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

Access comprehensive NFL data, including historic stats and results, with datasets available in various formats. Perfect for sports analysts and enthusiasts, this product offers a free trial to explore detailed National Football League data for insights and research.

Context

National Football League historic game and betting info

Content

National Football League (NFL) game results since 1966 with betting odds information since 1979. Dataset was created from a variety of sources including games and scores from a variety of public websites such as ESPN, NFL.com, and Pro Football Reference. Weather information is from NOAA data with NFLweather.com a good cross reference. Betting data was used from http://www.repole.com/sun4cast/data.html for 1978-2013 seasons. Pro-football-reference.com data was then cross referenced for betting lines and odds as well as weather data. From 2013 on betting data reflects lines available at sportsline.com.

Acknowledgements

Helpful sites with interest in football and sports betting include:

https://github.com/fivethirtyeight/nfl-elo-game

http://www.repole.com/sun4cast/data.html

https://www.pro-football-reference.com/

http://www.espn.com/nfl/

http://www.nflweather.com/

http://www.noaa.gov/weather

https://www.sportsline.com/

https://github.com/jp-wright/nfl_betting_market_analysis

http://www.aussportsbetting.com/data/historical-nfl-results-and-odds-data/

Inspiration

Can you build a predictive model to better predict NFL game outcomes and identify successful betting strategies?

Analysis of ‘NFL scores and betting data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tobycrabtree/nfl-scores-and-betting-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

National Football League historic game and betting info

Content

National Football League (NFL) game results since 1966 with betting odds information since 1979. Dataset was created from a variety of sources including games and scores from a variety of public websites such as ESPN, NFL.com, and Pro Football Reference. Weather information is from NOAA data with NFLweather.com a good cross reference. Betting data was used from http://www.repole.com/sun4cast/data.html for 1978-2013 seasons. Pro-football-reference.com data was then cross referenced for betting lines and odds as well as weather data. From 2013 on betting data reflects lines available at sportsline.com.

Acknowledgements

Helpful sites with interest in football and sports betting include:

https://github.com/fivethirtyeight/nfl-elo-game

http://www.repole.com/sun4cast/data.html

https://www.pro-football-reference.com/

http://www.espn.com/nfl/

http://www.nflweather.com/

http://www.noaa.gov/weather

https://www.sportsline.com/

https://github.com/jp-wright/nfl_betting_market_analysis

http://www.aussportsbetting.com/data/historical-nfl-results-and-odds-data/

Inspiration

Can you build a predictive model to better predict NFL game outcomes and identify successful betting strategies?

--- Original source retains full ownership of the source dataset ---

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.

Total attendance at National Football League (NFL) games reached about ***** million fans across the regular season in 2024. This represented a slight decrease over the previous year's figure of approximately **** million spectators. Attendance at NFL games Over the last few years the total attendance at regular season games of the National Football League has consistently been at more than ** million per season. The NFL is composed of 32 teams and each team plays a minimum of 8 home games during the regular season for a total of 256 games per season. The average attendance at National Football League games was at around ****** in the 2023 season. Dallas Cowboys drew record crowds In 2023, the Dallas Cowboys drew the most spectators to their home games with a total attendance of more than *** thousand. The Cowboys also had the highest average attendance that season with around ****** people attending each home game. The average price for a ticket to an NFL game was at ****** U.S. dollars in 2023. On average, tickets to Las Vegas Raiders games were the most expensive (****** U.S. dollars), while tickets for Arizona Cardinals games were the least expensive, with an average price of ***** U.S. dollars.

The difference represents . Significance codes: ***: p < .001, **: p < .01, *: p < .05. The home team advantage is also presented.

Dataset is updated through the 2024-25 season.

All data is scraped from ESPN's Team Stats page for each game. Seasons include all regular season games plus all playoff games.

Any errors or quirks in ESPN's data will be present in this dataset. For example, redzone conversions are missing prior to the 2006-07 season.

NFL Rushing stats between 2000 and 2016. For regular season only. Extracted from FoxSports by Christopher Hayles for Fantasy Football purposes.

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...

The American Football market has experienced a dynamic evolution, carving out a significant niche in both the sporting world and the broader entertainment landscape. As of recent evaluations, the market size stands robust, reflecting the profound popularity of the sport across demographics, particularly in the Unite

The American Football Club market represents a dynamic and ever-evolving segment within the larger sports industry, encompassing a vast array of professional and amateur leagues, clubs, and associations. With a current market size valued at several billion dollars, this sector has witnessed significant growth over t

The dataset contains weekly 2022 NFL team offense stats from the current NFL season (week 9).

The data was collected from Sports Reference then cleaned for data analysis.

Tabular data includes: - rank - team - games - points_scored - total_yards - offensive_plays - yards_per_play - turnovers_lost - fumbles_lost - 1st_downs - passes_completed - passes_attempted - passing_yards - passing_touchdowns - passing_interceptions - net_yards_per_pass_attempt - passing_1st_downs - rushing_attempts - rushing_yards - rushing_touchdowns - rushing_yards_per_attempt - rushing_1st_downs - penalties: Hit batter with pitch - penalty_yards - 1st_down_penalites - percentage_scoring_drives - percentage_turnover_drives - expected_points

American College Football network of Girvan and Newman Mark Newman provides a football.gml file which contains the network of American football games between Division IA colleges during regular season Fall 2000. The file asks you to cite M. Girvan and M. E. J. Newman, Community structure in social and biological networks, Proc. Natl. Acad. Sci. USA 99, 7821-7826 (2002). There are are two issues with the original GN file. First three teams met twice in one season so the graph is not simple. This is easily dealt with if required. Secondly, the assignments to conferences, the node values, seem to be for the 2001 season and not the 2000 season. The games do appear to be for the 2000 season as stated. For instance the Big West conference existed for football till 2000 while the Sun Belt conference was only started in 2001. Also there were 11 conferences and 5 independents in 2001 but 10 conferences and 8 independents in 2000. I have provided a set of files footballTSE* which define a simple graph with the correct conference assignments in the archive here. There is a read me file included with more details. Further information about the problems with this data and the solutions are given in T.S. Evans, “Clique Graphs and Overlapping Communities”, J. Stat. Mech. (2010) P12037 [arXiv:1009.0638] which would be the appropriate source to cite along with the original GN publication.Note that Gschwind et al, 2015, Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques, independently finds similar errors in this data.

Significance codes: ***: p < .001, **: p < .01, *: p < .05.

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.

Comprehensive Cleveland Browns NFL team statistics including rankings, performance metrics, schedule, betting data, and advanced analytics.

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.

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.