Analyze Japanese Baseball Stats with Python and Pandas

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Pythonと野球統計学「セイバーメトリクス」を用いて,2016年セ・リーグ優勝の広島東洋カープの強さの秘密は何だったのか?に迫ります. PyCon mini Hiroshima 2016発表資料 http://hiroshima.pycon.jp/

Datos y análisis

Py “Baseball” Data
PyCon mini Hirosima 2016
Python
Shinichi Nakagawa(Baseball Analyst&Pythonista)

Starting Member
• Who am I?( )
• PyData
• PyData / #
• Python
• PyData + (FIP/RC27)
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Python Python Library
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http://www.slideshare.net/
iktakahiro/pydata-67913897

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# Python 3 (3.4 ) ( )
$ pip install ipython pandas beautifulsoup4 numpy lxml html5lib
# ipython ( Jupyter )
$ ipython

Python×pandas
#
import pandas as pd
import numpy as np
# ( )
df = pd.read_html('http://baseball.yahoo.co.jp/npb/standings/')
#
df_cl = df[0].drop([0]) #

Python×pandas
#
# ( )
df_cl.columns = ['rank', 'name', 'games', 'win', 'lose', 'draw',
'pct', 'gb', 're_games', 'r', 'er', 'hr', 'sb', 'ba', 'era']
#
df_cl['win'] = df_cl['win'].fillna(0).astype(np.int64) #
df_cl['lose'] = df_cl['lose'].fillna(0).astype(np.int64) #
df_cl['pct'] = df_cl['pct'].fillna(0).astype(np.float64) #
df_cl['r'] = df_cl['r'].fillna(0).astype(np.int64) #
df_cl['er'] = df_cl['er'].fillna(0).astype(np.int64) #

Python×pandas
#
df_cl['difference'] = df_cl['r'] - df_cl['er']
#
df_cl['pythagorean_win_per'] = (df_cl['r'] ** 2) / (df_cl['r'] ** 2
+ df_cl['er'] ** 2)
#
df_cl['pythagorean_win'] = (df_cl['pythagorean_win_per'] *
143).fillna(0).astype(np.int64)
df_cl['pythagorean_lose'] = 143 - df_cl['pythagorean_win']
#
df_cl.sort_values(by='pythagorean_win_per', ascending=False)

https://gist.github.com/Shinichi-Nakagawa/8ff55af83390fcd2e2dd34bcb914868c

×PyData
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Py "Baseball" Data - Python
※pandas, Re:dash (& )
MonotaRO TechTalk #4
http://www.kokuchpro.com/event/monotarotech4/

&
Shinichi Nakagawa(Twitter/Facebook/visasQ:@shinyorke)

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Analyze Japanese Baseball Stats with Python and Pandas

1. Py “Baseball” Data PyCon mini Hirosima 2016 Python Shinichi Nakagawa(Baseball Analyst&Pythonista)

2. Starting Member • Who am I?( ) • PyData • PyData / # • Python • PyData + (FIP/RC27) •

3. Who am I? • Shinichi Nakagawa(@shinyorke) • Python , Hack ※ Python • HR . • Python/Agile/PyData/SABRmetrics( ) • • ( ) .

4. • ( ) HR . • 1 2 . • (Django) Python . • https://service.visasq.com • https://tech.visasq.com

5. • • • • & • etc… • Web Python

6. • • IPython + pandas   (Hello World ) •

7. •   .  . • Deep Learning ,   . • (Pandas )  & .

8. PyData / #

9. PyData

10. “”” PyData Python Python Library “”” ※@iktakahiro http://www.slideshare.net/ iktakahiro/pydata-67913897

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22. Python×Pandas

23. Python×pandas # Python 3 (3.4 ) ( ) $ pip install ipython pandas beautifulsoup4 numpy lxml html5lib # ipython ( Jupyter ) $ ipython

24. Python×pandas # import pandas as pd import numpy as np # ( ) df = pd.read_html('http://baseball.yahoo.co.jp/npb/standings/') # df_cl = df[0].drop([0]) #

25. Python×pandas # # ( ) df_cl.columns = ['rank', 'name', 'games', 'win', 'lose', 'draw', 'pct', 'gb', 're_games', 'r', 'er', 'hr', 'sb', 'ba', 'era'] # df_cl['win'] = df_cl['win'].fillna(0).astype(np.int64) # df_cl['lose'] = df_cl['lose'].fillna(0).astype(np.int64) # df_cl['pct'] = df_cl['pct'].fillna(0).astype(np.float64) # df_cl['r'] = df_cl['r'].fillna(0).astype(np.int64) # df_cl['er'] = df_cl['er'].fillna(0).astype(np.int64) #

26. Python×pandas # df_cl['difference'] = df_cl['r'] - df_cl['er'] # df_cl['pythagorean_win_per'] = (df_cl['r'] ** 2) / (df_cl['r'] ** 2 + df_cl['er'] ** 2) # df_cl['pythagorean_win'] = (df_cl['pythagorean_win_per'] * 143).fillna(0).astype(np.int64) df_cl['pythagorean_lose'] = 143 - df_cl['pythagorean_win'] # df_cl.sort_values(by='pythagorean_win_per', ascending=False)

27. https://gist.github.com/Shinichi-Nakagawa/8ff55af83390fcd2e2dd34bcb914868c

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