Data Science in Sports: 10 Project Ideas
So you’re into data science, programming, and sports. How about combining these things?
It doesn’t matter if you’re just starting with data science or have some experience.
It can allow you to have a unique portfolio project, work on something you enjoy, and perhaps even turn your project into a side hustle or business.
There’s nothing worse than completing or studying courses without actually building anything or doing well-known boring projects that everybody else does (iris/titanic dataset, anyone?).
Sounds great, right?
We’ll discuss project ideas, including why they are important, where to get the data, what analysis or visualizations would be cool, which tools to use, insights to look for when doing your analysis, and how to tell a great story with the data you’ve gathered and analyzed.
1. NFL Drafting Efficiency
Importance
This analysis goes beyond just identifying successful picks; it uncovers the methodology behind team decisions in the draft, which can significantly influence a team’s competitive advantage and long-term success.
Efficient drafting can lead to sustained success over many seasons.
Data Sources
NFL.com provides detailed draft pick records and player performance metrics.
Pro-Football-Reference offers a comprehensive historical dataset that includes player statistics, team performance, and draft history.
Tools
Python stands out for its data manipulation and visualization capabilities.
Pandas is ideal for dataset manipulation, cleaning, and aggregation, while Matplotlib and Seaborn offer robust options for creating insightful visualizations.
Analysis and Visualizations
Bar charts can highlight the average performance score of players by draft round, offering a quick visual insight into where value is found.
Scatter plots with performance metrics (e.g., Pro Bowl selections, starts, awards) plotted against draft positions can show how well teams identify talent.
Key Insights
Teams that consistently perform well in the draft often have robust scouting departments and a clear strategic vision.
Late-round draft picks that become key contributors are indicators of a team’s drafting efficiency and can be pivotal in team success stories.
Storytelling with Data
Focus on case studies of teams that have built successful rosters through the draft.
Highlight specific picks that exceeded expectations and the impact of those picks on the team’s success.
Discuss strategies employed by teams to identify talent in later rounds.
2. Expected vs. Actual Goals in Premier League
Importance
This project can reveal teams or players that might be due for a change in fortune.
For example, a team significantly underperforming its expected goals (xG) may improve as luck normalizes, while one overperforming might regress.
Data Sources
Football-data.co.uk offers detailed match stats and betting odds that can be useful for calculating expected goals.
Opta provides granular player and team performance data, including shot location, type, and outcome, crucial for calculating xG.
Tools
R is particularly suited for this analysis due to its strong statistical analysis capabilities. dplyr is efficient for data manipulation, while ggplot2 excels in creating advanced visualizations.
Analysis and Visualizations
Line charts tracking the expected vs. actual goals over the course of a season can illustrate trends and deviations for teams and players.
Scatter plots may be used to compare all Premier League teams on the same graph, showcasing those significantly above or below the line of parity between expected and actual goals.
Key Insights
Identifying outliers can pinpoint teams or players that may see a reversal in fortune.
Understanding the reasons behind discrepancies, such as luck, skill, or perhaps systematic factors like defensive strategies or goalkeeper performance, can provide deeper insights.
Storytelling with Data
Delve into stories of teams that defied their expected goals for better or worse. Analyze how this metric correlates with league position, success, or failure.
Discuss potential strategies teams could adopt based on their performance relative to expected goals, highlighting the role of analytics in modern football.
3. Tennis Court Specialists
Importance
Understanding player performance variations across different court surfaces (hard, clay, grass) can be pivotal for predicting match outcomes, especially in tournaments where certain players historically excel on specific surfaces.
Data Sources
ATP and WTA websites offer comprehensive player stats, including performance by surface.
Tennis Abstract provides in-depth analytical articles and databases for a deeper analysis of player performance and surface preferences.
Tools
Python, with its versatile libraries, is perfect for analyzing and visualizing complex datasets.
Pandas is great for data manipulation, while Matplotlib and Seaborn can produce a wide range of visualizations to compare players’ performances across surfaces.
Analysis and Visualizations
Heat maps to display a player’s win rate, average serve speed, or break point conversion rate by surface.
Radar charts to visually represent multiple aspects of a player’s game across different surfaces, facilitating direct comparison between players.
Key Insights
Players might show significantly better performance on one surface over others, suggesting a specialization that could influence match predictions and betting odds.
Historical data could identify emerging trends or shifts in player performance on specific surfaces over their career.
Storytelling with Data
Craft narratives around players known for their dominance on a particular surface, such as Rafael Nadal on clay.
Discuss how these specialists prepare for their preferred surfaces and the impact this has on their legacy and strategy in tournament play.
4. NFL Offensive Player Value
Importance
This analysis helps to identify the offensive players who provide the best value for their teams based on their production relative to their salaries.
