Metric reliability
Does a player or team metric hold up from one sample to the next, and how many games before it means something?
You get: reliability estimates, stabilization points, and a short report with reproducible code.
Measurement for sports data
Most sports analytics reports a number and never says how much to trust it. I audit the reliability and calibration of the metrics, models, and assessments that teams, vendors, and analysts rely on, and I explain the results in plain language.
Three kinds of questions come up again and again, and each one has a standard, defensible way to answer it.
Does a player or team metric hold up from one sample to the next, and how many games before it means something?
You get: reliability estimates, stabilization points, and a short report with reproducible code.
When your model says 70%, does it happen about 70% of the time? Applies to win probability, expected goals, injury risk, and similar models.
You get: calibration curves, Brier scores, and a clear verdict on where the model over- or under-reaches.
Are wellness questionnaires and tests measuring what staff think, and is a change in an athlete's score real or just measurement error?
You get: validation evidence, reliable-change thresholds, and guidance staff can use.
Fixed scope, fixed fee, no surprises.
How many at-bats or attempts before a rate stat is mostly skill? Enter what you know, or paste player totals and get an estimate with an interval.
Short, occasional emails on why small samples fool us and how to tell signal from noise in sports numbers. I'll also tell you when the free stabilization-point calculator launches.
Sports Analytics Consulting is run by Nedim Yel, PhD, a measurement and statistics consultant whose training is in psychometrics, multilevel modeling, and research design. The same methods that tell you whether a test is reliable tell you whether a batting average is.