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

In baseball, every play on the field is quantitatively evaluated and the statistics have an effect on individual and team strategies. The weighted on base average (wOBA) is well known as a measure of a batter’s hitting contribution. However, this measure ignores the game situation, such as the runners on base, which coaches and batters are known to consider when employing multiple hitting strategies, yet, the effectiveness of these strategies is unknown. This is probably because (1) we cannot obtain the batter’s strategy and (2) it is difficult to estimate the effect of the strategies. Here, we propose a new method for estimating the effect using counterfactual batting simulation. The entire framework consists of two phases: (i) generate a counter-factual batter’s ability based on their actual performances and (ii) simulate games with the batting simulator. To realize (i), we propose a deep learning model that transforms batting ability when batting strategy is changed. This method can estimate the effects of various strategies, which has been traditionally difficult with actual game data. We found that, when the switching cost of batting strategies can be ignored, the use of different strategies increased runs. When the switching cost is considered, the conditions for increasing runs were limited. Our results suggest that players and coaches should be careful when employing multiple batting strategies given the trade-offs thereof. We also discuss practical baseball use-cases to use this simulation.
</abstract>

Estimating the effect of hitting strategies in baseball using counterfactual virtual simulation with deep learning

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

Simulating and predicting tournament outcomes has become an increasingly popular research topic. The outcomes can be influenced by several factors, such as attack, defence and home advantage strength values, as well as tournament structures. However, the claim that different structures, such as knockout (KO), round-robin (RR) and hybrid structures, have their own time restraints and requirements has limited the evaluation of the best structure for a particular type of sports tournament using quantitative approaches. To address this issue, this study develops a decision support system (DSS) using Microsoft Visual Basic, based on the object-oriented programming approach, to simulate and forecast the impact of the various tournament structures on soccer tournament outcomes. The DSS utilized the attack, defence and home advantage values of the teams involved in the Malaysia Super League 2018 to make better prediction. The rankings produced by the DSS were then compared to the actual rankings using Spearman correlation to reveal the simulated accuracy level. The results indicate that a double RR produces a higher correlation value than a single RR, indicating that more matches played provide more data to create better predictions. Additionally, a random KO predicts better than a ranking KO, suggesting that pre-ranking teams before a tournament starts does not significantly impact the prediction. The findings of this study can help tournament organizers plan forthcoming games by simulating various tournament structures to determine the most suitable one for their needs.
</abstract>

A Decision Support System for Simulating and Predicting the Impacts of Various Tournament Structures on Tournament Outcomes

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

In sports science, cardiopulmonary data is used to assess exercise intensity, performance and health status of athletes and derive relevant target values. However, sensors may produce flawed data and data may include a wide variety of artifacts, which could potentially lead to false conclusions. Thus, appropriate and customized pre-processing algorithms are a vital prerequisite for producing reliable and valid analysis results. To find adequate outlier detection methods for this type of data, we compared three algorithms by applying them on seven ergospirometric measures of junior ski racing athletes and applied a model to predict fatigue during skiing based on the pre-processed data. While values that lie outside a realistic spectrum were consistently labelled as outliers by all methods, and mean values and standard deviations changed in similar ways, methods differed from each other when it comes to changing trends, recurring patterns, and subsequent outliers. Decomposing the sensor data into different components (trend, seasonality, remainder) before dealing with outliers increased average predictive performance the most. However, pre-processing remarkably improved prediction results for certain study participants and not for others. Thus, handling outliers correctly prior to deriving information from ergospirometric data is recommended but more research should be conducted to find methods that achieve more consistent improvement.
</abstract>

Detecting Outliers in Cardiopulmonary Exercise Testing Data of Ski Racers – A Comparison of Methods and their Effect on the Performance of Fatigue Prediction

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

In the past 20 years, performance analysis in soccer has accumulated a wide variety of key performance indicators (KPI’s) aimed at reflecting a team’s strength and success. Thanks to rapidly advancing technologies and data analytics more sophisticated metrics, requiring high resolution data acquisition and big data methods, are developed. This includes many position-data-based KPI’s, which incorporate precise spatial and temporal information about every player and the ball on the field.
The present study contributes to this research by performing a large-scale comparison of several metrics mainly based on player positions and passing events. Their association with team’s success (derived from goals scored) and team’s strength (estimated from pre-game betting odds) is analysed.
The systematic analysis revealed relevant results for further KPI research: First, the magnitude of overall correlation coefficients was higher for relative metrics than for absolute metrics. Second, the correlation of metrics with the strength of a team is stronger than the correlation with the game success of a team. Third, correlation analysis with team strength indicated more positive associations, while correlation analysis with success is most likely confounded by the intermediate score line of a game and revealed more negative associations.
</abstract>

