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 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.

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.

Super Bowl Ads (Year-by-Year)

Analyzing Funny, Patriotic, and Product Promotion Ads Year By Year

By data.world's Admin [source]

About this dataset

This dataset contains a comprehensive collection of Super Bowl Ads broadcasted. Our data comes from superbowl-ads.com, providing us with the URL's to watch each ad on YouTube. We have included seven defining characteristics of these advertisements - including funniness, patriotism, celebrity presence, animals featured, and use of sex to sell the product - that will offer unique insights into the cultural trends present in each year's advertising campaigns. Furthermore, this dataset implores us to ask questions about the relationship between popular culture and the kinds of ads companies have used in order to both promote their products as well as better relate with their audience through utilizing images and themes which reflect current society. With so much data available in an easily accessible format than ever before thanks to modern technology; exploring this content could give way to unprecedented opportunities for marketers who want gain an advantage in understanding their target demographic or can provide a fresh perspective for those looking consume something new

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

There are a few different ways you can use this data to uncover America’s secrets through Super Bowl ads. Let’s explore some potential uses!

• Analyze changes in the types of themes across years: By looking at the data for each year separately and trying to identify trends or similarities across years in particular themes (like funny ads or dangerous ad), you can gain an understanding of any changes in how Americans view these aspects of their entertainment. For example, is there a trend towards more funny ads? Or more patriotic ones?

• Utilize Brand Analysis: pull up all of an individual brand’s data from all years and ask what types of messages this brand has been sending throughout its Super Bowl advertising over time– Do they like animals? Are their famous people in most ads? An understanding what type brands put out will allow insight into how Americans perceive them overall.

• Analyze correlations between themes: Find correlations between different aspects by performing analyses that compare two columns at a time over multiple years; some examples may include correlation between using sex vs using animals in advertising or correlation between having a celebrity spokesperson/actor/actress vs being patriotic with ad content could also be interesting to analyze.

Research Ideas

• Creating an interactive visualization that allows users to explore the different trends surrounding Super Bowl ads over the last two decades. This could include visuals such as bar graphs, line charts and scatter plots that show how often certain characteristics are used in ads, and how these characteristics have evolved over time.

• Running a classifier model to predict which characteristics will be used in an upcoming Super Bowl ad. This could use factors such as past data from similar brands or from the same company over multiple years.

• Using the data to create a machine learning algorithm that recommends which kinds of elements (i.e funny jokes, celebrity appearances, animals ect.) should be included in a new ad based on user input about their desired outcome for the ad (i.e increase brand awareness or position brand image)

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: superbowl-ads.csv | Column name | Description | |:------------------------------|:--------------------------------------------------------------| | year | The year the ad was broadcasted. (Integer) | | brand | The brand associated with the ad. (String) | | superbowl_ads_dot_com_url | The URL of the ad on Superbowl-ads.com. (String) | | youtube_url | The URL of the ad on YouTube. (String) ...

The Super Bowl Ads dataset provides a comprehensive analysis of advertisements that have aired during the Super Bowl, one of the most watched television events in the United States. The Super Bowl is not just a sporting event, but also a cultural phenomenon, attracting a massive audience that is eagerly anticipating the commercials that air during the game.

The dataset includes information on the advertisers, their products or services, the length of the ad, and the cost of airing the ad during the Super Bowl. It also provides data on the popularity and success of each ad, such as viewer ratings and social media engagement. Additionally, the dataset contains information on the trends in advertising during the Super Bowl, such as the types of products and services that are most commonly advertised and the advertising strategies used by companies.

This dataset is valuable for marketers, advertisers, and business analysts who are interested in understanding the success rates and patterns of advertising during the Super Bowl. The dataset can be used to identify trends in successful ads, as well as to analyze the factors that contribute to an ad's success, such as creativity, humor, and emotional appeal. Additionally, the dataset may have practical applications in fields such as marketing and advertising, as it provides insights into the types of ads that are most effective in reaching and engaging with consumers during one of the most high-profile events of the year.

