on wax

KEEPING SCORE

From the stands to the semantic layer.

178 games I was actually in the stands for — joined to the historical record, modeled in dbt on BigQuery, tested, and documented down to the lineage graph. My grandfather kept score by hand. I carried the tradition. This is the same diamond, a generation later, answered with new tools.

dbt BigQuery Retrosheet Semantic Layer Baseball

The whole project is live and browsable — models, tests, and the full lineage graph.

Explore the live dbt docs →

#What it is

KEEPING SCORE is a personal data project: every MLB game I've ever attended — 178 of them, going back to childhood — turned into a queryable, tested, documented dataset. My attendance record joined to Retrosheet's historical game logs, modeled in dbt on Google BigQuery, with a semantic layer on top so the questions can be asked in plain language.

It started analog. My grandfather kept score by hand at the ballpark, and I grew up doing the same — pencil, scorecard, the little diamond you fill in when someone reaches base. Those scorecards, his and mine, are the seed. This is what happens when you point modern data tooling at a shoebox full of them.

#How it works

Five pieces, each doing one job:

The attendance record — 178 games, hand-assembled from tickets, scorecards, and memory. The one source no database could give me.
Retrosheet — the historical truth. Every MLB game ever played, with box-score detail. I match my games to it on date and teams to pull the full record of each one I was at.
dbt on BigQuery — staging views clean and curate the raw data; a fact table joins my attendance to the historical record, with derived columns like who won, the margin, and whether it was a Yankees game.
26 dbt tests — not there to always pass. They're there to surface what the data is actually doing. One of them found ~1,800 duplicate game IDs hiding in the historical source — the kind of thing you only learn by testing.
Docs + lineage — every model documented, the full lineage graph generated, and the whole thing auto-published on every push. That's the button up top.

The move here: treat a personal archive like production data. Sources, staging, marts, tests, docs — the same discipline a team would use, applied to a shoebox of scorecards. The dataset is sentimental; the engineering is real.

#Same questions, new tools

A scorecard answers questions. Who's up? What did he do last time? How did this game actually go? Fans have asked the same handful of questions for a hundred years.

The tools to answer them keep changing. First the scorecard in your lap. Then the box score in the morning paper. Then the stat sites. Now: a semantic model an agent can query in plain English.

Same questions every baseball fan ever asked. New tools answering them — scorecard, box score, now agentic. This project is the literal proof of that thesis. The same diamond my grandfather filled in by hand, now a tested data model with a lineage graph of its own.