Lock in $30 Savings on PRO—Offer Ends Soon! ⏳
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
Shinichi Nakagawa
PRO
December 17, 2020
Research
0
3k
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
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
230
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
120
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
5.5k
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
6.1k
AI・LLM事業部のSREとタスクの自動運転
shinyorke
PRO
0
490
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
3.8k
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
3.5k
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
4.2k
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
90k
Other Decks in Research
See All in Research
SREのためのテレメトリー技術の探究 / Telemetry for SRE
yuukit
12
2.2k
地域丸ごとデイサービス「Go トレ」の紹介
smartfukushilab1
0
500
Stealing LUKS Keys via TPM and UUID Spoofing in 10 Minutes - BSides 2025
anykeyshik
0
160
Minimax and Bayes Optimal Best-arm Identification: Adaptive Experimental Design for Treatment Choice
masakat0
0
200
"主観で終わらせない"定性データ活用 ― プロダクトディスカバリーを加速させるインサイトマネジメント / Utilizing qualitative data that "doesn't end with subjectivity" - Insight management that accelerates product discovery
kaminashi
12
6.4k
SNLP2025:Can Language Models Reason about Individualistic Human Values and Preferences?
yukizenimoto
0
220
令和最新技術で伝統掲示板を再構築: HonoX で作る型安全なスレッドフロート型掲示板 / かろっく@calloc134 - Hono Conference 2025
calloc134
0
440
[IBIS 2025] 深層基盤モデルのための強化学習驚きから理論にもとづく納得へ
akifumi_wachi
14
7.8k
能動適応的実験計画
masakat0
2
1.1k
EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues
satai
3
370
世界の人気アプリ100個を分析して見えたペイウォール設計の心得
akihiro_kokubo
PRO
63
34k
Integrating Static Optimization and Dynamic Nature in JavaScript (GPCE 2025)
tadd
0
160
Featured
See All Featured
How To Stay Up To Date on Web Technology
chriscoyier
791
250k
Scaling GitHub
holman
464
140k
How to train your dragon (web standard)
notwaldorf
97
6.4k
The Invisible Side of Design
smashingmag
302
51k
Keith and Marios Guide to Fast Websites
keithpitt
413
23k
How to Create Impact in a Changing Tech Landscape [PerfNow 2023]
tammyeverts
55
3.1k
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
285
14k
Six Lessons from altMBA
skipperchong
29
4.1k
The Illustrated Children's Guide to Kubernetes
chrisshort
51
51k
Facilitating Awesome Meetings
lara
57
6.7k
We Have a Design System, Now What?
morganepeng
54
7.9k
Stop Working from a Prison Cell
hatefulcrawdad
273
21k
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)