In 2023, the greatest share of players by ethnic group in the National Football League (NFL) were black or African American athletes, constituting just over ** percent of players within the NFL. Despite the large population of Hispanic or Latino people within the United States, there is a substantial underrepresentation within the NFL, with only *** percent of players identifying as such. National Football League The National Football League (NFL) is a professional American football league that was established in 1920 and now consists of 32 clubs divided into two conferences, the National Football Conference (NFC) and the American Football Conference (AFC). The league culminates in the Super Bowl, the NFL's annual championship game. As the league’s championship game, the Super Bowl has grown into one of the world's largest single-day sporting events, attracting high television ratings and generating billions of dollars in consumer spending. NFL revenues The NFL is one of the most profitable sports leagues in the world, generating a staggering **** billion U.S. dollars in 2022. This total revenue of all ** NFL teams has constantly increased over the past 15 years and, although this figure dropped significantly in 2020, this was largely as a result of the impact of coronavirus (COVID-19) containment measures. This significant drop in revenue demonstrates one of the primary impacts of COVID-19 on professional sports leagues. NFL franchises As a result of this profitability in non-pandemic times, the franchises of the NFL are attributed extremely high market values. The Dallas Cowboys were by far the most valuable franchise in the NFL, with a market value of **** billion US dollars in 2023. The high value of NFL franchises can be seen clearly when compared to those of the NBA, MLB, and NHL. Franchises within the NFL had an average market value of approximately *** billion U.S. dollars in 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. 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.

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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Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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...

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 popular professional sports leagues in the United States, with televised games attracting millions of viewers each week. As of April 2025, a survey determined that 26 percent of respondents in the United States considered the NFL to be their top interest.

2020 NFL Stats Web Scrape

This dataset consists of basic statistics and career statistics provided by the NFL on their official website (http://www.nfl.com) for all players, active and retired.

Summary

All of the data was web scraped using Python code, which can be found and downloaded here: https://github.com/ytrevor81/NFL-Stats-Web-Scrape

Explanation of Data

Before we go into the specifics, it's important to note in the basic statistics and career statistics CSV files that all players are assigned a 'Player_Id'. This is the same ID used by the official NFL website to identify each player. This is useful in case of, for example, importing these CSV files in a SQL database for an app.

1. The first main group of stats is the basic stats provided for each player. This data is stored in the CSV file titled Active_Player_Basic_Stats.csv and Retired_Player_Basic_Stats.csv.

The data pulled for each player in Active_Player_Basic_Stats.csv is as follows: a. Player ID b. Full Name c. Position d. Number e. Current Team f. Height g. Height h. Weight i. Experience j. Age k. College

The data pulled for each player in Retired_Player_Basic_Stats.csv differs slightly from the previous data set. The data is as follows: a. Player ID b. Full Name c. Position f. Height g. Height h. Weight j. College k. Hall of Fame Status

1. The second main group of stats gathered for each player are their career statistics. Due to the NFL having a various amount of positions that players occupy, the career statistics are divided into statistics categories. The stats for active players and retired players are structured the same, but are stored in separate CSV files (ActivePlayer_(category)_Stats.csv and RetiredPlayer_(category)_Stats.csv). The following are the career statistics categories and accompanying CSV file names: a. Defensive Stats - ..._Defense_Stats.csv b. Fumbles Stats - ..._Fumbles_Stats.csv c. Kick Returns Stats - ..._KickReturns_Stats.csv d. Field Goal Kicking Stats - ..._Kicking_Stats.csv e. Passing Stats - ..._Passing_Stats.csv f. Punt Returns Stats - ..._PuntReturns_Stats.csv g. Punting Stats - ..._Punting_Stats.csv h. Receiving Stats - ..._Receiving_Stats.csv i. Rushing Stats - ..._Rushing_Stats.csv

Of the big four professional sports leagues in North America, the NFL and the NBA have the highest percentage of African American players. In 2024, around **** percent of NFL players were African American, as well as around **** percent of assistant coaches.

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 is one of the most popular sports in the world. Many of us are stat geeks who understanding not what just happened but also who and why. This NFL dataset provides a comprehensive view of NFL games, statistics, participation, and much more. The dataset includes NFL play data from 2004 to the present.

This NFL dataset provides play-by-play data from the 2004 to 2019 seasons. Dataset also includes play and participation information for players, coaches, and game officials. Additional data tables included in this file includes NFL Draft from 1989 to present, NFL Combine 1999 to present, NFL rosters from 1998 to present, NFL schedules, stadium information and much more. The granularity of NFL statistics varies by NFL season. The current version of NFL statistics has been collected since 2012. All information sources used to create this dataset are from publically accessible websites and the NFL GSIS dataset.

