.7.2
# Older versions will most likely work but are unsupported
library("jsonlite")
# Requires a minimum of version 1.4.2
# Older versions will most likely work but are unsupported
library("httr")
require("abettor")

Login to Betfair

loggedin <- loginBF(username = "YourBFUsername", password = "YourBFPassword", applicationKey = "YourAppKey")

You will need an Application Key and the guide to obtaining one is in the initial tutorial

Betfair provides further details here.

You can check that the log in was successful by looking at the value of loggedin which should be "SUCCESS:"

Choose event

Following on from the initial tutorial, we look at a win market for a horse race in Great Britain. We can easily get a list of events by using the function.

marketCat <- listMarketCatalogue(eventTypeIds = "7", marketCountries = "GB", marketTypeCodes = "WIN")

This returns a list of all horse racing win markets in Great Britain starting in the next 24 hours which is stored in a dataframe called marketCat. There is a lot of data in this dataframe and we don't need all of that for now. We're looking for a specific race and we can review the relevant information for each race using:

cbind(marketCat$event$venue,marketCat$marketName,marketCat$marketStartTime,marketCat$marketId)

The starting time shown here is in UTC so you may want to check you have the correct event. The initial tutorial goes through how you can subset a race using R. You can either use that code or identify the marketId visually and store it in myMarketId.

To simplify things, we want just our market which we can get using:

mc <- marketCat[marketCat$marketId == myMarketId,]

Get exchange information for your chosen market

To get information on the current exchange position there is another function called listMarketBook.

winOdds <- listMarketBook(marketId = myMarketId, priceData = "EX_BEST_OFFERS")

The information about the current market position is in the runners dataframe but there are some other useful items here worth noting. The status field gives the current event status and you may want to use this as a check in your code. There is also an inplay field which may be helpful. Fields numberOfRunners and numberOfActiveRunners show you if there have been anyscratchings and how many horses are currently expected to run.

There is more information that can be retrieved using listMarketBook using the priceProjection, orderProjetion and matchProjection flags and you can adapt this code to get exactly the data you want.

The market information for each horse is in the runners dataframe which you can access with

winOdds$runners

If there have been any scratchings in the race since the field was posted by Betfair, they will still appear in the dataframe with the status field equal to REMOVED. If a horse was scratched before the field was posted by Betfair it won't appear.

The code below extracts the back and lay offers and puts the best price and size for each of these into a dataframe. Any missing entries will be left as NA.

numRunners <- winOdds$numberOfRunners
if(winOdds$status == "OPEN"){
  runnerData <- data.frame(runnerName = mc$runners[[1]][,"runnerName"])
  allRunnerData <- winOdds$runners[[1]]
  runnerData[,"selectionId"] <- allRunnerData$selectionId
  for(j in 1:numRunners){
    if(allRunnerData$status[[j]]=="ACTIVE"){
      #Check that there are orders in the stack for this runner or it causes an error
      if(nrow(allRunnerData$ex[[1]][[j]])>0){
        runnerData[j,"bestLayPrice"] <- allRunnerData$ex[[1]][[j]][[1,"price"]]
        runnerData[j,"layAmount"] <- allRunnerData$ex[[1]][[j]][[1,"size"]]
      } 
      if(nrow(winOdds$runners[[1]]$ex[[2]][[j]])>0){
        runnerData[j,"bestBackPrice"] <- allRunnerData$ex[[2]][[j]][[1,"price"]]
        runnerData[j,"backAmount"] <- allRunnerData$ex[[2]][[j]][[1,"size"]]
      } 
    } 
  }
}

What next?

This tutorial shows how you can extract the current best market price for each runner to feed into your bot. There are more variations of what you can get from listMarketBook including information on BSP. You may want to read the documentation for the listMarketBook function and explore other data that is available.

Your bot can now use this information to place a bet. If you need help with this step then you should read the initial tutorial on placing your first bet

Final Words

If you ever need to know more about abettor functions from within R, simply type ?functionName.

The Betfair API-NG developer documentation also contains a wealth of information and answers to your questions.

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phillc73/abettor documentation built on March 29, 2025, 10:44 a.m.