NFL Football Stats

My family has always been serious about fantasy football. I've managed my own team since elementary school. It's a fun reason to talk with each other on a weekly basis for almost half the year.

Ever since I was in 8th grade I've dreamed of building an AI that could draft players and choose lineups for me. I started off in Excel and have since worked my way up to more sophisticated machine learning. The one thing that I've been lacking is really good data, which is why I decided to scrape pro-football-reference.com for all recorded NFL player data.

From what I've been able to determine researching, this is the most complete public source of NFL player stats available online. I scraped every NFL player in their database going back to the 1940s. That's over 25,000 players who have played over 1,000,000 football games.

The scraper code can be found here. Feel free to user, alter, or contribute to the repository.

The data was scraped 12/1/17-12/4/17

Shameless plug

When I uploaded this dataset back in 2017, I had two people reach out to me who shared my passion for fantasy football and data science. We quickly decided to band together to create machine-learning-generated fantasy football predictions. Our website is https://gridironai.com. Over the last several years, we've worked to add dozens of data sources to our data stream that's collected weekly. Feel free to use this scraper for basic stats, but if you'd like a more complete dataset that's updated every week, check out our site.

The data is broken into two parts. There is a players table where each player has been assigned an ID and a game stats table that has one entry per game played. These tables can be linked together using the player ID.

Player Profile Fields

• Player ID: The assigned ID for the player.
• Name: The player's full name.
• Position: The position the player played abbreviated to two characters. If the player played more than one position, the position field will be a comma-separated list of positions (i.e. "hb,qb").
• Height: The height of the player in feet and inches. The data format is

Our NFL Data product offers extensive access to historic and current National Football League statistics and results, available in multiple formats. Whether you're a sports analyst, data scientist, fantasy football enthusiast, or a developer building sports-related apps, this dataset provides everything you need to dive deep into NFL performance insights.

Key Benefits:

Comprehensive Coverage: Includes historic and real-time data on NFL stats, game results, team performance, player metrics, and more.

Multiple Formats: Datasets are available in various formats (CSV, JSON, XML) for easy integration into your tools and applications.

User-Friendly Access: Whether you are an advanced analyst or a beginner, you can easily access and manipulate data to suit your needs.

Free Trial: Explore the full range of data with our free trial before committing, ensuring the product meets your expectations.

Customizable: Filter and download only the data you need, tailored to specific seasons, teams, or players.

API Access: Developers can integrate real-time NFL data into their apps with API support, allowing seamless updates and user engagement.

Use Cases:

Fantasy Football Players: Use the data to analyze player performance, helping to draft winning teams and make better game-day decisions.

Sports Analysts: Dive deep into historical and current NFL stats for research, articles, and game predictions.

Developers: Build custom sports apps and dashboards by integrating NFL data directly through API access.

Betting & Prediction Models: Use data to create accurate predictions for NFL games, helping sportsbooks and bettors alike.

Media Outlets: Enhance game previews, post-game analysis, and highlight reels with accurate, detailed NFL stats.

Our NFL Data product ensures you have the most reliable, up-to-date information to drive your projects, whether it's enhancing user experiences, creating predictive models, or simply enjoying in-depth football analysis.

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.

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.

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.

NFL Injury Analysis 2012-2017

NFL Injuries 2012-2017: Yearly, injury type, scenario, and season type data

By Throwback Thursday [source]

About this dataset

This dataset provides comprehensive information on injuries that occurred in the National Football League (NFL) during the period from 2012 to 2017. The dataset includes details such as the type of injury sustained by players, the specific situation or event that led to the injury, and the type of season (regular season or playoffs) during which each injury occurred.

The Injury Type column categorizes the various types of injuries suffered by players, providing insights into specific anatomical areas or specific conditions. For example, it may include injuries like concussions, ankle sprains, knee ligament tears, shoulder dislocations, and many others.

The Scenario column offers further granularity by describing the specific situation or event that caused each injury. It can provide context about whether an injury happened during a tackle, collision with another player or object on field (such as goalposts), blocking maneuvers gone wrong, falls to the ground resulting from being off-balance while making plays, and other possible scenarios leading to player harm.

The Season Type column classifies when exactly each injury occurred within a particular year. It differentiates between regular season games and playoff matches – identifying whether an incident took place during high-stakes postseason competition or routine games throughout the regular season.

The Injuries column represents numeric data detailing how many times a particular combination of year-injury type-scenario-season type has occurred within this dataset's timeframe – measuring both occurrence frequency and severity for each unique combination.

Overall, this extensive dataset provides valuable insight into NFL injuries over a six-year span. By understanding which types of injuries are most prevalent under certain scenarios and during different seasons of play - such as regular seasons versus playoffs - stakeholders within professional football can identify potential areas for improvement in safety measures and develop strategies aimed at reducing player harm on-field

How to use the dataset

The dataset contains six columns:

• Year: This column represents the year in which the injury occurred. It allows you to filter and analyze data based on specific years.

• Injury Type: This column indicates the specific type of injury sustained by players. It includes various categories such as concussions, fractures, sprains, strains, etc.

• Scenario: The scenario column describes the situation or event that led to each injury. It provides context for understanding how injuries occur during football games.

