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慶大野球部のリーグ戦中にアナリストの僕が取り組んだこと
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Sports Analyst Meetup #9での発表資料です。 Twitter : https://twitter.com/batterrrrrrrR31
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慶大野球部のリーグ戦中にアナリストの僕が取り組んだこと
1.
慶大野球部のリーグ戦中に アナリストの僕が取り組んだこと Sports Analyst Meetup
#9 raipachi
2.
自己紹介 2 柴田 頼仁(Shibata Raijin) らいじん@batterrrrrrrR31 慶應義塾大学理工学研究科修士2年、応用統計解析の研究 慶應義塾高校野球部在籍中、スポーツ×統計学に興味を持ち理工学部へ進学 第8回スポーツデータ解析コンペティション・野球部門最優秀賞 Kaggle
Expert 2020年夏から慶大野球部のデータ班をサポート
3.
慶應義塾大学野球部について 3 東京六大学野球リーグに所属 主なプロ野球OB:高橋由伸、郡司裕也、木澤尚文 2020年度リーグ戦は春・秋ともに2位 ラプソードを導入するなど、データ活用に精力的 2020年度からアナリスト枠が新設(僕は所属していないです)
4.
リーグ戦期間のデータ活用の現状と課題 4 • 翌週対戦するチームについてのレポートがメイン • 自チームに対するフィードバックは薄い •
trackman社からリーグ戦全試合のトラッキングデータが提供されている が技術的に扱える人がおらず、活用できていない • トラッキングデータを自チームの育成のために活用したいと考えている • 投手陣は特にデータ活用に対する抵抗感が少ない トラッキングデータを用いた自チームの 投手陣に対するフィードバック ※リーグ戦期間中2回実行
5.
使用したデータ 5 • 基礎集計レベルのスコア情報 • trackman社から提供されたリーグ戦のトラッキングデータ
6.
フィードバックの際に意識したこと 6 • 選手のアウトプットベースで分析内容を考える • 速報性 •
事実と考察をはっきり分ける • 過去の試合やシーズンと比較して、成績の変化の原因を探す • 分かりやすい内容
7.
具体例 非公開 Sports Analyst Meetup
#9 ではトラッキングデータを用いた実際の分析例と それを受けた選手の取り組みについて紹介。
8.
まとめ 8 • 難しい分析は必要ない、当たり前の結果も丁寧に伝えれば選手には有用な フィードバックになる • 選手がどう取り組むかのアウトプットベースで分析内容を考えるのが大事 (フィードバックを受けてできることは限られる、迷いを消すための分析) •
trackman社提供のトラッキングデータを扱える日本で数少ないチャンス がある六大学野球は、アナリスト志望の学生に超おすすめ!
9.
ご清聴ありがとうございました データ提供にご協力いただいた六大学野球連盟様、TrackMan社様、 慶應義塾大学野球部様に厚く御礼申し上げます。
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