Baseball Databank is a compilation of historical baseball data in a convenient, tidy format, distributed under Open Data terms.

This version of the Baseball databank was downloaded from Sean Lahman's website.

Note that as of v1, this dataset is missing a few tables because of a restriction on the number of individual files that can be added. This is in the process of being fixed. The missing tables are Parks, HomeGames, CollegePlaying, Schools, Appearances, and FieldingPost.

The Data

The design follows these general principles. Each player is assigned a unique number (playerID). All of the information relating to that player is tagged with his playerID. The playerIDs are linked to names and birthdates in the MASTER table.

The database is comprised of the following main tables:

• MASTER - Player names, DOB, and biographical info
• Batting - batting statistics
• Pitching - pitching statistics
• Fielding - fielding statistics

It is supplemented by these tables:

• AllStarFull - All-Star appearances
• HallofFame - Hall of Fame voting data
• Managers - managerial statistics
• Teams - yearly stats and standings
• BattingPost - post-season batting statistics
• PitchingPost - post-season pitching statistics
• TeamFranchises - franchise information
• FieldingOF - outfield position data
• FieldingPost- post-season fielding data
• ManagersHalf - split season data for managers
• TeamsHalf - split season data for teams
• Salaries - player salary data
• SeriesPost - post-season series information
• AwardsManagers - awards won by managers
• AwardsPlayers - awards won by players
• AwardsShareManagers - award voting for manager awards
• AwardsSharePlayers - award voting for player awards
• Appearances - details on the positions a player appeared at
• Schools - list of colleges that players attended
• CollegePlaying - list of players and the colleges they attended

Descriptions of each of these tables can be found attached to their associated files, below.

Acknowledgments

This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. For details see: http://creativecommons.org/licenses/by-sa/3.0/

Person identification and demographics data are provided by Chadwick Baseball Bureau (http://www.chadwick-bureau.com), from its Register of baseball personnel.

Player performance data for 1871 through 2014 is based on the Lahman Baseball Database, version 2015-01-24, which is Copyright (C) 1996-2015 by Sean Lahman.

The tables Parks.csv and HomeGames.csv are based on the game logs and park code table published by Retrosheet. This information is available free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at http://www.retrosheet.org.

Baffled why your team traded for that 34-year-old pitcher? Convinced you can create a new and improved version of WAR? Wondering what made the 1907 Cubs great and if can they do it again?

The History of Baseball is a reformatted version of the famous Lahman’s Baseball Database. It contains Major League Baseball’s complete batting and pitching statistics from 1871 to 2015, plus fielding statistics, standings, team stats, park stats, player demographics, managerial records, awards, post-season data, and more.

Scripts, Kaggle’s free, in-browser analytics tool, makes it easy to share detailed sabermetrics, predict the next hall of fame inductee, illustrate how speed scores runs, or publish a definitive analysis on why the Los Angeles Dodgers will never win another World Series.

We have more ideas for analysis than games in a season, but here are a few we’d really love to see:

• Is there a most error-prone position?
• When do players at different positions peak?
• Are the best performers selected for all-star game?
• How many walks does it take for a starting pitcher to get pulled?
• Do players with a high ground into double play (GIDP) have a lower batting average?
• Which players are the most likely to choke during the post-season?
• Why should or shouldn’t the National League adopt the designated hitter rule?

See the full SQLite schema.

Context

This was created to evaluate MLB baseball salaries adjusted for Inflation

Content

This is a join from two datasets. The first is baseball salaries. I took the data from the consumer index, transposed it, and used SQL to join the two tables:

SELECT * FROM Salaries LEFT JOIN USIndex ON Salaries.yearID = USIndex.Year

Acknowledgements

The baseball tables are from Lahman's Baseball Database The Consumer Price Index is from World Bank Data

Inspiration

I'm practicing using SQL and R using baseball