Off-ball behavior in association football: A data-driven model to measure changes in individual defensive pressure-Reference-Cited by-同舟云学术

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Off-ball behavior in association football: A data-driven model to measure changes in individual defensive pressure

Published:2022-05-31 Issue:12 Volume:40 Page:1412-1425
ISSN:0264-0414
Container-title:Journal of Sports Sciences
language:en
Short-container-title:Journal of Sports Sciences

Author:

Herold Mat¹²,Hecksteden A.¹,Radke D.¹,Goes F.³,Nopp S.²,Meyer T.¹,Kempe M.³

Affiliation:

1. Institute of Sports and Preventive Medicine, Saarland University, Saarbrücken, Germany

2. Deutscher Fußball-Bund, Frankfurt am Main, Germany

3. Center for Human Movement Sciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands

Funder

The author(s) reported that there is no funding associated with the work featured in this article

Publisher

Informa UK Limited

Subject

Physical Therapy, Sports Therapy and Rehabilitation,Orthopedics and Sports Medicine

Link

https://www.tandfonline.com/doi/pdf/10.1080/02640414.2022.2081405

Reference64 articles.

1. Diminutions of acceleration and deceleration output during professional football match play

2. What variables can differentiate winning and losing teams in the group and final stages of the 2018 FIFA World Cup?

3. Technical-Tactical Analysis of The Players of the Left and Right Wing in Elite Soccer

4. A survey of methods for time series change point detection

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1. How to win in FIFA World Cup Qatar 2022? A study on the configurations of technical and tactical indicators based on fuzzy-set qualitative comparative analysis;Frontiers in Psychology;2024-01-26

2. The Success Factors of Rest Defense in Soccer – A Mixed-Methods Approach of Expert Interviews, Tracking Data, and Machine Learning;Journal of Sports Science and Medicine;2023-12-01

3. Prediction of defensive success in elite soccer using machine learning - Tactical analysis of defensive play using tracking data and explainable AI;Science and Medicine in Football;2023-08-04

4. Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football;2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS);2023-07-04

5. Comparison of the physiological responses and time-motion characteristics during football small-sided games: effect of pressure on the ball;Frontiers in Physiology;2023-05-19

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