6MapNet: Representing soccer players from tracking data by a triplet network

by   Hyunsung Kim, et al.

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.



There are no comments yet.


page 4

page 5

page 6


Bayesian Learning of Play Styles in Multiplayer Video Games

The complexity of game play in online multiplayer games has generated st...

A Methodology for Learning Players' Styles from Game Records

We describe a preliminary investigation into learning a Chess player's s...

Analyzing In-Game Movements of Soccer Players at Scale

It is challenging to get access to datasets related to the physical perf...

QPass: a Merit-based Evaluation of Soccer Passes

Quantitative analysis of soccer players' passing ability focuses on desc...

Multi-Modal Trajectory Prediction of NBA Players

National Basketball Association (NBA) players are highly motivated and s...

Are the Players in an Interactive Belief Model Meta-certain of the Model Itself?

In an interactive belief model, are the players "commonly meta-certain" ...

Vid2Player: Controllable Video Sprites that Behave and Appear like Professional Tennis Players

We present a system that converts annotated broadcast video of tennis ma...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.