https://github.com/benjamincrom/baseball
Library to download, analyze, and visualize events in Major League Baseball games
https://github.com/benjamincrom/baseball
baseball baseball-analysis-packages baseball-statistics baseball-stats
Last synced: 11 months ago
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Library to download, analyze, and visualize events in Major League Baseball games
- Host: GitHub
- URL: https://github.com/benjamincrom/baseball
- Owner: benjamincrom
- License: mit
- Created: 2017-12-14T23:15:40.000Z (over 8 years ago)
- Default Branch: main
- Last Pushed: 2025-05-15T13:48:23.000Z (about 1 year ago)
- Last Synced: 2025-05-15T14:45:03.510Z (about 1 year ago)
- Topics: baseball, baseball-analysis-packages, baseball-statistics, baseball-stats
- Language: HTML
- Homepage: http://livebaseballscorecards.com
- Size: 11.2 MB
- Stars: 88
- Watchers: 10
- Forks: 15
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
**Table of Contents**
- [Baseball](#baseball)
- [Installing from pypi](#installing-from-pypi)
- [Installing from source](#installing-from-source)
- [Fetch individual MLB game](#fetch-individual-mlb-game)
- [Game Class Structure](#game-class-structure)
- [Game](#game)
- [Team](#team)
- [Inning](#inning)
- [PlateAppearance](#plateappearance)
- [Player](#player)
- [PlayerAppearance](#playerappearance)
- [Pitch](#pitch)
- [Pickoff](#pickoff)
- [RunnerAdvance](#runneradvance)
- [Substitution](#substitution)
- [Switch](#switch)
- [Analyze a game: 2017 World Series - Game 7](#analyze-a-game-2017-world-series---game-7)
- [Analyze a player's season: R.A. Dickey - 2017](#analyze-a-players-season-ra-dickey---2017)
- [Analyze a lineup of pitchers: Atlanta Braves - 2017 Regular Season](#analyze-a-lineup-of-pitchers-atlanta-braves---2017-regular-season)
# Baseball
This package fetches and parses event data for Major League Baseball games. [Game](#game) objects generated via the **\_from\_url** methods pull data from MLB endpoints where events are published within about 30 seconds of occurring. This [XML/JSON source data zip file](https://spaces-host.nyc3.digitaloceanspaces.com/livebaseballscorecards-artifacts/baseball_1974_2021.zip) contains event data from MLB games 1974 - 2020.
## Installing from pypi
```
pip3 install baseball
```
## Installing from source
```
git clone git@github.com:benjamincrom/baseball.git
cd baseball/
python3 setup.py install
```
## Fetch individual MLB game
* __get_game_from_url(__*date_str, away_code, home_code, game_number*__)__
Fetch an object which contains metadata and events for a single MLB game.
```python
import baseball
game_id, game = baseball.get_game_from_url('2017-11-1', 'HOU', 'LAD', 1)
game_dict = game._asdict()
game_json_str = game.json()
```
Write scorecard as SVG image:
```python
with open(game_id + '.svg', 'w') as fh:
fh.write(game.get_svg_str())
```
2017-11-01-HOU-LAD-1.svg
## Game Class Structure
#### Game
- away_batter_box_score_dict
- away_pitcher_box_score_dict
- away_team ([Team](#team))
- away_team_stats
- start_datetime
- expected_start_datetime
- game_date_str
- home_batter_box_score_dict
- home_pitcher_box_score_dict
- home_team ([Team](#team))
- home_team_stats
- inning_list ([Inning](#inning) list)
- end_datetime
- location
- attendance
- weather
- temp
- timezone_str
- is_postponed
- is_suspended
- is_doubleheader
- is_today
- get_svg_str()
- json()
- \_asdict()
#### Team
- abbreviation
- batting_order_list_list (list of nine [PlayerAppearance](#playerappearance) lists)
- name
- pitcher_list ([PlayerAppearance](#playerappearance) list)
- player_id_dict
- player_last_name_dict
- player_name_dict
- \_asdict()
#### Inning
- bottom_half_appearance_list ([PlateAppearance](#plateappearance) list)
- bottom_half_inning_stats
- top_half_appearance_list ([PlateAppearance](#plateappearance) list)
- top_half_inning_stats
- \_asdict()
#### PlateAppearance
- start_datetime
- end_datetime
