Data Analysis of Sport Specific Variables for Performance Consistency Evaluation in Critical Moments of College Tennis Matches
Bibliographic record
Abstract
Performance under pressure typically represents a significant factor in achieving desired athletic goals and competition outcomes. Dealing with performance under pressure can be an extremely challenging task for many athletes as competitions are not necessarily decided by talent or technique alone, but also by performance quality under pressure conditions. Even though coaches and fans can be quick to claim that recognition of suboptimal or superior performance under pressure is simple, clear definitions of individual performance patterns under pressure have not yet been established. This work introduces an approach for the identification of crucial moments in college tennis matches, presents a data collection methodology designed to capture measurements and observations of college tennis specific variables, and proposes data analysis procedures designed to evaluate performance quality in crucial moments of college tennis competitions. The crucial moments of college tennis matches were defined as a newly introduced variable, called the crucial point, and its identification was based on match scores. The study data were collected with 15 college tennis players in 60 tennis sessions using two wearable sensors. College tennis specific parameters evaluated in the study were swing speed and heartrate variables. The obtained results showed specific performance data pattern relationships between the study variables and the identified crucial points. The results provide evidence that the presented performance under pressure evaluation model, if developed further, could help college tennis coaches and players to develop individually tailored game plans and strategies to improve their chances for success in crucial moments of their college tennis competitions.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".