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
坂本勇人選手はいつ通算3,000安打を達成するか? AIに聞いてみました / Hayato S...
Search
Shinichi Nakagawa
PRO
December 13, 2020
Research
940
1
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
坂本勇人選手はいつ通算3,000安打を達成するか? AIに聞いてみました / Hayato Sakamoto Performance Prediction Using Feature Engineering with Machine Learning and Python
Sports Analytics Meetup #9 2020/12/13 LT
#Baseball #SABRmetrics #ML #Python
Shinichi Nakagawa
PRO
December 13, 2020
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
480
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
460
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
300
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
8k
AI・LLM事業部のSREとタスクの自動運転
shinyorke
PRO
0
560
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
4.5k
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
敵対生成プロンプト同時探索による内省型プロンプト最適化
kinoue_smarthr
0
200
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
130
第66回コンピュータビジョン勉強会@関東 Epona: Autoregressive Diffusion World Model for Autonomous Driving
kentosasaki
0
630
[BlackHatAsia2026] Hidden Telemetry: Uncovering TraceLogging ETW Providers You're Not Using (Yet)
asuna_jp
1
530
Ghost in the 7‑Zip: The Shadow of Residential Proxies Creeping into Your Life
nttcom
0
1.1k
RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent
satai
2
300
Data Visualization Tools in the Age of AI
flekschas
0
160
Ankylosing Spondylitis
ankh2054
0
170
NII S. Koyama's Lab Research Overview AY2026
skoyamalab
0
310
AIで最適化を解けるか?
mickey_kubo
0
120
Harness Engineering and Al Agent
kzinmr
3
1.7k
CyberAgent AI Lab研修 / Social Implementation Anti-Patterns in AI Lab
chck
7
4.7k
Featured
See All Featured
Building Flexible Design Systems
yeseniaperezcruz
330
40k
Lightning Talk: Beautiful Slides for Beginners
inesmontani
PRO
2
580
A brief & incomplete history of UX Design for the World Wide Web: 1989–2019
jct
2
400
