Table of Contents

Baseball

Baseball

This package fetches and parses event data for Major League Baseball games. 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 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.

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:

with open(game_id + '.svg', 'w') as fh:
    fh.write(game.get_svg_str())

2017-11-01-HOU-LAD-1.svg

Fetch list of MLB games

get_game_list_from_file_range(start_date_str, end_date_str, input_dir)

Fetch a list of game objects which each contain metadata and events for a single MLB game.

First, download and unzip the source data zip file:

wget https://spaces-host.nyc3.digitaloceanspaces.com/livebaseballscorecards-artifacts/baseball_files_2008-2017.zip
unzip baseball_files_2008-2017.zip -d ./baseball_files_2008-2017

Then import the files in Python using this library:

import baseball
game_tuple_list = baseball.get_game_list_from_file_range('1-1-2017', '12-31-2017', 'baseball_files_2008-2017')

Get Game generator given target directory and date range

get_game_generator_from_file_range(start_date_str, end_date_str, input_dir)

Returns generator which yields (game_id, Game) tuples

Get raw XML files for an individual MLB game

get_game_xml_from_url(date_str, away_code, home_code, game_number)

Returns game_id and three strings containing XML documents: (game_id, boxscore_raw_xml, players_raw_xml, inning_raw_xml)

Convert XML documents into Game object

get_game_from_xml_strings(boxscore_raw_xml, players_raw_xml, inning_raw_xml)

Returns Game object if enough information to create one is provided. Otherwise returns None.

Write scorecard SVGs for all MLB games on a given date

write_games_for_date(this_datetime, output_dir)

Writes SVG files for all games played on the given date

Game Class Structure

Game

away_batter_box_score_dict
away_pitcher_box_score_dict
away_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)
home_team_stats
inning_list (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 lists)
name
pitcher_list (PlayerAppearance list)
player_id_dict
player_last_name_dict
player_name_dict
_asdict()

Inning

bottom_half_appearance_list (PlateAppearance list)
bottom_half_inning_stats
top_half_appearance_list (PlateAppearance list)
top_half_inning_stats
_asdict()

PlateAppearance

start_datetime
end_datetime
batter (Player)
batting_team (Team)
error_str
event_list (list of Pitch, Pickoff, RunnerAdvance, Substitution, Switch objects)
got_on_base
hit_location
inning_outs
out_runners_list (Player list)
pitcher (Player)
plate_appearance_description
plate_appearance_summary
runners_batted_in_list (Player list)
scorecard_summary
scoring_runners_list (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)
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)
start_base
end_base
runner_scored
run_earned
is_rbi
_asdict()

Substitution

substitution_datetime
incoming_player (Player)
outgoing_player (Player)
batting_order
position
_asdict()

Switch

switch_datetime
player (Player)
old_position_num
new_position_num
new_batting_order
_asdict()

Analyze a game: 2017 World Series - Game 7

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

data['Pitcher'].value_counts().plot.bar()

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()

plt.axis('equal')
data['Pitch Description'].value_counts().plot(kind='pie', radius=1.5, autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2)

data.plot.kde()

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()

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()

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

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()

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()

df.dropna(inplace=True)
ax.set_xlim(50, 100)
df.plot.kde()
ax.legend()

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

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()

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()

ALeggeUp / baseball