Enhancing volleyball training: empowering athletes and coaches through advanced sensing and analysis.

Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform. The first application is an automatic video-tagging protocol that marks key events (captured on video) based on the automatic recognition of volleyball-specific actions with an unweighted average recall of 78.71% in the 10-fold cross-validation setting with convolution neural network and 73.84% in leave-one-subject-out cross-validation setting with active data representation method using wearable sensors, as an exemplification of how dashboard and retrieval systems would work with the platform. In the context of action recognition, we have evaluated statistical functions and their transformation using active data representation besides raw signal of IMUs sensor. The second application is the "bump-set-spike" trainer, which uses automatic action recognition to provide real-time feedback about performance to steer player behaviour in volleyball, as an example of rich learning environments enabled by live action detection. In addition to describing these applications, we detail the system components and architecture and discuss the implications that our system might have for sports in general and for volleyball in particular.

"Dear IOC": Considerations for the Governance, Valuation, and Evaluation of Trends and Developments in eSports.

In 2021, the International Olympic Committee ventured virtual space by launching their first ever Olympic Virtual Series - featuring virtual baseball, cycling, rowing, sailing and motor racing. Interestingly, all these virtual events take strongly after their physical counterparts. Which begs the question: Where are the massively popular esports games like Fortnite, League of Legends, and Dota?-What do the Olympic Virtual Series have that these popular video games do not? Here, we argue for the inclusion of esports within the Olympic program. In many respects, esports "act" and "behave" just like traditional sports. We argue that esports and traditional sports share many of the same values, like the values of meritocracy, competition, fair play, and the value of having a "level playing field". Yet, in esports, many of these values remain underappreciated, losing out to negative values such as physical inactivity and game-addiction. To preserve what is worth preserving, we borrow from Value Sensitive Design to ameliorate the design-tensions that are foregrounded in esports. Thereby, paving possible ways toward the inclusion of esports in the Olympic program. Ultimately, the question for the IOC should not be "does it look like 'real sport', as we know it?", but rather: are they sporting, rule-led, and fair activities worth preserving and setting an example for a new digitally savvy generation?

The affordance of catchability in running to intercept fly balls.

How do outfielders control their locomotor behavior in running to catch fly balls? This question has been the topic of many empirical studies. It is interesting that a little addressed but highly relevant issue in this regard is that of the influence of perceived catchability on locomotor control. We examined what factors determine catchability and whether catchability can be reliably perceived. We had participants run to catch fly balls that could either be catchable or uncatchable. Participants performed two tasks. In the catching task, they were instructed to attempt to catch the ball and to keep running even when they felt that a ball was uncatchable. In the judging task, they were instructed to call "no" as soon as they perceived a ball to be uncatchable. Using Generalized Linear Mixed Effects Regression (GLMER) on data from the catching task, we modeled catchability, identifying five behaviorally relevant agent-environment variables that together explained 84.4% of the variance in catching performance. Next, we examined whether judgments of catchability were accurate. Using the GLMER-model, the catchability of every fly ball in the judging task was predicted and subsequently compared with participants' judgments. Participants were able to correctly judge the catchability of a fly ball on 85.4% of the trials. It is interesting that participants' judgments of fly balls to be uncatchable most often were given only after they had started running. Present findings provide a valuable step toward the formalization of an affordance-based control strategy for running to catch fly balls. (PsycINFO Database Record

When a Fly Ball Is Out of Reach: Catchability Judgments Are Not Based on Optical Acceleration Cancelation.

The optical acceleration cancelation (OAC) strategy, based on Chapman's (1968) analysis of the outfielder problem, has been the dominant account for the control of running to intercept fly balls approaching head on. According to the OAC strategy, outfielders will arrive at the interception location just in time to catch the ball when they keep optical acceleration zero. However, the affordance aspect of this task, that is, whether or not an approaching fly ball is catchable, is not part of this account. The present contribution examines whether the scope of the OAC strategy can be extended to also include the affordance aspect of running to catch a fly ball. This is done by considering a fielder's action boundaries (i.e., maximum running velocity and -acceleration) in the context of the OAC strategy. From this, only when running velocity is maximal and optical acceleration is non-zero, a fielder would use OAC to perceive a fly ball as uncatchable. The present contribution puts this hypothesis to the test. Participants were required to try to intercept fly balls projected along their sagittal plane. Some fly balls were catchable whereas others were not. Participants were required to catch as many fly balls as possible and to call 'no' when they perceived a fly ball to be uncatchable. Participants' running velocity and -acceleration at the moment of calling 'no' were examined. Results showed that participants' running velocity was submaximal before or while calling 'no'. Also running acceleration was often submaximal. These results cannot be explained by the use of OAC in judging catchability and ultimately call for a new strategy of locomotor control in running to catch a fly ball.

Keeping your eyes continuously on the ball while running for catchable and uncatchable fly balls.

When faced with a fly ball approaching along the sagittal plane, fielders need information for the control of their running to the interception location. This information could be available in the initial part of the ball trajectory, such that the interception location can be predicted from its initial conditions. Alternatively, such predictive information is not available, and running to the interception location involves continuous visual guidance. The latter type of control would predict that fielders keep looking at the approaching ball for most of its flight, whereas the former type of control would fit with looking at the ball during the early part of the ball's flight; keeping the eyes on the ball during the remainder of its trajectory would not be necessary when the interception location can be inferred from the first part of the ball trajectory. The present contribution studied visual tracking of approaching fly balls. Participants were equipped with a mobile eye tracker. They were confronted with tennis balls approaching from about 20 m, and projected in such a way that some balls were catchable and others were not. In all situations, participants almost exclusively tracked the ball with their gaze until just before the catch or until they indicated that a ball was uncatchable. This continuous tracking of the ball, even when running close to their maximum speeds, suggests that participants employed continuous visual control rather than running to an interception location known from looking at the early part of the ball flight.