Betfair Model

Models Betfair has a team of Data Scientists, both in-house and external contractors, that build prediction models. They use dozens of quantitative variables, specific to each sport, that identify betting opportunities. Betfair’s own Data Scientists have created a prediction model which produces ratings that you can use as Horse Racing Tips for nearly every thoroughbred meeting in Australia. The Model is built using Punting Form data. This includes Sectional Time data, benchmark ratings. Betfair’s model, which allows anonymous punters the ability to set lines, can cause corruption. This is especially true in horse racing where a punter can lay a particular horse to lose. It’s much easier to throw a horse race than it is to throw a major sporting event. Betfair counters these critics by stating.

Want to learn how to create your own predictive model using sports or racing data, but you don’t know where to start? We’re here to help.

The Data Scientists at Betfair have put together the first few steps we suggest you take to get you started on your data modelling journey. We also run occasional data modelling workshops to help you get the basics down – reach out and let us know if you’re interested in being notified about upcoming data events.

Choose your language¶

There are lots of programming languages to choose from. For our data modelling workshops we work in R and Python, as they’re both relatively easy to learn and designed for working with data.

If you’re new to these languages, here are some resources that will help get you set up.

Intro to modelling. Want to learn how to create your own predictive model using sports or racing data, but you don’t know where to start? We’re here to help. The Data Scientists at Betfair have put together the first few steps we suggest you take to get you started on your data modelling journey.

Language 1: R¶

• Download and install R – get the language set up on your computer
• Download and install RStudio – you’ll need a program to develop in, and this one is custom-designed to work with R
• Take a look at the some of the existing R libraries you can use if you want to connect to our API, including abettor and our Data Scientists’ R repo.

Language 2: Python¶

• Download and install Anaconda Distribution – this will install Python and a heap of data science packages along with it

Find a data source¶

Finding quality data is crucial to being able to create a successful model. We have lots of historical Exchange data that we’re happy to share, and there are lots of other sources of sports or racing specific data available online, depending on what you’re looking for.

For our workshops we use historical NBA odds data from the Exchange (which you can download directly from here, along with NBA game data from a variety of sources including:

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Learn to Program¶

Okay, so easier said than done, but you don't actually need a high level of programming knowledge to be able to build a decent model, and there are so many excellent resources available online that the barrier to entry is much lower than it's been in the past.

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These are some of our favourites if you want to learn to use R or Python for data modelling:

• Dataquest – free coding resource for learning both Python and R for data science
• Datacamp – another popular free resource to learn both R and Python for data science
• Codeacademy – free online programming courses with community engagement

We've also shared a R repo for connecting with our API, which might make that part of the learning process easier for you, if you go down that path.

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Learn how to model data¶

We’ve put together some articles to give you an introduction to some of the different approaches you can take to modelling data, but again there are also lots of resources available online. Here are some good places to start:

• Work through the modelling tutorials we've put together using AFL and soccer data
• This Introduction to Tennis Modelling gives a good overview of ranking-based models, regression-based models and point-based models
• How we used ELO and machine learning as different approaches to modelling the recent World Cup

Get your hands dirty¶

The best way to learn is by doing. Make sure you have a solid foundation knowledge to work from, then get excited, get your hands dirty and see what you can create! Here are a final few thoughts to help you decide where to from here:

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• Make sure you’ve got your betting basics and wagering fundamentals knowledge solid
• Learn about the importance of ratings and prices and get inspired by the models created by our Data Scientists
• Consider how you could use our API in building and automating your model
• Read about how successful some of our customers have been in their modelling journeys