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Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badminton

AAAI 2023 · Graph learning › Knowledge and temporal graphs · approach: Continuous-time dynamic graph learning · 19 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add dynamic GCN in place of static GCN (ablation)DyMF · Badminton singles, τ=8MSE location: 1.1838 -> 1.0827Helped
Add player-player fusion (ablation)DyMF · Badminton singles, τ=8MSE location: 1.1346 -> 1.0827Helped
Add dynamic weights for rally interactions (ablation)DyMF · Badminton singles, τ=8MSE location: 1.1375 -> 1.0827Helped
Add dynamic weights for player-player styles (ablation)DyMF · Badminton singles, τ=8MSE location: 1.1270 -> 1.0827Helped
Add dynamic GCN in place of static GCN (ablation)DyMF · Badminton singles, τ=8CE shot type: 1.9650 -> 1.9570Helped
Add player-player fusion (ablation)DyMF · Badminton singles, τ=8CE shot type: 1.9607 -> 1.9570Helped
Add dynamic weights for rally interactions (ablation)DyMF · Badminton singles, τ=8CE shot type: 1.9883 -> 1.9570Helped
Add dynamic weights for player-player styles (ablation)DyMF · Badminton singles, τ=8CE shot type: 1.9674 -> 1.9570Helped
Use a PM graph instead of a complete graph (ablation)GCN · Badminton singles, τ=8MSE location: 3.2579 -> 1.2676Helped
Use a PM graph instead of a complete graph (ablation)R-GCN · Badminton singles, τ=8MSE location: 1.1296 -> 1.2006Hurt
Use a PM graph instead of a complete graph (ablation)DyMF · Badminton singles, τ=8MSE location: 1.1366 -> 1.0827Helped
Use DyMF rather than ShuttleNetMovement forecasting models · Badminton singles, τ=2, τ=4, and τ=8MSE location: 1.1345 -> 1.1006; 1.1539 -> 1.1061; 1.1399 -> 1.0827Helped

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