Individual factors associated with baseball pitching performance: scoping review
Bibliographic record
Abstract
BACKGROUND: Ball velocity, accuracy and game statistics represent three methods used to measure pitching performance. However, individual determinants of pitching performance are more elusive. OBJECTIVES: The aims of this study were to classify the performance factors associated with baseball pitchers, to identify the methods used to quantify their abilities through all features of the game and to document relationships between performance factors and indicators. DESIGN: Scoping review. DATA SOURCES: Electronic searches of MEDLINE, Academic Search Complete, CINAHL, SportDiscus, PubMed, PsycINFO, Cochrane and of grey literature were undertaken from inception to January 2019. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Cross-sectional studies that investigated the relationship between performance indicators and individual performance factors in healthy baseball pitchers were selected. RESULTS: Thirty-four cross-sectional studies investigating individual potential factors of pitching performance met the inclusion criteria. The primary performance factors investigated were kinematic, kinetic, timing outcomes, personal characteristics, physical tests and range of motion. Shoulder horizontal adduction (SHA), upper torso forward flexion, maximal shoulder external rotation, upper torso rotation angle, upper torso lateral flexion, lead knee flexion (LKF) and forward trunk tilt (FTT) were identified as key kinematic features associated with increased ball velocity. Shoulder proximal force and peak elbow proximal forces were associated with greater ball velocity. Individual performances in jumping tests and body weight (BW) are also associated with pitching performance. SUMMARY/CONCLUSION: Based on studies presenting low and moderate risk of bias, we conclude that BW, age and kinematics, such as FTT, LKF, SHA and lateral trunk tilt, are associated with pitching performance.
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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 | 0.012 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".