Making A Sports Betting Model
While most MLB models make projections based on how a team's been hitting as a whole, our offensive projections are based on each and every player included in that particular team's lineup for the day. This means our model waits for each lineup to be posted (usually within a few hours before first pitch), then analyzes it on a player-by-player basis. This method is to ensure the highest accuracy in predicting a team's performance.
- Making A Sports Betting Model In R
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- Making A Sports Betting Model
Google Sheets Sports Betting Tracker. The sports betting tracker is also available on Google Sheets. While the features are the same as the Excel file, Google Sheets has some notable benefits. This is where it all began. I built my first NBA betting model back in 2013 as a hobby to see if I could crack the NBA betting code. I had already dipped my toe into sports betting, but the idea of manually handicapping each game seemed incredibly time-consuming and left myself vulnerable to my own biases and assumptions – which as a passionate Chicago sports.
The pitching/hitting evaluation component of the model uses advanced MLB metrics that go way over the casual baseball fan's head. Exit velocity, batted ball profiles, splits, plate discipline metrics, park factors, performance with or against certain pitches/velocities (combined with pitch usage rates), BABIP, FIP/xFIP, SIERA, and wRC+ are among the many metrics incorporated in the model. The challenge of MLB is analyzing advanced data to determine which players have been lucky and unlucky in relation to their actual performance. This is something that public/square bettors are very poor at figuring out, leaving a lot of value on the table in the betting market. Much like a player projection system, our model identifies a 'true' performance level for players and projects games accordingly.
Step 1: 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.
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
Step 2: 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:
Step 3: Learn to program
It’s daunting at first but there are lots of resources out there to help get you started. These are some of our favourites if you want to learn to use R or Python for data modelling:
Making A Sports Betting Model In R
- 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
Step 4: 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:
- 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
- We also have resources on our GitHub repo, where our Data Scientists have shared modelling tutorials using AFL and soccer data, along with a R repo for connecting with our API
Step 5: 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:
- 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
- Take a look at our Automated Betting Station and 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
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