NFL Player Suspensions by Kaggle | Sports

About this Dataset

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.

Tables

Nfl Suspensions Data

@kaggle.thedevastator_nfl_player_suspensions_2005_2015_data.nfl_suspensions_data

21.67 KB
269 rows
8 columns


CREATE TABLE nfl_suspensions_data (
  "index" BIGINT,
  "name" VARCHAR,
  "team" VARCHAR,
  "games" VARCHAR,
  "category" VARCHAR,
  "desc" VARCHAR,
  "year" VARCHAR,
  "source" VARCHAR
);