Bilateral And Split Stance Isometric Midthigh Pulls Can Equally Predict Change Of Direction Ability
Bibliographic record
Abstract
The isometric midthigh pull (IMTP) has been shown to be able to predict change of direction (COD) ability. This is of interest for sports that require COD movements, as it could potentially be used to identify standout athletes in their respective sports. However, no literature to our knowledge has assessed split stance isometric midthigh pull (SSIMTP) as a predictor of COD ability compared to the IMTP. PURPOSE: The purpose of this study was to determine whether SSIMTP is a better predictor of COD ability than the IMTP. METHODS: Seventeen (12M and 5F) university aged adults participated in the following study. Mean height, weight, and age were 176±8.6cm, 172±24.2kg, and 22±3 years, respectively. Participants completed two sessions randomized in order that were two to five days apart; four trials of the SSIMTP (two trials per lead leg) and the other session involved two trials of the IMTP. Each day also consisted of four trials of the 505-agility test. Two trials involved pivoting off of the left foot and two trials pivoting off of the right foot, which was randomized in order. RESULTS: All results are Pearson Correlation Coefficients presented with their respective p-value. The combined peak force (PF) of the IMTP and the best 505-agility time for the left and right foot had r values of -0.648 (p<0.09) and -0.464 (p<0.08), respectively. The r values during the SSIMTP condition were -0.722 (p=0.02) and -0.462 (p=0.07) when the left leg is the lead leg and COD to the left and when the right leg is the lead leg and COD to the right, respectively. CONCLUSION: The main finding of this study is that the combined PF produced during the IMTP and the PF produced by the lead leg during the SSIMTP can both uniformly predict COD. For the SSIMTP condition, a significant strong inverse correlation exists between left COD ability and left leg when it is forward and a moderate inverse correlation between right COD ability and the right leg when it is forward. For the IMTP condition, there is a strong inverse correlation between the combined PF and COD ability to the left and moderate inverse correlation between the combined PF and COD to the right. Previous literature has found correlations between IMTP and COD ability anywhere between -0.47 to -0.89.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".