The National Football League comprises 32 teams from across the United States competing in two conferences split roughly by region. The NFL is one of the most popular professional sports leagues in the United States, with televised games attracting millions of viewers each week. This survey depicts the level of interest in the NFL in the United States, and it showed that 42 percent of Black respondents were avid fans of the NFL as of April 2023.

The National Football League comprises 32 teams from across the United States competing in two conferences split roughly by region. The NFL is one of the most popular professional sports leagues in the United States, with televised games attracting millions of viewers each week. An April 2025 survey found that 29 percent of Americans aged 55 and above considered the NFL to be their top interest.

As of August 2023, around half of NFL fans aged 18-29 were white. Meanwhile, ** percent of NFL fans aged 30 or older were Black, while this figure stood at ** percent among those aged 18 to 29.

The NFL is one of the most widely televised sporting leagues in the world. The average television viewership of a regular season NFL game in 2024 was calculated to be **** million. Attendance In addition to this record-breaking TV viewership, the NFL attracts thousands of fans to the stadiums each week to see their favorite teams in action. The NFL has consistently been the major American sports league with the highest average attendance, with an average of almost ** thousand people attending each game in the 2023 season. Moreover, during the 2024 regular season, the average total home attendance per team across the entire NFL was calculated to be *******. The franchise with the highest total attendance for its eight regular-season home games was the Dallas Cowboys, perhaps in part to their comparatively reasonably priced tickets, costing nearly *** U.S. dollars on average, just less than the average ticket price across the NFL at *** U.S. dollars per ticket. Best players Since 1957, the Associated Press NFL Most Valuable Player Award has been given to the NFL player considered to have been the most outstanding during the season. Since the award's introduction, Peyton Manning has received the award the greatest number of times during his career, closely followed by Aaron Rodgers, who has received the award on **** occasions. One other potential indicator of the success of an NFL player is their annual salary. The highest-paid NFL player in 2024 was Dak Prescott, quarterback for the Dallas Cowboys, who earned **** million U.S. dollars from salary and bonuses and ** million U.S. dollars from endorsement deals during the 2024 season.

A January 2023 survey illustrated the average weekly viewership of National Football League games by respondents of different ethnicities in the United States. According to the survey, ** percent of Asian respondents stated that they spent between *** and **** hours a week following NFL games, whereas this figure amounted to ** percent for white survey participants. Meanwhile, ** percent of African American/Black and Latino respondents spent between *** and **** hours weekly watching football.

The data below comes from the official ESPN's NFL Statistics website. Included in this dataset are a count of 12 files.

All of the Passing files represents the statistics for the Top 50 Passers based on Passing Yards for each year's regular season only.

All of the Rushing files represents the statistics for the the Top 100 Rushers based on Rushing Yards for each year's regular season only.

All of the Receiving files represents the statistics for the the Top 100 Receivers based on Receiving Yards for each year's regular season only.

The purpose of this dataset is to encourage Exploratory Data Analysis on NFL statistics which can be very interesting for the average NFL fan, or even someone looking to do some Fantasy Football Research.

The data is very beginner friendly because of the detailed breakdown for each column shown below.

NFL Players Performance and Salary

Uncover Trends, Make Predictions and Analyze Demographics

By Ben Jones [source]

About this dataset

This Kaggle dataset contains unique and fascinating insights into the 2018-2019 season of the NFL. It provides comprehensive data such as player #, position, height, weight, age, experience level in years, college attended and the team they are playing for. All these attributes can be used to expand on research within the NFL community. From uncovering demographics of individual teams to discovering correlations between players' salaries and performance - this dataset has endless possibilities for researchers to dive deeply into. Whether you are searching for predictions about future seasons or creating complex analyses using this data - it will give you a detailed view of the 2018-2019 season like never before! Explore why each team is special, who shone individually that year and what strategies could have been employed more efficiently throughout with this captivating collection of 2019-2018 NFL Players Stats & Salaries!

