Football Player Passing Statistics (UEFA 2022–2023)

Dataset link: football-player-stats-2022-2023

  Video Presentation

Link: https://youtu.be/QyE0cujCIVg

  Overview

This dataset contains detailed statistics of professional football players across major European leagues for the 2022–2023 season.The data was cleaned and prepared for Exploratory Data Analysis (EDA) focusing on passing performance and positional differences.

  Research Focus

The main goal of… See the full description on the dataset page: https://huggingface.co/datasets/talcabalo/football-player-stats-2022-2023.

Introduction

The lack of publicly available National Football League (NFL) data sources has been a major obstacle in the creation of modern, reproducible research in football analytics. While clean play-by-play data is available via open-source software packages in other sports (e.g. nhlscrapr for hockey; PitchF/x data in baseball; the Basketball Reference for basketball), the equivalent datasets are not freely available for researchers interested in the statistical analysis of the NFL. To solve this issue, a group of Carnegie Mellon University statistical researchers including Maksim Horowitz, Ron Yurko, and Sam Ventura, built and released nflscrapR an R package which uses an API maintained by the NFL to scrape, clean, parse, and output clean datasets at the individual play, player, game, and season levels. Using the data outputted by the package, the trio went on to develop reproducible methods for building expected point and win probability models for the NFL. The outputs of these models are included in this dataset and can be accessed using the nflscrapR package.

Content

The dataset made available on Kaggle contains all the regular season plays from the 2009-2016 NFL seasons. The dataset has 356,768 rows and 100 columns. Each play is broken down into great detail containing information on: game situation, players involved, results, and advanced metrics such as expected point and win probability values. Detailed information about the dataset can be found at the following web page, along with more NFL data: https://github.com/ryurko/nflscrapR-data.

Acknowledgements

This dataset was compiled by Ron Yurko, Sam Ventura, and myself. Special shout-out to Ron for improving our current expected points and win probability models and compiling this dataset. All three of us are proud founders of the Carnegie Mellon Sports Analytics Club.

Inspiration

This dataset is meant to both grow and bring together the community of sports analytics by providing clean and easily accessible NFL data that has never been availabe on this scale for free.

American Football Player Detection

## Overview

American Football Player Detection is a dataset for object detection tasks - it contains American Football Players annotations for 171 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Card for football-player-segmentation

This dataset is specifically designed for computer vision tasks related to player detection and segmentation in foot goalkeeperders, and forwards, captured from various angles and distances.

This is a FiftyOne dataset with 512 samples.

  Installation

If you haven't already, install FiftyOne: pip install -U fiftyone

  Usage

import fiftyone as fo import fiftyone.utils.huggingface as fouh

Load the dataset

… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Football-Player-Segmentation.

Dataset Labels

['football', 'player']

  Number of Images

{'valid': 87, 'train': 119}

  How to Use

Install datasets:

pip install datasets

Load the dataset:

from datasets import load_dataset

ds = load_dataset("manot/football-players", name="full") example = ds['train'][0]

  Roboflow Dataset Page

https://universe.roboflow.com/konstantin-sargsyan-wucpb/football-players-2l81z/dataset/1

  Citation

@misc{… See the full description on the dataset page: https://huggingface.co/datasets/manot/football-players.

Comprehensive dataset of college football teams ranked by all-americans. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

Football Manager Players Dataset

Overview

Dive into the ultimate treasure trove for football enthusiasts, data analysts, and gaming aficionados! The Football Manager Players Dataset is a comprehensive collection of player data extracted from a popular football management simulation game, offering an unparalleled look into the virtual world of football talent. This dataset includes detailed attributes for thousands of players across multiple leagues worldwide, making it a goldmine for analyzing player profiles, scouting virtual stars, and building predictive models for football strategies.

Whether you're a data scientist exploring sports analytics, a football fan curious about your favorite virtual players, or a game developer seeking inspiration, this dataset is your ticket to unlocking endless possibilities!

Dataset Description

This dataset is a meticulously curated compilation of player statistics from five CSV files, merged into a single, unified dataset (merged_players.csv). It captures a diverse range of attributes for players from various clubs, nations, and leagues, including top-tier competitions like the English Premier Division, Argentina's Premier Division, and lower divisions across the globe.