It’s a critical component for team management in salary cap leagues like the NFL, aiding in contract negotiations and team building.
Data Sources
OverTheCap offers detailed information on player contracts and salaries.
Pro-Football-Reference provides exhaustive player statistics for performance evaluation.
Tools
Python, with Pandas for data manipulation and Plotly for creating interactive visualizations, allows users to explore data dynamically.
This combination is effective for analyzing and presenting complex salary and performance datasets.
Analysis and Visualizations
Scatter plots comparing player salaries to key performance metrics (e.g., yards gained, touchdowns, receptions) to identify outliers who perform well above their pay grade.
Bubble charts could add another dimension, such as years in the league or team wins, to the analysis.
Key Insights
Identifying underpaid players who may be due for significant raises.
Recognizing overpaid players who might not be living up to their salary cap hit, informing potential roster adjustments.
Storytelling with Data
Highlight success stories of players who outperformed their contracts and how they impacted their teams’ success.
Conversely, explore cases of overpaid players and the challenges teams face managing the salary cap.
5. Home Advantage Analysis in NBA/NFL
Importance
This project investigates how various external factors, beyond just the crowd support, contribute to the home advantage phenomenon.
It explores whether and how elements like city elevation, weather conditions, or local demographics impact game outcomes.
Data Sources
Official league websites for comprehensive game statistics and outcomes.
Government and public databases for city-specific data such as weather, elevation, population, and crime rates.
Tools
R, particularly its tidyverse collection of packages for data manipulation and ggplot2 for visualization, is well-suited for this analysis.
These tools can handle large datasets and produce a wide range of visualizations to explore complex relationships.
Analysis and Visualizations
Correlation analysis to explore relationships between city-specific factors and home team performance metrics.
Maps to visually represent the geographical spread and intensity of home advantage across teams.
Bar charts to compare home advantage metrics across different cities or teams.
Key Insights
Determining the most significant non-sporting factors contributing to home advantage.
Identifying teams that may have an “unnatural” home advantage due to external factors, offering insights into how teams could leverage or mitigate these influences.
Storytelling with Data
Tell the story of how different teams’ performances are influenced by their city’s unique characteristics.
Highlight specific teams that defy the odds due to external factors and discuss potential strategies for teams looking to optimize their home advantage.
6. NBA Team Valuation vs. Winning
Importance
This analysis sheds light on the business side of sports, illustrating how team performance affects financial outcomes.
Understanding this relationship is crucial for team owners, investors, and even fans interested in the economics of sports.
Data Sources
Forbes is a primary source for up-to-date team valuations, offering annual insights into the financial aspects of NBA teams.
Basketball-Reference provides comprehensive data on team performance, including win-loss records, playoff successes, and more, necessary for correlating sports performance with financial valuation.
Tools
Python, with its Pandas library for data manipulation and Seaborn for visualization, is ideal for handling large datasets and creating clear, compelling visualizations to showcase the relationship between team performance and valuation.
Analysis and Visualizations
Linear regression analysis helps in understanding the relationship between team success metrics (e.g., wins, playoff appearances) and their valuations.
Line and scatter plots visually represent this relationship, highlighting trends and outliers that deviate from the expected patterns.
Key Insights
Discovering the strength of the correlation between on-court success and team valuation can reveal how much winning contributes to a team’s financial health.
Identifying outliers—teams that are valued either much higher or lower than their on-court success would predict—can lead to fascinating insights into what other factors might influence team valuation.
Storytelling with Data
Case studies of specific NBA teams can make the data relatable and engaging.
Stories might include teams that have managed to increase their valuation through strategic management and winning titles, as well as those whose valuations don’t necessarily reflect their performance.
7. Hall of Fame Probability
Importance
This project connects past, present, and future by analyzing the likelihood of current players becoming Hall of Famers.
It engages a wide audience, including fans, statisticians, and sports historians, by providing a data-driven look into sports legacy and achievement.
Data Sources
Career stats from Pro-Football-Reference, Basketball-Reference, and Baseball-Reference are critical for modeling a player’s career trajectory against the backdrop of Hall of Fame standards.
HOF websites for each sport often list induction criteria, providing a baseline for what statistical milestones players should aim for.
Tools
Python is effective for this analysis, with Scikit-learn offering powerful machine learning libraries to build predictive models.
These tools can handle the complexity of comparing current players’ careers to those of past Hall of Famers.
Analysis and Visualizations
Logistic regression or machine learning models can predict HOF induction chances based on a variety of career statistics.
Visualization tools like bar charts or ROC curves effectively communicate the probability of induction, making it easier for audiences to understand the data.
Key Insights
Identifying which current players are on track for HOF induction and which factors most strongly predict HOF success.
Comparing players across eras, adjusting for changes in the game over time that affect statistical outputs.