- batter ([Player](#player))
- batting_team ([Team](#team))
- error_str
- event_list (list of [Pitch](#pitch), [Pickoff](#pickoff), [RunnerAdvance](#runneradvance), [Substitution](#substitution), [Switch](#switch) objects)
- got_on_base
- hit_location
- inning_outs
- out_runners_list ([Player](#player) list)
- pitcher ([Player](#player))
- plate_appearance_description
- plate_appearance_summary
- runners_batted_in_list ([Player](#player) list)
- scorecard_summary
- scoring_runners_list ([Player](#player) list)
- \_asdict()
#### Player
- era
- first_name
- last_name
- mlb_id
- number
- obp
- slg
- \_asdict()
#### PlayerAppearance
- start_inning_batter_num
- start_inning_half
- start_inning_num
- end_inning_batter_num
- end_inning_half
- end_inning_num
- pitcher_credit_code
- player_obj ([Player](#player))
- position
- \_asdict()
#### Pitch
- pitch_datetime
- pitch_description
- pitch_position
- pitch_speed
- pitch_type
- \_asdict()
#### Pickoff
- pickoff_description
- pickoff_base
- pickoff_was_successful
- \_asdict()
#### RunnerAdvance
- runner_advance_datetime
- run_description
- runner ([Player](#player))
- start_base
- end_base
- runner_scored
- run_earned
- is_rbi
- \_asdict()
#### Substitution
- substitution_datetime
- incoming_player ([Player](#player))
- outgoing_player ([Player](#player))
- batting_order
- position
- \_asdict()
#### Switch
- switch_datetime
- player ([Player](#player))
- old_position_num
- new_position_num
- new_batting_order
- \_asdict()
## Analyze a game: 2017 World Series - Game 7
```python
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import baseball
%matplotlib inline
game_id, game = baseball.get_game_from_url('11-1-2017', 'HOU', 'LAD', 1)
pitch_tuple_list = []
for inning in game.inning_list:
for appearance in inning.top_half_appearance_list:
for event in appearance.event_list:
if isinstance(event, baseball.Pitch):
pitch_tuple_list.append(
(str(appearance.pitcher),
event.pitch_description,
event.pitch_position,
event.pitch_speed,
event.pitch_type)
)
data = pd.DataFrame(data=pitch_tuple_list, columns=['Pitcher', 'Pitch Description', 'Pitch Coordinate', 'Pitch Speed', 'Pitch Type'])
data.head()
```
Pitcher
Pitch Description
Pitch Coordinate
Pitch Speed
Pitch Type
0
21 Yu Darvish
Ball
(155.47, 160.83)
96.0
FF
1
21 Yu Darvish
Called Strike
(107.0, 171.09)
83.9
FC
2
21 Yu Darvish
In play, no out
(115.36, 183.1)
83.9
SL
3
21 Yu Darvish
In play, run(s)
(80.06, 168.03)
96.6
FF
4
21 Yu Darvish
Ball
(54.1, 216.52)
84.6
SL
```python
data['Pitcher'].value_counts().plot.bar()
```
```python
for pitcher in data['Pitcher'].unique():
plt.ylim(0, 125)
plt.xlim(0, 250)
bx = [250 - x[2][0] for x in pitch_tuple_list if x[0] == pitcher if 'Ball' in x[1]]
by = [250 - x[2][1] for x in pitch_tuple_list if x[0] == pitcher if 'Ball' in x[1]]
cx = [250 - x[2][0] for x in pitch_tuple_list if x[0] == pitcher if 'Called Strike' in x[1]]
cy = [250 - x[2][1] for x in pitch_tuple_list if x[0] == pitcher if 'Called Strike' in x[1]]
ox = [250 - x[2][0] for x in pitch_tuple_list if x[0] == pitcher if ('Ball' not in x[1] and 'Called Strike' not in x[1])]
oy = [250 - x[2][1] for x in pitch_tuple_list if x[0] == pitcher if ('Ball' not in x[1] and 'Called Strike' not in x[1])]
b = plt.scatter(bx, by, c='b')
c = plt.scatter(cx, cy, c='r')
o = plt.scatter(ox, oy, c='g')
plt.legend((b, c, o),
('Ball', 'Called Strike', 'Other'),
scatterpoints=1,
loc='upper right',
ncol=1,
fontsize=8)
plt.title(pitcher)
plt.show()
```
```python
plt.axis('equal')
data['Pitch Description'].value_counts().plot(kind='pie', radius=1.5, autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
```
```python
data.plot.kde()
```
```python
fig, ax = plt.subplots()
ax.set_xlim(50, 120)
for pitcher in data['Pitcher'].unique():
s = data[data['Pitcher'] == pitcher]['Pitch Speed']
s.plot.kde(ax=ax, label=pitcher)
ax.legend()
```
```python
fig, ax = plt.subplots()
ax.set_xlim(50, 120)
for desc in data['Pitch Type'].unique():