Key Features

• Rich Player Attributes: Over 70 columns covering essential metrics such as:
  • Basic Info: UID, Name, Date of Birth (DOB), Nationality, Height, Weight, Age
  • Club & Position: Club, Position (e.g., AM, DM, GK), Based (league/division)
  • Performance Stats: Caps, Appearances (AT Apps), Goals (AT Gls), League Appearances, League Goals
  • Technical Skills: Acceleration, Passing, Dribbling, Finishing, Tackling, and more
  • Mental Attributes: Work Rate, Vision, Leadership, Determination
  • Physical Attributes: Pace, Strength, Stamina, Agility
  • Market Value: Transfer Value (e.g., $0 to millions)
  • Miscellaneous: Preferred Foot, Media Handling, Injury Proneness
• Global Coverage: Players from diverse regions, including Europe (England, Spain, Italy), South America (Argentina, Brazil), Asia (South Korea, China), Africa (Ivory Coast, Burkina Faso), and North America (USA, Mexico).
• Varied Player Types: From young prospects (15–18 years old) to veteran stars (up to 45 years old), including amateurs, youth players, and professionals.
• Realistic Insights: Includes attributes like Media Description (e.g., "Young winger," "Veteran striker") and injury status, mirroring real-world football dynamics.

Dataset Size

• Rows: Thousands of player records (exact count depends on deduplication).
• Columns: 70+ attributes per player.
• File: merged_players.csv (UTF-8 encoded for compatibility with special characters).

Potential Use Cases

• Sports Analytics:
  • Analyze player attributes to identify key traits for success by position (e.g., what makes a top goalkeeper?).
  • Predict transfer values based on skills, age, and performance stats.
  • Cluster players by playing style or potential using machine learning.
• Scouting & Strategy:
  • Build a dream team by filtering players based on specific attributes (e.g., high Pace and Dribbling for wingers).
  • Compare young talents vs. experienced veterans for team-building strategies.
• Gaming & Modding:
  • Create custom Football Manager databases or mods.
  • Analyze game balance by studying attribute distributions.
• Visualization:
  • Develop interactive dashboards to explore player stats by league, nationality, or position.
  • Map player origins to visualize global football talent distribution.
• Education & Research:
  • Use as a teaching tool for data science, exploring data cleaning, merging, and analysis.
  • Study correlations between mental/physical attributes and in-game performance.

Why This Dataset Stands Out

• Comprehensive: Covers every aspect of a player's profile, from technical skills to personality traits.
• Diverse: Includes players from top-tier to lower divisions, offering a broad spectrum of talent.
• Engaging: Perfect for football fans and data enthusiasts alike, blending gaming with real-world analytics.
• Ready-to-Use: Merged and cleaned for immediate analysis, with consistent column structure across all records.

Getting Started

1. Download: Grab merged_players.csv and load it into your favorite tool (Python/pandas, R, Excel, etc.).
2. Explore: Check out columns like Transfer Value, Position, and Media Description to start your analysis.
3. Analyze: Use Python (e.g., pandas, scikit-learn) or visualization tools (e.g., Tableau, Power BI) to uncover insights.
4. Share: Build models, visualizations, or scouting reports and share your findings with the Kaggle community!

Example Questions to Explore

• Which young players (<18 years) have the highest poten...

Football Transfer Records Dataset

This dataset contains detailed information on the transfer prices of football players. The dataset includes data on player names, positions, ages, market values, nationalities, the clubs they transferred from and to, the seasons of transfer, and the transfer fees involved.

  Dataset Details

Total Records: 250 Fields Included: Player Position Age Market Value Nationality Left Club Joined Club Season Transfer Fee

  Usage

This… See the full description on the dataset page: https://huggingface.co/datasets/swisscondor/football-transfers.

Roboflow Dataset Page

https://universe.roboflow.com/augmented-startups/football-player-detection-kucab

  Citation

@misc{ football-player-detection-kucab_dataset, title = { Football-Player-Detection Dataset }, type = { Open Source Dataset }, author = { Augmented Startups }, howpublished = { \url{ https://universe.roboflow.com/augmented-startups/football-player-detection-kucab } }, url = {… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/football-object-detection.

ProScout Players — Synthetic Football Scouting Dataset (1,112 rows)

A clean, fully synthetic dataset of realistic football player profiles built for text → top‑3 player recommendations (ProScout). It’s classroom‑safe (no PII, no scraping), diverse across positions/nations, and structured for retrieval demos.

