Enable JavaScript in your browser settings to use the full functionality of our website.
trustnbet.com

How to Build a Sports Betting Model in Python?

Want to crack the code on how to make the best sports predictions using betting models? By harnessing the power of data analytics and machine learning, you can develop a sports betting model in Python code that could be just what you need to beat the bookies!

In this article, we’ll carefully explore how to build a sports betting model in Python, including everything from data collection, cleaning and preparation, feature engineering, model selection, training and testing, and finally evaluating and tweaking for better performance.

Key Takeaways

  • Understand how to build a sports betting model in Python from start to finish.
  • Learn how to implement your sports betting model in your betting strategies.

Why Use Python for Sports Betting Models?

Thanks to its suitability for building robust sports betting models, Python has become:

The standard language for data science and machine learning in the sports industry.

SciSports

This indicates that it offers several advantages, the most important of which are its versatility, extensive libraries, community support, and others.

Let us explain these advantages:

  • Versatility: Python has the ability to handle various tasks, from data scraping to machine learning, making it the ideal choice among sports analysts and bettors for building end-to-end sports betting models. 
  • Comprehensive libraries: There are also libraries that provide effective data manipulation, modeling capabilities, and solid betting analysis. These libraries include Pandas, NumPy, scikit-learn, scipy, etc.
  • Community support: Because of the large community base, there are a greater number of resources available, including libraries specifically designed to help with sports analytics. Prominent among these libraries are SportsAnalytics and PySports.

Prerequisites for Building a Sports Betting Model

To successfully build a standard Python sports betting model, one must have the necessary skills, as well as mastering the usage of numerous tools.

Here are some of them:

  • Knowledge of Python programming: This includes the ability to use Python syntax, data structures, and control structures.
  • The ability to understand basic statistical concepts: Such as probability, regression, and data analysis is also a must.
  • The ability to work with large data sets: Like data collection and cleaning, feature engineering, and machine learning fundamentals are all prerequisites for building a sports betting model.
  • Python library tools: You will also need the help of certain Python library tools, including:
    • Panda for data manipulation and analysis
    • Numpy for numerical calculations
    • Scikit-learn for machine learning and algorithms.
  • Coursera’s Statistics with Python Specialization: To get started with a tutorial course in statistics and machine learning, Coursera’s Statistics with Python Specialization is perfect for a sports bettor looking to dive into the world of building sports betting models with Python.

A Step-by-Step Guide to Building a Sports Betting Model

Let’s take a step-by-step look at how to successfully build a sports betting model using Python code.

1 — Data Collection 

Sports data providers such as Sportradar offer APIs for bettors to access their data.

You can also extract data from authoritative websites such as ESPN using web scraping techniques. Most of these websites provide comprehensive datasets on different sports, so you can easily collect data with enough information and a long enough time span to improve the reliability of the model.

You can also collect data on platforms such as Kaggle and the UCI Machine Learning Repository. 

2 — Data cleaning and preparation 

This is another critical step in the process of building a predictive model. From handling missing values to removing duplicates to normalizing data, you need to ensure that the data you collect is clean, consistent, and well-prepared to improve model performance. 

When handling missing values, you can use techniques such as mean imputation or median imputation. Use libraries such as Pandas for efficient data manipulation.

Below is a sample code snippet from a GitHub repository:

Data Cleaning and Preprocessing:
from pandas import read_csv
from numpy import nan
from pandas import DataFrame

load data
data = read_csv(‘data.csv’)

Replace missing values with mean:
data[‘column_name’].fillna((data[‘column_name’].mean()), inplace=True)

Remove duplicates
data.drop_duplicates(inplace=True)

Normalize the data:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data)

3 — Feature Engineering 

This is the process of creating meaningful features from raw data that can help improve model performance. At this stage, it is often important to select features that are more appropriate for your model. With the goal of improving data quality, model interpretability, and accuracy, this step is critical.

Example: In soccer, most data scientists prefer features such as team possession percentage, passing accuracy, or shots on goal percentage. Why? Because they can be more meaningful than basic statistics such as the number of goals scored and the final score.

Feature engineering is an art that requires a deep understanding of the data and the domain. It’s not just about throwing a bunch of features into a model and hoping for the best.

David, a professor and data scientist

This means that effective feature engineering requires profound domain knowledge, data understanding, statistical skills, and creativity.