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How to use the dataset

• Get familiar with the characteristics of each column in our data set: Rk, Player, Pos, Tm, Cap Hit Player # , HT , WT Age , Exp College Team Rk Tm . Understanding these columns is key for further analysis since you can use each attribute for unique insights about NFL players' salaries and performance during this season. For example, HT (height) and WT (weight) are useful information if you want to study any correlations between player body types and their salaries or game performances. Another example would be Pos (position); it is a critical factor that determines how much a team pays its players for specific roles on the field such as quarterbacks or running backs etc.
• Use some visualizations on your data as it helps us better understand what we observe from statistical data points when placed into graphical forms like scatter plots or bar charts. Graphical representations are fantastic at helping us see correlations in our datasets; they let us draw conclusions quickly by comparing datasets side by side or juxtaposing various attributes together in order explore varying trends across different teams of players etc.. Additionally, you could also represent all 32 teams graphically according to their Cap Hits so that viewers can spot any outlier values quickly without having to scan a table full of numbers – map based visualizations come extremely handy here!
• Employ analytical techniques such as regular expression matching (RegEx) if needed; RegEx enables us detect patterns within text fields within your datasets making them exceptionally useful when trying discovering insights from large strings like college team name URLSs [for example] . This could potentially lead you towards deeper exploration into why certain franchises may have higher salaried players than others etc..
• Finally don't forget all mathematical tools available at your disposal; statistics involves sophisticated operations like proportions / ratios/ averages/ medians - be sure take advantage these basic math features because quite often they end up revealing dazzling new facets inside your datasets which help uncover more interesting connections & relationships between two separate entities such as how does height compare against drafted college etc..?

We hope these tips help those looking forward unlocking hidden gems hidden

Research Ideas

• Analyzing the impact of position on salaries: This dataset can be used to compare salaries across different positions and analyze the correlations between players’ performance, experience, and salaries.
• Predicting future NFL MVP candidates: By analyzing popular statistical categories such as passing yards, touchdowns, interceptions and rushing yards for individual players over several seasons, researchers could use this data to predict future NFL MVPs each season.
• Exploring team demographics: By looking into individual teams' player statistics such as age, height and weight distribution, researchers can analyze and compare demographic trends across the league or within a single team during any given season

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even co...

In the 2023 NFL season, the average number of a "Thursday Night Football" (TNF) game exclusively available on Amazon Prime Video peaked at around ***** million in the U.S., up from **** million in the previous season. Younger audiences in particular tend to watch NFL games on streaming services. Among the age group 18 to 49 years, the viewership of TNF grew by around ******* between 2022 and 2023.

This data set contains around 3000 football player's statistics scrapped from SoFIFA.com using beautiful Soup.

A total of 64 data columns and can be used as a useful dataset for creating a regression model to predict player value.

The share of 18 to 29 year olds who declared themselves casual fans of NFL increased from ** percent in 2021 to ** percent in 2023. Meanwhile, ********* of respondents from the same age group in 2023 stated that they were NFL fans.

This game-level, player-level data is sourced from the Python package nfl_data_py, which is a Python library for interacting with NFL data sourced from nflfastR, nfldata, dynastyprocess, and Draft Scout.

Includes import functions for play-by-play data, weekly data, seasonal data, rosters, win totals, scoring lines, officials, draft picks, draft pick values, schedules, team descriptive info, combine results and id mappings across various sites.

Content

NFL Favorite Team

This folder contains data behind the story The Rams Are Dead To Me, So I Answered 3,352 Questions To Find A New NFL Team.

team-picking-categories.csv contains grades for each NFL franchise in 16 categories, to be used to pick a new favorite team.

Should be used in conjunction with weights derived from a survey structured like this: http://www.allourideas.org/nflteampickingsample.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

NFL-Statistics-Scrape

Here are the basic statistics, career statistics and game logs provided by the NFL on their website (http://www.nfl.com) for all players past and present.