Building Applications with DynamoDB
mza
96
7.1k
Performance Is Good for Brains [We Love Speed 2024]
tammyeverts
12
1.7k
The Illustrated Guide to Node.js - THAT Conference 2024
reverentgeek
1
390
Creating an realtime collaboration tool: Agile Flush - .NET Oxford
marcduiker
35
2.5k
Tell your own story through comics
letsgokoyo
1
950
SERP Conf. Vienna - Web Accessibility: Optimizing for Inclusivity and SEO
sarafernandez
2
1.5k
Believing is Seeing
oripsolob
1
150
CoffeeScript is Beautiful & I Never Want to Write Plain JavaScript Again
sstephenson
162
16k
Joys of Absence: A Defence of Solitary Play
codingconduct
1
390
Transcript
ӫޫͷഎ൪߸6⃣ ࡔຊ༐ਓ3,000ຊ҆ଧه೦LT Shinichi Nakagawa(@shinyorke) Sports Analyst Meetup #9 2020/12/13
ʁʁʁʮ༐ਓ·ͩ2,000ຊ҆ଧΖʯ
ͦͷͱ͓ΓͰ͍͟͝·͢, ࣦྱ͠·ͨ͠
ࡔຊ༐ਓ͍ͭ௨ࢉ3,000ຊ҆ଧΛ ୡ͢Δ͔AIʹฉ͍ͯΈ·ͨ͠ Shinichi Nakagawa(@shinyorke) Sports Analyst Meetup #9 2020/12/13
ຊͷςʔϚ • ࡔຊ༐ਓ͕͍ͭ͝Ζ௨ࢉ3,000ຊ҆ଧΛୡ͢Δ͔༧͢Δ • ਅ໘ͳ, ༧ଌͲ͜·ͰͰ͖Δ͔ࢼͯ͠ΈΔ • ʮӫޫͷഎ൪߸6⃣ࡔຊ༐ਓ3,000ຊ҆ଧͷಓʯ͕ Կޙʹ์ө͞ΕΔ͔Θ͔Δ΄͏͕͍͍ΑͶʢదʣ
Who am I ?ʢ͓લ୭Αʣ • Shinichi Nakagawaʢத ৳Ұʣ • େͷSNSͰʮshinyorkeʢ͠ΜΑʔ͘ʣʯͱ໊͍ͬͯ·͢
• JX Press Corporation Senior Engineer ʢJX௨৴ࣾ γχΞɾΤϯδχΞʣ • Baseball Engineer, Data Scientist ʢੜͷٿΤϯδχΞɾσʔλαΠΤϯςΟετʣ • Ҏલ͓ࣄͰٿΤϯδχΞʮͩͬͨʯਓ
ʲCMʳαʔόʔαΠυΠϯλʔϯืूͯ͠·͢ https://www.wantedly.com/projects/543767 ※ֶੜ͞ΜݶఆͰ͢&ผʹεϙʔπͷࣄͬͯ༁͡Όͳ͍Ͱ͢
26.4ඵͰৼΓฦΔ2020ͷϓϩٿ • ιϑτόϯΫϗʔΫεຊҰʢ4࿈ʣ • όϯςϦϯυʔϜφΰϠ&౦ژυʔϜͷձࣾ(ry • ࡔຊ༐ਓʢڊਓʣ, ӈଧऀͱͯ͠࠷গͰ2,000ຊ҆ଧୡ ͦͷଞʹ͍ͬͺ͍͋Δ͚ͲׂѪʢదʣ
ࡔຊ༐ਓબखͳΒ3,000ຊ҆ଧ༨༟Ͱ • 31ࡀ10ϲ݄Ͱͷୡӈଧऀ࠷ • গͳ͘ͱ͋ͱ4, 5ݱ͢ΔͰ͠ΐ γϣʔτͰݩؾʹΠέͯ·͢͠. • ͡Ό͍͋ͭࠒ3,000ຊ҆ଧΔͷ͞?
͜Εͬͯաڈͷσʔλ͔Β͏·͍۩߹ʹΕ༧ଌՄೳͰ? https://www.nikkansports.com/baseball/news/202011080000831.html
ͱ͍͏Θ͚Ͱ༧ଌϞσϧΛ࡞Γ·ͨ͠. ࠓճPyCon JP 2020ͰͬͨͭΛݩʹͪΐͬͱΞϨϯδͯ͠࡞Γ·ͨ͠. https://shinyorke.hatenablog.com/entry/baseball-and-ml-with-python
ࠓճͷΞϓϩʔνʢΊͬͪΌཁʣ • ϝδϟʔϦʔάͷσʔλΛͬͯ 1.࠷ۙ୳ࡧܥͷΞϧΰϦζϜͰ͍ۙબख୳͠ 2.֬ʢͬΆ͍ʣํ๏Ͱ༧ଌΛ࡞Δ • ↑ͷ݁ՌΛStreamlitͰՄࢹԽ
ͳͥϝδϟʔͷσʔλͳͷ͔ • 3,000ຊ҆ଧୡऀ, ຊϓϩٿҰਓ͔͍͠ͳ͍ʢ͠ʣ ※ʮ୭Ͱ͔͢ʁʯ࣭ͬͯ׃ͧ • ϝδϟʔେਖ਼ٛΠνϩʔ༷ଞ, 3,000ຊ҆ଧୡऀ͕ଟ͍. •
σʔλͷϥΠηϯε&εΫϨΠϐϯάͱ͔େมͰ͠ΐ.
