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
坂本勇人さん改め山田哲人さんの成績予測をやってみた / Baseball Play Study...
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
Shinichi Nakagawa
PRO
December 17, 2020
Research
3.1k
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
坂本勇人さん改め山田哲人さんの成績予測をやってみた / Baseball Play Study 2020 Winter
Baseball Play Study 2020冬 LT資料
https://bpstudy.connpass.com/event/197652/
Shinichi Nakagawa
PRO
December 17, 2020
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
実例から見るLLMのマンガ理解:実務VQAタスクによる長期的文脈と視覚情報の定性評価
kzmssk
0
100
[ACL 2026 Demo] Fast-MIA: Efficient and Scalable Membership Inference for LLMs
upura
0
110
シングルチャネルマルチトーカー音声認識の進展
ryomasumura
0
260
Cross-Media Human-Information Interaction
signer
PRO
0
220
[最先端NLP勉強会2026] Agentic Rubrics as Contextual Verifiers for SWE Agents
rfujii
1
340
LA-Bench 2025:実験指示から実行可能手順を生成するためのデータセット/LA-Bench 2025: A Dataset for Generating Executable Experimental Procedures from Experimental Instructions
stktu
0
150
SAM3を用いたコマ・吹き出しの 領域検出と分割構造からの読み順推定
kzmssk
0
110
2026年度 生成AI を活用した論文執筆ガイド/ワークショップ / 2026 Academic Year Guide to Writing Papers Using Generative AI - Workshop
ks91
PRO
0
230
最先端NLP 2026 論文紹介: Wait, Wait, Wait... Why Do Reasoning Models Loop? / SNLP Paper Review: Wait, Wait, Wait... Why Do Reasoning Models Loop?
tkng
0
220
某助成金プロジェクト採択に向けて企業研究所のアウトリーチ専任者がやったこと
afroscript
0
180
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
Featured
See All Featured
What does AI have to do with Human Rights?
axbom
PRO
1
2.4k
<Decoding/> the Language of Devs - We Love SEO 2024
nikkihalliwell
1
320
Agile that works and the tools we love
rasmusluckow
331
22k
Navigating Weather and Climate Data
rabernat
0
510
Rebuilding a faster, lazier Slack
samanthasiow
85
9.6k
Paper Plane
katiecoart
PRO
2
53k
Building Experiences: Design Systems, User Experience, and Full Site Editing
marktimemedia
0
600
Bootstrapping a Software Product
garrettdimon
PRO
306
120k
Max Prin - Stacking Signals: How International SEO Comes Together (And Falls Apart)
techseoconnect
PRO
0
450
Agile Actions for Facilitating Distributed Teams - ADO2019
mkilby
0
270
Facilitating Awesome Meetings
lara
57
7.1k
Connecting the Dots Between Site Speed, User Experience & Your Business [WebExpo 2025]
tammyeverts
11
1k
Transcript
ࡔຊ༐ਓ͍ͭ௨ࢉ3,000ຊ҆ଧΛ ୡ͢Δ͔AIʹฉ͍ͯΈ·ͨ͠ Baseball Play Study 2020ౙ - γʔζϯৼΓฦΓεϖγϟϧ 2020/12/17 Shinichi
Nakagawa(@shinyorke)
ϫΠʮઌಉ͡ΛଞॴͰͨ͠Α͏ͳʯ
͋ͬʢ͠ʣ ༵ʹʮSports Analyst Meetup #9ʯͰLTͪ͠Όͬͯ·ͨ͠ https://speakerdeck.com/shinyorke/hayato-sakamoto-performance-prediction-using-feature-engineering-with-machine-learning-and-python
ʲ݁ʳࡔຊ༐ਓબखͷ༧ଌ 39ࡀͷγʔζϯ, ͖ͬͱΈΜͳʹॕ͞ΕΔͰ͠ΐ͏
ʲ݁ʳࡔຊ͞Μ3,000҆ଧ39ࡀ ※2028γʔζϯ, ͋͘·ͰݟࠐΈͰ͢
ΊͰͨ͠ΊͰͨ͠ ͝ਗ਼ௌ͋Γ͕ͱ͏͍͟͝·ͨ͠
͍͍, ͜ΕͰऴΘΕΜͩΖʢ͑ʣ
ͪΌΜͱωλ, ༻ҙͯ͠·͢
ॕɾࢁాਓ༷ϠΫϧτཹ ظܖظؒͷΛ AIʹ፻ͤ͞ฉ͍ͯΈ·ͨ͠ Baseball Play Study 2020ౙ - γʔζϯৼΓฦΓεϖγϟϧ
2020/12/17 Shinichi Nakagawa(@shinyorke)
ຊͷςʔϚ • ϠΫϧτ͍ຊϓϩٿͷਓؒࠃๅͱݴͬͯաݴͰͳ͍ ࢁాਓબख͕ҰମͲΕ΄ͲͷΛࠓޙ͢ͷ͔͏ • ͿͬͪΌ͚ܖֹۚͷ׆༂͢Δͷ͔?͖ʹͳΔ • ͖͏AI͍ͥͬͯ͢͝Θ͔ͬͯ͘ΕͨΒخ͍͠ʢ͜ͳΈʣ
Who am I ?ʢ͓લ୭Αʣ • Shinichi Nakagawaʢத ৳Ұʣ • େͷSNSͰʮshinyorkeʢ͠ΜΑʔ͘ʣʯͱ໊͍ͬͯ·͢
• JX Press Corporation Senior Engineer ʢJX௨৴ࣾ γχΞɾΤϯδχΞʣ • Baseball Engineer, Data Scientist ʢੜͷٿΤϯδχΞɾσʔλαΠΤϯςΟετʣ • ࣗশʮBaseball Play StudyͷϨδΣϯυʯ, ݩɾϓϩͷٿΤϯδχΞ • ࠷ۙ, 12ٿஂതѪओٛऀʹͳΓ·ͨ͠ʢ͕ݩւಓͳͷͰϋϜ͖ʹͳΔʣ.
