"\"I consider VR Table Tennis to be my secret weapon!\": An Analysis of the VR Table Tennis Players' Experiences Outside the Lab"
Bibliographic record
Abstract
Thanks to stand-alone Virtual Reality (VR) advances, users can play realistic simulations of real-life sports at their homes. In these game simulations, players control their avatars by doing the same movements as in real life (RL) while playing against a person or AI opponent, making VR sports attractive for the players. In this paper, we surveyed a popular VR table tennis game community, focusing on understanding their demographics, challenges, and experiences with skill transfers between VR and RL. Our results show that, on average, VR table tennis players are primarily men, live in Europe/Asia, and are 38 years old. We also found that the current state of VR technology affects the player’s experience and that players see VR as a convenient way to play matches but that RL is better for socialization. Finally, we identified skills like backhand and forehand strikes that players perceived to be transferred from VR to RL and vice versa. Our research findings have the potential to serve as a valuable resource for VR table tennis game developers seeking to integrate mid-air controllers into their future projects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".