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

Typical player management processes focus on managing an athlete’s physical, physiological, psychological, technical and tactical preparation and performance. Current literature illustrates limited attempts to optimize such processes in sports. Therefore, this study aimed to analyze the application of Business Process Management (BPM) in healthcare (a service industry resembling sports) and formulate a model to optimize data driven player management processes in professional sports. A systematic review, adhering to PRISMA framework was conducted on articles extracted from seven databases, focused on using BPM to digitally optimize patient related healthcare processes. Literature reviews by authors was the main mode of healthcare process identification for BPM interventions. Interviews with process owners followed by process modelling were common modes of process discovery. Stakeholder and value-based analysis highlighted potential optimization areas. In most articles, details on process redesign strategies were not explicitly provided. New digital system developments and implementation of Business Process Management Systems were common. Optimized processes were evaluated using usability assessments and pre-post statistical analysis of key process performance indicators. However, the scientific rigor of most experiments designed for such latter evaluations were suboptimal. From the findings, a stepwise approach to optimize data driven player management processes in professional sports has been proposed.
</abstract>

Optimizing Player Management Processes in Sports: Translating Lessons from Healthcare Process Improvements to Sports

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

Predicting and avoiding an injury is a challenging task. By exploiting data mining techniques, this paper aims to identify existing relationships between modifiable and non-modifiable risk factors, with the final goal of predicting non-contact injuries. Twenty-three young soccer players were monitored during an entire season, with a total of fifty-seven non-contact injuries identified. Anthropometric data were collected, and the maturity offset was calculated for each player. To quantify internal training/match load and recovery status of the players, we daily employed the session-RPE method and the total quality recovery (TQR) scale. Cumulative workloads and the acute: chronic workload ratio (ACWR) were calculated. To explore the relationship between the various risk factors and the onset of non-contact injuries, we performed a classification tree analysis. The classification tree model exhibited an acceptable discrimination (AUC=0.76), after receiver operating characteristic curve (ROC) analysis. A low state of recovery, a rapid increase in the training load, cumulative workload, and maturity offset were recognized by the data mining algorithm as the most important injury risk factors.
</abstract>

Department of Movement, Human and Health Sciences

Research Center for Sport and Physical Activity, Faculty of Sport Science and Physical Education

Computer, Control and Management Engineering Department

A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players

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

Quantification of athletic performance via analysis of scores of off-season fitness tests has become an essential part of the modern strength and conditioning coach (SCC). Player Efficiency Rating (PER) and Efficiency index (EFF) are two of the most used in-season basketball performance metrics in the US. We collected data from male and female basketball players of a National Collegiate Athletic Association (NCAA) program. Based on sex, we examined a) if unadjusted PER (uPER) and EFF reflect different amounts of information and b) which fitness tests predict those two indices more accurately. Our results showed lower means and less variability of the fitness tests scores in women than men. The correlation between uPER and EFF in men was moderate and strong in women. In men, no strong correlation was found between any fitness test and EFF, while full court sprint was strongly correlated with uPER. In women, strong correlations were detected between a) the T-drill and EFF and b) the foul court sprint, the vertical jump, and the T-drill and uPER. The collegiate SCCs should consider that off-season scores of a) the foul court drill may predict uPER more accurately in both men and women and b) the T-drill may predict both EFF and uPER more precisely in women.
</abstract>

Offseason Fitness Tests a Collegiate Basketball Strength Coach Should Choose to Predict In-Season 	Perfomance Based on Sex

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

To aid the implementation of athlete surveillance systems relative to logistical circumstances, easy-to-access information that summarises the extent to which methods of acquiring data are used in practice to monitor athletes is required. In this scoping review, Social Network Analysis and Mining (SNAM) techniques were used to summarise and identify the most prevalent combinations of methods used to monitor athletes in research studying team, individual, field- and court-based sports (357 articles; SPORTDiscus, MEDLINE, CINHAL, and WebOfScience; 2014-2018 inc.) . The most prevalent combination in team and field-based sports were HR and/or sRPE (internal) and GPS, whereas in individual and court-based sports, internal methods (e.g., HR and sRPE) were most prevalent. In court-based sports, where external methods were occasionally collected in combination with internal methods of acquiring data, the use of accelerometers or inertial measuring units (ACC/IMU) were most prevalent. Whilst individual and court-based sports are less researched, this SNAM-based summary reveals that court-based sports may lead the way in using ACC/IMU to monitor athletes. Questionnaires and self-reported methods of acquiring data are common in all categories of sport. This scoping review provides coaches, sport-scientists and researchers with a data-driven visual resource to aid the selection of methods of acquiring data from athletes in all categories of sport relative to logistical circumstances. A guide on how to practically implement a surveillance system based on the visual summaries provided herein, is also presented.
</abstract>

A scoping review using social network analysis techniques to summarise the prevalance of methods used to acquire data for athlete survelliance in sport

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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. 

International Journal of Computer Science in Sport

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

Optimizing Player Management Processes in Sports: Translating Lessons from Healthcare Process Improvements to Sports

A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players

Offseason Fitness Tests a Collegiate Basketball Strength Coach Should Choose to Predict In-Season Perfomance Based on Sex

A scoping review using social network analysis techniques to summarise the prevalance of methods used to acquire data for athlete survelliance in sport

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