4 — Model Selection 

There are several machine learning algorithms that can be used to select the right model. These include:

  • Logistic regression
  • A popular choice for binary classification tasks such as predicting win/loss outcomes
  • Decision trees for effectively handling categorical variables and non-linear relationships
  • Neural networks for complex pattern recognition
  • High-dimensional data ensemble methods such as random forest for combining multiple decision trees for better accuracy, etc.

In addition, many studies have compared these algorithms for sports prediction, highlighting their strengths and weaknesses and helping you choose what works best for you. Below are a few of these studies:

  • Sports Betting Prediction Using Machine Learning by J. Xu et al. (2020)
  • A Comparative Study of Machine Learning Algorithms for Sports Prediction by S. Singh et al. (2019)
  • Predicting Sports Outcomes Using Neural Networks by J. Chen et al. (2018)

5 — Model Training and Testing

Model training and testing involves training the model on historical data and evaluating its performance using cross-validation. By using this cross-validation method, you ensure a more accurate estimate of the model’s generalization performance.

Example: Here is an example of training a logistic regression model in Python using scikit-learn:

“`

from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import logistic_regression

# Split data into training and test sets
x_train, x_test, y_train, y_test = train_test_split(features, targets, test_size=0.2, random_state=42)

# Train model
model = LogisticRegression()
model.fit(x_train, y_train)

# Cross-Validation
scores = cross_val_score(model, x_train, y_train, cv=5)
print(“Cross-Validation Accuracy:”, scores.mean())

“`

Performance metrics:

“`

Accuracy: 0.85
Precision: 0.82
Recall: 0.83
F1 score: 0.82
MAE: 0.12
MSE: 0.25

“`

These metrics define the performance of the model and help you refine and improve it.

6 — Model Evaluation and Optimization

The final step is to evaluate the accuracy of the model. Metrics such as accuracy, precision, recall, and F1 score are used to determine the capability of the model. Adjustments are also made to fine-tune and optimize the model for better performance. 

For instance, successful data scientists often use GridSearchCV for hyperparameter optimization or apply feature importance techniques to refine feature sets.

In a study by Harris & Schmidt (2023), they optimized their sports betting model by combining decision trees with ensemble learning methods, significantly improving their prediction accuracy.*

*Source: Sports Betting Models: Improving Performance with Machine Learning

Implementing the Model for betting

To implement the Python model you’ve created, you need to integrate it into your betting strategy. This way, it can be used to make the best betting decisions.

Let us take a look at how this can be done:

  • Automate predictions: Start by using your model to make predictions about upcoming events, and then automate the process using tools such as Python scripts or APIs.
  • Set your betting criteria: Use your model’s predictions to determine when is the best time to place bets. It is often recommended to do this when the predicted probability of an event outcome is greater than 60%. 
  • Integrate with bookmakers: Connect your model to bookmaker data for automated bet placement.
  • Monitor and adjust your strategies: You will notice changes in your model’s predictions from time to time. Monitor it continuously, evaluate its performance, and adjust your strategies as needed.

Remember, implementing your model and integrating it into your betting plan could be just what you need to beat the bookies.

Take notes: Consider the story of Sharpe, a bettor who used a machine learning model to predict game outcomes for NBA betting, automating his bets and increasing his winnings by 15% over the course of a season.

Tools and Resources for Continuous Learning 

If you’re looking for some additional resources and tools to help sharpen your Python skills and strengthen your knowledge of building sports betting models on Python, here are some you should take a look at:

  • Sebastian Raschka’s book, Python Machine Learning Provides you with a comprehensive guide to machine learning with Python, a book that has been endorsed by data scientist Daniel Whitenack, who described it as a ‘must-read for anyone looking to get started with machine learning in Python’.
  • Online courses like Python for Data Science on Coursera are a perfect fit, and it is highly recommended by the University of Michigan to use Python for data science.
  • Machine Learning with Python on edX is another good online course that focuses on machine learning with Python. 
  • Kaggle platforms and other sports betting forums alike are communities that you can join to learn and meet data scientists and sports betting enthusiasts. 

So, Will You Build a Sports Betting Model with Python Tonight? 

Did this article teach you everything you need to know about how to build a model with Python? We bet it did! So why not put that knowledge to good use and build a model for your sports predictions tonight? This could kickstart the start of a purple patch for you!

Perhaps you would like to learn about other important concepts in sports betting education instead? If you are, then check out our dedicated Betting 101 page!