ࡔຊ༐ਓʹ͍ۙϝδϟʔϦʔΨʔ ࢲʢshinyorkeʣ࡞, ʮzobristʯϞσϧͰग़ͨ݁͠Ռʢ΄΅ANNͰ͢ʣ ϝδϟʔϦʔάΛͬͯΔਓ͔ΒΈΔͱೲಘͷ݁ՌͩͱࢥΘΕ ໊͓લνʔϜ ʢ௨ࢉʣ ଧຊྥଧ௨ࢉ҆ଧ ಛͱ͔ 9BOEFS#PHBFSUT
ʢ3FE4PYʣ ଧ੮ӈଧ ௨ࢉ014 ݱ۶ࢦͷ߈ܸܕγϣʔτ %FSFL+FUFS ʢ:BOLFFTʣ ଧ੮ӈଧ આ໌ෆཁͷελʔ खʹݶΔͱ௨ࢉ҆ଧҐ 5SPZ5VMPXJU[LJ ʢ3PDLJFT FUDʜʣ ଧ੮ӈଧ ௨ࢉ014 ߈ܸܕγϣʔτ ͳ͓ຊڌ +JNNZ3PMMJOT ʢ1IJMMJFT FUDʜʣ ଧ੮྆ଧ कඋܕͳγϣʔτ ࣮ಇͷແࣄ೭໊അ
σϞ͠·͢
ࡔຊ༐ਓͷࠓޙ - ҆ଧɾຊྥଧɾଧ ࣅ͍ͯΔϝδϟʔϦʔΨʔXਓͷΛ75%λΠϧͰࢉग़
ࡔຊ༐ਓͷࠓޙ - ଧ ࣅ͍ͯΔϝδϟʔϦʔΨʔXਓͷΛ75%λΠϧͰࢉग़
ࡔຊ༐ਓͷࠓޙΛ·ͱΊΔͱ 2027ʢ38ࡀʣ·Ͱنఆଧ֬อͰ͖ΔͬΆ͍. ※نఆଧ443ଧ੮ʢ2019ͷࢼ߹143×3.1Ͱܭࢉ, ࢛ࣺޒೖʣ ྸ ଧ ҆ଧ ຊྥଧ
ଧ ଧ
ࡔຊ༐ਓબख, ௨ࢉʢ༧ଌʣ ͜ΕͰγϣʔτͩͬͨΒڧ͗͢Ͱʢ͑ʣ ظؒ ଧ ҆ଧ ຊྥଧ ଧ ଧ ·Ͱ
˞ݱ࣮ ˞༧ଌ ௨ࢉʢ༧ଌʣ
ߟ • 39ࡀ͝Ζʹ3,000ຊ҆ଧୡ…ͷϖʔε·͋·͋͋Γͦ͏. ͨͩ͠ྼԽආ͚ΒΕͳ͍. • 36ࡀ͔ΒͷٸܹྼԽकඋҐஔมߋͱ͔ͰઌԆ͠Ͱ͖ͦ͏. ʲࢀߟʳѨ෦৻೭ॿ36ࡀ͔Βัख->ϑΝʔετʹίϯόʔτ •
௨ࢉຊྥଧʢ༧ଌʣ321ຊ…334ຊߦͬͯཉ͍͚͠ͲͲ͏͔
͜ͷ͓͠·͍Ͱ͢…͕ʂʁ • ༧ଌϞσϧ࡞ΓηΠόʔϝτϦΫεແ͠ͰͰ͖ͳ͔ͬͨ • ʮRʹΑΔηΠόʔϝτϦΫεೖʯग़ͨ͠, ͜ͷลΛಛྔΤϯδχΞϦϯάతʹৼΓฦΓ͍ͨ • ͍ͬͯ͏ϩϯάτʔΫ͕Ͱ͖ͨΒ͍͍ͳ⚾
ʢҙ༁ɿࠓճંͬͨϞσϧͷΛ͍ͨ͠ʣ ӡӦͷօ༷, ͝ݕ౼ΑΖ͓͘͠ئ͍͠·͢
ήʔϜηοτ⚾ ͝ਗ਼ௌ͋Γ͕ͱ͏͍͟͝·ͨ͠. Shinichi Nakagawa(Twitter/Facebook/etc… @shinyorke)