Super Bowl Game Results

Super Bowl game results: Winning teams, scores, and viewer data

By Throwback Thursday [source]

About this dataset

How to use the dataset

• Winning and Losing Teams: The dataset includes columns for both the winning team and the losing team. You can analyze trends or patterns in team performance over time by comparing their records in different Super Bowl games.

• Scores: The final score of each Super Bowl game is provided in two separate columns: Winning Team Points and Losing Team Points. You can explore which games had high-scoring or low-scoring outcomes, and identify any interesting patterns or outliers.

• Conferences: The dataset includes information about the conferences to which both the winning and losing teams belong. You can analyze the success rates of teams from different conferences or compare their performances in specific seasons.

• Venue and City: You can find information about where each Super Bowl game was played by referring to the Venue and City columns. This allows you to explore geographical aspects of the games' locations.

• Attendance: The number of people in attendance at each Super Bowl game is provided under the column Attendance. This data point allows you to understand how popular a particular game was among fans.

• Networks: Television networks that broadcasted each Super Bowl game are included in this dataset under Network. Analyzing network preferences for airing these games may reveal interesting insights into TV viewership habits over time.

• Average U.S.Viewers,Rating,and Share: Columns like Average U.S.Viewers provide valuable information regarding viewership trends across different years while Rating provides insight into audience interest as measured by ratings.Advertisers may be interested in exploring instances where the Cost Per 30s Ad increased in line with higher ratings.

• Cost Per 30s Ad: The cost of a 30-second advertisement during each Super Bowl game is listed under the Cost Per 30s Ad column. This allows you to examine trends in advertising costs or identify Super Bowl games that commanded particularly high advertising rates.

• Notes: Additional notes or details about each Super Bowl game are provided under the Notes column. These notes may contain interesting information, trivia, or historical context that can enrich your analysis.

Remember not to include dates as per your requirement for this guide.

With

Research Ideas

• Analyzing the popularity of Super Bowl games: With data on average U.S. viewers, rating, share, and cost per 30s ad, this dataset can be used to analyze the popularity and viewership trends of different Super Bowl games over the years. This can help identify patterns and factors that contribute to a successful Super Bowl event.
• Comparing team performance: By analyzing the winning and losing team points for each game, as well as their conferences, this dataset can be used to compare the performance of different teams in Super Bowl games. It can help determine which conferences or teams have historically performed better or worse in these high-stakes games.
• Studying advertising trends: The cost per 30s ad information in this dataset allows for an analysis of advertising trends during the Super Bowl. By examining how ad costs have changed over time, advertisers can gain insights into the value and effectiveness of Super Bowl commercials, as well as understand shifts in consumer behavior and preferences during these major sporting events

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: ThrowbackDataThursday 2019 Week 5 - Super Bowl.csv | Column name | Description | |:----------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------| | Game | The number assigned to each Super Bowl game. (Numeric) | | Date | The date on which the Super Bowl game took place. (Date) | | Winning team | The name of the team ...

The Super Bowl is the highlight of the NFL season, watched by millions in the United States and many more across the world. During a 2025 survey in the United States, around 78 percent of respondents stated that they planned to watch Super Bowl LIX between the Kansas City Chiefs and the San Francisco 49ers.

Content

Whether or not you like football, the Super Bowl is a spectacle. There's a little something for everyone at your Super Bowl party. Drama in the form of blowouts, comebacks, and controversy for the sports fan. There are the ridiculously expensive ads, some hilarious, others gut-wrenching, thought-provoking, and weird. The half-time shows with the biggest musicians in the world, sometimes riding giant mechanical tigers or leaping from the roof of the stadium. In this notebook, we're going to find out how some of the elements of this show interact with each other. After exploring and cleaning our data a little, we're going to answer questions like:

1. What are the most extreme game outcomes?
2. How does the game affect television viewership?
3. How have viewership, TV ratings, and ad cost evolved over time?
4. Who are the most prolific musicians in terms of halftime show performances?

Acknowledgements

The dataset we'll use was scraped and polished from Wikipedia. It is made up of three CSV files, one with game data, one with TV data, and one with halftime musician data for all 52 Super Bowls through 2018.

Reference

This dataset is one of the projects of Data Scientist with Python Career Track at DataCamp. Link: https://www.datacamp.com/projects/684

The Super Bowl is one of the highlights of the sporting calendar, but many viewers tune in for more than just the game itself. During a January 2025 survey in the United States, almost 35 percent of respondents stated that the famous Super Bowl commercials were one of the parts of the event they were looking forward to.

Whether or not you like football, the Super Bowl is a spectacle. There's drama in the form of blowouts, comebacks, and controversy in the games themselves. There are the ridiculously expensive ads, some hilarious, others gut-wrenching, thought-provoking, and weird. The half-time shows with the biggest musicians in the world, sometimes riding giant mechanical tigers or leaping from the roof of the stadium. And in this project, you will find out how some of the elements of this show interact with each other. You will answer questions like:

What are the most extreme game outcomes? How does the game affect television viewership? How have viewership, TV ratings, and ad cost evolved over time? Who are the most prolific musicians in terms of halftime show performances? This project gives you an opportunity to apply the skills from Intermediate Python for Data Science.

The dataset used in this project was scraped and polished from Wikipedia. It is made up of three CSV files, one with game data, one with TV data, and one with halftime musician data for all 52 Super Bowls through 2018.

I worked on the above small project from Datacamp website after attending few Data science courses in it. I felt these kind of assignments will help for the beginners like me

Project Description Whether or not you like football, the Super Bowl is a spectacle. There's drama in the form of blowouts, comebacks, and controversy in the games themselves. There are the ridiculously expensive ads, some hilarious, others gut-wrenching, thought-provoking, and weird. The half-time shows with the biggest musicians in the world And in this project, you will find out how some of the elements of this show interact with each other. You will answer questions like:

1. - What are the most extreme game outcomes? How does the game affect television viewership? How have viewership, TV ratings, and ad cost evolved over time? Who are the most prolific musicians in terms of halftime show performances?