What Is A Regression Model in Sports Betting?

Sports bettors are constantly looking for ways to gain an advantage and increase profitability. One tool that can help is a regression model, which can help isolate key factors that lead to determining the likelihood of given outcomes.

On this page, we’ll explain how regression models work and their applications in sports betting. We will also cover how you can build your own model that best matches your betting objectives and interests.

What Are Regression Models?

Regression models are statistical equations that analyze the relationship between a dependent variable (the outcome you’re trying to predict) and one or more independent variables (any factor that could influence the outcome). Using these models to analyze data can help to predict future outcomes.

Some websites provide modeling software for you, but you can also build them yourself (as we’ll explain below). Using existing software is a good starting point, but learning how to build your own can help you assign weight to variables and adjust your regression models accordingly.

How Can I Use Regression Models for Sports Betting?

You can use regression models for sports betting by analyzing historical data to predict future results. Using multiple regression models can allow you to adjust for different scenarios and variables leading to greater accuracy in gambling predictions.

As is the case with any regression model, the more data you can add to the calculation, the more accurate and useful your findings will be. While specific betting data (such as odds and lines) can be more difficult and time consuming to track, but including them is worth that investment.

Types of Regression Models Used in Sports Betting

Here are a few of the most common types of regression models used in sports betting.

Linear Regression

Linear regression models are the most straightforward type of regression analysis. Using a single dependent variable and one or more independent variables (though they tend to work best with only one independent variable), a linear regression tries to find the line of best fit (or plane of best fit when using multiple independent variables).

Applying this to sports betting could find the number of runs a baseball team is likely to score, the number of shots on goal from a hockey team, or a football team’s total rushing attempts to predict their game totals.

Logistic Regression

Logistic regressions analyze binary outcomes (yes/no or win/loss scenarios). Unlike linear progressions that try to predict a precise total, logistic regressions are useful for predicting whether a team will win or lose by accounting for factors such as weather, injuries, and home/away performances.

Multiple Regression

Multiple regression models expand on the linear model by using multiple independent variables in a more complex manner than linear regressions. Accounting for more variables leads to more accurate predictions through the inclusion of more data.

These models do well with advanced metrics like expected goals, hard hit ball percentage, EPA/play, etc.

Polynomial Regression

Finally, polynomial regression works well when the relationship between the dependent and independent variables does not necessarily yield linear results. These non-linear relationships can show things like change in team or player performance over the course of a season or for calculating optimal bet size.

How to Build a Regression Model

You can build a regression model by following these steps:

1. Collect Your Data: As a general rule, the more data you can collect, the more accurate your model will be. Using data from league websites, reference pages, and other trusted sources is key.
2. Choose Key Variables: The variables you choose to include in your model should have a direct impact on the type of bet you’re planning to back. Consider all possible factors that could influence the end result and be sure they’re addressed in some way.
3. Input the Data: Using your preferred software (or even something basic like Excel or Google Sheets), include the necessary data for your model.
   Run the Calculations: Next, choose the given dependent and independent variables to study and track the results.
4. Analyze the Results: Finally, test the results to ensure your findings are statistically significant. In particular, you should consider the p-value, r-value, and regression coefficients. If your model shows inaccurate or insignificant relationships between your variables, your model needs adjustments (through more data or different variables).

Pros and Cons of Regression Models for Sports Betting

Before you choose to use a regression model for sports betting, we hope you’ll weigh the pros and cons of their application.

Pros:

• Ability to identify reasonable betting opportunities
• Allows you to limit or remove personal biases from your betting research
• Vast data aggregation leads to more accurate gambling predictions

Cons:

• It can be easy to create a model that is too complex and overfits your data, leaning too heavily on historical data and less on recent trends or causing inaccurate results at the start of a season.
• Reading your models incorrectly can lead you to assume causation when the only relationship present is a correlation. Causations and correlations should always be considered differently and not as equals.
• Creating your own model can lead you to overlook key variables or have omitted necessary data.

Conclusion

Regression models are a useful tool for sports betting that can lead you to making more accurate, data-driven wagers. However, no model is 100% accurate 100% of the time.

Instead, regression analysis is best conducted alongside research on recent betting trends to help balance short-term and long-term results. This will also help to reduce the impact of conscious and unconscious biases in your analysis. 

Part of the challenge (and fun) of sports betting is using these tools effectively and adjusting your strategies to yield consistent results. Remember – it is ALWAYS better to walk away from the sportsbooks than to continue pursuing risky wagers when your models aren’t working. 

Good luck, have fun, and always gamble responsibly!