Linear Regression For Sports Prediction

No matter what sport you are betting on, you want to have the best chance possible at winning your bet. As we have said in other articles, part of successful sports betting (in fact, a BIG part of it) is math. The sports books use statistical information on different matches in order to come up with the spreads and lines you make a wager on. These statistics are based on everything from which side is receiving the most bets to which side has the best chance of winning based on points per possession, and so on.

There are two big problems for the average sports bettor when it comes to the mathematics side of sports betting. The first is that, honestly, most of us lack either the patience or the brains to really come up with those calculations on our own. A quick search around the 'net will result in lots of sites that make it sound as if these calculations are easy but trust us, they are NOT. If they were, the books wouldn't be making all that money every year!

The second problem with mathematics in sports betting is that it really strips a lot of fun out of the action. Most of us like to wager on sporting events for the fun of it. We enjoy the sport in question, we have some knowledge of the game, and we like the chance to win a little money on the side. Concentrating on who is offering what odds and pure numbers can turn a fun pastime into something resembling a bad high school course.

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What is linear regression?

Linear regression analysis is used to predict the value of one variable based on the value of another. The variable you want to predict is called the dependent variable. One variable used to predict the value of another variable is called an independent variable.

This form of analysis involves fitting the coefficients of a linear equation with one or more independent variables so that the equation best predicts the value of the dependent variable. The linear regression results can be represented as a straight line in the plane minimizing the difference between the predicted and actual values. There are simple linear regression calculators that use the least squares method to compute the optimal flow to fit a data set. Then, the value of X (the dependent variable) is estimated from the value of Y (the independent variable).

Linear regression can be used in many different programs and environments, for example:

• Linear regression in R
• Linear regression in MATLAB
• Linear regression in Sklearn
• Linear regression in Python
• Linear regression in Excel

Why linear regression problems are important

The linear regression models are relatively simple and offer easy-to-understand mathematical prediction formulas. Linear regression can be applied in different fields of science and business.

Linear regression is used everywhere: in biology, behavioral and environmental studies, social studies and business. Linear regression modeling has proven to be a scientifically reliable method for predicting the future. Since linear regression is a well-researched statistical procedure, the properties of the linear regression model are well understood and easy to learn.

A scientifically reliable way to predict the future.

Leaders can use linear regression to improve the quality of their decisions. Organizations collect large amounts of data and linear regression help them use this data instead of experience and intuition to optimize their interactions with the reality around them. It is therefore possible to convert large amounts of data into useful information.

Linear regression can also be used to improve the quality of information by analyzing the patterns and relationships your colleagues have seen and think they understand. For example, sales and purchase data analysis can help identify purchase patterns on specific days of the week or times of the day. The information obtained through regression analysis helps to predict the times when their company's product will be in high demand.

Linear regression in sports forecasting - applicable to sports betting:

- by video Linear Regression For Sports Prediction - Regression models for forecasting goals and results in professional football

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Learn more: Best Football Prediction Site

Key assumptions for efficient linear regression

Assumptions considered in linear regression analysis:

For each variable: Take into account the number of allowed cases, mean value and standard deviation.

• For each model: Consider regression coefficient, correlation matrix, partial correlation, correlation coefficient multiple, coefficient of determination, adjusted coefficient of determination, change of the coefficient of determination. Estimated standard error, analysis table of variance, predicted value and error. Also taking into account a 95% confidence interval for each regression coefficient, the variance and covariance matrix, the variance growth coefficient, tolerance, the Durbin-Watson test, distance measurements (Mahalanobis value, Cook and leverage), DfBeta, DfFit, predictive range and case-specific diagnostic information.
• Charts: Review scatter charts, section charts, bar charts, and standard distribution charts.
• Data: The dependent and independent variables must be numeric. Categorical data, such as religion, educational background or region of residence, must be stored in binary variables or other comparable variables.
• Other assumptions: For each value of the independent variable, the distribution of the dependent variable must be standard. The variance of the distribution of the dependent variable must be constant for all values ​​of the independent variable. The relationship between the dependent variable and each independent variable must be linear and all observations must be independent.

Also see: Random Forest Classifier for Sports Bettin

Make sure your data follows linear regression assumptions.

Before performing linear regression, you need to ensure that your data can be analyzed using this method. Data must meet certain assumptions.

• How to verify that these assumptions are met:
• Variables must be measured continuously. Examples of continuous variables: time, sales, weight, test results.
• Using scatter charts, you can quickly determine if there is a linear relationship between two variables.
• Observations must be independent of each other.
• There should be no significant exceptions in the data.
• Check for data on the homogenization - uniformity of variance of the random error of the regression model.
• The variance of the random error of the regression model must have a normal distribution.