Baseball

MLB analysis. Most posts here are sourced from Statcast pitch-level data via a daily automated pipeline into BigQuery; any post that draws on a different data source, like a static academic dataset, says so explicitly.

The Contact Quality Decline Held Up Through a Full August

Baseball

Now that August is complete, a follow-up on hard-hit rate’s five-month decline, a still-flat whiff rate, and where home run rate and wOBA landed after their June peak.

Contact Quality Has Been Falling Since Spring, and Whiffs Aren’t the Reason

Baseball

Hard-hit rate and exit velocity have declined for months running, home run rate and wOBA cooled off after peaking in June, and whiff rate hasn’t moved at all, a league-wide Statcast trend check through early August.

First Half in Review: Hitting and Pitching Trends, Team Momentum, and the Reds

Baseball

How league-wide hitting and pitching have shifted since March, which teams are actually trending up or down heading into the second half, and a data-driven look at where Cincinnati’s offense and pitching staff really stand.

Fan Vote vs. the Numbers: Grading the 2026 All-Star Rosters

Baseball

The starters are elected by fans, and that’s fine. Here’s who the stat lines say actually earned it.

First Pitches: Standing Up the Statcast Pipeline

Baseball

Proving the pipeline end-to-end - from Cloud Run to BigQuery to this chart.

Coursework Deep Dive: What Actually Predicts a Whiff

Baseball

Featured Research

A GRAD 505 (Purdue, Foundations in Data Science) project on 2.87 million MLB pitches from 2015-2018: hypothesis tests, a regression, and an ANOVA, and the same lesson repeated three times, statistical significance is cheap at this scale, effect size is not.