Applications of Discrete Markov Chains to Predicting Baseball Game Outcomes
Bibliographic record
Abstract
There are two fundamental notions which justify the use of Markov chains in the analysis of baseball game outcomes. The first is simply a statement of the consequences of the rules of Baseball itself-- namely that for any batter, exactly one of three possible outcomes will have occurred by the time their turn at bat is finished. They will have scored a run, or they will find themselves on base, or they will be “out”. As a consequence, the evolving state of a game of baseball can be entirely characterized in terms of batters in sequence moving from their turn atbat into the appropriate variation of one of these three foundational “states”. In particular, from the beginning of a half-inning to its conclusion, every single possible configuration of bases occupied, number of outs, and number of points scored can therefore be arranged sequentially and, as I will demonstrate, the discrete Markov chain is the perhaps the most natural framework in which to do this. Discipline: Mathematics Faculty Mentor: Dr. Cristina Anton
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".