Core use case: a scout types a brief (e.g., “22–25 left‑footed inverted winger with pace+dribbling in European competition”), the system ranks candidates, and returns the top 3… See the full description on the dataset page: https://huggingface.co/datasets/Ilayr222/proscout-players-1112.

Major US Sports Venues Usage and Affiliations

Team, League, Conference and Population Usage Records

By Homeland Infrastructure Foundation [source]

About this dataset

This dataset provides detailed information on major sport venues, along with their usage and affiliations. It includes data related to the National Association for Stock Car Auto Racing, Indy Racing League, Major League Soccer, Major League Baseball, National Basketball Association, Women's National Basketball Association, National Hockey League, National Football League, PGA Tour, NCAA Division 1 FBS Football, NCAA Division 1 Basketball and thoroughbred horse racing.* This dataset contains columns such as USE (which describes the type of use for the venue), TEAM (the team associated with the venue), LEAGUE (the league associated with the venue) , CONFERENCE (the conference associated with the venue), DIVISION (the division associated with the venue), INST_AFFIL(the institution affiliation associatedwith the venue), TRACK_TYPE(type of track at a specific point in time or over its complete life-cycle) as well as LENGTH_MILEGE ('length of track in milege') ROOF_TYPE(The type of roof covering used at a specific point in time or over its complete life-cycle) and plenty other variables. With this astounding range and quantity of data points -- spanning countries across different continents and leagues -- explore patterns in sports games you never even thought were possible!

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Featured Notebooks

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How to use the dataset

The MajorUS Sports Venues Usage and Affiliations dataset includes data on major sports venues from leagues including National Association for Stock Car Auto Racing (NASCAR), Indy Racing League (IRL), Major League Soccer (MLS), Major League Baseball (MLB), National Basketball Association (NBA), Women's National Basketball Association (WNBA), National Hockey League (NHL), National Football League(NFL), PGA Tour, NCAA Division 1 FBS Football, NCAA Division 1 Basketball, and thoroughbred horse racing. The columns provided include USE_, USE_POP, TEAM, LEAGUE,CONFERENCE,DIVISION ,INST_AFFIL,TRACK_TYPE. LENGTH_MI,ROOF_TYPESTADIUM_SH,`ADDDATAE , USEWEBSITE',and'COMMENTS'.

The `USE~ column specifies the type of usage of each venue at which point can be college athletics or professional athletics. The corresponding column to this is the ‘USE~POP’ which informs you about how many people are using each venue for a particular sport at a given time. For example if there were 6 NHL games being played that day then USE~ would say “professional Athletics” while USE~POP would state “NNN” reflecting there were NNN people spectating those events collectively: The next column is TEAM which represents what team sponsors or manages each venue or what teams will be playing in them.

 Following on from TEAM is LEAGUE; here you can find out what league each team represents such as MLB, NBA etc… The next three columns CONFERENCE/DIVISION/INST ~ AFFIL provide more specific details as they blur into collegiate level as well where CONFERENCE indicates which conference they belong within their respective division: while INST ~ AFFIL states its affiliated school body e.g.: Southeastern Conference > University of Arkansas Razorbacks . Rounding up our overview these last three columns TRACK ~ TYPE/LENGTH

Research Ideas

• Analyzing the affiliations and usage of different sports venues to determine which teams or leagues have the most presence across a certain geographic area.
• Comparing different stadiums within a given conference in terms of their roof type, track length, and stadium shape for optimal design features for new construction projects.
• Placing sponsorships or advertisements within each sporting arena based on audience size, league popularity, and team affiliation within a given conference or division

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 commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contribut...

Sam Football Training Dataset

Training dataset for Sam, an AI football commentator designed for young fans (11-12 years old).

  Dataset Description

Sam provides live football commentary that is:

Educational: Teaches football rules, tactics, and player facts Respectful: Celebrates great play from any team Age-appropriate: Uses language and concepts suitable for 11-12 year olds Enthusiastic: Captures the excitement of live football

  Statistics

Total samples: 2… See the full description on the dataset page: https://huggingface.co/datasets/IlkkaU/sam-football-training.

Data on athletes' professional career lengths in the sports of baseball, basketball, and American football. The data was compiled from baseball-reference.com, pro-football-reference.com, and basketball-reference.com. The data is split into three different files, one for each sport, identified by the title: baseball_career_length.csv, basketball_career_length.csv, football_career_length.csv.