ඵͰৼΓฦΔ2020ͷϓϩٿ • όϯςϦϯυʔϜφΰϠ, ര • ౦ژυʔϜબख, Ҡ੶ʢ༧ఆʣ • 26 -
4ʢ͠ʣ • ࢁాਓબख, 7૯ֹ40ԯԁʢਪఆʣͰϠΫϧτཹ
ࢁాਓ͞Μͷ740ԯԁͱ͔͍͏ܖ • เ5ԯԁʢʴΠϯηϯςΟϒʣ×7, Β͍͠. • ϑΝϯΈΜͳخ͍͠Ͱ͠ΐ͏, ϫΠخ͍͠Ͱ͢. • ͏ҰਓͷϫΠʮ40ԯԁͬͯݩ͕औΕΔΜΖ͔ʯ
…ͱ͍͏༁Ͱ, ٿAI͞Μʹฉ͍ͯΈ·ͨ͠.
ࠓճ͏͖͏ͷਓೳ PyCon JP 2020ͰͬͨʔͭΛͦͷ··͍·ͨ͠ʢ#spoana ͱಉ͡Ͱ͢ʣ. https://shinyorke.hatenablog.com/entry/baseball-and-ml-with-python
ͻͱ·ͣ݁ՌΛ͓ݟͤ͠·͢.
ࢁాਓ༷ͷࠓޙ - ҆ଧɾຊྥଧɾଧ 150҆ଧͪΐ͍, 17ʙ19ຊྥଧΛՔ͗ͭͭ, 70ଧҎ্Ք͙
ࢁాਓ༷ͷࠓޙ - ଧ 32, 33ࡀ͋ͨΓͰಥવଧʹ֮Ίͯͯ໘ന͍݁Ռʹ
ࢁాਓ༷ͷࠓޙΛ·ͱΊΔͱ ͜ΕͰͣͬͱηΧϯυͬͯ͘ΕΔͳΒ͗͢͢͝Ͱ ͑?τϦϓϧεϦʔ??͏ʔʔΜ ྸ ଧ ҆ଧ ຊྥଧ ଧ ଧ
ࢁాਓ༷ͷ௨ࢉʢ༧ଌʣ ͜ΕͰηΧϯυͬͯڧ͗͢͠·ͤΜ͔ʢ͑ʣ ظؒ ଧ ҆ଧ ຊྥଧ ଧ ଧ ·Ͱ ˞ݱ࣮
˞༧ଌ ௨ࢉʢ༧ଌʣ
ࢁాਓ༷ͷ௨ࢉʢ༧ଌʣ ͜ΕͰηΧϯυͬͯڧ͗͢͠·ͤΜ͔ʢ͑ʣ ظؒ ଧ ҆ଧ ຊྥଧ ଧ ଧ ·Ͱ ˞ݱ࣮
˞༧ଌ ௨ࢉʢ༧ଌʣ 334ͪΌ͏Μ͔ʔ͍
ࢁాਓ༷2027ʢ34ʣ͕͢ه • ௨ࢉຊྥଧɾଧɾଧͰߴकಓࢯΛ͑Δ • ௨ࢉ2,236҆ଧͰ໊ٿձೖΓ·ͬͨͳ͠ • ໊࣮ͱʹϓϩٿ্࢙࠷ڧͷηΧϯυʹͳΔՄೳੑ
ͱ͍͑Ͱ͢Α • 7ܖதͷτϦϓϧεϦʔʢ3ׂ30ຊྥଧ30౪ྥʣଟແཧ • ਓೳ500ଧ੮Ҏ্Ք͙༧ଌΛ͍ͯ͠Δ͚Ͳ, ਓ༷Ҋ֎ނোͱ͔͋Δͷ͕ͪΐͬͱ৺ • ηΧϯυकඋෛ୲͕͔ͳΓ͋ΔϙδγϣϯͳͷͰ
ଧྗΛ׆͔ͨ͢Ίͷίϯόʔτ͋Δ͔͠Εͳ͍
݁ • ຊҰͷηΧϯυʹͳΓͦ͏ͳͷͰ740ԯͷܖଟଥ • ͱ͍͑େࣄʹͬͯཉ͍͠, ͋ΔҙຯਓؒࠃๅͰ͢͠ • ͘Ε͙ΕମʹؾΛ͚ͭͯؤுͬͯ΄͍͠ʂ
ήʔϜηοτ⚾ ͝ਗ਼ௌ͋Γ͕ͱ͏͍͟͝·ͨ͠. Shinichi Nakagawa(Twitter/Facebook/etc… @shinyorke)