Developing a New Expected Goals Metric to Quantify Performance in a Virtual Reality Soccer Goalkeeping App Called CleanSheet
As virtual reality (VR) sports training apps start to become more mainstream, it is important that human performance is measured from VR gameplay interaction data in a more meaningful way. <i>CleanSheet</i> is a VR training app that is played by over 100,000 users around the world. Many...
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7527 |
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| author | Matthew Simpson Cathy Craig |
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| description | As virtual reality (VR) sports training apps start to become more mainstream, it is important that human performance is measured from VR gameplay interaction data in a more meaningful way. <i>CleanSheet</i> is a VR training app that is played by over 100,000 users around the world. Many of those players are aspiring goalkeepers who want to use the app as a new way to train and improve their general goalkeeping performance. Whilst the leaderboards display how many shots players saved, these data do not take into account the difficulty of the shot faced. This study presents a regression model developed from a combination of existing expected goals (xG) models, goalkeeper performance metrics, and psychological research to produce a new shot difficulty metric called CSxG. Utilizing user save rate data as the target variable, a model was developed that incorporated three input variables relating to ball flight and in-goal positioning. Our analysis showed that the required rate of closure (RROC), adapted from Tau theory, was the most significant predictor of the proportion of goals conceded. A validation process evaluated the new xG model for <i>CleanSheet</i> by comparing its difficulty predictions against user performance data across players of varying skill levels. CSxG effectively predicted shot difficulty at the extremes but showed less accuracy for mid-range scores (0.4 to 0.8). Additional variables influencing shot difficulty, such as build-up play and goalpost size, were identified for future model enhancements. This research contributes to the advancement of predictive modeling in sports performance analysis, highlighting the potential for improved goalkeeper training and strategy development using VR technology. |
| format | Article |
| id | doaj-art-73e19c695c2942c586c65b4e29abe9f5 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-73e19c695c2942c586c65b4e29abe9f52024-12-13T16:31:49ZengMDPI AGSensors1424-82202024-11-012423752710.3390/s24237527Developing a New Expected Goals Metric to Quantify Performance in a Virtual Reality Soccer Goalkeeping App Called CleanSheetMatthew Simpson0Cathy Craig1School of Maths & Physics, Queens University Belfast, Belfast BT7 1NN, UKSchool of Psychology, Ulster University, Cromore Road, Coleraine BT52 1SA, UKAs virtual reality (VR) sports training apps start to become more mainstream, it is important that human performance is measured from VR gameplay interaction data in a more meaningful way. <i>CleanSheet</i> is a VR training app that is played by over 100,000 users around the world. Many of those players are aspiring goalkeepers who want to use the app as a new way to train and improve their general goalkeeping performance. Whilst the leaderboards display how many shots players saved, these data do not take into account the difficulty of the shot faced. This study presents a regression model developed from a combination of existing expected goals (xG) models, goalkeeper performance metrics, and psychological research to produce a new shot difficulty metric called CSxG. Utilizing user save rate data as the target variable, a model was developed that incorporated three input variables relating to ball flight and in-goal positioning. Our analysis showed that the required rate of closure (RROC), adapted from Tau theory, was the most significant predictor of the proportion of goals conceded. A validation process evaluated the new xG model for <i>CleanSheet</i> by comparing its difficulty predictions against user performance data across players of varying skill levels. CSxG effectively predicted shot difficulty at the extremes but showed less accuracy for mid-range scores (0.4 to 0.8). Additional variables influencing shot difficulty, such as build-up play and goalpost size, were identified for future model enhancements. This research contributes to the advancement of predictive modeling in sports performance analysis, highlighting the potential for improved goalkeeper training and strategy development using VR technology.https://www.mdpi.com/1424-8220/24/23/7527VR trainingsports performancexG modelsgoalkeeping |
| spellingShingle | Matthew Simpson Cathy Craig Developing a New Expected Goals Metric to Quantify Performance in a Virtual Reality Soccer Goalkeeping App Called CleanSheet Sensors VR training sports performance xG models goalkeeping |
| title | Developing a New Expected Goals Metric to Quantify Performance in a Virtual Reality Soccer Goalkeeping App Called CleanSheet |
| title_full | Developing a New Expected Goals Metric to Quantify Performance in a Virtual Reality Soccer Goalkeeping App Called CleanSheet |
| title_fullStr | Developing a New Expected Goals Metric to Quantify Performance in a Virtual Reality Soccer Goalkeeping App Called CleanSheet |
| title_full_unstemmed | Developing a New Expected Goals Metric to Quantify Performance in a Virtual Reality Soccer Goalkeeping App Called CleanSheet |
| title_short | Developing a New Expected Goals Metric to Quantify Performance in a Virtual Reality Soccer Goalkeeping App Called CleanSheet |
| title_sort | developing a new expected goals metric to quantify performance in a virtual reality soccer goalkeeping app called cleansheet |
| topic | VR training sports performance xG models goalkeeping |
| url | https://www.mdpi.com/1424-8220/24/23/7527 |
| work_keys_str_mv | AT matthewsimpson developinganewexpectedgoalsmetrictoquantifyperformanceinavirtualrealitysoccergoalkeepingappcalledcleansheet AT cathycraig developinganewexpectedgoalsmetrictoquantifyperformanceinavirtualrealitysoccergoalkeepingappcalledcleansheet |