(First release: 07/16/17)
pybaseball is a Python package for baseball data analysis. This package scrapes baseball-reference.com and baseballsavant.com so you don't have to. So far, the package performs four main tasks: retrieving statcast data, pitching stats, batting stats, and division standings/team records.
Statcast data include pitch-level features such as Perceived Velocity (PV), Spin Rate (SR), Exit Velocity (EV), pitch X, Y, and Z coordinates, and more. The function statcast(start_dt, end_dt) pulls this data from baseballsavant.com.
>>> from pybaseball import statcast
>>> data = statcast(start_dt='2017-06-24', end_dt='2017-06-27')
>>> data.head(2)
index pitch_type game_date release_speed release_pos_x release_pos_z
0 314 CU 2017-06-27 79.7 -1.3441 5.4075
1 332 FF 2017-06-27 98.1 -1.3547 5.4196
player_name batter pitcher events ... release_pos_y
0 Matt Bush 608070.0 456713.0 field_out ... 54.8585
1 Matt Bush 429665.0 456713.0 field_out ... 54.3470
estimated_ba_using_speedangle estimated_woba_using_speedangle woba_value
0 0.100 0.137 0.0
1 0.269 0.258 0.0
woba_denom babip_value iso_value launch_speed_angle at_bat_number pitch_number
0 1.0 0.0 0.0 3.0 64.0 1.0
1 1.0 0.0 0.0 3.0 63.0 3.0
[2 rows x 79 columns]
If start_dt and end_dt are supplied, it will return all statcast data between those two dates. If not, it will return yesterday's data. The argument team may also be supplied with a team's city abbreviation (i.e. BOS) to obtain only observations for games containing that team.
For a player-specific statcast query, pull pitching or batting data using the statcast_pitcher and statcast_batter functions. These take the same start_dt and end_dt arguments as the statcast function, as well as a player_id argument. This ID comes from MLB Advanced Media, and can be obtained using the function playerid_lookup. A complete example:
# Find Clayton Kershaw's player id
>>> from pybaseball import playerid_lookup
>>> from pybaseball import statcast_pitcher
>>> playerid_lookup('kershaw', 'clayton')
Gathering player lookup table. This may take a moment.
name_last name_first key_mlbam key_retro key_bbref key_fangraphs
0 kershaw clayton 477132 kersc001 kershcl01 2036
mlb_played_first mlb_played_last
0 2008.0 2017.0
# His MLBAM ID is 477132, so we feed that as the player_id argument to the following function
>>> kershaw_stats = statcast_pitcher('2017-06-01', '2017-07-01', 477132)
>>> kershaw_stats.head(2)
pitch_type game_date release_speed release_pos_x release_pos_z
0 SL 2017-06-29 87.2 1.0865 6.4034
1 SL 2017-06-29 86.9 1.0195 6.4324
player_name batter pitcher events description
0 Clayton Kershaw 458913 477132 strikeout swinging_strike_blocked
1 Clayton Kershaw 458913 477132 null ball
... release_pos_y estimated_ba_using_speedangle
0 ... 54.5463 0.0
1 ... 54.7625 0.0
estimated_woba_using_speedangle woba_value woba_denom babip_value
0 0.0 0.00 1 0
1 0.0 null null null
iso_value launch_speed_angle at_bat_number pitch_number
0 0 null 57 6
1 null null 57 5
[2 rows x 78 columns]
Pitching Stats: pitching stats for players across multiple seasons, single seasons, or during a specified time period
This library contains two main functions for obtaining pitching data. For league-wide season-level pitching data, use the function pitching_stats(start_season, end_season). This will return one row per player per season, and provide all metrics made available by FanGraphs.
The second is pitching_stats_range(start_dt, end_dt). This allows you to obtain pitching data over a specific time interval, allowing you to get more granular than the FanGraphs function (for example, to see which pitcher had the strongest month of May). This query pulls data from Baseball Reference. Note that all dates should be in YYYY-MM-DD format.
If you prefer Baseball Reference to FanGraphs, there is actually a third option called pitching_stats_bref(season). This works the same as pitching_stats, but retrieves its data from Baseball Reference instead. This is typically not recommended, however, because the Baseball Reference query currently can only retrieve one season's worth of data per request.
>>> from pybaseball import pitching_stats
>>> data = pitching_stats(2012, 2016)
>>> data.head()
Season Name Team Age W L ERA WAR G GS
336 2015.0 Clayton Kershaw Dodgers 27.0 16.0 7.0 2.13 8.6 33.0 33.0
236 2014.0 Clayton Kershaw Dodgers 26.0 21.0 3.0 1.77 7.6 27.0 27.0
472 2014.0 Corey Kluber Indians 28.0 18.0 9.0 2.44 7.4 34.0 34.0
235 2015.0 Jake Arrieta Cubs 29.0 22.0 6.0 1.77 7.3 33.0 33.0
256 2013.0 Clayton Kershaw Dodgers 25.0 16.0 9.0 1.83 7.1 33.0 33.0
... wSL/C (pi) wXX/C (pi) O-Swing% (pi) Z-Swing% (pi)
336 ... 1.76 22.85 0.364 0.665
236 ... 2.62 NaN 0.371 0.670
472 ... 3.92 NaN 0.336 0.598
235 ... 2.42 NaN 0.329 0.618
256 ... 0.74 NaN 0.339 0.635
Swing% (pi) O-Contact% (pi) Z-Contact% (pi) Contact% (pi) Zone% (pi)
336 0.511 0.478 0.811 0.689 0.487
236 0.525 0.536 0.831 0.730 0.515
472 0.468 0.485 0.886 0.744 0.505
235 0.468 0.595 0.856 0.762 0.483
256 0.484 0.563 0.873 0.763 0.492
Pace (pi)
336 23.4
236 23.7
472 24.6
235 23.3
256 23.4
[5 rows x 299 columns]
Batting stats are obtained similar to pitching stats. The function call for getting a season-level stats is batting_stats(start_season, end_season), and for a particular time range it is batting_stats_range(start_dt, end_dt). The Baseball Reference equivalent for season-level data is batting_stats_bref(season).
