How the math works

The league had two methodologies: the original Google Sheet model (we’ll call it v1) and a new z-score model (v2). Both are computed from every season’s real data; you can toggle between them anywhere on the site.

The v2 z-score model in plain English

• Magnitude preservation. A z-score says how many standard deviations above or below the league mean a manager landed that season. +1σ on win % means exactly the same thing as +1σ on points, regardless of how spread out the league was.
• Comparability across metrics.Final standing, win %, share of points, and all-play win % have wildly different scales. Z-scoring puts them on the same axis so the weighted average is meaningful.
• Era-comparability. Yahoo era (2019–2022) had no weekly score data, so it uses 3 components renormalized to 1/3 each. The Sleeper era (2023+) adds the all-play component and uses 4 × 0.25. Both produce comparable career numbers because z-scoring strips out scale differences.
• Win % excludes the median game.2024 added the median bonus; counting it would silently inflate Sleeper-era win % relative to Yahoo. The v2 model uses head-to-head wins only, so the same season has the same meaning across eras.
• Display scale. The raw career number is roughly in [-1.0, +1.0]. We rescale to 1250 + 100·raw (Elo-anchored): 1250 ≈ a perfectly average career, +1σ ≈ 1350.
• Low-confidence flag.A manager with fewer than 3 seasons has a career mean that’s very sensitive to a single outlier season. We visually flag those rows so a 2-season manager isn’t mistaken for a steady performer.

v1 ↔ v2 side-by-side

Same managers, same seven seasons, two different formulas. Δ is how far a manager moves when you switch from v1 to v2 (positive = climbs in v2).

Worked example: Kevin Hulsebosch (v2 rank 1)

Each row is one season. The weighted column is that season’s contribution to the career mean. The σ columns are the per-component z-scores (higher = better).