グラデーション回帰モデルに基づくアスリートのパフォーマンスの予測.
Xiaolei Wei1, Shuang Liang2, Wei Diao1
1Physical Education Teaching and Research Department, Changchun University of Technology, Changchun, Jilin, 130012, China.
Scientific reports
|February 18, 2026
まとめ
新しいグラデント回帰モデルは,従来の方法と比較して,アスリートのパフォーマンスの予測精度 (R2=0.923) を大幅に改善します. このデータベースのアプローチは,トレーニングと人材管理のためのスポーツ科学の洞察を強化します.
科学分野:
- スポーツ科学とアナリティクス
- スポーツにおける機械学習
背景:
- 従来の統計モデルは,アスリートの生理学,ライフスタイル,および文脈的なデータの複雑な非線形性と闘っています.
- 正確なアスリートのパフォーマンスの予測は,トレーニングデザイン,怪我の予防,才能の管理に不可欠です.
研究 の 目的:
- スポーツ選手のパフォーマンススコアをテーブルデータを使って予測し,予測有効性と一般化に重点を置く.
- SHAPベースの説明を通じて解釈しやすさを高め,実用的な展開のための計算効率を確保する.
- 主要なパフォーマンスの決定要因を特定し,スポーツコーチングの決定に情報を提供するためのデータ主導のプラットフォームを提供すること.
主な方法:
- Kaggle Athlete Performance Prediction Datasetを利用し,人口統計学,トレーニング,生理学,ライフスタイルの特徴を網羅しました.
- 計算,正規化,エンコーディング,機能エンジニアリングを含むデータ前処理技術を適用します.
- 10倍クロス検証でグラデント回帰モデルを訓練し,そのパフォーマンスを線形回帰,リッジ回帰,サポートベクトル回帰,ランダムフォレスト,ニューラルネットワークと比較した.
主要な成果:
- 梯子回帰モデルは,0.923の優れたR2を達成し,ニューラルネットワーク (R2=0.901) とランダムフォレスト (R2=0.887) を上回った.
- 残留とエラーの分析は最小のバイアスとバリエンスを確認し,学習ダイナミクスは効率的な収束と安定性を示した.
- このモデルは,ベースラインモデルよりも正確で解釈しやすいことが証明されました.
結論:
- Gradient Regression Modelは,アスリートのパフォーマンスの予測のための堅牢で解釈可能なソリューションを提供します.
- 発見は,個別化されたトレーニングプログラムと継続的なパフォーマンスモニタリングのアプリケーションをサポートします.
- 将来の研究は,より大規模で縦断的な研究と,生体力学および心理学的データを組み込んだハイブリッドフレームワークを探求すべきである.
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