Analysis
Longer looks at what the deserve-to-win model is seeing — article-length dives and short quick hits, all built on the same numbers as the rest of the site.
More from dtwbaseball
Longer methodology write-ups and short explainer threads, published off-site.
Who Deserved to Win? Building an MLB Game Outcome SimulatorRead →The original methodology, motivation, and results — how the game simulator was built and what it found.Applying Bayesian Hierarchical Methods to MLB Season Win ProbabilitiesRead →Rolling the per-game deserve-to-win results up into an estimate of each team's true, underlying strength.What is EB/PA?Read →How we estimate a hitter's true production — and why sample size matters.Can a Simple Formula Predict Baseball Scores?Read →Why the Poisson distribution fails for MLB runs — and what works better.Understanding Player ProjectionsRead →How multi-year Bayesian models handle aging, uncertainty, and small samples.How Much Should You Trust the First Week?Read →Why 5 games tells you almost nothing — and when Bayesian estimates start to converge.