野球テ?ータを可視化してみたニコニコ学会

Mar 8, 20141 like1,629 views

Shinichi Nakagawa

野球データを可視化してみた
～メジャーリーグ編～

2014/3/8
第4回ニコニコ学会β データ研究会LT
shinyorke@野良野球エンジニア

shinyorke選手
(呼び名：シンさん、シン選手)
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1979.9.6生=34歳北海道

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右投げ右打ち 183cm/71kg

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専門学校→ソフトウェアハウス
→独立系コンサルティングファー
ム=13年

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プログラマー?コンサルタント?
野球、三拍子った万能エンジ
ニア。今シーズンの課題はデー
タ解析?統計学スキルの向上。

共通していること
?

情報が非常に充実している

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文字と数字のオンパレードで読みにくい

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ほしい情報が一発で見つからない(年俸いくら？最
近調子いいの？)

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専門用語のオンパレード(ERA,BB/9,xFIP…???)

?

英語多すぎィ

やったこと
?

Baseball-Reference.comのデータをスクレイピン
グ

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スクレイピングしたデータから、特に意味あるデー
タをキャッシュ

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Webアプリにしてグラフを描画

これからやること
?

「やさしい野球選手名鑑」としてアプリにする

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データの著作権等の問題を解決する（ほぼ目処立っ
てる）

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ファンにとって意味がある独自の指標を作る

宣伝！

プロ野球開幕直前！2014年シーズンを前に、
野球好きたちが語り合う会
@haru860 VS @gothedistance VS @shinyorke
今年の野球をエンジニアが熱く語る！
詳しくは http://connpass.com/event/5295/

This document discusses using Python and PyData tools for baseball analytics. It introduces Shinichi Nakagawa, a baseball analyst and Python expert. It explains common PyData tools like Grafana, Redash and Jupyter Notebook, and how they can be used to visualize and analyze baseball metrics and stats. It also discusses using Python and scraping to analyze run creation (RC) and run creation per 27 outs (RC27) stats to evaluate player and team performance.

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This document summarizes Shinichi Nakagawa's presentation on analyzing baseball data with Python. It introduces Shinichi as a Python developer and baseball analyst. It then discusses how he uses the Python pitchpx library and Jupyter notebooks to analyze pitching data downloaded from MLB.com using pandas and matplotlib. Finally, it provides Shinichi's contact information for anyone interested in Python or baseball data analysis projects.

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