Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
やきう選手の撮れ高(打者編) #kwskrb 2019/2/27 LT資料
Search
Shinichi Nakagawa
PRO
February 27, 2019
Research
0
390
やきう選手の撮れ高(打者編) #kwskrb 2019/2/27 LT資料
kawasaki.rb #69 LTの資料に色々と足したり引いたりしたもの
Shinichi Nakagawa
PRO
February 27, 2019
Tweet
Share
More Decks by Shinichi Nakagawa
See All by Shinichi Nakagawa
自らを強いエンジニアにするための3つの習慣 2025/ Fitter happier more productive
shinyorke
PRO
0
270
生成AI時代におけるSREの進化とキャリア戦略 / Building an Embedded SRE team and my career
shinyorke
PRO
0
130
生成AIを活用した野球データ分析 - メジャーリーグ編 / Baseball Analytics for Gen AI
shinyorke
PRO
1
5.9k
ゼロから始めるSREの事業貢献 - 生成AI時代のSRE成長戦略と実践 / Starting SRE from Day One
shinyorke
PRO
2
6.7k
AI・LLM事業部のSREとタスクの自動運転
shinyorke
PRO
0
520
実践Dash - 手を抜きながら本気で作るデータApplicationの基本と応用 / Dash for Python and Baseball
shinyorke
PRO
2
4.1k
Terraform, GitHub Actions, Cloud Buildでデータ基盤をProvisioningする / Data Platform provisioning for Google Cloud and Terraform
shinyorke
PRO
2
3.6k
Cloud RunとCloud PubSubでサーバレスなデータ基盤2024 with Terraform / Cloud Run and PubSub with Terraform
shinyorke
PRO
9
4.3k
自らを強いエンジニアにするための3つの習慣 / I need to be myself, I can't be no one else
shinyorke
PRO
86
91k
Other Decks in Research
See All in Research
生成AI による論文執筆サポート・ワークショップ 論文執筆・推敲編 / Generative AI-Assisted Paper Writing Support Workshop: Drafting and Revision Edition
ks91
PRO
0
120
生成AI による論文執筆サポート・ワークショップ ─ サーベイ/リサーチクエスチョン編 / Workshop on AI-Assisted Paper Writing Support: Survey/Research Question Edition
ks91
PRO
0
140
情報技術の社会実装に向けた応用と課題:ニュースメディアの事例から / appmech-jsce 2025
upura
0
310
R&Dチームを起ち上げる
shibuiwilliam
1
160
Pythonでジオを使い倒そう! 〜それとFOSS4G Hiroshima 2026のご紹介を少し〜
wata909
0
1.3k
超高速データサイエンス
matsui_528
2
380
Multi-Agent Large Language Models for Code Intelligence: Opportunities, Challenges, and Research Directions
fatemeh_fard
0
120
Upgrading Multi-Agent Pathfinding for the Real World
kei18
0
210
その推薦システムの評価指標、ユーザーの感覚とズレてるかも
kuri8ive
1
310
Remote sensing × Multi-modal meta survey
satai
4
710
空間音響処理における物理法則に基づく機械学習
skoyamalab
0
190
HU Berlin: Industrial-Strength Natural Language Processing with spaCy and Prodigy
inesmontani
PRO
0
220
Featured
See All Featured
Exploring the relationship between traditional SERPs and Gen AI search
raygrieselhuber
PRO
2
3.6k
Art, The Web, and Tiny UX
lynnandtonic
304
21k
Visual Storytelling: How to be a Superhuman Communicator
reverentgeek
2
430
Unsuck your backbone
ammeep
671
58k
The Pragmatic Product Professional
lauravandoore
37
7.1k
Java REST API Framework Comparison - PWX 2021
mraible
34
9.1k
Test your architecture with Archunit
thirion
1
2.2k
Building Experiences: Design Systems, User Experience, and Full Site Editing
marktimemedia
0
410
ReactJS: Keep Simple. Everything can be a component!
