As a Master of Science in Analytics student at the Institute for Advanced Analytics, I am always seeking new ways to apply my statistical knowledge and programming skills. Earlier this year, I realized that one of the most engaging ways to practice data collection, analysis, and visualization is right on your game shelf. By combining my love for spreadsheets and statistics with board games, I transformed the board game Thunder Alley into a comprehensive NASCAR-style analytics season. Turns out that board games make the perfect way to hone data science skills while having an absolute blast with friends.
Image source: “Game Night Reviews: Thunder Alley” by Chris Bowler. Unboxed the Board Game Blog, Feb 13, 2019.
Thunder Alley: Simulating NASCAR
Thunder Alley is a stock car racing board game that simulates the excitement of NASCAR racing. Players control teams of multiple cars using card-driven movement mechanics that create realistic drafting, pit strategy, and race dynamics. From an analytics perspective, this game is really cool in its similarity to actual NASCAR operations: multiple cars per team owner, scoring systems, and strategic decision-making that parallels real motor sports.
The following link will take you to an online PDF rulebook for the game. Thunder Alley Rules (2014). My main focus was on Section 11.0, “Season Rules.” After learning how to play the game with friends, we wanted to expand the experience into a full season. To make that format more realistic and suitable for data collection and analysis, I began modifying the rules—turning our informal season into a structured, trackable system.
Customizing the Season Rules
The first major addition I made to Thunder Alley was implementing NASCAR’s elimination-style playoff format. The system mirrors real NASCAR playoffs but is modified to fit the board game’s dynamics. This entails drivers accumulating playoff points based on finish position in races.
Another change I introduced stemmed from Thunder Alley’s rule that each owner controls six drivers, but can only field at most four cars in each race. This constraint creates both a strategic and analytical challenge: how do you determine which drivers qualify for the next race in a way that’s fair and reflects existing performance trends, making the data more realistic?
To solve this, I developed a weighted random selection algorithm that transforms recent racing performance into qualifying probabilities. Here’s how it works:
- It calculates each driver’s weight by dividing their individual qualifying score by the team’s total qualifying points.
- Using a cumulative distribution approach, the system creates cumulative weights for all six drivers on a team.
- It then generates random numbers to select drivers. For each random number, the chosen driver is the one whose cumulative weight range contains that number.
- This selection process repeats until four unique drivers are chosen for the team.
By following these steps, consistently strong performers are more likely to be selected while still preserving an important element of uncertainty.
The following link leads to a written Google Document of the rule additions I created for the game: Thunder Alley: Season Rules Additions. These rules expand on the existing Season section in the official rulebook and provide greater detail than what is covered in this article.
Building an Analytics System
My Thunder Alley season tracking system captures extensive performance metrics for each race, creating a rich dataset. For every driver in every race in a season, I collect:
- Starting position and finishing position
- Points earned (based on finishing position and bonuses)
- Turns led throughout the race
- Playoff points accumulated
- Qualifying points from the pre-race qualifying system
- Relative team performance rankings
- …and more!
Starting position, finishing position, points, and turns led are manually entered after each race. In contrast, qualification points, playoff points, and overall standings are automatically calculated using Google Sheets formulas.
The aggregation relies on Google Apps Script automation to calculate statistics across multiple race sheets, ensuring accuracy and enabling updates to both driver and owner standings. Functions like `totalPoints()`, `countWins()`, and `totalTurnsLed()` automatically aggregate performance data.
All of this data is organized and managed through a dedicated Thunder Alley season stats Google Spreadsheet. You can view the system in action here: Thunder Alley: Season Stats Spreadsheet.
The spreadsheet tracks three key aspects: individual drivers, driver playoff points, and team owners. For team owners, the standings are calculated by aggregating the results of all drivers under each owner’s banner, which highlights which owners perform best throughout the season. This approach provides a comprehensive view of both individual achievements and overall team success, making it easy to analyze strategic outcomes and competitive trends.
For future work, I plan to migrate these data processing and aggregation tasks to a Python environment. Utilizing pandas, this transition would enable faster calculations and greater scalability, supporting more analytics as the dataset grows.
Visualizing the Story with Tableau
But with the standing calculations, there are also patterns and trends in the data that tell a story throughout the Thunder Alley Season. Using Tableau, I can visualize this story and immerse my friends and myself into the excitement.
For example, my team—Quaker-Stubbs Motorsports in Quaker, Ohio—faced a daunting deficit of 58 points behind the leader at one point in the season. However, through consistent improvements and three consecutive race wins by a Quaker-Stubbs driver, I managed to cut the gap to just 8 points behind the leader.
Using Tableau, I was able to create interactive graphs that tell the evolving story of our Thunder Alley season. This is something that static spreadsheets couldn’t capture. These interactive graphs illustrate movements in the Playoff Standings from race to race, allowing us to watch point gaps close and witness, often dramatic, lead changes between teams. My friends and I are able to track when a driver consistently gains positions, moving from the back of the pack to the front by race end, revealing patterns of high-performing drivers and strategic comebacks.
Insights Beyond Board Games
This approach turns the ordinary race data into an immersive narrative, providing both competitive insight and a more engaging season experience for us all. The same principles and techniques can easily be applied to other board games, or even extended to different hobbies.
The key insight from this project is that the most effective way to learn data science isn’t always through textbook problems or sanitized datasets. Sometimes it’s through pursuing your passions and applying analytical thinking to activities you already enjoy. Start collecting data, building algorithms, visualizing the gameplay or hobbies you experience— you’ll be surprised by how much you can learn.
Columnist: Andrew Clark