<abstract xmlns="http://www.w3.org/1999/xhtml">

The aim of this study is to integrate an inertial sensor inside a hammer to allow a realtime feedback. In the first step we build our own prototype to measure the radial acceleration. In the second step there is a validation with an infrared camera system. It is a comparison between the radial acceleration along the wire axis, that is measured by the sensor against the velocity that is delivered by the infrared camera system. As a result, significant correlation was observed between the measured velocity and the acceleration (r = 0.99, p &lt; 0.001). These suggest that this system can used in the training to improve the technique of the hammer throw.
</abstract>

Wireless inertial sensor system for hammer throwing

<abstract xmlns="http://www.w3.org/1999/xhtml">

The Association of Tennis Professionals (ATP) distributes a considerable amount of money in prizes each year. Studies have shown that only the top 100 ranked players can self-finance; hence, it is convenient to introduce changes to the prize distribution to promote a more sustainable system. A Linear Programming model to distribute the tournament’s budget under a new concept for the fair distribution of prize money is proposed. Additionally, to distribute the prizes, a function based on the effort of the players is designed. The model was applied to tournaments to demonstrate the impact on improving the player’s prizes distribution.
</abstract>

Department of Industrial Engineering, Faculty of Engineering

Pontificia Universidad Católica de Chile

An optimization model for the fair distribution of prize money in ATP tournaments

<abstract xmlns="http://www.w3.org/1999/xhtml">

This review explores the avenues for the application of meta-heuristics in sports. The necessity of sophisticated algorithms to investigate different NP hard problems encountered in sports analytics was established in the recent past. Meta-heuristics have been applied as a promising approach to such problems. We identified team selection, optimal lineups, sports equipment optimization, scheduling and ranking, performance analysis, predictions in sports, and player tracking as seven major categories where meta-heuristics were implemented in research in sports. Some of our findings include (a) genetic algorithm and particle swarm optimization have been extensively used in the literature, (b) meta-heuristics have been widely applied in the sports of cricket and soccer, (c) the limitations and challenges of using meta-heuristics in sports. Through awareness and discussion on implementation of meta-heuristics, sports analytics research can be rich in the future.
</abstract>

Department of Computer Science, Faculty of Applied Sciences

Department of Statistics, Faculty of Applied Sciences

Meta-heuristics meet sports: a systematic review from the viewpoint of nature inspired algorithms

<abstract xmlns="http://www.w3.org/1999/xhtml">

Cloud-based services revolutionize how applications are designed and provisioned in more and more application domains. Operating a cloud application, however, requires careful choices of configuration settings so that the quality of service is acceptable at all times, while cloud costs remain reasonable. We propose an analytical queuing model for cloud resource provisioning that provides an approximation on end-to-end application latency and on cloud resource usage, and we evaluate its performance. We pick an emerging use case of cloud deployment for validation: sports analytics. We have created a low-cost, cloud-based soccer player tracking system. We present the optimization of the cloud-deployed data processing of this system: we set the parameters with the aim of sacrificing as least as possible on accuracy, i.e., quality of service, while keeping latency and cloud costs low. We demonstrate that the analytical model we propose to estimate the end-to-end latency of a microservice-type cloud native application falls within a close range of what the measurements of the real implementation show. The model is therefore suitable for the planning of the cloud deployment costs for microservice-type applications as well.
</abstract>

Optimizing and dimensioning a data intensive cloud application for soccer player tracking

<abstract xmlns="http://www.w3.org/1999/xhtml">

Virtual reality (VR) is a tool used in sports to train specific situations under standardized conditions. However, it remains unclear whether improved performances from VR training can be transferred into real world (RW). Therefore, the current study compares beginner training of balance beam tasks in VR (simulated balance beam height, n = 17) with similar training in RW (n = 15). Both groups completed 12 training sessions (each 20 min) within six weeks in their respective environment. The training aimed to learn the one leg full turn on a balance beam with a height of 120 cm. Criteria were defined to analyze the movement quality before and after the intervention. Statistical analyses showed similar improvements in movement quality in RW for both training groups after the intervention (p &lt; .05). These results indicate that the skills adapted in VR could be transferred into RW and that the VR training was as effective as the RW training in improving the movement quality of balance beam elements. Thereby, VR provides the advantages of a reduced risk of injury due to a simulated beam height, a faster beam height adjustment, and spacial independence from specific gyms.
</abstract>