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

Context

I originally wanted to see how NFL an player's game stats impact their Madden rating. I could not find a comprehensive Database of Madden Ratings and updates anywhere, so I created my own.

Content

The dataset contains weekly rating updates in Madden 21 for the top ~800 players. It includes overall ratings as well as ratings for each player attribute that Madden scores.

Acknowledgements

I'd like to acknowledge EA sports for posting ratings updates every week on their website ea.com.

Inspiration

Feel free to explore the ratings and see what players made big jumps or took big falls, and what may have contributed to these changes in rating.

Overview

Mountain Dew is running a $1,000,000 counting contest. Computer Vision can help you win.

:fa-spacer:

Watch our video explaining how to use this dataset.

https://i.imgur.com/ED4jpM3.png" alt="Mountain Dew">

During Super Bowl LV, Mountain Dew sponsored an ad that encourages viewers to count all unique occurrences of Mountain Dew bottles. You can watch the full ad here. The first person to tweet the exactly correct count at Mountain Dew is eligible to win $1 million (see rules here).

Counting things is a perfect place for where computer vision can help.

We uploaded the Mountain Dew video to Roboflow, created three images per each second of the commercial (91 images from ~30 seconds of commercial), and annotated all bottles we could see. This dataset is the result.

We trained a model to recognize the Mountain Dew bottles, and then ran the original commercial back through this model. This helps identify Mountain Dew bottles that the human eye may have missed when completing counts.

https://i.imgur.com/rjZCS2a.png" alt="Image example">

Getting Started

Click "Fork" in the upper right hand corner or download the raw annotations in your desired format.

Note that while the images are property of PepsiCo, we are using them here as fair-use for educational purposes and have released the annotations under a Creative Commons license.

About Roboflow

Roboflow enables teams to use computer vision. :fa-spacer: Our end-to-end platform enables developers to collect, organize, annotate, train, deploy, and improve their computer vision models -- all without needing to hire a new ML engineering team. :fa-spacer:

Context The Challenge - One challenge of modeling retail data is the need to make decisions based on limited history. Holidays and select major events come once a year, and so does the chance to see how strategic decisions impacted the bottom line. In addition, markdowns are known to affect sales – the challenge is to predict which departments will be affected and to what extent.

Content You are provided with historical sales data for 45 stores located in different regions - each store contains a number of departments. The company also runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks.

Within the Excel Sheet, there are 3 Tabs – Stores, Features and Sales

Stores Anonymized information about the 45 stores, indicating the type and size of store

Features Contains additional data related to the store, department, and regional activity for the given dates.

Store - the store number Date - the week Temperature - average temperature in the region Fuel_Price - cost of fuel in the region MarkDown1-5 - anonymized data related to promotional markdowns. MarkDown data is only available after Nov 2011, and is not available for all stores all the time. Any missing value is marked with an NA CPI - the consumer price index Unemployment - the unemployment rate IsHoliday - whether the week is a special holiday week Sales Historical sales data, which covers to 2010-02-05 to 2012-11-01. Within this tab you will find the following fields:

Store - the store number Dept - the department number Date - the week Weekly_Sales - sales for the given department in the given store IsHoliday - whether the week is a special holiday week The Task Predict the department-wide sales for each store for the following year Model the effects of markdowns on holiday weeks Provide recommended actions based on the insights drawn, with prioritization placed on largest business impact

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FEATURES

stores.csv

This file contains anonymized information about the 45 stores, indicating the type and size of store.

train.csv

This is the historical training data, which covers to 2010-02-05 to 2012-11-01. Within this file you will find the following fields:

• Store - the store number
• Dept - the department number
• Date - the week
• Weekly_Sales - sales for the given department in the given store
• IsHoliday - whether the week is a special holiday week

test.csv This file is identical to train.csv, except we have withheld the weekly sales. You must predict the sales for each triplet of store, department, and date in this file.

features.csv This file contains additional data related to the store, department, and regional activity for the given dates. It contains the following fields:

