Analysis
These are longer looks at what the simulator is seeing, from full articles to short quick hits. Every post is built on the same numbers as the rest of the site.
- Analysis
Logan Gilbert Enters the Spin Zone
Logan Gilbert's four-seam fastball went from the 6th percentile in spin to league average in a single start, and nothing in his delivery explains it.July 26, 2026 - Analysis
Welcome to the analysis section
A new home for longer looks at what the deserve-to-win model is seeing — and why.July 24, 2026
More from dtwbaseball
These methodology write-ups and explainer threads live off-site. For the daily numbers behind them, go straight to hitters, pitchers, teams and games.
Who Deserved to Win? Building an MLB Game Outcome SimulatorRead →This is the original write-up of how I built the game simulator, why, and what it found.Applying Bayesian Hierarchical Methods to MLB Season Win ProbabilitiesRead →This one rolls the per-game deserve-to-win results up into an estimate of each team's true strength.What is EB/PA?Read →This thread shows how the simulator estimates a hitter's true production, and why sample size matters.Can a Simple Formula Predict Baseball Scores?Read →This thread shows why the Poisson distribution fails for MLB runs, and what works better.Understanding Player ProjectionsRead →This thread shows how multi-year Bayesian projections handle aging, uncertainty and small samples.How Much Should You Trust the First Week?Read →Five games tell you almost nothing. This thread shows when the estimates start to settle.