Influence of expertise level on techniques of applying top and back spins in cue sports
Bibliographic record
Abstract
This study compared the kinematics of upper body and cue stick among players of various skill levels when performing back spin and top spin shots. Twenty-eight male cue sports players were assigned to the novice (n = 10), intermediate (n = 9), or skilled groups (n = 9). The back spin and top spin tests were administrated while kinematic data were recorded using a 3D motion capture system. The results revealed greater upper limb joint ranges of motions (all p < 0.05), maximum angular velocities (all p < 0.05), and cue tip speed in the back spin than top spin shots (p < 0.001). None of joint kinematic or shot performance variables investigated was significantly different among the three skill levels (all p > 0.05). For the head movement, the novice group exhibited greater anteroposterior displacement than the skilled group (p = 0.020). In conclusion, except for the head movement, the upper body and cue stick kinematics did not significantly differ among players with varied skill levels. Greater joint ranges of motions and angular velocities were required to generate a faster cue tip speed for the back spin shots when compared with the top spin shots.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".