Summary

The data was scraped using a Python code. The code can be located at Github: https://github.com/kendallgillies/NFL-Statistics-Scrape

Explanation of Data

1. The first main group of statistics is the basic statistics provided for each player. This data is stored in the CSV file titled Basic_Stats.csv along with the player’s name and URL identifier. If available the data pulled for each player is as follows:
   1. Number
   2. Position
   3. Current Team
   4. Height
   5. Weight
   6. Age
   7. Birthday
   8. Birth Place
   9. College Attended
   10. High School Attended
   11. High School Location
   12. Experience
2. The second main group of statistics gathered for each player are their career statistics. While each player has a main position they play, they will have statistics in other areas; therefore, the career statistics are divided into statistics types. The statistics are then stored in CSV files based on statistic type along with the player name, URL identifier and position (if available). The following are the career statistics types and accompanying CSV file names:
   1. Defensive Statistics – Career_Stats_Defensive.csv
   2. Field Goal Kickers - Career_Stats_Field_Goal_Kickers.csv
   3. Fumbles - Career_Stats_Fumbles.csv
   4. Kick Return - Career_Stats_Kick_Return.csv
   5. Kickoff - Career_Stats_Kickoff.csv
   6. Offensive Line - Career_Stats_Offensive_Line.csv
   7. Passing - Career_Stats_Passing.csv
   8. Punt Return - Career_Stats_Punt_Return.csv
   9. Punting - Career_Stats_Punting.csv
   10. Receiving - Career_Stats_Receiving.csv
   11. Rushing - Career_Stats_Rushing.csv
3. The final group of statistics is the game logs for each player. The game logs are stored by position and have the player name, URL identifier and position (if available). The following are the game log types and accompanying CSV file names:
   1. Quarterback – Game_Logs_Quarterback.csv
   2. Running back – Game_Logs_Runningback.csv
   3. Wide Receiver and Tight End – Game_Logs_Wide_Receiver_and_Tight_End.csv
   4. Offensive Line – Game_Logs_Offensive_Line.csv
   5. Defensive Lineman – Game_Logs_Defensive_Lineman.csv
   6. Kickers – Game_Logs_Kickers.csv
   7. Punters – Game_Logs_Punters.csv

Glossary

While most of the abbreviations used by the NFL have been translated in the table headers in the data files, there are still a couple of abbreviations used.

• FG: Field Goal
• TD: Touchdown
• Int: Interception

The statistic shows figures on the television ratings and viewership figures for NFL games in the United States in the 2015/16 season. The week one game between the Pittsburgh Steelers and the New England Patriots was watched by 27.4 million viewers, and had a household rating of 16.2. The viewership figures and ratings for the 2016/17 season can be found here.

Fantasy Football Market Outlook

In 2023, the global fantasy football market size was valued at approximately USD 24.4 billion, and it is projected to reach USD 48.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.9%. This robust growth is driven by the increasing popularity of sports betting, the expansion of internet penetration, and the evolving digital landscape that has made fantasy sports more accessible to a global audience.

The burgeoning interest in fantasy football is significantly fueled by the thrill associated with virtual sports management and the competitive spirit it invokes among participants. The advent of high-speed internet and the proliferation of smartphones have considerably lowered entry barriers, enabling users from diverse demographics to engage with fantasy football platforms. Enhanced user interfaces and the strategic inclusion of real-time data and analytics have further enriched the user experience, making the game more immersive and engaging. Additionally, the growing partnerships between fantasy sports platforms and major sports leagues have enhanced the credibility and reach of the market.

Another crucial growth factor is the increasing monetization avenues within the fantasy football ecosystem. Platforms are leveraging ad revenues, subscription models, and in-app purchases to enhance their profitability. The introduction of innovative revenue streams like virtual goods, personalized content, and premium features provides substantial growth opportunities. Furthermore, the gamification of fantasy sports, including interactive features like social sharing and leaderboards, has significantly contributed to user retention and engagement.

Public perception and societal trends have also played a pivotal role in the market's growth. The cultural acceptance of fantasy sports as a mainstream activity has expanded its demographic reach beyond traditional sports enthusiasts. The integration of fantasy sports into mainstream media, including dedicated shows and podcasts, has increased visibility and user adoption. This cultural shift has also led to the formation of fantasy football communities, fostering a sense of camaraderie and collective enthusiasm.

