New Paper: “xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning”

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We are pleased to announce that our latest paper “xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning“ is now published at Sensors MDPI. With xLength we developed the first ski jump length prediction. Therefore, we analyzed 2523 ski jumps from 205 athletes on five venues. For every jump, the dataset includes the 3D trajectory, 3D velocity, skis’ orientation, and metadata such as wind, starting gate, and ski jumping hill data. Due to the real-time transmission of the sensor’s data, xLength can be updated during the flight phase and used in live TV broadcasting. xLength could also be used as an analysis tool for experts to quantify the quality of the take-off and flight phases.

The Paper is published open access: 

Congratulations to all authors Johannes Link, Leo Schwinn, Falk Pulsmeyer, Thomas Kautz and Bjoern Eskofier