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How to Build a Sports Betting Model in Excel? 

Do you want to know the secret to making data-driven bets and staying ahead in the game of sports betting? With the power of Excel, one can actually create a model that helps you make the best picks in your betting activities. 

In this article, we will explore how to build a sports betting model from scratch in Excel, and every step you need to take from data collection to probability calculation.

We hope, in the end, you won’t need to rely just on your gut feelings or luck to make your picks!

Key Takeaways

  • Understanding how to build sports betting models on Excel.
  • Knowing the steps involved in building a model on Excel.

Why Use Excel for Sports Betting Models? 

There can be numerous merits for bettors who use Excel for creating sports betting models. Prominent among them are its accessibility and versatility, as well as its powerful data analysis tools.

Let’s explain these advantages:

  • Accessibility: Widely available for almost every bettor, Excel is readily accessible to many users, making it the best choice for sports betting enthusiasts who may not have advanced programming skills. 
  • Versatility: One can easily import and calculate data using Excel, as well as create sports betting Excel formulas and calculate probability. Moreover, you can visualize charts and analyze graphs with it, underlining its versatile ability.
  • Powerful data analysis tools: Excel offers a range of built-in functions and analysis tools, such as regression analysis, probability calculator, and data visualization tools, making it ideal for data-driven sports betting models.

A sports bettor, Sarah Bethoven told me,

I have tried different pieces of software, but none of them comes close. Not only does Excel allow me to work with large datasets and perform complex calculations quickly, I can also easily import data, create formulas and visualize trends with it.

Sarah Bethoven

Prerequisites for Building a Sports Betting Model 

Building a sports betting model can be helpful for bettors who are looking to use data analysis to make decent betting decisions. To achieve this, however, certain skills and tools are required to keep you well-equipped.

Below are a few must-have skills:

  • Proficiency in Excel: One must possess advanced knowledge of Excel formulas, functions, and data analysis tools. That way, you can be able to use advanced Excel features like Power Query, Power Pivot, and others.
  • Understanding of basic statistics: You must need to be familiar with statistical concepts like probability, regression, and data visualization. You should also be knowledgeable about using software programs like Python or MATLAB.
  • Access to sports data: Having access to large sports datasets and being able to analyze and interpret them is also needed. Some of these sports data tools include Opta Sports, SportsDataIO, WhoScored etc.

Sports bettors looking to learn more about Excel proficiency that aligns with their betting needs might need to enroll for special courses with Coursera, or other platforms alike.

Step-by-Step Guide to Building a Sports Betting Model in Excel 

Let’s explore in steps what it takes to build a sports betting model suitable to make the best betting decisions using Excel.

Step 1: Data Collection

Gathering sports data is crucial when building a sports betting model on Excel. This process includes web scraping, API integration, data importation, data cleaning, and analysis. One can get the necessary data required for this from numerous suitable sources. 

Opta Sports, Stats Perform and SportsDataIO are known to provide detailed data, including play-by-play data through their APIs. 

Other websites like ESPN and OddsBible offer historical sports data including scores, statistics, and game logs for various sports. Other sports databases like SportsDB are also good at offering historical sports data.

Step 2: Data Cleaning and Preparation 

This stage is another important stage in the data analysis process. So, let’s present an overview of the process, including some code snippets and examples:

  • Handling Missing Values:
    • Identify missing values: df.isnull( ).sum( )
    • Drop missing values: df.dropna()
    • Fill missing values with mean/median/imputed values: `df.fillna(df.mean())`

Example from Kaggle:

  • import pandas as pd
    • # Load data
    • df = pd.read_csv(‘data.csv’)
  • # Handle missing values
    • df = df.dropna()  # Drop rows with missing values
  • Normalizing data: 
    • Scale data using StandardScaler from sklearn.
    • Preprocessing import StandardScaler
  • Normalize data using MinMaxScaler from sklearn.
    • Preprocessing import MinMaxScaler

Step 3: Feature Engineering

This is the act of creating meaningful features from raw data that can improve model performance. Calculating team performance metrics like points per game, and player stats like passing accuracy, tackling success, and historical odds can go a long way providing valuable insights. 

Some effective feature engineering techniques include feature extraction (deriving new features from existing ones), dimensionality reduction, and transforming data formats to improve model compatibility. 

