Get pitch-by-pitch data from MLB's API in a friendly format. Named after DIPS-inventor Voros McCracken.
In [1]: from voros import Scraper
In [2]: from datetime import date
In [3]: import pandas as pd
In [4]: s = Scraper()
In [5]: df = pd.DataFrame(s.run(date(2008, 4, 1), date(2008, 4, 2)))
In [6]: df.head()
Out[6]:
away_team ax ay az batter break_angle break_length break_y \
0 ari 16.894 29.125 -21.117 455759 -32.6 6.5 23.7
1 ari 9.003 23.273 -24.415 455759 -16.6 7.9 23.8
2 ari 9.655 22.735 -29.378 455759 -15.3 9.1 23.8
3 ari 16.483 29.234 -24.125 455759 -29.3 6.6 23.7
4 ari 18.276 28.46 -22.469 455759 -33.5 7.0 23.7
cc des ... type_confidence vx0 vy0 vz0 x \
0 Called Strike ... 2.000 -12.388 -128.615 -4.967 103.86
1 Called Strike ... 2.000 -8.481 -115.609 -2.181 78.11
2 Ball ... 2.000 -7.97 -117.315 -4.537 66.95
3 Ball ... 2.000 -13.938 -128.972 1.812 122.75
4 In play, run(s) ... 2.000 -12.679 -127.693 -4.988 107.30
x0 y y0 z0 zone
0 3.448 151.10 50.0 5.971 8
1 3.48 132.97 50.0 6.271 3
2 3.619 171.83 50.0 6.17 14
3 3.495 88.07 50.0 6.046 11
4 3.357 149.38 50.0 6.048 4
[5 rows x 49 columns]
Scraper().run(start_date, end_date) is the only thing you really need. The
method returns a generator
that yields a dict for every pitch. Each dict contains all the per-pitch
data MLB provides, as well as per-inning data. If you look here,
it combines the attributes of the inning and atbat elements with those
of each pitch element.
In the interest of convenience, errors get converted to warnings. Thus, if your wifi cuts out midway through, you'll still get something back.
In the interest of reducing the number of pointless HTTP requests made, I tried to be reasonable about dates. Games played between April and November are scraped, with a modulo-ish mechanism resulting in the first game in April immediately following the last game of the last year's playoffs.
There's no per-game date info, unfortunately. The only date information is in the
URL, and I couldn't think of an elegant way to propagate that through the parsing
process. Maybe I will someday! Coincidentally, I'm also accepting PRs, hint hint.
(There are actually two separate date fields in the data itself—start_tfs_zulu
and tfs_zulu. They're even already in UTC for you! Local game times could be
recreated with a library like pytz and
a mapping between home teams and timezones, but that's Beyond The Scope Of This
Work.)
You can get A LOT OF DATA this way. The code uses generators wherever
reasonable to cut down on memory usage, but you'll still probably regret trying
to turn an entire season's worth of games into a single in-memory DataFrame,
so...don't? You can either throttle in the scraping, or use something like
itertools.islice
on the generator that gets returned, e.g.:
lmt = 100
idx = 0
g = voros.Scraper().run(start, end)
res = islice(g, lmt)
while res:
fname = 'pitch_data_{}.csv'.format(idx)
# seems like overkill until you see your 9th error about
# `csv.DictWriter` encountering an unfamiliar field
pd.DataFrame(res).to_csv(fname)
idx += 1
res = islice(g, lmt)
Inspired by John Choiniere's pfx_parser.
Written by Sam Raker.