All information sources used to create this dataset are from publically accessible websites and NFL documentation. Although my current life is focused on data science, this project has a special place in my heart, since it links my previous profession in the NFL with my current passion for data analysis.

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.

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

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

This dataset contains following data: - CSV of All NFL Weather From 1960 to 2013 - Every NFL Player in CSV format - Play by Play data from 2013 till 2022

Original data

Acknowlegement

Foto von Muyuan Ma auf Unsplash

Dataset

This dataset was created by Farzad Yousefi

Released under CC0: Public Domain

Contents

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.

Collection of college statistics, draft team information, and NFL career statistics for every quarterback drafted since the year 2000 until the 2024 offseason. Originally created in an attempt to train a neural network that predicts NFL success level of a quarterback at the time of being drafted.

This database was only made possible by the many NFL stat keeping websites I discovered in the data collection process:

• Draft Data: https://www.drafthistory.com/index.php/positions/qb
• College Statistics: https://www.sports-reference.com/cfb
• Physical Combine Measurements: https://www.pro-football-reference.com
• Assorted Missing Stats: https://www.walterfootball.com
• NCAA Conference Strength Metric: https://www.jhowell.net/cf/
• Assorted Missing QB Stats: https://www.ncaa.com and respective school websites
• NFL Team Records: https://www.jt-sw.com/football/
• Coach Tenure: https://www.pro-football-history.com/
• NFL Stats: https://www.stathead.com/ (paid subscription)

Database Feature Glossary

year-drafted: The year drafted into the NFL

qb-num-picked: The number taken relative to other quarterbacks (1 = first quarterback selected, 2 = second selected, etc.)

rd-picked: The round of the NFL draft the player was selected

num-picked: The overall draft position the player was drafted at

name: Name of player

height (in): Player height in inches as reported at the NFL Draft

weight (lbs): Player weight in pounds as reported at the NFL Draft

nfl-team: The NFL team that drafted the player

coach-tenure: The number of years the head coach had been employed by the team that drafted the player at the time of the draft

drafted-team-winpr: The win percentage in the most recent season of the team that drafted the player at the time of drafting

drafted_team_ppg_rk: The points per game ranking in the most recent season of the team that drafted the player at the time of drafting

college: The college the player attended at the time of drafting

conf: The conference of the college the player participated in

conf-str: The calculated strength of the conference in the final year the quarterback played (reference link above)

p-cmp: Pass completions in college career

p-att: Pass attempts in college career

cmp-pct: Pass completion percentage in college career

p-yds: Total pass yards in college career

p-ypa: Passing yards per attempt in college career

p-adj-ypa: Adjusted passing yards per attempt in college career

p-td: Passing touchdowns in college career

int: Interceptions in college career

rate: Passing efficiency rating (reference link above)

r-att: Rushing attempt count in college career

r-yds: Rushing yards in college career

r-avg: Average yards per rush in college career

r-tds: Rushing touchdowns in college career

nfl-starts: Total number of started games in the NFL

nfl-wins: Total games won in the NFL

nfl-losses: Total games lost in the NFL

nfl-ties: Total games tied in the NFL

nfl-winpr: Total win percentage as a starter in the NFL

nfl-qbr: Quarterback rating in the NFL

nfl-cmp: Total pass completions in the NFL

nfl-att: Total pass attempts in the NFL

nfl-inc: Total incompletions thrown in the NFL

nfl-comp%: Career completion percentage in the NFL

nfl-yds: Total passing yards in the NFL

nfl-tds: Total passing touchdowns in the NFL

nfl-int: Total interceptions thrown in the NFL

nfl-pick6: Number of interceptions thrown that were returned for touchdowns in the NFL

nfl-int%: Percentage of NFL throws that were interceptions

nfl-sack%: Percentage of NFL passing plays the player gave up a sack

nfl-y/a: Yards per passing attempt in the NFL

nfl-ay/a: Adjusted yards per passing attempt in the NFL

nfl-any/a: Adjusted net yards per passing attempt in the NFL

nfl-y/c: Passing yards per completion in the NFL

nfl-y/g: Passing yards per game in the NFL

nfl-succ%: Passing success rate in the NFL (reference link above)

nfl-4qc: 4th quarter comebacks completed in the NFL

nfl-gwd: Game winning drives completed in the NFL

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.

NFL passing statistics since 2001. Contains record of every player who attempted a pass within the time period. Tracked metrics include passing yards, passing touchdowns, pass attempts, completions, interceptions, and touchdown/interception/completion percentages. More advanced metrics like yards per attempt, adjusted net yards per attempt, and other similar metrics are also included. I used this dataset, accompanied with the NFL Rushing Statistics dataset to predict the NFL MVP winner in 2024.

Players rated by appearance for an analysis of the racial demographics of club rosters