Dataset Features available in all files: - name: The name of the athlete. - start_year: The year that the athletes professional career started. - end_year: The last year of the athletes professional career. - hall_of_fame: True for athletes who have been admitted to the hall of fame, False otherwise. - status: True if the athlete has finished their career, False otherwise. - career_length: The total number of years the athlete was actively playing professionally. - sport: The sport of the athlete.

Additional Dataset Features available for football_career_length.csv: - position: The position that the athlete played in their sport. If they played multiple positions they are separated by a '-'.

Additional Dataset Features available for basketball_career_length.csv: - position: The position that the athlete played in their sport. If they played multiple positions they are separated by a '-'. - height: The height of the athlete in inches. - weight: The weight of the athlete in pounds. - birth_date: The date of the athlete's birth.

Previous research has sought to quantify head impact exposure using wearable kinematic sensors. However, many sensors suffer from poor accuracy in estimating impact kinematics and count, motivating the need for additional independent impact exposure quantification for comparison. Here, we equipped seven collegiate American football players with instrumented mouthguards, and video recorded practices and games to compare video-based and sensor-based exposure rates and impact location distributions. Over 50 player-hours, we identified 271 helmet contact periods in video, while the instrumented mouthguard sensor recorded 2,032 discrete head impacts. Matching video and mouthguard real-time stamps yielded 193 video-identified helmet contact periods and 217 sensor-recorded impacts. To compare impact locations, we binned matched impacts into frontal, rear, side, oblique, and top locations based on video observations and sensor kinematics. While both video-based and sensor-based methods found similar location distributions, our best method utilizing integrated linear and angular position only correctly predicted 81 of 217 impacts. Finally, based on the activity timeline from video assessment, we also developed a new exposure metric unique to American football quantifying number of cross-verified sensor impacts per player-play. We found significantly higher exposure during games (0.35, 95% CI: 0.29–0.42) than practices (0.20, 95% CI: 0.17–0.23) (p

NFL Wide Receiver Career Stats and Aging Trends

Examining Target, Yards-Per-Attempt, and Overall Rating Data

By FiveThirtyEight [source]

About this dataset

This repository contains a comprehensive database on the careers of NFL wide receivers, examining their performance over time to offer insights into physical changes and playing style over the years. With data stretching back all the way to 1990, it reveals key changes in stats and ratings -- including age_from/age_to, trypg_change, career_try/career_ranypa/career_wowy, and bcs_rating -- that provide essential information for football fans looking to understand the history and evolution of this position in American football. This dataset is made available under Creative Commons Attribution 4.0 International License as well as MIT License with hopes of facilitating more public understanding and transparency on this subject. We invite anyone who finds it useful to share their stories by contacting us at andrei.scheinkmanfivethirtyeight.com

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

In order to get started using this dataset: - Read through the columns of data to better understand what is being measured and how it relates to an individual player's performance. - Explore the data by filtering it in different ways (such as looking at only high rated players or seeing how older players fared compared with younger ones). - See if there are any patterns in how certain traits (such as age) affect a player's performance over time by creating graphs or other visualizations that explore these relationships over time.
- Use these findings to draw your own conclusions about trends in NFL wide receiver aging curves or team strategies related to scouting opportunities for certain players throughout different stages of their career development journey from rookies all the way through veterans who are retiring from playing football professionally on any given year during an off-season year . . . or even beyond!

Research Ideas

• Analyzing the performance of NFL wide receivers over time by comparing their age-from and age-to stats.
• Comparing the AV rating of NFL wide receivers to their total career receiving yards per attempt.
• Comparing the career wowy stats of NFL wide receivers to their total career targets in order to assess efficiency levels across different players

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: try-per-game-aging-curve.csv | Column name | Description | |:-----------------|:------------------------------------------------------------------------------------------------------------| | age_from | Age when the career started. (Integer) | | age_to | Age when the career ended. (Integer) | | trypg_change | Change in the wide receiver's total receiving yards per game from the start to end of their career. (Float) |

File: advanced-historical.csv | Column name | Description | |:------------------|:-----------------------------------------------------------| | player_name | Name of the NFL wide receiver. (String) | | career_try | Total number of career targets. (Integer) | | career_ranypa | Average number of receiving yards per attempt. (Float) | | career_wowy | Average number of yards per target. (Float) | | bcs_rating | Player's overall rating according to BCS system. (Integer) |

Acknowledgements

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

Context

In our Data Science lesson, we tried to predict the value of some soccer players, using their performance and their last market value. As we have not found a dataset on Kaggle that was convenient to us, we have tried to create our own dataset merging two ones finding on this platform. The 2 datasets are : ''Soccer players values and their statistics'' and ''Top Football Leagues Scorers''.

