Part IV — Scoring Elite Code
Part IV — Scoring Elite Code
The instrument, its calibration against real results, and one team’s four-year climb.
Parts I–III argue what elite robot code looks like and how to build it. Part IV is how you measure it — and the evidence that the measurement means something. Three chapters, three questions: what do you measure?, does it actually track winning?, and what does the climb look like on one real team?
Part IV — Scoring Elite Code
The instrument, its calibration against real results, and one team’s four-year climb.
Parts I–III argue what elite robot code looks like and how to build it. Part IV is how you measure it — and the evidence that the measurement means something. Three chapters, three questions: what do you measure?, does it actually track winning?, and what does the climb look like on one real team?
Chapters
- The rubric in full — the eight-dimension instrument, every 0–4 anchor and the grep/AST cheat-sheet, scored directly.
- The San Diego scoresheet — the rubric run on 24 teams against season-matched Statbotics EPA: does better code track winning? (Yes — moderately, and unevenly.)
- The Patribots, four years — one team scored season by season with full commit history: the rubric in motion, and the two rules it illustrates.
Two validation datasets appear across these chapters and are easy to conflate: ch. 33 cites a cross-validated study over the full indexed corpus (55 teams; the cross-validation pooled every available season repo per team — 232 team-years — while rubric scoring uses each team’s most recent season), and ch. 34 is a hand-scored companion study of 24 San Diego teams paired with season-matched EPA. They are different samples answering related questions.
The method behind the rubric — how the corpus was read and why the dimensions are what they are — lives in Appendix A — How We Developed This.