The Impact of Virtual Reality Training on Learning Gymnastic Elements on a Balance Beam with Simulated Height

<abstract xmlns="http://www.w3.org/1999/xhtml">

Classifying multi-agent cooperative behavior is a fundamental problem in various scientific and engineering domains. In team sports, many cooperative plays can be manually labelled by experts. However, it requires high labour costs and a large amount of unlabelled data is not utilised. This paper examines semi-supervised learning methods for the classification of strategic cooperative plays (called screen plays) in basketball using a smaller labelled dataset and a larger unlabelled dataset. We compared the classification performance of two basic semi-supervised learning methods: self-training and label-propagation. Results show that the classification performance of the semi-supervised learning approaches improved upon the conventional supervised approach (SVM: support vector machine) for minor types of screen-plays (flare, pin, back, cross, and hand-off screen). For the feature importance, we found that self-training obtained similar or higher Sharpley values than SVM. Our approach has the potential to reduce manual labelling costs for detecting various cooperative behaviors.
</abstract>

Cooperative play classification in team sports via semi-supervised learning

AHEAD OF PRINT

Volume 23 (2024): Issue 1 (February 2024)

Volume 22 (2023): Issue 2 (August 2023)

Volume 22 (2023): Issue 1 (March 2023)

Volume 21 (2022): Issue 2 (December 2022)

Volume 21 (2022): Issue 1 (March 2022)

Volume 20 (2021): Issue 2 (December 2021)

Volume 20 (2021): Issue 1 (July 2021)

Volume 19 (2020): Issue 2 (December 2020)

Volume 19 (2020): Issue 1 (July 2020)

Volume 18 (2019): Issue 3 (December 2019)

Volume 18 (2019): Issue 2 (September 2019) Special Issue: Selected papers presented at the 12th Symposium of the Section Computer Science in Sport of the German Association of Sport Science (September 4.-7., 2018)

Volume 18 (2019): Issue 1 (July 2019)

Volume 17 (2018): Issue 2 (December 2018)

Volume 17 (2018): Issue 1 (July 2018)

Volume 16 (2017): Issue 3 (December 2017)

Volume 16 (2017): Issue 2 (November 2017)

Volume 16 (2017): Issue 1 (July 2017)

Volume 15 (2016): Issue 2 (December 2016)

Volume 15 (2016): Issue 1 (July 2016)

International Journal of Computer Science in Sport

 The International Journal of Computer Science in Sport (IJCSS) is published in association with the International Association of Computer Science in Sport (IACSS).  It is a refereed electronic journal that is published biannually. Research results with an emphasis on the following topics regarding the application of Computer Science and Mathematics in supporting the development of theory and practice in sport are considered:   Modeling (mathematical, informatics, biomechanical, physiological)  Computer aided applications (software, hardware)  Data acquisition and processing  Data Bases  Simulation (interactive, animation etc.)  Development of theories  Multimedia / Internet  Presentation  Education   The journal does not only serve as a forum for current research from around the world in the field of computer technology in sport. Moreover, there is place to present theories, methods and concepts, which are developed in Computer Science and Mathematics, and applied to Sport Science.  Articles submitted to the IJCSS are reviewed by at least two anonymous referees. The reviewing process usually takes about a month. The authors are obliged to take the reviewers comments into account when preparing the final version of their article.  Why subscribe and read   Current research in all fields of Computer Science in Sport.  Forum for both established and young scholars.  No particular theories or trends favored.   Why submit   Fair and constructive double blind review.  Fast and efficient publication process.   Rejection Rate  &gt;30%  Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 