David, a data scientist, said:

Effective feature engineering requires a deep understanding of the data, and the problem you’re trying to solve. It’s not just about creating new features, but about creating the right features that add value to your model.

David

Step 4: Model Selection and Implementation

Model selections and implementations are critical steps in building a sports betting model. There are several statistical methods and formulas one can employ, and they include:

  • Regression analysis (simple and multiple)
  • Moving averages (simple and exponential)
  • Logistic regression
  • Decision trees: random forests and neural networks

Research has also compared different statistical methods and formulas for sports prediction, highlighting the strengths and limitations of each method, and helping you to choose the best approach for the model you’re creating.

Some of these research publications include:

  • A Comparative Study of Sports Prediction Models (Journal of Sports Analytics)
  • Evaluating Sports Betting Models: A Review of the Literature (International Journal of Forecasting)
  • Sports Prediction using Machine Learning Algorithms (IEEE Transactions on Knowledge and Data Engineering)

Step 5: Model Training and Testing

This step involves training the model on historical data and testing its performance using back testing.

This process involves splitting data into training and testing sets, training the model on the testing set, and evaluating the model’s performance on the testing set (using metrics like MAE [Mean Absolute Error], MSE [Mean squared error] and R-squared) and then fine-tuning the model by adjusting parameters and features.

Here are some code and performance metrics examples:

  • # Train the model
    • model.fit(X_train, y_train)
  • # Evaluate the model
    • y_pred = model.predict(X_test)
    • mae = mean_absolute_error(y_test, y_pred)
    • mse = mean_squared_error(y_test, y_pred)
    • r2 = r2_score(y_test, y_pred)
  • Performance metrics:
    • MAE: 2.5
    • MSE: 5.2
    • R-squared: 0.8

Step 6: Model Evaluation and Optimization

Finally, here is where trying out the model’s accuracy and optimizing its performance takes place. Using MAE and MSE metrics, the model’s predictive nature is accessed. Furthermore, areas that need improvement are identified and worked on using special features and algorithms. 

Continue to fine-tune the model for better performance through techniques like:

  • Hyperparameter tuning
  • Regularization
  • Ensemble method

Fact: Recall the story of Haralabob, a seasoned bettor who utilized his proprietary algorithm, Ewing, nicknamed after former NBA star Patrick Ewing, to predict game totals. Having incorporated additional information such as team momentum and player injuries, an up to 10% increase in accuracy became evident.

Implementing the Model for Betting

When you’ve been able to successfully build a sports betting model from start to finish, then you need to integrate it into your betting strategy. That way, you begin to reap the dividends of having such a powerful tool.

Here’s how to do it:

  1. Generate predictions on events: Using your model, make predictions on games you intend to bet on and use tools like Python scripts or API to automate the process.
  2. Set betting criteria based on your model’s predictions: Know when to place your bets. Look out for important things like your model’s expected value thresholds or minimum probability thresholds, and follow them.
  3. Connect to betting platforms: Use APIs to connect your model to online betting platforms to automate bet placement.
  4. Always monitor your model’s performance: And adjust your betting strategy to suit it. 

That said, there have been success stories recorded in the past backing up this.

A professional sports bettor, Donovan had used a sports betting model he created with Excel to increase his winnings by 25% over six months. Then, a sports betting syndicate was said to have implemented a model like this and reportedly earned a 15% increase in profits in over a year. 

Tools and Resources for Continuous Learning  

To continue to enhance one’s Excel skills and ability to develop top-quality betting models, there are certain books, courses, and forums one must look out for. We highly recommend these and though the list is not exhaustive, we think a number of well-known sports data scientists and betting experts have endorsed them. 

For books:

Interested in online courses?

  • Coursera’s Data Science Specialization is supported by data scientist, Roger Peng.
  • Udemy’s Sports Betting Strategies, a course focused on sports betting strategies, is recommended by Bill Walters.

If you are keen to join forums where you could meet like minds:

  • Kaggle’s Sports Betting Competition Forum, endorsed by Ray Li, is a community for sports betting enthusiasts and data scientists.

So, Will You Build a Model with Excel Tonight? 

With all these shared formulas and tips, you now have a more profound understanding of how to build your sports betting model with Excel. Start by gathering data and make sure to always integrate our strategies into your bettings to achieve favorable results.

So, will you build a model tonight and inculcate it in your betting? Check out our section 101 here and you might find something helpful!