Systematic Analysis of Position-Data-based Key Performance Indicators

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

NBA teams rely heavily on their star players, though an ever-increasing tendency shows that proper ball movement is key for building a successful offense. According to experts, one of the most crucial individual contributions for this aspect is ‘making the extra pass’ – meaning to pass on a decent shooting opportunity to create an even better one. However, judging this ability is subjective, even a precise definition is missing. In this analysis, we conceptualize the event and design a method to measure this skill on an individual player level. Using this model, we analyze directly assisted shots – whether they could have been turned down to make the extra pass. In-season statistics are used to calculate the scoring efficiency of the player from the particular zone given the distance of the closest defender. Our method helps to automatically find individual situations where the extra pass could have been played to gain a margin in Expected Points and scaled up to a whole season, we are able to identify which areas of the court are the most often overlooked. By detecting these missed opportunities of extra passes, experts can easily point out situations where better teamwork can lead to better scoring opportunities.
</abstract>

Modeling the extra pass in basketball – an assessment of one of the most crucial skills for creating great ball movement

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

Change of direction speed, reaction time, sprint speed, and explosive strength are important factors that determine athletes’ performance in the majority of sports. From the practical standpoint, it is of interest to investigate to what extent they differ among athletes of team and individual sports. We compared 7 handball, 11 basketball, and 15 male table tennis players in four reaction time tests, 505 Agility test, 5m and 20m sprints, squat, countermovement, and drop jumps. Basketball players performed better in reaction time to fast generating stimuli (12.6%, p=.001) and countermovement jump height (14.5%, p=.05) than handball players. In addition, they achieved a higher reactive strength index (25%, p=.01) than table tennis players. Handball players were faster in the 505 Agility test compared to table tennis players (4.6%, p=.04). Results revealed that performance of basketball players is mainly determined by explosive strength, handball players by change of direction speed, and table tennis by speed of response to visual stimuli. These differences may be ascribed to long-term adaptation to sport-specific stimuli. Novel assessment methods and devices should better determine key performance factors of athletes with regard to sport-specific tasks.
</abstract>

Department of Track and Field, Faculty of Physical Education and Sport

Department of Biological and Medical Sciences, Faculty of Physical Education and Sport

Sport-specific differences in key performance factors among handball, basketball and table tennis players

<abstract xmlns="http://www.w3.org/1999/xhtml">
Fencing is a sport requiring high levels of physical and mental abilities from athletes. Amongst others traits, fencers need to be able to hit small targets with high accuracy. In order to be able to investigate changes in the accuracy of fencers over prolonged periods of training, a training device needs to be accepted by its users. This article presents a low-cost feedback system that can be used to train and monitor accuracy. The system was evaluated for its acceptance and intended use by potential users using a qualitative version of the Unified Theory of Acceptance and Use of Technology (UTAUT2) (Venkatesh et al. 2012). Nine athletes participated in the evaluation. After conducting a standardized session, qualitative interviews were conducted with the athletes. Categorization was performed inductively along the dimensions of the UTAUT2 model. Results showed that the athletes were satisfied with the prototype and expressed their desire for a system with a simple setup. No effect of gender on usage intentions was found. However, an effect of age and/or experience on how athletes intend to use such as system was identified. More experienced athletes intended to use the system in dedicated parts of their training while novice athletes desired to integrate it into existing training sessions. 
</abstract>

Acceptance and Intended Use of a Feedback System for Fencing 

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

The Gothenburg Half Marathon is one of the world’s largest half marathon races with over 40 000 participants each year. In order to reduce the number of runners risking over-straining, injury, or collapse, we would like to provide runners with advice to appropriately plan their pacing. Many participants are older or without extensive training experience and may particularly benefit from such pacing assistance. Our aim is to provide this with the help of machine learning. We first analyze a large publicly available dataset of results from the years 2010 - 2019 (n = 423 496) to identify pacing patterns related to age, sex, ability, and temperature of the race day. These features are then used to train machine learning models for predicting runner’s finish time and to identify which runners are at risk of making severe pacing errors and which ones seem set to pace well. We find that prediction of finish time improves over the current baseline, while identification of pacing patterns correctly identifies over 70% of runners at risk of severe slowdowns, albeit with many false positives.
</abstract>

Pacing Patterns of Half-Marathon Runners: An analysis of ten years of results from Gothenburg Half Marathon

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. 