If you eat, sleep, and breathe sports, then you may want to consider turning your passion into a career in sports analytics. If you are a baseball, football, or basketball fanatic, this knowledge can benefit you in this career path, but you will still need to develop technical knowledge and skills. Completing sports analytics projects can make learning more fun and challenging.
These beginner to expert-level projects are a great way to land a job in the field of sports analytics. Not only will they help you enhance the skills necessary for the profession, but they can also be an opportunity to develop the perfect portfolio and impress potential employers. We’ve compiled a list of project ideas for you to try and help you prosper in your career.
5 Skills That Sports Analytics Projects Can Help You Practice
Sports analytics is a crucial ingredient for professional sports leagues to gain a competitive edge. Winning is no longer only about training and working hard. Using data such as player statistics to create predictive models can help organizations make well-informed decisions. Many seek professionals with sports analytics skills to improve player and team performance.
- Statistics. Advanced math and statistics are a big part of this profession. You will be dealing with complex formulas to obtain relevant data. Part of your job also includes assessing statistical models to gain insights into trends and data.
- Data visualization. Data visualization is a must-have skill for professionals working in the field of sports analytics. Many clients won’t have the advanced sports statistics knowledge to understand what crucial numbers and data imply. Sometimes, you will need to translate insights into a visual context to make it easier to understand.
- Python. A sports analytics professional must also be equipped with technical knowledge of programming languages like Python. When you get into the industry, this is one of the popular and widely used tools. Python helps users analyze and handle complex data and calculations.
- R programming language. Aside from Python, R is another programming language used by many data analysts, data scientists, and sports analytics professionals. It offers an excellent environment for statistical computing and graphics.
- Machine learning. Aside from statistical techniques, you will be using various machine learning models in most advanced sports analytics projects. Organizations often use machine learning and AI technology for predictive analysis. It can provide rapid feedback on a team’s performance and help them improve certain areas of their operations.
Best Sports Analytics Project Ideas for Beginners
If you’re a beginner who wants to analyze your favorite sport and assist your team in winning, or a professional looking for a career change, you’ll need a basic understanding of sports analytics. These sports analytics projects are for beginners who want to grasp the concepts and techniques of data analysis. They will help you develop the basic skills required in the industry.
Create Radar Plots to Show Soccer Player Statistics
- Sports Analytics Skills Practiced: R programming and data visualization
Radar plots or charts are often used in sports analysis to illustrate a breakdown of player rating, attributes, or performance. For this project, you will use R and RStudio to visualize summary statistics of soccer players. You will display their expected goals, successful dribbles, completed passes, shots, and key passes.
Build an Expected Goals Model Using Python
- Sports Analytics Skills Practiced: Python programming
Football data and analytics can also be used to determine when a goal will be scored. You will create an xG or expected goals model in this project to help teams and fans understand the performances of players and how many goals they’ll score in the future. It’s an excellent activity for a student who is also getting familiarized with the concepts of Python.
Make an NBA Shots Chart
- Sports Analytics Skills Practiced: Python programming and data visualization
An NBA shots chart helps players, teams, and coaches identify trends, strengths, and weaknesses of a player. This basketball analytics project idea will let you create a simple shooting chart using Python and North Bound Interface (NBI) API. You can also design a basketball court with identical dimensions to those of the NBA.
Create a Graph in R Using Field Hockey Data
- Sports Analytics Skills Practiced: R programming and data visualization
Sports analysts who are new to the field will have an easy time with this project idea for beginners. You’ll be tasked with creating a graphic representation of the relationship between goals scored at home and away. You’ll need to import a field hockey data set or other data into R Studio.
Calculate the Home Court Advantage
- Sports Analytics Skills Practiced: Math and statistics
When your favorite team is playing on their home court, calculating the home-field advantage can be a fun sports analytics project idea. You won’t need any programming skills for this project, so it’s perfect for absolute beginners. All you will do is gather data from the past scoreboards and conduct some basic math and statistics calculations.