American Football has played a pivotal role in the evolution of fantasy sports, particularly in North America, where the National Football League (NFL) stands as the most popular league for fantasy football. The deep-rooted passion for American Football among fans has translated into a robust fantasy football culture, with millions of participants engaging in both daily and season-long leagues. The NFL's extensive media coverage and the availability of detailed player statistics have made it an ideal sport for fantasy leagues, offering fans an opportunity to test their managerial skills and engage with the sport on a deeper level. This engagement is further amplified by the NFL's active promotion of fantasy football, which has helped to sustain and grow its fan base over the years.

From a regional perspective, North America continues to dominate the fantasy football market, driven by the high penetration of internet services and the strong sports culture in the region. The United States alone accounts for a significant portion of the market owing to the popularity of the National Football League (NFL). Meanwhile, Europe and the Asia Pacific are emerging as significant growth regions. The increasing popularity of soccer and the rising number of internet users in countries like India and China are expected to contribute to the market's expansion in these regions.

Platform Analysis

The fantasy football market is segmented into mobile applications and websites based on the platform. Mobile applications have revolutionized the fantasy sports experience by offering users the convenience of managing their teams on the go. The advent of sophisticated mobile apps with user-friendly interfaces, real-time updates, and interactive features has significantly enhanced user engagement. The integration of advanced analytics and personalized recommendations in mobile applications has made it easier for users to make informed decisions, thereby increasing user satisfaction and retention.

On the other hand, websites continue to be a popular platform among a segment of users who prefer a more detailed and expansive interface. Websites offer a broader range of features and functionalities compared to mobile applicatio

Comprehensive YouTube channel statistics for NFL, featuring 15,700,000 subscribers and 13,703,521,591 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in US. Track 53,968 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

The "Big5 Soccer Leagues Players statistics Last5 Years" dataset offers a comprehensive collection of football player statistics spanning the last five years across the top five football leagues globally. With a rich repository of player data meticulously curated from prominent leagues including the Premier League, La Liga, Serie A, Bundesliga, and Ligue 1, this dataset serves as a valuable resource for in-depth analysis, research, and insights into the performance and trends of football players in elite competitions.

Encompassing a diverse array of player attributes, performance metrics, and match statistics, this dataset enables researchers, analysts, and football enthusiasts to delve deep into various aspects of player performance and team dynamics. From basic player information such as age, nationality, and position, to advanced metrics including goals scored, assists provided, successful passes, interceptions, and defensive contributions, this dataset offers a comprehensive overview of player performance across multiple seasons and competitions.

Key features of the dataset include:

Comprehensive Coverage: The dataset spans across multiple seasons, providing a comprehensive view of player performance trends over time. Multi-League Data: Data is sourced from the top five football leagues globally, offering insights into the performances of players across different footballing cultures and playing styles. Rich Player Attributes: In addition to basic demographic information, the dataset includes a wide range of player attributes and performance metrics, allowing for detailed analysis of player capabilities and contributions on the field. Versatile Analysis Opportunities: Whether it's evaluating player consistency, assessing team dynamics, identifying emerging talents, or uncovering tactical trends, this dataset presents versatile analysis opportunities for researchers and analysts alike. Utilizing this dataset, analysts can explore a myriad of research questions and hypotheses, ranging from individual player performance evaluations to broader team strategies and league-wide trends. From scouting potential transfer targets to optimizing team formations and tactics, the insights derived from this dataset can inform decision-making processes across various facets of football management and analysis.

In summary, the "Big5 Soccer Leagues Players statistics Last5 Years" dataset serves as a comprehensive repository of football player statistics, offering valuable insights and analysis opportunities for researchers, analysts, coaches, scouts, and football enthusiasts passionate about unraveling the intricacies of the beautiful game.

Get the latest NFL Football game predictions, power and performance rankings, offensive and defensive rankings, and other useful statistics from VersusSportsSimulator.com.

Financial overview and grant giving statistics of Nfl Player Care Foundation

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.