Multi-Armed Bandits in Python: Epsilon Greedy, UCB1, Bayesian UCB, and EXP3
This post explores four algorithms for solving the multi-armed bandit problem (Epsilon Greedy, EXP3, Bayesian UCB, and UCB1), with implementations in Python ...
Introducing pybaseball: an Open Source Package for Baseball Data Analysis
July 27, 2017
Baseball and statistics go together like peanut butter and jelly; it’s almost hard to imagine following one without involving the other. In recent years, the data that make this game enjoyable for so many have only gotten better. With the introduction of the Statcast system for measuring sci-fi-sounding statistics such as the spin speed of a thrown ball and its launch angle off a player’s bat, I believe we are at the beginning of an exciting new era for baseball statistics.
Unfortunately for Python-loving statisticians like myself, there has historically been no tool for bringing this data into one’s work without a painful process of manual curation. For this reason, I’m releasing pybaseball: an open source package for 21st century baseball data analysis in Python.
How does Aaron Judge hit baseballs into the stratosphere? Pybaseball and Statcast can help you find out (image: mlb.com)
What It Does
Pybaseball takes the pain out of collecting and cleaning baseball data from the internet. In short, I scraped Baseball Savant, FanGraphs, and Baseball Reference so you don’t have to. Currently, this means that you can retrieve pitch, season, and game-level data on individual players and teams, historic schedule and record data, and division standings with simple, Pythonic one-liners. The stats that this library provides range from the classics (BA, RBI, HR, W, L, K, IP), to the slightly more sophisticated (OBP, SLG, WHIP, WAR), to what would have sounded like science fiction a few short years ago (exit velocity, spin speed, pitch x, y, and z coordinates). The goal of this library is to provide the data necessary to answer any baseball research question.
How it Works
The statcast function returns statcast pitch-level data.
>>> from pybaseball import statcast
>>> data = statcast(start_dt='2017-06-15', end_dt='2017-06-28')
>>> 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]
Similarly, if you want player-level stats aggregated by season, you can pull 299 different features per player per season from FanGraphs using the pitching_stats and batting_stats functions.
>>> 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]
But wait, there’s more! Say you’re interested in comparing the performances of historic teams. That, too, is easy with pybaseball. With this package, one can dissect the 1927 “Murderers Row” Yankees season with a single line of Python.
>>> 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
These examples are just the tip of the iceberg, but hopefully this gives a taste of the power and versatility of pybaseball.
How to Install Pybaseball
Pybaseball is pip installable. Simply run pip install pybaseball and it’s on your machine.
Where to Read More
If any of this piqued your interest, full documentation and complete examples are available on the Github repo here. If you like what you’ve seen so far, please give it a star. If you really like what you’ve seen so far, drop me a suggestion or submit a code improvement. Last, if you end up using pybaseball for any type of project or analysis, I would love to hear about it. Send me a note or reach out on Twitter!
2020
Offline Evaluation of Multi-Armed Bandit Algorithms in Python using Replay
Multi-armed bandit algorithms are seeing renewed excitement, but evaluating their performance using a historic dataset is challenging. Here’s how I go about ...
2019
Understanding the AdTech Auctions in Your Browser: an Analysis of 30,000 Prebid.js Auctions
An analysis of auction dynamics in client-side header bidding
2018
Predicting The Shift: Boosting and Bagging for Strategic Infield Positioning
Using machine learning to predict strategic infield positioning using statcast data and contextual feature engineering.
Visualizing MLB Team Rankings with ggplot2 and Bump Charts
A quick tutorial on fetching MLB win-loss data with pybaseball and cleaning and visuzlizing it with the tidyverse (dplyr and ggplot).
2017
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Introducing pybaseball: an Open Source Package for Baseball Data Analysis
Throughout my baseball-facing work at MLB Advanced Media, I came to realize that there was no reliable Python tool available for sabermetric research and adv...
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2016
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