Assessing the validity and reliability of a baseball pitch discrimination online task
Bibliographic record
Abstract
There has been an increasing interest in training perceptual skills in sports through online video-based methods, particularly in baseball. However, there is little empirical evidence related to the reliability and validity of such online methods for the assessment of these skill. Here we developed an online task to assess pitch discrimination and evaluated (a) inter-item reliability, (b) reliability in assessment compared to an in-person task, also tapping into external validity and (c) discriminability across different skill groups. We also compared performance on a non-sport specific Dynamic Visual Acuity task (DVA), thought to tap into underlying visual skills comprising pitch discrimination. Skilled, Varsity-level baseball players (n = 17) were compared to novices (n = 14) when discriminating pitches thrown by two different pitchers, across three pitch types, edited to progressively remove sections of ball flight (3 time points). The online task discriminated across skill groups, showed good reliability across repeated viewings and from the online task to an in-person assessment of skilled athletes (n = 8). There were, however, differences in reliability and discriminant validity based on the type of pitcher, with one pitcher being responded to more accurately and reliably. Skilled participants showed good discriminability between fastballs and change-ups. There were no group differences for DVA, nor did it correlate with pitch discrimination for the skilled group. These data illustrate the reliability of online video assessments, but raise issues concerning discriminability across different pitchers and when standing ready to swing. Greater sensitivity testing of such assessments is still needed, within and across skill groups.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".