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
400
0
Share
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
More Decks by Shinichi Nakagawa
See All by Shinichi Nakagawa
野球解説AI Agentを開発してみた - 2026/02/27 LayerX社内LT会資料
shinyorke
PRO
0
450
WBCの解説は生成AIにやらせよう - 生成AIで野球解説者AI Agentを実現する / Baseball Commentator AI Agent for Gemini
shinyorke
PRO
1
430
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
290
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
160
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
6.3k
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
3
7.7k
AI・LLM事業部のSREとタスクの自動運転
shinyorke
PRO
0
550
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
4.4k
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
3.7k
Other Decks in Research
See All in Research
さくらインターネット研究所テックトーク2026春、研究開発Gr.25年度成果26年度方針
kikuzo
0
140
2026年3月1日(日)福島「除染土」の公共利用をかんがえる
atsukomasano2026
0
590
IEEE AIxVR 2026 Keynote Talk: "Beyond Visibility: Understanding Scenes and Humans under Challenging Conditions with Diverse Sensing"
miso2024
0
180
2026 東京科学大 情報通信系 研究室紹介 (すずかけ台)
icttitech
0
3.3k
20年前に50代だった人たちの今
hysmrk
0
200
LLM の Attention 機構まとめ — 数式・計算量・メモリ
puwaer
7
1.8k
Ankylosing Spondylitis
ankh2054
0
170
LOSの検討(λ Kansai 2026 in Winter)
motopu
0
120
[チュートリアル] 電波マップ構築入門 :研究動向と課題設定の勘所
k_sato
0
420
正規分布と最適化について
koide3
0
190
ScoreMatchingRiesz for Automatic Debiased Machine Learning and Policy Path Estimation with an Application to Japanese Monetary Policy Evaluation
masakat0
0
270
【SIGGRAPH Asia 2025】Lo-Fi Photograph with Lo-Fi Communication
toremolo72
0
160
Featured
See All Featured
Designing Dashboards & Data Visualisations in Web Apps
destraynor
231
54k
Navigating Weather and Climate Data
rabernat
0
190
Primal Persuasion: How to Engage the Brain for Learning That Lasts
tmiket
0
340
How to Build an AI Search Optimization Roadmap - Criteria and Steps to Take #SEOIRL
aleyda
1
2k
Winning Ecommerce Organic Search in an AI Era - #searchnstuff2025
aleyda
1
2k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
659
62k
XXLCSS - How to scale CSS and keep your sanity
sugarenia
250
1.3M
Agile Actions for Facilitating Distributed Teams - ADO2019
mkilby
0
190
Chrome DevTools: State of the Union 2024 - Debugging React & Beyond
addyosmani
10
1.2k
Connecting the Dots Between Site Speed, User Experience & Your Business [WebExpo 2025]
tammyeverts
11
910
Navigating Algorithm Shifts & AI Overviews - #SMXNext
aleyda
1
1.2k
Technical Leadership for Architectural Decision Making
baasie
3
360
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
͓͠·͍.