An Analysis of Playoff Performance Declines in Major League Baseball
Bibliographic record
Abstract
ABSTRACT: Conforti, CM, HBA, Crotin, RL, RSCC, C, and Oseguera, J. An analysis of playoff performance declines in Major League Baseball. J Strength Cond Res 35(12S): S36-S41, 2021-At present, it is unknown how athletes of varying talent in Major League Baseball (MLB) perform in the postseason as compared with the regular season. Anecdotal evidence from the authors' previous work experience in MLB established the hypothesis that players of higher caliber were expected to perform worse in the playoffs compared with lesser talented cohorts. Publicly available data on 1477 MLB players from 1994 to 2019 were used to separate athletes into excellent, above average, average, and below average pitching, hitting, and defensive groups with respect to Fielding Independent Pitching (FIP), Weighted Runs Created Plus (wRC+), and Errors per Inning Out (EpIO), respectively. Mixed-model analyses of variance with Tukey's Honest Significant Difference post hoc testing was used to determine whether the change in performance was significant within groups at an a priori alpha level of p < 0.05. Within-subject effects' tests were statistically significant within regular season talent groups for FIP, wRC+, and EpIO in comparison with their playoff performance (p < 0.001). Excellent performers suffered most with more than half depreciating in playoff hitting (58%) and pitching performance (52%), yet nearly 80% of 908 fielders retained defensive ability, which was unexpected. Results indicate that teams should consider providing greater mental performance support, implement periodization strategies to taper or lower training workloads, offer team support networks, and anxiety desensitization for excellent MLB performers in approach of the playoffs, as certain aspects of pitching and hitting significantly suffer.
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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) | 0.000 | 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) | 0.001 | 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".