Storytelling with Data
Creating narratives around players who are nearing the end of their careers or have recently retired, assessing their HOF chances, can generate engaging content for fans and sports media.
This analysis can also foster discussions about what makes a player truly “great” and how the criteria for HOF induction evolve with the game.
8. Predicting NBA Salaries
Importance
Predicting NBA salaries is not just about numbers; it’s about understanding the economics of basketball and the strategic elements behind team building.
This analysis is essential for teams to manage their salary caps effectively, ensuring they get the best value for their spending.
It also offers a glimpse into how performance, experience, and market demand interact to shape player salaries, providing fans and analysts with deeper insights into the business side of basketball.
Data Sources
HoopsHype and Spotrac are invaluable for detailed, up-to-date salary information.
Basketball-Reference and the NBA’s official statistics page offer comprehensive player performance statistics.
Tools
Python shines for its versatility in handling and analyzing data. Pandas is perfect for data wrangling, while Scikit-learn and statsmodels are powerful for predictive modeling.
Matplotlib and Seaborn are recommended for creating insightful visualizations that can communicate complex data relationships clearly.
Analysis and Visualizations
Developing predictive models to estimate salaries based on variables like scoring, assists, defensive stats, and advanced metrics such as PER and win shares.
Histograms to explore the salary distribution across the league, highlighting disparities.
Scatter plots to examine the relationship between individual performance metrics and salaries, potentially revealing what the market values most.
Line graphs could trace the evolution of salary norms over time for different positions or performance levels.
Key Insights
Identifying which performance metrics have the strongest correlation with salary can highlight market inefficiencies or changing trends in player valuation.
Analyzing how salaries vary by position, age, and experience can provide insights into the NBA’s economic and strategic landscape.
Spotting outliers where players are significantly overpaid or underpaid relative to their performance offers opportunities for deeper investigation.
Storytelling with Data
Create narratives around players who defy the norm, either by earning much more or less than their performance would suggest.
Explore the factors behind these anomalies, such as market timing, team needs, or negotiation prowess.
Highlight trends in how the valuation of player attributes has evolved, offering insights into the shifting priorities of NBA teams and the broader economic factors at play in professional sports.
9. Real-Time Odds Analyzer
Importance
This project is pivotal for identifying short-lived betting opportunities, enabling bettors to exploit discrepancies across different bookmakers for arbitrage betting.
It also provides insights into market dynamics and how events affect betting odds.
Data Sources
Scraping real-time data from a range of betting websites such as Bet365, DraftKings, and FanDuel.
This requires an approach that can handle frequent updates and differing website structures.
Tools
BeautifulSoup or Scrapy for efficient web scraping, capturing live odds from multiple sources.
Pandas for organizing and analyzing the scraped data, especially useful for handling time-sensitive data.
Analysis and Visualizations
Time series plots can track the movement of odds over time for selected events, highlighting when discrepancies appear and disappear.
Heatmaps are effective for visualizing the discrepancy levels between different bookmakers at a glance.
Key Insights
Identifying patterns in when and where the most significant odds discrepancies occur could indicate market inefficiencies or bookmaker strategies.
Storytelling with Data
Detail instances of successful arbitrage betting based on odds discrepancies and discuss the potential implications for the betting industry.
Offer insights into how real-time data analysis can lead to more informed betting strategies.
10. Sentiment Analysis of Athletes’ Social Posts
Importance
This analysis explores the psychological aspect of sports, providing a novel angle on how public sentiment and personal expression correlate with athletic performance.
Data Sources
Social media APIs like Twitter’s Tweepy and Instagram’s Graph API for fetching athletes’ posts.
Sentiment analysis tools such as TextBlob or NLTK to evaluate the sentiment of posts.
Tools
Tweepy for accessing Twitter data.
TextBlob or NLTK for performing sentiment analysis, offering insights into the emotional tone of text data.
Analysis and Visualizations
Line graphs to show sentiment trends alongside performance metrics, illustrating correlations.
Scatter plots to correlate specific performances with sentiment scores, identifying any direct impacts.
Key Insights
Observations might reveal how significant life events, public reactions, or even self-expression through social media can affect an athlete’s performance.
Storytelling with Data
Construct narratives around specific athletes, detailing how changes in social media sentiment preceded notable performances or slumps.
This could provide a more humanized view of athletes, highlighting the impact of psychological factors on professional performance.
These detailed breakdowns offer a deeper understanding of each project’s potential to generate impactful insights, illustrating how data science can uncover nuanced relationships in sports.
Final Thoughts
Alright, we’ve walked through many cool project ideas where sports meet data science.
These projects are your ticket to getting hands-on with sports analytics.
Whether you’re just starting out or have some data science tricks up your sleeve, diving into these projects is a fun way to sharpen your skills and maybe even see your favorite sports in a new light.
