The Pitch-Design Revolution: How Sweepers Changed Pitching
The sweeper went from niche to everywhere in a few seasons. How pitch design and Statcast tracking rewired modern pitching, with the usage data to prove it.
Statistics · Analytics · Data science
Baseball, run through the numbers.
Independent, original MLB analysis built on public data and open, re-runnable code. Explainers, tutorials, and deep dives — with every statistic traced back to a real data pull, never invented.
Data Deep-Dives
The Shift Ban, Two Seasons Later: What Actually Changed
Two seasons after the infield shift was restricted, what actually changed? League BABIP, pull-side outcomes, and the gap between the promise and the data.
Latest articles
Why Playoff Odds Models Disagree: A Look Under the Hood
FanGraphs says 62%, another model says 48%. A look under the hood at how playoff-odds simulations are built — and why reasonable models reach different answers.
FIP and xFIP Explained: Judging Pitchers Beyond ERA
ERA blames pitchers for things they don't control. FIP and xFIP focus on what they do. The formulas, the FIP constant, and how to judge a pitcher beyond runs allowed.
Revisiting Five Famous Trades with WAR Hindsight
Trades are judged the day they happen. WAR judges them years later. Revisiting five famous deals with the benefit of wins-above-replacement hindsight.
Anatomy of a Breakout: A Statcast Framework for Spotting Real Improvement
Is the breakout real or a mirage? A repeatable Statcast framework — expected stats, batted-ball quality, plate discipline — for telling signal from noise.
The Dead-Ball Era Through Modern Metrics
What would the dead-ball era look like with Statcast? Re-reading 1900–1920 baseball through run environment, BABIP, and the metrics we use today.
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Old games, new lenses.
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How the models think.
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Signal over narrative.
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Data Deep-Dives
Long-form, data-first investigations into league-wide trends — usage patterns, rule-change effects, and the stories hiding in the aggregate numbers..