Do Bad NFL Teams Drive More Search Traffic?

I recently came across an intriguing question in a conversation with an SEO and analytics leader at a digital media company: Do NFL teams that are either really bad or really good get more search traffic compared to the mediocre ones? The theory is that extreme performance—whether great or terrible—draws more attention than middle-of-the-road results. So, I decided to put this to the test using data from Google Trends and NFL win-loss records.

In this video, I walk you through how I gathered the data, processed it in Gigasheet, and used Python to automate parts of the data collection. While Google Trends is easily accessible, finding reliable APIs for NFL win-loss data was a challenge. Fortunately, I found a workaround, and I’ll share how we pulled that data. Plus, the Python script I used is available on our GitHub if you want to try it out.

Here’s the process I followed:

1. Gathering the data: I pulled Google Trends data for all 32 NFL teams using team-specific search terms like “Vikings news” and “Vikings injuries.” The data came in hourly increments, which allowed me to group and analyze it over time.
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2. Analyzing search volume: I created a new column in Gigasheet to sum the search volume across the various terms for each team. This gave me a clear picture of the total traffic for each team.
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3. Adding win-loss data: To compare team performance with search traffic, I needed win-loss records. After some API struggles, I found a way to scrape the data from a public site and integrated it into my analysis.
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4. Cleaning and enriching the data: Using Gigasheet's enrichment features (and a bit of help from GPT), I cleaned up the team names and joined the win-loss data with the search trends.
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5. Visualizing the results: Finally, I created a combination chart in Gigasheet to compare win percentage with search volume. While there wasn't a clear trend initially, this process gives us a solid method for checking in as the season progresses.

For now, the early results are inconclusive, but I plan to revisit this analysis in a few weeks to see if patterns emerge as the season continues. Be sure to stay tuned for that follow-up!

Want to dive into the data yourself? Check out the full video and grab an account on Gigasheet.
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