S22 / Sports analytics and baseball
Pitcher development signals that survive the next season
A player-development group needs to distinguish durable pitch quality from small-sample outcomes. Test whether pitch characteristics and context improve forecasts of future swing-and-miss and contact quality beyond recent results.
Awaiting prerequisite
Hierarchical shrinkage; staged probabilities; temporal transfer
Research question and proposed design
Create pitcher/pitch-type season histories from Statcast with explicit tracking-era and field-availability checks. Model swing, miss and contact outcomes in stages, using count, handedness and pitch characteristics available at the relevant stage. Pool sparse pitcher/pitch-type estimates hierarchically and forecast a later season.
Evaluation: Use season-forward holdouts and pitcher-level uncertainty. Compare with prior-season rates and league/pitch-type averages. Report calibration, log loss for binary outcomes, contact-quality error and rank stability across pitch-count thresholds and tracking eras.
Boundary: Observed pitch selection depends on opponent and situation. The study cannot prove that changing pitch mix improves performance, and outcome-stage variables must not leak into pre-pitch forecasts.
No completed finding is claimed for this topic.
Statcast / Baseball Savant ↗
Original dataset selection position 1 within this industry. This is research-selection metadata, not measured model performance.
S23 / Sports analytics and baseball
Baseball strategy as a risk-sensitive decision problem
A baseball strategy analyst wants to quantify when a stolen-base attempt or other base-running choice is worth the risk. Estimate the break-even success probability under different base-out states and scoring environments.
Planned
Markov reward processes; expected utility; partial pooling
Research question and proposed design
Parse Retrosheet events into validated base-out transitions and inning runs. Estimate era-specific run-expectancy tables with shrinkage for sparse states. For a stolen-base decision, compare expected runs after success, failure and no attempt using documented transition assumptions.
Evaluation: Hold out seasons and check run-expectancy calibration and transition validity. Compare pooled and era-specific tables, bootstrap by game, and test rare-state sensitivity. Validate the transition engine with known game-state identities and explicitly distinguish predictive checks from causal policy evaluation.
Boundary: Managers select attempts non-randomly, and observed non-attempts are not randomized controls. Historical decision replay cannot by itself establish that a new strategy would cause more wins.
No completed finding is claimed for this topic.
Retrosheet play-by-play ↗
Original dataset selection position 2 within this industry. This is research-selection metadata, not measured model performance.
S24 / Sports analytics and baseball
Roster allocation under aging and performance uncertainty
A roster planner must allocate a fixed budget across players with uncertain future production. Test whether modeling aging, playing time and downside risk changes a roster relative to ranking last season's statistics.
Planned
Hierarchical aging curves; survivor-bias analysis; portfolio optimization
Research question and proposed design
Build historical player-season panels with era normalization and a prespecified offensive run-value formula. Forecast playing time and offensive production jointly using hierarchical age curves and flexible challengers. Optimize a simplified roster under position and budget constraints, using documented salary-covered seasons or clearly synthetic costs.
Evaluation: Use season-forward holdouts and compare against last-season production and age-neutral forecasts. Report production error, interval coverage and roster-performance distributions in historical replay. Stress retirement/selection assumptions and salary coverage; keep hindsight-optimal comparisons confined to the simulation.
Boundary: Lahman is not a complete current payroll or scouting database. A simplified offensive roster omits defense, injuries and contractual constraints unless explicitly added; it cannot establish current front-office value.
No completed finding is claimed for this topic.
Lahman Baseball Database ↗
Original dataset selection position 3 within this industry. This is research-selection metadata, not measured model performance.