• Store - the store number
• Date - the week
• Temperature - average temperature in the region
• Fuel_Price - cost of fuel in the region
• MarkDown1-5 - anonymized data related to promotional markdowns that Walmart is running. MarkDown data is only available after Nov 2011, and is not available for all stores all the time. Any missing value is -marked with an NA.
• CPI - the consumer price index
• Unemployment - the unemployment rate
• IsHoliday - whether the week is a special holiday week

For convenience, the four holidays fall within the following weeks in the dataset (not all holidays are in the data):

Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labor Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

https://raw.githubusercontent.com/Masterx-AI/Project_Retail_Analysis_with_Walmart/main/Wallmart1.jpg" alt="">

Description:

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Acknowledgements

The dataset is taken from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

Men's T20 World Cup 2016, 2021 and 2022 Dataset

T20 WC 2024 dataset!!

Any Queries or requirements, Please feel free to share them with me!!

Author - Sahil Tailor

Columns:

• match_id (Primary Key Combining Season and Match_no)
• season (Year)
• phase ('Group Stage','Super 12/8','Playoffs')
• match_no (Match No. in a season)
• date (Match Date)
• venue (Stadium name and Location)
• batting_team (Team Name)
• bowling_team (Team Name)
• innings (1,2)
• over (0.1 to 19.6)
• striker (Player Name)
• bowler (Player Name)
• runs_of_bat (Runs scored by Striker)
• extras (Runs in Extras)
• wide (1,0)
• legbyes (1,0)
• byes (1,0)
• noballs (1,0)
• player_dismissed (Player Name)
• wicket_type ('Caught', 'Stumped', 'Bowled', 'Run out', 'LBW', 'Retired out', 'Retired hurt', 'obstructing the field')
• fielder (Player Name)

DESCRIPTION

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

**Dataset Description **

This is the historical data that covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields:

1. Store - the store number

2. Date - the week of sales

3. Weekly_Sales - sales for the given store

4. Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week

5. Temperature - Temperature on the day of sale

6. Fuel_Price - Cost of fuel in the region

7. CPI – Prevailing consumer price index

8. Unemployment - Prevailing unemployment rate

Holiday Events

Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Analysis Tasks

Basic Statistics tasks

1. Which store has maximum sales

2. Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation

3. Which store/s has good quarterly growth rate in Q3’2012

4. Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together

5. Provide a monthly and semester view of sales in units and give insights

Statistical Model

For Store 1 – Build prediction models to forecast demand

1. Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.

2. Change dates into days by creating new variable.

Conventional retail stores still play a prominent role in a world dominated by Ecommerce. Retail is the process of selling consumer goods or services to customers through multiple channels of distribution to earn a profit. From groceries to clothing to electronics, customers keep flooding the gates of retail stores to satisfy their needs. As time has passed, retailers have had to evolve in order to keep up with changes in demands and the ever-changing mindset of customers. One such retail industry juggernaut that has kept up with the demands of customers as well changed the face of the retail industry for the better is Walmart Inc.

Walmart Inc is an American multinational retail corporation that operates a chain of hypermarkets, discount department stores, and grocery stores, headquartered in Bentonville, Arkansas. They have many stores across the globe and it is the largest retail company by revenue.

We have historical sales data for 45 Walmart stores located in different regions. Each store contains a number of departments. Apart from these, weekly data of Fuel price, Holiday, Temperature with some other features are also present in the data set.

In addition, Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks.

Data consists of 421570 records of weekly sales from stores spanning between ’05-Feb-2010’ to ’26-Oct-2012’. This comprises of 143 Weeks of sales data.

Total 16 numbers of attributes are provided in the Data set including Target variable. Attribute definition is: Store: The store number Size: Size of the Store Dept: Department of the Store Date: Specifying the Week (Friday of every Week) Temperature: Average temperature in the region (in ℉) FuelPrice: Cost of fuel in the region MarkDown1-5: Anonymized data related to promotional markdowns that Walmart is running. Markdown data is only available after November 2011, and is not available for all stores all the time. Any missing value is marked with Null. CPI: Consumer price index Unemployment: Unemployment rate IsHoliday: Whether the week is a special holiday week

Using the above given features, we have to predict the weekly sales of the store with given parameters.