pedronauck
666
130k
Ethics towards AI in product and experience design
skipperchong
2
200
Exploring the Power of Turbo Streams & Action Cable | RailsConf2023
kevinliebholz
37
6.3k
Sam Torres - BigQuery for SEOs
techseoconnect
PRO
0
190
Transcript
ϝδϟʔϦʔΨʔͷ ࡱΕߴʢPythonฤʣ Shinichi Nakagawa(@shinyorke) kawasaki.rb #069 2019/2/27
Who am I? • Shinichi Nakagawa(@shinyorke) • ʢגʣωΫετϕʔε ٿΤϯδχΞ/CTO •
#rettypy ओ࠵ऀ • ΤϯδχΞ࠾༻ɾٕज़ใྺ3
ͦͷTechϒϩάຊʹඞཁͰ͔͢ʁ ʮ࠾༻ใʯΛޠΔใLTେձ#17@αΠϘζ #PRLT https://speakerdeck.com/shinyorke/sofalsetechburoguben-dang-nibi-yao-desuka-burogufalse-cuo-regao- number-prlt
ϒϩάͷࡱΕߴ=Ԡื ※ձࣾͷٕज़ϒϩάͷͰ͢ʂ ʢݸਓͲ͏͔Θ͔ΒΜʣ
ٿબखͷʮࡱΕߴʯ = ಘՁ ೋྥଧҰຊͲΕ͙Β͍ʹͭͳ͕Δʁ ૹΓόϯτΛఆྔతʹධՁͬͯʁʁ
ٿબखͷʮࡱΕߴʯࢉग़ • શଧ੮ͷϓϨʔΛಘͷߩݙͱͯ͠ఆྔԽ ʮಘظʯͱݺΕΔࢦඪɾߟ͑ํͰΔ • ଧ੮ʹཱͬͨ࣌ͷಘظͱɺ ଧ੮ऴྃޙͷಘظͷࠩͰ ʮϓϨʔ͕ಘʹͭͳ͕͔ͬͨʁʯΛग़͢ ˠಘՁͱݺΕΔͷ
ಘظͱಘՁʢৄ͘͠ʣ • ϥϯφʔͷ(8௨Γ)×ΞτΧϯτ(3௨Γ)=24௨Γͷঢ়گΛ ྨ,͔ͦ͜Β3ΞτऔΒΕΔ·Ͱʹ֫ಘͰ͖Δ(ͱࢥΘΕΔ)ฏۉత ͳಘΛʮಘظ(Run Expectancy)ʯͱݺͿ. • ϓϨʔ(ώοτ,ྥ,etc…)ʹΑͬͯ,ಘظΛ্͔͛ͨ(·ͨ Լ͔͛ͨ)ΛੵΈॏͶͯબखΛධՁ͢Δ. ͜ΕΛʮಘՁ(Run
Value)ʯͱݺͿ. • Α͘Θ͔Μͳ͍ਓɺAnalyzing Baseball Data with R ͘͠ϚωʔɾϘʔϧΛಡΜͰ͍ͩ͘͞ʂ
PythonͰࢉग़ͯ͠ΈΔ • Analyzing Baseball Data with Rͱ͍͏ຊʹɺ RͰͷܭࢉํ๏͕͋ΔͷͰͦΕΛRͰࣸܦ • R͔ΒPythonʹॻ͖͑
• جຊతʹpandasͷ͚ؔͩͰ࣮
ͪͳΈʹσʔλ • ࠓͷϝδϟʔϦʔάͷશଧ੮σʔλ retrosheet͍ͬͯ͏ެ։σʔληοτ • CSVϑΝΠϧɺ110MBͪΐ͍ • 19ສߦɺ96ྻʢ͏ͷ10ྻແ͍ʣ
ಘظʢMLB 2018ʣ த͕ಠࣗࢉग़, MLBͷαΠτͱಉ͡ͳͷͰਖ਼ղͷͣ ݩσʔλɿ https://github.com/chadwickbureau/baseballdatabank ແࢮ Ұࢮ ೋࢮ ϥϯφʔແ͠
0.49 0.26 0.10 Ұྥ 0.87 0.53 0.22 ೋྥ 1.13 0.68 0.32 ࡾྥ 1.43 1.00 0.35 Ұྥೋྥ 1.42 0.93 0.44 Ұྥࡾྥ 1.79 1.21 0.50 ೋྥࡾྥ 1.94 1.36 0.57 ຬྥ 2.35 1.47 0.77
Run Value = New State - State + Run Scored
Run valueɿಘՁʢࡱΕߴʣ New Stateɿଧ੮݁Ռͷಘظ Stateɿଧ੮ʹཱͭલͷಘظ Run Scoredɿ࣮ࡍʹೖͬͨಘʢ0ʙ4ʣ
ܭࢉྫ • ແࢮ1ྥ͔Β͕֮ΊΔೋྥଧͰແࢮ2,3ྥ 1.94(2,3ྥ) - 0.87(1ྥ) + 0() = 1.07
ࡱΕߴ͋Δύλʔϯ • ແࢮ1ྥ͔ΒଉΛٵ͏༻ʹόϯτޭ1ࢮ2ྥ 0.68(1ࢮ2ྥ) - 0.87(1ྥ) + 0() = -0.19 ΉΉʁΉ͠ΖԼ͕ͬͯΔͧʁʁ • ୯७ͳྫ͕ͩ͜ΕͰϓϨʔධՁՄೳ
͜ΕͰϝδϟʔϦʔΨʔʹͯΊ ධՁ͢ΔͱͲ͏ͳΔ͔ʁʁʁ
…ͱ͍͏ଓ͖ͷɺ ʮBaseball Play Study 2019य़ʯ Ͱ൸࿐͢Δʢ͔ʣ ※3/27(ਫ)ϓϨΠϘʔϧ⽁ #bpstudy
͓͠·͍.