6MapNet: Representing soccer players from tracking data by a triplet
network
- URL: http://arxiv.org/abs/2109.04720v1
- Date: Fri, 10 Sep 2021 07:57:12 GMT
- Title: 6MapNet: Representing soccer players from tracking data by a triplet
network
- Authors: Hyunsung Kim, Jihun Kim, Dongwook Chung, Jonghyun Lee, Jinsung Yoon,
Sang-Ki Ko
- Abstract summary: We build a triplet network named 6MapNet that can effectively capture the movement styles of players using in-game GPS data.
Ourworks then map these heatmap pairs into feature vectors whose similarity corresponds to the actual similarity of playing styles.
- Score: 19.343859572602558
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Although the values of individual soccer players have become astronomical,
subjective judgments still play a big part in the player analysis. Recently,
there have been new attempts to quantitatively grasp players' styles using
video-based event stream data. However, they have some limitations in
scalability due to high annotation costs and sparsity of event stream data. In
this paper, we build a triplet network named 6MapNet that can effectively
capture the movement styles of players using in-game GPS data. Without any
annotation of soccer-specific actions, we use players' locations and velocities
to generate two types of heatmaps. Our subnetworks then map these heatmap pairs
into feature vectors whose similarity corresponds to the actual similarity of
playing styles. The experimental results show that players can be accurately
identified with only a small number of matches by our method.
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