Sports Betting and Trading as a Data Scientist

As a data scientist you might be interested in earning some extra money using your data analysis and machine learning skills. Sports betting and trading can be an interesting investment opportunity for data analysts and data scientists. With a data focused career, you are most likely familiar with data collection, transformation, exploration and modelling. These are important skills that can be used to successfully earn money with algorithmic sports betting and trading.

Which of my Skills can I use for Automated Betting and Trading?

As a data scientist you might have some knowledge about data acquisition and data transformation (ETL). This includes parsing information from websites and saving them in an appropriate format, as well as combining different data sources and transforming the data. Even if a data engineering team is taking care of these tasks in your normal working environment you probably still have a basic understanding of it and it shouldn't be too difficult to learn it. When using an algorithmic approach towards sports betting, parsing information from websites / APIs, transforming data and storing it in a master database for strategy development and backtesting is an important aspect.

Data modelling and machine learning / AI techniques is probably the core of your skills. This is also a very valuable asset to model sports betting markets. In fact there are a number of scientific publications on sports modelling, such as the famous Dixon-Coles model. Your knowledge in statistics, machine learning and artificial intelligence is essential for sports modelling and the development of betting models and strategies.

Which Skills do I Need to Learn?

Once you have developed a winning betting or trading strategy you would need to deploy it in production. Data scientist typically posses very good scripting skills. Decent software engineering skills are required in order to deploy models into production as an automated betting bot. You would also need some knowledge of setting up and maintaining a server where you can deploy your betting and trading algorithms.

Another aspect to focus on is finance and risk management which is an important part of sports betting and trading. This includes portfolio / money management as well as staking.

How Much Can I Earn Applying Data Science on Sports Betting Markets?

It is very difficult to tell how much money you can earn with sports betting. Only few participants on betting exchange markets such as Betfair are successful over the long term. You don't earn an hourly wage and returns are variable, most likely some weeks are profitable and some are not.

How much you can earn depends on how much time you can spend on developing and deploying strategies, how profitable your strategies are and what size of bankroll you can use. Often betting or trading strategies can not be scaled with a larger bankroll due to limited liquidity in betting markets. Also expect to spend quite some time before earning any money: Typically you would start off with data collection and then develop models. Once you are satisfied with a model performance deployment can start which typically also takes some time. It is certainly not a get rich quick scheme and once the betting bot is deployed, you still need time to maintain and monitor it.

How to get started with Quantitative Betting as a Data Scientist?

Do lot's of research! There are plenty of resources available online which could help you getting started. You can follow along some sports modelling tutorials or test simple betting strategies on historical data. After spending a couple of hours with this topic you should figure out whether you enjoy this kind of work or not. I believe that motivation is key to succeed on the long term.

If you are interested in the topic of data science and sports betting markets, please do not hesitate to contact us!