Evaluation of the differences in competition strength of high-level table tennis matches: a case study based on a multiple regression model
Bibliographic record
Abstract
With the reforms in competition rules and equipment by the International Table Tennis Federation (ITTF), the number of rounds in table tennis matches has increased, placing higher demands on athletes' abilities to transition between technical and tactical offensive and defensive strategies, as well as on their physical and psychological qualities.Therefore, this study employs methods such as the strength difference evaluation, competition performance (CP), and multiple regression.Using 48 international important matches in which Player W (anonymous) participated as case studies, the study evaluates and predicts the competitive performance of W when facing athletes of different world rankings.The results indicate that in matches against athletes with different skill rankings, the phases where the technical strength difference significantly affects competition performance (CP) are the attack-after-serve phase and attack-after-receive phase, followed by the rally phase, and finally, the rally phase .The competitive level in serve rounds is superior to that in receive rounds.The Kruskal- Wallis test results reveal significant fluctuations in the competition performance (CP) during the rally phase , demonstrating highly significant differences (P < 0.01).In matches, Player W has a very low probability of winning when not holding an absolute advantage in key techniques (the first four strokes) -particularly pronounced when facing athletes ranked in the world top 20.The multiple regression model for the technical strength difference in table tennis matches plays a certain role in predicting the performance of athletes in terms of technical and tactical indicators during matches, offering a clear reflection of the effectiveness of these indicators.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".