Content

The data are only from the season 2019-2020. We have 88 players remaining. Our work is not finish and can be significantly improved, particularly by increasing the number of player.

Acknowledgements

Thanks to Mohamed Hany and RSKriegs for their datasets.

Weekly statistics showing how many U.S. Soccer coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

[en-us] This dataset gathers detailed information on the performance of football players and their market values, collected from two widely recognized sources in the sports world: Sofascore and Transfermarkt. The dataset includes a combination of on-field performance metrics and financial data related to the players' market valuation.

The performance data is derived from Sofascore, which provides detailed statistics on player performances in various competitions, including goals, assists, completed passes, tackles, and other performance indicators. Meanwhile, the players' market value information is sourced from Transfermarkt, a leading platform that tracks market fluctuations, the highest market values reached by players, and contract expiration dates.

This dataset is ideal for analyses involving the relationship between sports performance and market value, allowing insights into how on-field performance can impact players’ market value. It is useful for sports analysts, researchers, and enthusiasts looking to explore trends in football, observe the valuation of players over time, and make comparisons between leagues and competitions.

market_value: History of the player's contract values. partidas_sofascore: Game dates, championships, and match IDs. performance_tm: Some player statistics collected from the Transfermarkt website. players_tm: Information related to the club, URL, and player ID on Transfermarkt. statistics_game: Game statistics, with total values, first and second halves. statistics_player: Individual player statistics.

The championships collected are: - Campeonato Brasileiro Série A and B - Copa do Brasil - Copa Sudamericana - Copa Libertadores

The data coming until 2024-10-12.

At this initial stage, data has been extracted from championships related to Brazil and South America. More data on other European and South American championships will be added soon.

[pt-br] Este dataset reúne informações detalhadas sobre o desempenho de jogadores de futebol e seus valores de mercado, coletados de duas fontes amplamente reconhecidas no mundo esportivo: Sofascore e Transfermarkt. O conjunto de dados inclui uma combinação de métricas de desempenho em campo e dados financeiros relacionados à avaliação de mercado dos jogadores.

Os dados de desempenho são derivados do Sofascore, que fornece estatísticas detalhadas sobre as atuações dos jogadores em diversas competições, incluindo gols, assistências, passes completos, desarmes, entre outros indicadores de performance. Já as informações sobre o valor de mercado dos jogadores são extraídas do Transfermarkt, uma plataforma líder que acompanha as flutuações de mercado, maiores valores atingidos pelos jogadores e as datas de expiração de seus contratos.

Este dataset é ideal para análises que envolvem a relação entre o desempenho esportivo e o valor de mercado, permitindo insights sobre como a performance em campo pode impactar o valor de mercado dos jogadores. É útil para profissionais de análise esportiva, pesquisadores e entusiastas que buscam explorar tendências no futebol, observar a valorização de jogadores ao longo do tempo e realizar comparações entre ligas e competições.

market_value: Histórico dos valores do contrato do jogador.; partidas_sofascore: Referente a Data dos jogos, campeonatos e ID's das Partidas; peformance_tm : Algumas estatísticas coletadas do jogador no site do Transfermakt; players_tm: informações referentes ao Clube, URL e ID do jogador no Transfermakt.; statistics_game: Estatísticas do jogo, com valores totais, primeiro e segundo tempo; statistics_player : Estatisticas individuais dos jogadores.

Os campeonatos que forma coletados são: - Campeonato Brasileiro Série A e B; - Copa do Brasil; - Campeonato Sulamericana; - Taça Libertadores da América.

Os dados vão até o dia 12/10/2024.

Nesse primeiro momento foram extraídos dados dos campeonatos referente ao Brasil e a América do Sul. Em breve será adicionado mais dados referente a outros campeonatos europeus e sulamericanos.