s = data[data['Pitch Type'] == desc]['Pitch Speed']
s.plot.kde(ax=ax, label=desc)
ax.legend()
```
```python
fig, ax = plt.subplots(figsize=(15,7))
data.groupby(['Pitcher', 'Pitch Description']).size().unstack().plot.bar(ax=ax)
```
## Analyze a player's season: R.A. Dickey - 2017
```python
game_list_2017 = baseball.get_game_list_from_file_range('1-1-2017', '12-31-2017', '/Users/benjamincrom/repos/livebaseballscorecards-artifacts/baseball_files')
pitch_tuple_list_2 = []
for game_id, game in game_list_2017:
if game.home_team.name == 'Atlanta Braves' or game.away_team.name == 'Atlanta Braves':
for inning in game.inning_list:
for appearance in (inning.top_half_appearance_list +
(inning.bottom_half_appearance_list or [])):
if 'Dickey' in str(appearance.pitcher):
for event in appearance.event_list:
if isinstance(event, baseball.Pitch):
pitch_tuple_list_2.append(
(str(appearance.pitcher),
event.pitch_description,
event.pitch_position,
event.pitch_speed,
event.pitch_type)
)
df = pd.DataFrame(data=pitch_tuple_list_2, columns=['Pitcher', 'Pitch Description', 'Pitch Coordinate', 'Pitch Speed', 'Pitch Type'])
df['Pitch Type'].value_counts().plot.bar()
```
```python
plt.axis('equal')
df['Pitch Description'].value_counts().plot(kind='pie', radius=2, autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)
plt.ylabel('')
plt.show()
```
```python
df.dropna(inplace=True)
ax.set_xlim(50, 100)
df.plot.kde()
ax.legend()
```
```python
fig, ax = plt.subplots()
ax.set_xlim(50, 100)
for desc in df['Pitch Type'].unique():
if desc != 'PO':
s = df[df['Pitch Type'] == desc]['Pitch Speed']
s.plot.kde(ax=ax, label=desc)
ax.legend()
```
## Analyze a lineup of pitchers: Atlanta Braves - 2017 Regular Season
```python
import datetime
import dateutil.parser
import pytz
pitch_tuple_list_3 = []
for game_id, game in game_list_2017:
if game.home_team.name == 'Atlanta Braves' and dateutil.parser.parse(game.game_date_str) > datetime.datetime(2017, 3, 31):
for inning in game.inning_list:
for appearance in inning.top_half_appearance_list:
pitch_tuple_list_3.append(
(str(appearance.pitcher),
str(appearance.batter),
len(appearance.out_runners_list),
len(appearance.scoring_runners_list),
len(appearance.runners_batted_in_list),
appearance.scorecard_summary,
appearance.got_on_base,
appearance.plate_appearance_summary,
appearance.plate_appearance_description,
appearance.error_str,
appearance.inning_outs)
)
if game.away_team.name == 'Atlanta Braves' and dateutil.parser.parse(game.game_date_str) > datetime.datetime(2017, 3, 31):
for inning in game.inning_list:
if inning.bottom_half_appearance_list:
for appearance in inning.bottom_half_appearance_list:
pitch_tuple_list_3.append(
(str(appearance.pitcher),
str(appearance.batter),
len(appearance.out_runners_list),
len(appearance.scoring_runners_list),
len(appearance.runners_batted_in_list),
appearance.scorecard_summary,
appearance.got_on_base,
appearance.plate_appearance_summary,
appearance.plate_appearance_description,
appearance.error_str,
appearance.inning_outs)
)
df3 = pd.DataFrame(data=pitch_tuple_list_3, columns=['Pitcher',
'Batter',
'Out Runners',
'Scoring Runners',
'RBIs',
'Scorecard',
'On-base?',
'Plate Summary',
'Plate Description',
'Error',
'Inning Outs'])
for pitcher in df3['Pitcher'].unique():
summary = df3[df3['Pitcher'] == pitcher]['Plate Summary']
s = summary.value_counts(sort=False)
if len(summary) > 400:
fig, ax = plt.subplots()
ax.set_ylim(0, 250)
s.plot.bar()
plt.title(pitcher)
plt.show()
```
```python
x = []
for pitcher in df3['Pitcher'].unique():
#f = df3[df3['Pitcher'] == pitcher]['On-base?'].value_counts()[0]
s = df3[df3['Pitcher'] == pitcher]['On-base?'].value_counts()
if len(s) == 2:
f = s[0]
t = s[1]
x.append((str(pitcher), f, t))
df4 = pd.DataFrame(data=x, columns=['Pitcher',
'Did not get on base',
'Got on base'])
df4.index = df4['Pitcher']
df4.sort_values(by=['Got on base']).nlargest(10, 'Did not get on base').plot.bar()
```
