samshipengs/Coordinated-Multi-Agent-Imitation-Learning
This is an implementation of the paper "Coordinated Multi Agent Imitation Learning", or the Sloan version "Data-Driven Ghosting using Deep Imitation Learning" using Tensorflow
This project helps basketball coaching staff and analysts understand defensive strategies by predicting how a defending team would move in various game situations. You input raw game tracking data, specifically player and ball coordinates, and it outputs simulated defensive player trajectories. This tool is designed for sports analysts, coaches, and strategists who want to evaluate defensive play without manual annotation.
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Use this if you want to automatically generate 'ghost' defensive player movements to analyze team strategy, rather than manually marking positions.
Not ideal if you need to predict offensive player movements or analyze individual player defensive behavior rather than overall team defense.
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Jun 28, 2018
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