Title: 70 Football Leagues Data (2019-2023)

Dataset Description: This dataset provides comprehensive data on 70 football leagues from various countries around the world. The dataset covers the period from 2019 to 2023, offering a rich collection of football-related information for data analysis, research, and visualization purposes.

Content: The dataset contains a wealth of football-related data, including match statistics, team information, player details, and league standings. The dataset covers a diverse range of leagues, encompassing top-tier competitions as well as lower divisions, allowing users to explore football data at various levels.

Key Features:

Match Results Home Goals Away Goals Home Goals in First Half Away Goals in First Half Match Odds for 1X2 and O/U 2.5 Goals Total Goals in the Match

Potential Use Cases: - Statistical Analysis: Analyze match data, team performance, and player statistics to identify trends, patterns, and insights. - Predictive Modeling: Utilize historical data to build predictive models for match outcomes, goal predictions, or player performance. - Visualizations: Create visualizations, graphs, and charts to present key football data in an easily understandable format.

Data Source: The data for this dataset is collected from reliable sources, including official football websites, sports news portals, and reputable football data providers. The dataset is carefully curated and quality-checked to ensure accuracy and reliability.

Updates and Maintenance: The dataset will be periodically updated to include new seasons, leagues, and any necessary data corrections. User feedback and contributions are welcome to improve the dataset and keep it up-to-date.

Disclaimer: While utmost care has been taken to ensure the accuracy and reliability of the data, errors or inconsistencies may still exist. Users are encouraged to verify the data with official sources before making any critical decisions based on the dataset.

Acknowledgments: We would like to acknowledge the contributions of the data providers, football organizations, and sports enthusiasts whose efforts have made this dataset possible. Their dedication to collecting and sharing football data is greatly appreciated.

Note: Please be respectful of the data usage policy and terms of service of the dataset. Use the data responsibly and ensure compliance with any applicable legal requirements.

NFL Player Suspensions

Exploring Categories and Sources of Player Discipline

By FiveThirtyEight [source]

About this dataset

This dataset contains comprehensive information on NFL player suspensions. It includes detailed information such as the player's name, team they were playing for during the suspension, number of games suspended, and category of suspension. This data is ideal for anyone looking to analyze or research trends in NFL suspensions over time or compare different players' suspension records and can represent an invaluable source for new insights about professional football in America. So dive deep into this repository and see what meaningful stories you can tell—all under the Creative Commons Attribution 4.0 International License and MIT License. If you find this useful, let us know!

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How to use the dataset

Key Columns/Variables

The following is a list of key columns present in this dataset: - Name: Name of the player who was suspended. (String) - Team: The team that the player was playing for when suspension was issued. (String) - Games: The number of games suspended for which includes postseason games if applicable. (Integer) - Category: A description/categorization of why player was suspended e.g ‘substance abuse’ or ‘personal conduct’.(String) * Desc.: A brief synopsis describingsuspension further - often indicates what action led suspension to take place (e.g drug use).(String) Year: The year suspension originally took place.(Integer) Source: Information source behind suspension data.(String).

#### Exploring and Visualizing the Data

There are a variety of ways you can explore and analyze this data set including visualizations such as histograms, box plots, line graphs etc.. Additionally you can further explore correlations between various variables by performing linear regression or isolating individual instances by filtering out specific observations e.g all Substance Abuse offences committed against players in 2015 etc.. In order to identify meaningful relationships within data set we recommend performing univariate analysis i.e analyzing one variable at time and look for patterns which may be indicative wider trends within broader unit./population context which it represents! Here's example code snippet first step towards visualizing your own insights from NFL Suspension data set - generate histogram showing distribution type offense categories undertaken 2005 through 2015.

Research Ideas

• An analysis of suspension frequencies over time to determine overall trends in NFL player discipline.
• Comparing the types of suspensions for players on different teams to evaluate any differences in the consequences for violations of team rules and regulations.
• A cross-sectional analysis to assess correlations between types and length of suspensions issued given various violation categories, such as substance abuse or personal conduct violations

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: nfl-suspensions-data.csv | Column name | Description | |:--------------|:------------------------------------------------------------| | name | Name of the player who was suspended. (String) | | team | The team the player was suspended from. (String) | | games | The number of games the player was suspended for. (Integer) | | category | The category of the suspension. (String) | | desc. | A description of the suspension. (String) | | year | The year the suspension occurred. (Integer) | | source | The source of the suspension information. (String) |

Acknowledgements

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