Best Intermediate Sports Analytics Project Ideas
If you are already familiar with some of the basic concepts and techniques of sports analytics, then it’s time to challenge yourself with these intermediate-level projects. Increase your chances of obtaining the perfect job by learning more about sports analytics and honing the necessary skills for this field. You can even include some of these project ideas in your portfolio.
Find and Scrape Your Own Data
- Sports Analytics Skills Practiced: Web scraping and Python programming
If you’re tired of manually gathering information about players from various sources, consider creating your own script that scrapes data from the web. In this project, you will build a functional web scrape tool that will help you save time and money. Make sure you have prior knowledge of hypertext markup language (HTML) to navigate the source code of web pages.
Predict Football Player Positions with K-Nearest Neighbors Algorithm
- Sports Analytics Skills Practiced: Python programming
For this project, you will use the 2020 FIFA data set, which includes more than 18,000 player records, to forecast the position of new players. To make it an intermediate-level challenge, you could only predict whether a person will be a midfielder or a defender. If you want to test whether your model is working properly, you can use other sports data sets from previous years.
Predict MLB Wins per Season
- Sports Analytics Skills Practiced: Python programming and statistical modeling
In this intermediate-level baseball sports analytics project, you will forecast major league baseball (MLB) team wins per season using the linear regression model and K-means clustering model. Furthermore, you will test multiple machine learning models and use team statistics and other variables to forecast how many games a team will win.
Create Football Shot Maps With Tracking and Event Data
- Sports Analytics Skills Practiced: Python and data interpretation
A football shot map that uses both tracking and event data is another great project idea to challenge your basic sports analytics and programming skills. By combining both types of data, you’ll have a complete picture of what’s going on during a given moment of a match. For this project, you can try using the sample data from Metrica Sports.
Build a Simple Forecast Model to Predict NCAA Football Games
- Sports Analytics Skills Practiced: R programming and statistics
This project idea can help you predict the probable outcome of the match. You will use logistic and linear regression to create a forecast model that will predict National Collegiate Athletic Association (NCAA) football spreads and win probabilities. You might want to use recent sports data sets scraped from ESPN.
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Best Advanced Sports Analytics Project Ideas
Professionals with years of experience in sports analytics should continue sharpening their skills and learning new techniques. One of the best ways to do this is by taking on advanced sports analytics projects like the ones mentioned below. Not only will these projects help you become a senior sports analytics specialist but they can also be a great addition to your portfolio.
Build a Women’s Basketball Dream Team
- Sports Analytics Skills Practiced: Machine learning and data interpretation
One of the tasks you may encounter when working in sports analytics is helping teams with their draft. For this project, you will create a women’s basketball team by analyzing each player’s stats and using data science and machine learning tools. Since this is an advanced-level project, you can try creating two teams and simulating a match between them.
Build an NHL Game Prediction Model
- Sports Analytics Skills Practiced: Jupyter Notebook and statistics
In this project, you will focus on creating a model that may generate improved outcome possibilities to assist people in developing winning betting strategies. You can use the official National Hockey League (NHL) game results from the NHL API, and the team and goalie data from Natural Stat Trick.
Create a Tennis Match Simulator in Python
- Sports Analytics Skills Practiced: Python programming and statistics
To challenge your coding skills and help sharpen your sports analytics knowledge, consider creating a tennis match simulator in Python. In this project idea, you will have to apply a points-based model and develop an equation for the likelihood of the server winning a match. After that, you will have to run Python simulations to evaluate this derivation.
Build a Player-Tracking and Detection Algorithm for Basketball Video Analysis
- Sports Analytics Skills Practiced: Deep learning implementation
An algorithm that can track players can help teams, coaches, and fans gain insight into how teams play and future performance. You’ll make a player-tracking analysis tool to use on real-time basketball clips. To recognize all players, officials, and individuals attempting to shoot the basket, you’ll utilize deep learning, OpenCV, a pre-trained detection model, and OpenPose.
