We consider the problem of learning predictive
models for in-game sports play prediction. Focusing on basketball, we develop models for anticipating near-future events given the current game state. We employ a latent factor modeling approach, which leads to a compact data representation that enables efficient prediction given raw spatiotemporal tracking data. We validate our approach using tracking data from the 2012-2013 NBA season, and show that our model can make accurate in-game predictions. We provide a detailed inspection of our learned factors, and show that our model is interpretable and corresponds to known intuitions of basketball gameplay.
title: “Learning Fine-Grained Spatial Models for Dynamic Sports Play Prediction" info: title: "Learning Fine-Grained Spatial Models for Dynamic Sports Play Prediction" authors: "Yue, Yisong; Lucey, Patrick; Carr, Peter; Bialkowski, Alina; Matthews, Iain" labs: "Disney Research, Queensland University of Technology" conference: "" sport: "Basketball" sport_icon: "basketball" url: "https://ieeexplore.ieee.org/document/7023384" authors:
Gota Shirato date: 2019-06-20T00:00:00+09:00 description: "Summary" type: technical_note draft: true
要旨 Abstract
We consider the problem of learning predictive models for in-game sports play prediction. Focusing on basketball, we develop models for anticipating near-future events given the current game state. We employ a latent factor modeling approach, which leads to a compact data representation that enables efficient prediction given raw spatiotemporal tracking data. We validate our approach using tracking data from the 2012-2013 NBA season, and show that our model can make accurate in-game predictions. We provide a detailed inspection of our learned factors, and show that our model is interpretable and corresponds to known intuitions of basketball gameplay.
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従来のアプローチとはどのように異なるか
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