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Related Experiment Video

Updated: Jun 4, 2025

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Published on: May 19, 2023

Developing a New Expected Goals Metric to Quantify Performance in a Virtual Reality Soccer Goalkeeping App Called

Matthew Simpson¹, Cathy Craig²

¹School of Maths & Physics, Queens University Belfast, Belfast BT7 1NN, UK.

Sensors (Basel, Switzerland)

|December 17, 2024

Summary

This study introduces CSxG, a new metric for virtual reality (VR) goalkeeping training apps. It measures shot difficulty by analyzing ball flight and positioning, improving performance analysis beyond simple save rates.

Keywords:

VR training goalkeeping sports performance xG models

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Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

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Published on: September 18, 2017

Area of Science:

Sports Science
Human-Computer Interaction
Data Science

Background:

Virtual reality (VR) sports training apps are increasingly popular for athlete development.
Current VR goalkeeping apps like CleanSheet lack nuanced performance metrics, primarily tracking save rates without considering shot difficulty.
Aspiring goalkeepers need more sophisticated tools to train effectively using VR technology.

Purpose of the Study:

To develop a novel shot difficulty metric, termed CSxG (CleanSheet Expected Goals), for VR goalkeeping training.
To enhance the meaningfulness of performance data derived from VR gameplay.
To improve the accuracy of goalkeeper performance analysis in VR environments.

Main Methods:

Developed a regression model combining existing expected goals (xG) models, goalkeeper performance metrics, and psychological research.
Utilized user save rate data from the CleanSheet VR app as the target variable.
Incorporated input variables related to ball flight and in-goal positioning, including the required rate of closure (RROC) from Tau theory.

Main Results:

The developed CSxG model identified the required rate of closure (RROC) as the most significant predictor of goals conceded.
Validation showed CSxG accurately predicted shot difficulty at the extremes but had lower accuracy for mid-range difficulty scores (0.4-0.8).
Identified additional factors like build-up play and goalpost size for future model improvements.

Conclusions:

The CSxG metric offers a more advanced way to assess goalkeeper performance in VR training.
This research advances predictive modeling in sports analytics, particularly for VR-based training.
The findings support the potential for enhanced goalkeeper training strategies and performance development through VR technology.