Effect Of Adjunt Sports Specific Drill Training on Selected Fitness Parameters in Amateur Lawn Tennis Players
Bibliographic record
Abstract
Lawn tennis, game in which two opposing players or pairs of players use tautly strung rackets to hit a ball of specified size, weight, and bounce over a net on a rectangular court. Tennis is characterized by the execution of a series of high intensity and explosive actions, sprints, changes of direction and abrupt deceleration; these specific movements put the tennis player under physical stress. 1 this component of training is as such termed as agility. The percentage of lower limb injuries in lawn tennis in players is 73%. Hence there is a need of designing an optimal conditioning program for improvement of agility and sprint performance which may reduce the risk of injuries and to improve the quality of performance. AIM was to find the effect of sports specific drill training on selected fitness parameters in amateur lawn tennis players. 50 purposive sample, using experimental study for 6 weeks in lawn tennis players. Result The pre and post training results of descriptive statistics and paired T test shows significant improvement in experimental group for agility test (t = 5.220 and p=<0.0001 ) And 10 meter sprint (t=5.432 and p= <0.0001)There is Statistically significant difference post training with illionos agility test and ten meter sprint test in experimental group as compared to control group was seen post training session. Conclusion The present study concluded an effect of sports specific drill training has effect on Agility test and sprint test timings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".