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
410
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
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
LLMの出力を"いい感じに"する技術 / Taming LLM Output: AI Agent Design Patterns with FastAPI
shinyorke
PRO
4
1.8k
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
700
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
690
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
320
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
190
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
6.7k
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
9.6k
AI・LLM事業部のSREとタスクの自動運転
shinyorke
PRO
0
590
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
4.9k
Other Decks in Research
See All in Research
PGDM: Physically Guided Diffusion Model for L Downscaling
satai
3
460
VLMの推論を高速化する視覚トークン削減の仕組み
tattaka
2
300
全国町字単位空き家率推定データver1.0データ仕様
microbaseinc
0
220
[BlackHatAsia2026] Hidden Telemetry: Uncovering TraceLogging ETW Providers You're Not Using (Yet)
asuna_jp
1
700
Anthropic が提案する LLM の内部状態を自然言語で説明可能にした Natural Language Autoencoders / Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations
shunk031
0
200
Spatial Active Noise Control Based onSound Field Interpolation Incorporating Physical Constraints
skoyamalab
0
180
最先端NLP勉強会2026 論文紹介:Reasoning with Sampling: Your Base Model is Smarter Than You Think (ICLR 2026 paper)
kogoro
4
590
多様なデータを許容し学習し続ける模倣学習 / Advanced Imitation Learning for VLA
prinlab
0
290
CVPR2026論文紹介_VLMにとって良いvision encoderとは何か?Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance
kobayashi31
1
210
SoftMatcha 2: 1兆語規模コーパスの超高速かつ柔らかい検索
e869120_sub
7
3.8k
Karkada さんの論文 × 2 の紹介: (1) Closed-Form Training Dynamics Reveal Learned Features and Linear Structure in Word2Vec-like Models, (2) Symmetry in language statistics shapes the geometry of model representations
eumesy
PRO
1
670
東京大学工学部計数工学科、計数工学特別講義の説明資料
kikuzo
0
650
Featured
See All Featured
XXLCSS - How to scale CSS and keep your sanity
sugarenia
249
1.3M
Navigating the Design Leadership Dip - Product Design Week Design Leaders+ Conference 2024
apolaine
2
410
Measuring & Analyzing Core Web Vitals
bluesmoon
9
990
Effective software design: The role of men in debugging patriarchy in IT @ Voxxed Days AMS
baasie
0
510
GitHub's CSS Performance
jonrohan
1033
470k
Bioeconomy Workshop: Dr. Julius Ecuru, Opportunities for a Bioeconomy in West Africa
akademiya2063
PRO
1
340
The SEO identity crisis: Don't let AI make you average
varn
0
550
RailsConf 2023
tenderlove
30
1.5k
How to Get Subject Matter Experts Bought In and Actively Contributing to SEO & PR Initiatives.
livdayseo
0
190
Stop Working from a Prison Cell
hatefulcrawdad
274
21k
The Impact of AI in SEO - AI Overviews June 2024 Edition
aleyda
6
1.2k
The browser strikes back
jonoalderson
0
1.6k
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
͓͠·͍.