• Season Type: This column categorizes injuries based on whether they occurred during regular season games or playoff games.

• Injuries: The number of injuries recorded for each specific combination of year, injury type, scenario, and season type is mentioned in this column's numeric values.

Using this dataset effectively involves several steps:

• Data Exploration: Start by examining all available columns carefully and making note of their meanings and data types (categorical or numeric).

• Filtering Data by Year or Season Type: If you are interested in analyzing injuries during a particular year(s) or specific seasons (regular vs playoffs), apply filters accordingly using either one or both these columns respectively.

3a. Analyzing Injury Types: To gain insights into different types of reported injuries over time periods specified by your filters (e.g., a given year), group data based on Injury Type and calculate aggregate statistics like maximum occurrences or average frequency across years/seaso

3b.Scenario-based Analysis:/frequency across years/seasons. Group the data based on Scenario and calculate aggregate values to determine which situations or events lead to more injuries.

• Exploring Injury Trends: Explore the overall trend of injuries throughout the 2012-2017 period to identify any significant patterns, spikes, or declines in injury occurrence.

• Visualizing Data: Utilize appropriate visualization techniques such as bar graphs, line charts, or pie charts to present your findings effectively. These visualizations will help you communicate your analysis concisely and provide clear insights into both common injuries and specific scenarios.

• Drawing Conclusions: Based on your analysis of the

Research Ideas

• Understanding trends in NFL injuries: This dataset can be used to analyze the number and types of in...

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.

Sports Analytics Market size was valued at USD 3.84 Billion in 2023 and is poised to grow from USD 4.75 Billion in 2024 to USD 5.88 Billion by 2032, growing at a CAGR of 23.8% during the forecast period (2025-2032).

Over the past few years, the US has seen a significant shift in the political sentiment surrounding sports betting and gambling, following the Supreme Court overruling a law which forbade states from...

In 2023, the 32 teams of the National Football League (NFL) generated a total revenue of approximately **** billion U.S. dollars. This shows an increase of nearly *** billion U.S. dollars over the previous year. How does the NFL compare to other major sports leagues? The NFL is the most profitable professional sports league in the United States. Between 2001 and 2019, the total revenue of all 32 NFL teams steadily increased, reaching ** billion U.S. dollars in 2019. This figure dropped to approximately **** billion U.S. dollars in 2020, due to the impact of the coronavirus (COVID-19) pandemic, however, shot back up in 2021 to exceed pre-pandemic levels. In comparison, the revenue generated by Major League Baseball (MLB) teams amounted to around ***** billion U.S. dollars in 2023, while the revenue of the National Basketball Association (NBA) was ***** billion U.S. dollars in the 2022/23 season. NFL revenue streams In the list of most watched TV programs in the U.S., football games dominate the top spots. Duly, the NFL has a diverse array of lucrative revenue streams, such as sponsorships, media partnerships (both broadcasting and digital), ticket sales and concessions. As of February 2024, media deals between the NFL and networks in the United States alone accounted for over ** billion U.S. dollars annually. Meanwhile, NFL league and team sponsorships provided nearly *** billion U.S. dollars in revenue in 2022. Which team generates the most income? In 2023, the five-time Super Bowl champion Dallas Cowboys topped the rankings of NFL teams with the highest revenues. That year, the Cowboys were the only team to generate more than *** billion U.S. dollars in revenue. The Las Vegas Raiders ranked second with approximately *** million U.S. dollars in revenue. Meanwhile, the team with the most Super Bowl titles of the last 20 years - the New England Patriots - sat in fourth place with *** million U.S. dollars in revenue.

As one of the most popular sports leagues in the world, the NFL attracts huge television audiences and thousands of fans flock to the stadiums every week to see their favorite teams in action. However, fans of the Las Vegas Raiders, a franchise which moved from Oakland ahead of the 2020 season, had to fork out a league-high average of over 168 U.S. dollars to see their team play live at the RingCentral Coliseum. In contrast, the Arizona Cardinals charged a comparatively low 98.54 U.S. dollars for an average home game.

In 2023, the Dallas Cowboys were the only NFL franchise to report revenue of over one billion U.S. dollars. In that year, America's Team generated revenue of *** billion U.S. dollars. Meanwhile, the Detroit Lions generated less than **** that amount. The Dallas Cowboys As well as being the NFL franchise with the highest revenue, the Dallas Cowboys was also the most valuable NFL franchise. As of August 2024, the franchise was valued at **** billion U.S. dollars. This success off the pitch, however, has not translated to on-field success in recent years. Despite winning an impressive * Super Bowl titles, the last of these was back in 1995. While the Cowboys made it to the playoffs in the 2022 season, they lost out to the San Francisco 49ers in the divisional round. NFL revenue streams Sponsorships, media, partnerships, ticket and concession sales are some of the most important revenue streams for the NFL. In 2023, the revenue of all 32 NFL teams totaled ***** billion U.S. dollars, the highest figure to-date. Meanwhile, NFL league and team sponsorship generated **** billion U.S. dollars that same year. Some of the main sponsors for the league include Verizon, Pepsi, and Nike.