Moneyball sports analyzer using machine learning

Our world is heavily driven by technology and one of the newest facets of technology which is getting a lot of attention is machine learning. This field of study hopes to transform simple machines into smart machines that can think and analyze problems, much like how we do! In this post, we will be looking at one of the large scale applications of machine learning which is sports analytics. So here’s a look at a Moneyball inspired Sports Analyser project that works on the principles of Machine Learning.

Looking to build projects on Machine Learning?:

Machine Learning Kit will be shipped to you and you can learn and build using tutorials. You can start for free today!

1. Machine Learning (Career Building Course)

2. Fraud Detection using Machine Learning

3. Machine Learning using Python

4. Movie Recommendation using ML

5. Handwritten Digits Recognition using ML

6. Machine Learning Training & Internship

7. Brain Tumor Detection using Deep Learning

Project Description

Sports analytics made front news when the 2011 movie Moneyball, came out. This movie portrayed the triumphs of the Oakland Athletics team who made history by consecutively winning 20 games in 2002. Most of this success was credited to the team’s Managers, Billy Beane and Paul DePodesta.

DePodesta, who has a degree in Economics from Harvard University used the analysis of baseball statistics to purchase and choose players. This approach proved to be very successful as in 2002, Billy Beane bought several undervalued players, helping his team do extremely well in that season in a cost-effective manner. The movie which saw stars such as Brad Pitt and Jonah Hill take the stage, proved to be an instant hit as it brought to the limelight a concept, that few people were aware of at the time- Sports analytics and its use of Machine Learning. This project will be a simple recreation of their algorithm in an attempt to statistically study and analyze players by using Linear Regression to fundamentally identify strong and weak players.

Uses of Sports analytics

• Predict the outcome of matches
• Predict the performance of teams or players across a time period
• Build new strategies
• Help in deciding when and how much to pay for a player
• Connect players to sponsors
• Spot natural talent early on

Did you know

Skyfi Labs helps students learn practical skills by building real-world projects.

You can enrol with friends and receive kits at your doorstep

You can learn from experts, build working projects, showcase skills to the world and grab the best jobs.
Get started today!

Concepts Used

1. Algorithm Making
2. Machine Learning
3. Fundamentals of Python
4. R Programming
5. Linear Regression
6. Understanding of Statistics

Project Implementation

• Linear regression is derived from fundamental statistical principles and is used to model the relationship between a dependent variable and one or more independent variables.
• When there is only one independent variable, it is called Simple Linear Regression and the presence of multiple independent variables makes it Multiple Linear Regression.
• Regression coefficients help define the relationship between the two variables.
• The most important step in building such a system is finding the right dataset to do so.
• For a sports analyser, the dataset can be statistics that may be sourced from a reputed sports website, such as baseball-reference.com. for baseball, NBA monitoring sites for basketball and IPL analyzing sites for cricket.
• The data must then be fed into Python and this can be done using the pandas function or library.
• Define the variables that translate to metrics when it comes to calculating sporting efficiency or average, such as Overs bowled, runs scored, wickets taken and so on, corresponding to the sport you want to analyze.
• Set a time period for which you want to analyze the data. For example, this could be all players who have been active since 2002.
• Next, plot the distributions of the variables and map the relation between them.
• Cluster and classify the data using pre-set techniques to create a model for a team.
• Use principal component analysis to trim the feature space if needed.
• Then apply basic regression principles to gauge each player in your datasheet.
• On performing a descriptive analysis of the clustered data, you will be able to understand which model works best.
• Through continued training of this model, you will be able to build the best team roster using minimal cost.

• No kit required

• Machine Learning
• Python

Moneyball sports analyzer using machine learning