>>> from pybaseball import batting_stats_range
>>> data = batting_stats_range('2017-05-01', '2017-05-08')
>>> data.head()
Name Age #days Lev Tm G PA AB R H ... HBP
1 Jose Abreu 30 69 MLB-AL Chicago 7 31 30 5 9 ... 0
2 Lane Adams 27 69 MLB-NL Atlanta 6 6 6 0 2 ... 0
3 Matt Adams 28 68 MLB-NL St. Louis 6 9 9 2 4 ... 0
4 Jim Adduci 32 69 MLB-AL Detroit 6 24 21 3 5 ... 0
5 Tim Adleman 29 72 MLB-NL Cincinnati 1 2 2 0 0 ... 0
SH SF GDP SB CS BA OBP SLG OPS
1 0 0 1 0 0 0.300 0.323 0.667 0.989
2 0 0 1 1 0 0.333 0.333 0.333 0.667
3 0 0 0 0 0 0.444 0.444 0.778 1.222
4 0 0 0 0 0 0.238 0.333 0.381 0.714
5 0 0 0 0 0 0.000 0.000 0.000 0.000
[5 rows x 27 columns]
The schedule_and_record function returns a team's game-by-game results for a given season, including game date, home and away teams, end result (W/L/Tie), score, winning/losing/saving pitchers, attendance, and division standing at that date. The function's only two arguments are season and team, where team is the team's abbreviation (i.e. NYY for New York Yankees, SEA for Seattle Mariners). If the season argument is set to the current season, the query returns results for past games and the schedule for those that have not occurred yet.
# Example: Let's take a look at the individual-game results of the 1927 Yankees
>>> from pybaseball import schedule_and_record
>>> data = schedule_and_record(1927, 'NYY')
>>> data.head()
Date Tm Home_Away Opp W/L R RA Inn W-L Rank \
1 Tuesday, Apr 12 NYY Home PHA W 8.0 3.0 9.0 1-0 1.0
2 Wednesday, Apr 13 NYY Home PHA W 10.0 4.0 9.0 2-0 1.0
3 Thursday, Apr 14 NYY Home PHA T 9.0 9.0 10.0 2-0 1.0
4 Friday, Apr 15 NYY Home PHA W 6.0 3.0 9.0 3-0 1.0
5 Saturday, Apr 16 NYY Home BOS W 5.0 2.0 9.0 4-0 1.0
GB Win Loss Save Time D/N Attendance Streak
1 Tied Hoyt Grove None 2:05 D 72000.0 1
2 up 0.5 Ruether Gray None 2:15 D 8000.0 2
3 Tied None None None 2:50 D 9000.0 2
4 Tied Pennock Ehmke None 2:27 D 16000.0 3
5 up 1.0 Shocker Ruffing None 2:05 D 25000.0 4
The standings(season) function gives division standings for a given season. If the current season is chosen, it will give the most current set of standings. Otherwise, it will give the end-of-season standings for each division for the chosen season.
This function returns a list of dataframes. Each dataframe is the standings for one of MLB's six divisions.
>>> from pybaseball import standings
>>> data = standings(2016)[4]
>>> print(data)
Tm W L W-L% GB
1 Chicago Cubs 103 58 .640 --
2 St. Louis Cardinals 86 76 .531 17.5
3 Pittsburgh Pirates 78 83 .484 25.0
4 Milwaukee Brewers 73 89 .451 30.5
5 Cincinnati Reds 68 94 .420 35.5
So far this has provided a basic overview of what this package can do and how you can use it. For full documentation on available functions and their arguments, see the docs folder.
Need some inspiration? See some examples of classic baseball studies replicated using this package here.
This pacakge was inspired by Bill Petti's excellent R package baseballr, which to date has no Python equivalent. My hope is to fill that void with this library.
To install pybaseball, simply run
pip install pybaseball
or, for the version currently on the repo (which may at times be more up to date):
git clone https://github.com/jldbc/pybaseball
cd pybaseball
python setup.py install
Moving forward, I intend to:
- Make this pip-installable
- Implement custom metrics such as Statcast edge percentages, historical Elo ratings, wOBA, etc.
- Identify edge cases where these queries fail (please open up an issue if you find one!)
- Add examples
This library depends on: Pandas, NumPy, bs4 (beautiful soup), and Requests.