Build a Model for Sports Injury Analysis
- Sports Analytics Skills Practiced: Statistical analysis and machine learning implementation
Because sports injuries have the potential to alter game outcomes and cause teams to lose income, organizations are looking for techniques to predict potential injuries. For this project, you will build a model that will use previous injuries and workload data to help predict possible injuries. It should also assist teams in identifying major contributing factors for these issues.
Sports Analytics Starter Project Templates
Sports analytics often involve complex data and statistics that can be confusing for the analyst and their audience to tackle. Additionally, the coding aspect of developing models and algorithms can also be time-consuming. Using starter project templates for your sports analytics tasks can help make things easier to understand and create.
- Python project template. If you’re doing a sports analytics project that requires Python programming, this template can come in handy. It contains basic commands, including install, test, lint, format, and release.
- Generic template for data analysis projects. This generic template can be used for Python-based data science projects. To run the code, you will need the command-line interface (CLI) cookiecutter on your Python environment.
- Web scraping template. Use this web scraping template for your projects to save time and energy searching the web for data. Keep in mind that this tool may not be suitable for all websites.
- Machine learning models for R and Python. This template consists of machine learning models for data processing, classification, regression, clustering, natural language processing (NLP), neural networks, cross-validation, principal component analysis (PCA), and linear discriminant analysis (LDA) for R and Python.
- Football radar chart template. If you’re not versed in programming languages and don’t know how to create visualization through coding, you can use and customize this template. You can apply it to presentations to ensure that your audience understands your findings.
Next Steps: Start Organizing Your Sports Analytics Portfolio
A sports analytics portfolio is one of the most effective tools for showcasing the type and quality of the work you can do. Your portfolio must be presentable and organized to effectively communicate your skills and experience in data and sports analytics. Follow the tips below to help increase your chances of landing a job in the sports industry.
Showcase Projects Relevant to the Job or Organization
The goal is to prove to your potential employer that you have what it takes to fulfill the requirements of the job you want. Your portfolio should contain sports analytics projects that highlight the skills and experiences relevant to the specific job. If you’re planning on working as a sports consultant, consider showcasing more consulting projects on your portfolio.
Provide a Clear Explanatory Text for Your Projects
While organizing your portfolio, make sure you explain what each of the sports analytics projects entails. Describe the process you followed to complete the project and what makes it important or relevant. If you have used particular analytical tools or analysis techniques, explain why and how that decision lead to relevant findings.
Take Advantage of Data Visualization Tools
Instead of preparing a sports analytical portfolio that’s full of texts and numbers, make one that’s interactive and engaging. Some people process information easier and faster through images, graphs, charts, or maps. To make your portfolio more than just a wall of text, consider using data visualization tools like Tableau, Microsoft Power BI, Plot.Ly, and Sisense.
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Sports Analytics Projects FAQ
Analytics is an integral part of the industry as it allows coaches to make decisions and develop strategies that help the team win. They gather the opposing team’s statistics, individual player performance, and other relevant data to prepare for each game. Team scouts can use data to recruit new members, while sports enthusiasts can use them to place bets.
Almost all professional sports use data analytics to enhance and maximize team performance and team ratings. There are a few team-based sports that frequently use this strategy. For example, American football, cricket, basketball, baseball, hockey, and soccer, and have used analytical techniques with great success.
To start your career in sports analytics, you’ll need to gain the proper education, skills, and experience. Look for academic or certificate programs regarding data science, applications of statistics, sports management, computer programming, and general business. Aside from statistical analyses and principles, you’ll have to learn the rules and dynamics of sports.
You can study sports analytics by enrolling in a bachelor’s, master’s, or certificate program at institutions including the University of Michigan, Northwestern University, University of North Texas, and more. If certificate programs or four-year academic programs at universities are out in your budget, data analytics bootcamps offer a cheaper and quicker alternative.