Inputs

start_season
(int) The start year whose data we need. Eg. 2015

end_season
(int) The end year whose data we need. Eg. 2017

league
(str) The league of the teams we want to consider. 'AL' for American League, 'NL' for National League and 'all' for both.

ind
(int) ind = 1 indicates that we want one row per team per year whereas ind = 0 indicates that we want one row per team containing the aggregate data for start_season to end_season.

Outputs

pandas DataFrame containing the following headers:

Season: Year
Team: Team name
W: Wins
L: Losses
SV: Saves. Measures number of finishes by a relief pitcher for the winning team.
G: Games played.
GS: Games started. Measures number of times a pitcher opens a game.
IP: Innings pitched.
SO: Strike-outs.
K/9: Strike-outs per 9 innings.
BB/9: Bases-on-balls per 9 innings.
HR/9: Home-runs per 9 innings.
BABIP: Batting Average on Balls in Play. Measures batting average without considering the opposing team's performance.
LOB%: Left-on-Base %. Number of men who remain on base at the end of an inning.
GB%: Ground-ball rate. Percentage of balls hit into the field of play that are ground balls. HR/FB: Home Run To Fly Ball Rate. Number of home runs to number of fly-balls.
ERA: Earned Runs Average. Number of error- and aid-free runs a that pitcher allows per nine innings.
FIP: Fielding Independent Pitching. Considers home-runs, hits-by-pitches, strike-outs, and unintentional walks to give a number in the same scale as ERA.
xFIP: Expected Fielding Independent Pitching. Similar to FIP but uses projected home-run rate in place of real home-run rate.
WAR: Wins Above Replacement. Gives a value to a player depending upon whether they are replaceable or not. It tells us how many wins the player can get in comparison to a replacement.