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

We make a unique contribution to momentum research by proposing a way to quantify momentum with performance indicators (i.e., features). We argue that due to measurable randomness in the NHL, sequential outcomes’ dependence or independence may not be the best way to approach momentum. Instead, we quantify momentum using a small sample of a team’s recent games and a linear line of best-fit to determine the trend of a team’s performances before an upcoming game. We show that with the use of SVM and logistic regression these momentum- based features have more predictive power than traditional frequency-based features in a pre-game prediction model which only uses each team’s three most recent games to assess team quality. While a random forest favors the use of both feature sets combined. The predictive power of these momentum-based features suggests that momentum is a real phenomenon in the NHL and may have more effect on the outcome of games than suggested by previous research. In addition, we believe that how our momentum-based features were designed and compared to frequency-based features could form a framework for comparing the short-term effects of momentum on any individual sport or team.
</abstract>

Memorial University of Newfoundland and Labrador

Department of Computer Science and Media Technology

The Use of Momentum-Inspired Features in Pre-Game Prediction Models for the Sport of Ice Hockey

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

Automatic fault detection is a major challenge in many sports. In race walking, judges visually detect faults according to the rules. Hence, automatic fault detection systems will help a training of race walking without experts’ visual judgement. Some studies have attempted to use sensors and machine learning to automatically detect faults. However, there are problems associated with sensor attachments and equipment such as a high-speed camera, which conflict with the visual judgement of judges, and the interpretability of the fault detection models. In this study, we proposed an automatic fault detection system for non-contact measurement. We used pose estimation and machine learning models trained based on the judgements of multiple qualified judges to realize fair fault judgement. We verified them using smartphone videos of normal race walking and walking with intentional faults in several athletes including the medalist of the Tokyo Olympics. The results show that the proposed system detected faults with an average accuracy of over 90%. We also revealed that the machine learning model detects faults according to the rules. In addition, the intentional faulty walking movement of the medalist was different from that of other walkers. This finding informs realization of a more general fault detection model.
</abstract>

Automatic Detection of Faults in Simulated Race Walking from a Fixed Smartphone Camera

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

The spin of the ball plays a crucial role in table tennis tactics. However, it has rarely been measured and reported for the broadcast audience to better understand table tennis matches. This paper introduces a system designed to measure the spin of a table tennis ball without using electrically synchronized shutters or high-speed cameras. The system employs multiple unsynchronized cameras to detect the logos printed on the ball and estimates its three-dimensional translational motion to determine the spin rate (rotational velocity expressed in the revolutions per unit time) and spin axis (imaginary line around which the ball rotates). An experimental analysis indicated median errors of 0.78 rps and 12.5° in spin rate and axis, respectively. Additionally, the system exhibited sufficient resolution to analyze the spin rate and axis of a service ball in table tennis, distinguishing between spin axes that differ by 30° with 95.8% confidence. The developed system was used in the Japanese T-League to report the spin of several services after the live streaming of matches. The developed system successfully measured the spins of 92.1% of the served balls, confirming that the system has sufficient capability to feedback spin data immediately after a match.
</abstract>

Department of Sports and Health, Faculty of Human Health Sciences

Spin measurement system for table tennis balls based on asynchronous non-high-speed cameras

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

In the growing field of data analysis in soccer tracking data is analyzed utilizing increasingly complex methods to account for the dynamic, multifactorial nature of the game. One promising approach is the Success-Score combining ball control and space control. The resulting metric is hypothesized to indicate performance levels and to distinguish performance from playing style. Position datasets from one season of the German Bundesliga were analyzed by calculating Success-Scores based on different interval lengths for two different areas. The relative goalscoring frequency above resp. below the 80th percentile and the rank order correlation between goals and Success-Scores was used to assess the relevance of the Success- Score for goalscoring. The influence of the Success-Score on match outcome, accounting for possession and opponent quality was analyzed via mixed linear models. Results indicated a relation between goalscoring and the Success-Scores, as well as a considerable influence of the Success-Scores on match outcome. The mixed linear models allowed to conclude that Success-Scores capture performance rather than just playing style. The results highlight the potential of the general concept of the Success-Score, combining space and ball control. However, the practical value of the Success-Score in its current implementation appears limited and requires further development.
</abstract>

The Success-Score in Professional Football: a metric of playing style or a metric of match outcome?

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. 