Learn to Combine Sports Betting, Data Science and Programming

With this article I would like to explain how you can combine sports betting, trading, data science and programming to develop and apply profitable betting and trading strategies. I prefer this approach over manual trading since it is data-driven, time efficient due to automation and less prone to errors and emotions resulting in larger profits from sports betting and trading.

Strategies are always tested on historical data. With a back test you can get a understanding of how successful your strategy was in the past which is probably the best indicator for future profits. Statistics can be calculated for a betting strategy such as expected returns, maximum drawdown and drawdown periods. If the strategy has proven to be successful in the past, it might be deployed as a fully automated trading or betting bot.

The advantage of automation is that it does not take away your time. You don't need to sit in front of a computer all the time. The bot places bets for you and hopefully makes a profit whilst you can focus on more important things in your life.

Quantitative Betting Cycle

What is Required for Data-Driven Sports Betting?

A quantitative approach to sports betting is not suitable for everyone: Most important prerequisites are access to betting markets, sufficient capital as well as statistics and programming skills. Creativity and mental strength are equally important for this betting and trading approach.

Access to Betting Markets

First of all, you need to have access to a betting market. The betting market should offer you access via API (application programming interface) so that you can use computer software to connect to the betting market and automate the placement of bets and orders. The betting market should also have liquidity and offer decent odds as this will simplify the process of crafting a successful betting or trading strategy. As a start we recommend to look at betfair or betdaq, both are betting exchange platforms that offer peer-to-peer betting. They have a well-written documentation for their API which you can find under https://developer.betfair.com/en/exchange-api/ and https://betdaqpro.com/ respectively.


Starting off with a smaller bank size might be beneficial as it will give you the option to develop bots and learn whilst the risk of losing money due to programming or trading system errors is limited to a smaller balance. However, once you plan to scale your strategies a larger bankroll is required. This is necessary to persist longer drawdowns that inevitably will occur and also generate sufficient profit on the longer term.

Statistics Skills

You need to have some maths knowledge to backtest betting and trading systems. In addition to knowledge in statistics, it might also be beneficial to know about probability distributions as well as machine learning approaches.

Programming Skills

Betting exchanges typically offer sample code for development of betting systems in various languages such as Python, C#, PHP, Java and Excel/VBA. If you already know how to code that's great, you can then focus more on other areas such as strategy development. Otherwise, you might start with learning how to code, however, this process takes quite some time, especially if done in your spare time. Another option is to outsource the programming part and pay someone to program a trading bot for you. The critical part here is to protect your IP (trading or betting system).

The Right Mindset

When talking about the required mindset two aspects are important:

Firstly, creativity is needed. You need to be creative, you need to think outside the box if you want to come up with betting or trading strategies that beat the market and generate profit on the long run.

The other aspect is mental strength: Due to the automated nature of this betting approach emotions are reduced to a minimum. However, all strategies show some drawdown and such periods of drawdown might put mental pressure on you as you are losing money. To become successful with an algorithmic betting or trading approach, you need to find a way to overcome such situations.

Betting and Trading Strategy Development

The development of a successful betting or trading strategy is the most critical step. This involves an idea pipeline where constantly ideas are added, then backtested, refined and deployed if successful.

Developing Ideas for a Betting or Trading Strategy

The easiest way to get started with an algorithmic approach is to use publicly discussed betting or trading strategies, create your own backtest and compare to published figures. You will learn quite a bit as you need to collect data reproduce the strategy on historical data. A famous starting point might be the "Lay the Draw" strategy. We also provide a list of backtested betting and trading strategies trying to help you getting started.

Another approach is scraping and exploring data from sports betting markets. You might be able to identify patterns and reverse engineer successful betting strategies

Strategies can also be sourced from betting or trading forums as well as scientific publications.

Data Sources for Sports Betting and Trading

Once you have defined an idea for a betting or trading strategy you need to collect historical data to evaluate and assess the strategy. The assumption is here that the results of a strategy in the past might indicate some future performance, however this is not guaranteed. All the data needs to be collected that is consumed by the strategy as well as actual price data from a betting market or betting exchange to evaluate the strategy.

Helpful for this process might be the following pages:

Data Processing and Data Engineering

Before you can start your data analysis you might need to process the data from the source into a format that is suitable for analytics tasks. This could include downloading and processing data from webserver and saving data or a subset of the data into a database. 


Once you have collected data you can run a backtest which means that your strategy is evaluated on historical data. You can calculate the return of your strategy in the past year for instance. You can also derive maximum drawdown or drawdown periods to get a better feeling of your strategy. Once you are satisfied with the results of a backtest you can finally move to the next step and deploy your strategy in production.


Congratulations - you have accomplished quite a lot once you are at this stage. The task is now to automate your strategy: Assuming that you have sufficient programming skills you can write a betting or trading bot yourself. The alternative is to hire someone to create a betting bot for you.

You might be able to run your bot from your local machine, however there are some disadvantages such as possible network interruption, power consumption and latency. In most cases it makes more sense to move your betting bot to a virtual private server (VPS) or dedicated server for automation. Selecting a suitable virtual private server for betting bots that is closer to the betting market also helps to reduce latency. However, deploying a strategy on someone else's infrastructure might leak your betting or trading strategy. 


Once you deployed your strategy to the betting market it is advisable to continuously monitor your system and compare it against backtesting results. This will help you to identify possible mistakes that were done during system creation, backtest or deployment. Often biases, limited liquidity on markets etc will be revealed at this stage. You might also decide to adjust the stake for your strategy or retire a strategy that is no longer successful.