Python for Sports Betting

Feb. 20, 2021 by SportsBettingQuant

Python Programming Language

Python is a programming language that was first released in 1991 and has evolved since then. Python is a general-purpose programming language that you can use to write software for many applications. There is support for all major operating systems such as Windows, Linux or MacOS.

I would say that Python is easier to learn compared to other programming languages such as Java or C++. You will find that you can become effective after short time and typically will be able to build first prototypes or MVPs with a small code base.

Python is highly extensible through packages that bring in additional functionality so that you don't need to re-implement existing features. You can browse Python packages at PyPI - some example functionalities of Python packages include:

  • Database connectors (e.g. psycopg, SQLAlchemy, etc)
  • Array and matrix computations (e.g. numpy)
  • Scientific computing such as optimization, interpolation (e.g. scipy)
  • Web Frameworks (e.g. Django, Flask)
  • and many more

Why is Python Suitable for Sports Betting?

There are various reasons why Python is a good choice for programming tasks in the domain of sports betting.

First of all, Python has a rich ecosystem in the domain of scientific computing and especially in the world of data analysis. You can do a lot of statistical modeling with Python but also do all sorts of things in the domain of machine learning and artificial intelligence since Python is the most popular programming language in this specific domain.

There are also packages available that allow you to directly integrate with betting exchanges or bookmakers such as the betfairlightweight package for instance. For betdaq and b365 similar Python wrappers are available that allow you to use their API through an API.

Python is a relatively easy to learn programming language that allows you to quickly develop software for sports betting or trading applications. A rich ecosystem allows you to collect and process betting-related data, develop profitable models and automate your bet or trade execution.

9 Example Use-Cases for Python in Sports Betting

Looking at some examples of what you can do with Python in the world of Sports Betting and Trading I have put together 9 use cases that you can address with Python.

1. Crawler to Collect Information

There is a lot of information available on the Internet that might be worth considering when placing bets with a bookmaker or trading on a betting exchange: There are various sites that provide team or player statistics, news information or similar information.

With Python you can create a bot that automatically fetches information from the internet and extract information that might be important for you. Popular Python packages for web crawling include requests, Beautiful Soup and selenium. Using such tooling you can create a web crawler that visits web sites in regular intervals, extracts relevant information from the site and saves it to a database for instance so that you can analyse the data later. The issue with web crawler is that the website might change the layout or the way how information is displayed. This could cause issues and your crawler might need some adjustment to work again.

You might not need to create a web crawler as certain sites offer an API. Still you can most likely use Python to access the API and get the desired information from a (hopefully) well documented API endpoint. If it is a HTTP-based API you could again use the requests package from Python to use the endpoint. Maybe the API provider even offers a Python API that you could use and easily integrate into your application.

Examples:

2. Data Processing and Cleaning (ETL)

Python is also a popular choice for data processing and data engineering tasks. Let's assume that you have created a crawler that collects some information on the web. Most likely you will not want to work with the raw data but rather need to transform or clean the data. Such transformation could for instance include the extraction of the number of runners in a horse race.

Example:

3. Build Models and Strategies

If you want to earn money with sports betting on the long run you need a betting model or strategy that exploits some market efficiency. With Python you can develop such models or strategies.

One example is the use of statistical models such as the Dixon-Coles model for instance that you can easily implement in Python.

Another type of model that can be build with Python are machine learning or AI-based models: Python has many packages in the domain of AI that you can use to build betting models such as scikit-learn, pytorch or tensorflow - to just name a few.

Examples:

4. Create a Backtesting Engine

You might want to test your betting or trading strategy on historical data for validation. With Python you can create a backtesting engine that would simulate your betting strategy on historical data and derive relevant metrics such as ROI, maximum drawdown etc. for you.

5. Place Bets or Execute Trades

You can use Python to fully automate the placement of bets or trades on a betting exchange or with a bookmaker. Many betting sites offer an API that you can use for automated bet placement. Python offers various packages to access APIs. For HTTP-based endpoints you can use the requests package for instance.

For various betting sites there are also packages available that offer you an an SDK to place orders so there is no need to work with the lower level API of the betting site. Instead, you can just simply use the existing functionality in your application to automate the bet or trade execution.

6. Run Simulations

Simulations can help you to develop certain models that improve your betting operations: Monte Carlo simulations can help you with risk analysis or maybe you can just simulate different staking methods to improve your betting performance.

With Python you can easily create simulations. Starting with simple simulations that would just require some random number generation up to more complex and sophisticated approaches such as Monte Carlo simulations.

Examples:

7. Monitor Your Bet Execution

Once you decide to put your betting or trading strategy into production you might want to compare the orders against the backtesting engine that you used to derive the strategy. Such monitoring can help you to identify systematic errors early on so that you can still reduce potential losses in case of issues. In a backtest you might assume endless liquidity in the market which can turn out to be problematic in reality resulting in unmatched bets or only partially matched bets.

With Python you can extract the orders that were placed on the exchange or bookie site and compare those with your backtesting results. You can automatically highlight any deviation that you can then manually verify and investigate in more detail.

8. Send out Alerts

If you want to automate parts of your betting strategy it might be helpful to use some sort of alerting mechanism. This could include sending out emails if certain events happen or integrate with some sort of social media interaction or messenger integration.

Python has a package in the standard library that allows you to send out emails, which is called smtplib. Along with a mailserver you can use this package to send out notification emails, for instance when a certain event happens in a betting market or whenever a crawler identifies some arbitrage opportunity.

There are also various Python packages that allow you to integrate with social media. You could for instance create a bot for Facebook, Twitter and Co to automatically publish your bets or predictions. Similar integration is possible with messenger or communication platforms such as Whatsapp, Slack or Co.

9. Visualize Information

Python has also a rich ecosystem for charting and plotting. There are multiple libraries that allow you to nicely visualize your data which is an important aspect of working with data.

Popular packages for data visualization include matplotlib, seaborn and bokeh.

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