6 Sports analytics project ideas for resume: Beginner to advanced

In the competitive landscape of professional sports, the intersection of data and strategy has become a game-changer. Sports analytics isn't just a trend; it's a fundamental shift in how teams and athletes approach the game. Whether it's understanding player performance, predicting outcomes, or refining in-game strategies, the impact of data analysis is undeniable.

Aspiring to become a sports analyst? Here's the point of entry. The significance of sports analytics isn't confined to boardrooms—it's transforming how we perceive and play sports. These projects are your stepping stones into this dynamic field. They offer more than just technical skills; they provide a practical understanding of real-world scenarios in sports analysis.

Ready to make data work for you in the world of sports? Let's get started.

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1. Create Radar Plots to Show Soccer Player Statistics

Radar plots, commonly employed in sports analysis, serve as an effective tools to depict a comprehensive overview of player ratings, attributes, and performance metrics. In this project, the utilization of R and RStudio is proposed to visually represent summary statistics pertaining to soccer players. However you can use Python and your choice of IDE to execute this project. In this project the focus will be on showcasing key metrics such as expected goals, successful dribbles, completed passes, shots, and key passes. 

Level : Beginner 

What you’ll learn

• Statistical analysis techniques specific to soccer player performance.
• Exploratory data analysis to identify key performance indicators.
• Data visualization skills using radar plots for a holistic player overview.
• Data wrangling for effective analysis and visualization.

Tools you’ll use

• R for statistical analysis and data manipulation.
• RStudio for creating and visualizing radar plots.

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2. Create a Graph in R Using Field Hockey Data

This beginner-friendly project is great for sports analysts who are just starting out. Your goal is to make a visual representation of how many goals are scored at home versus away. 

To do this, you'll use R Studio and bring in a data set, such as one for field hockey.

Level: Beginner 

What you’ll learn

• Statistical analysis of field hockey data to understand scoring patterns.
• Exploratory data analysis for insights into home and away game dynamics.
• Data visualization skills to represent goal differentials effectively.
• Data wrangling techniques for preparing data for analysis.

Tools you’ll use

• R for statistical analysis and data manipulation.
• RStudio for creating visual representations of field hockey data.

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3. Predict MLB Wins per Season

This baseball sports analytics project, designed for those with intermediate-level skills, it involves predicting the number of wins per season for Major League Baseball (MLB) teams. You'll use both the linear regression model and the K-means clustering model for this. Additionally, you'll experiment with various machine learning models, considering team statistics and other factors to forecast a team's total wins.

Level: Intermediate

What you’ll learn

• Advanced statistical analysis using linear regression and K-means clustering.
• Exploratory data analysis focusing on MLB team statistics.
• Machine learning model experimentation and evaluation.
• Insight into the factors influencing a baseball team's total wins.

Tools you’ll use

• R for statistical analysis and modeling.
• Python for machine learning experimentation.

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4. Create Football Shot Maps With Tracking and Event Data

Now here's a compelling project for honing your fundamental sports analytics and programming skills: Create a football shot map using a blend of tracking and event data. This approach provides a comprehensive view of the action during specific moments in a match. To undertake this project, consider working with sample data from Metrica Sports. Find the dataset below.

Level: Intermediate

What you’ll learn

• Advanced data manipulation techniques for handling tracking and event data.
• Programming skills for creating interactive football shot maps.
• Exploratory data analysis to derive meaningful insights from complex data.
• Visualization techniques to present detailed football match action.

Tools you’ll use

• Python for data manipulation and programming.
• Jupyter Notebooks for interactive coding and analysis.

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5. Predict Football Player Positions with K-Nearest Neighbors Algorithm

In this project, you'll leverage the extensive 2020 FIFA dataset, comprising over 18,000 player records, to predict the positions of new players. Elevating the challenge to an intermediate level, you may opt to predict only whether a player will be a midfielder or a defender. To validate the effectiveness of your model, consider testing it with additional sports datasets from previous years.

Level: Advanced

What you’ll learn

• Advanced machine learning techniques using the K-Nearest Neighbors algorithm.
• Feature engineering for effective position prediction.
• Evaluation methods for validating machine learning models.
• Skill in handling extensive datasets for sports analytics.

Tools you’ll use

• Python for machine learning implementation.
• Scikit-learn library for K-Nearest Neighbors algorithm.
• Pandas for data manipulation.

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6. Build a Simple Forecast Model to Predict NCAA Football Games

Now let’s look at this project concept that allows you to anticipate the likely result of a game. Employing logistic and linear regression, you'll craft a forecasting model to predict National Collegiate Athletic Association (NCAA) football spreads and win probabilities. Consider utilizing freshly collected sports datasets scraped from ESPN to enhance the accuracy of your predictions.

Level: Advanced

What you’ll learn

• Logistic and linear regression for sports outcome prediction.
• Feature selection for enhancing forecasting model accuracy.
• Web scraping techniques for collecting sports datasets.
• Model validation and performance assessment.

Tools you’ll use

• Python for implementing regression models.
•  BeautifulSoup for web scraping sports data.
• Pandas for data manipulation and analysis.

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These projects not only provide a structured path for enhancing your sports analytics skills but also equip you with a diverse set of tools crucial for success in this dynamic field. From statistical analysis to advanced machine learning, each project contributes to your proficiency, ensuring you stand out as a capable sports analyst in a competitive landscape.

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