Acquisition Leverage

For each win of FA/trade talent acquired, the playoff-probability points it added — given the roster it joined. Did a GM buy wins where they actually matter?

What it measures

Playoff odds rise steeply only near the contention threshold, so a bought win is worth a lot on a near-contender and almost nothing on a baseless roster. Acquisition Leverage grades where a GM spent — a process measure, independent of how the players then performed.

Step 1 — the win curve

Playoff probability is an S-curve in roster WAR. Fit a logistic on every full team-season (1985–2025):

\[ P(\text{playoff}\mid W) = \sigma(\alpha + \beta W), \qquad \sigma(x)=\frac{1}{1+e^{-x}} \]

The slope \(\frac{dP}{dW}=\beta\,\sigma(1-\sigma)\) is maximal at the ~45-WAR threshold and ≈ 0 in the tails. Fitted: P = σ(−9.09 + 0.200·W); ceiling slope ≈ 5.0 pts/WAR.

Step 2 — split the roster, measure the climb

Split each season's roster WAR into the home-grown base (entered as the club's amateur) and the acquired (FA + trade) WAR on top. The leverage of that season's acquisitions is the playoff probability they added:

\[ \text{lev} = \big[\,P(W_\text{base}+W_\text{acq}) - P(W_\text{base})\,\big]\times 100 \]

Aggregate over a GM's tenure and divide by the WAR they bought:

\[ \text{Acq Leverage} = \frac{\sum \text{lev}}{\sum W_\text{acq}} \quad\text{(playoff points per bought WAR)} \] Win curve and leverage distribution

How to read it

Units: playoff-probability points per bought WAR. League average ≈ 1.3; ceiling ≈ 5.0.
~2.5 (elite) — Schuerholz, Friedman, Cashman: bought wins onto near-threshold contenders where they flip playoff odds. ~0.5 (weak) — bought onto rosters too far from contention.
A process measure: buying a star onto a baseless roster scores low whether he becomes an MVP or a bust.

Is it a skill, and does it predict? — our best GM-skill signal

Of every metric on this site, Acquisition Leverage is the one we'd hand a hiring committee. It's the rare combination of repeatable and predictive of the outcomes it was never fit on — most metrics give you one or the other.

Stickiness (within-GM, year-over-year): +0.49 — comfortably stickier than WAB (+0.38) and TAB (+0.48). More importantly, it holds at +0.37 even after residualizing out total team WAR, so it is not just "good teams stay good" — the same executive keeps making high-leverage buys across different rosters and franchises.
Predictiveness: it correlates +0.59 with reaching the playoffs, +0.30 with winning the pennant, and +0.19 with the World Series — the highest World-Series correlation of any metric on this site. The key point: leverage is built only from regular-season playoff odds, so it has no October information baked in, yet it still predicts October. That's the cleanest evidence we have that buying wins where they matter is a genuine, transferable executive skill.
Why it beats the alternatives: the Surplus metrics are stickier (~+0.66) but predict titles at ≈0 — efficiency without aim. WAB/TAB predict reaching the playoffs but lean heavily on the wins/talent a GM partly inherited. Acquisition Leverage isolates the decision — where you spent the marginal win — and that decision both repeats and pays off in October.

Unlike WAB or Surplus, we don't publish a "playoff rate by leverage bucket" table here: leverage is defined from the playoff-probability curve, so binning it against playoff rate would be circular. Its honest validation is the one above — it forecasts pennants and World Series, outcomes it was never fit on.

Worked example — the 2019 Dodgers

Inputs: home-grown base \(W_\text{base}=45.2\), acquired \(W_\text{acq}=16.6\), total = 61.8 WAR.

1. \(P(45.2)=\sigma(-9.09+0.200\times45.2)=\sigma(-0.05)=0.488\) — the core alone was a ~50% playoff team, right at the threshold.

2. \(P(61.8)=\sigma(-9.09+0.200\times61.8)=\sigma(3.27)=0.963\)

3. \(\text{lev}=(0.963-0.488)\times100 = +47.5\) playoff points

4. per bought WAR: \(47.5/16.6 = \mathbf{2.9}\) — high-leverage (vs ~1.3 average): every bought win sat on the steepest part of the curve, pushing a coin-flip team to a near-lock 96%.

Fit on full team-seasons (≥100 games), 1985–2024/25. All figures are franchise-level outcomes credited to the decision-maker in the relevant year — see the GM profiles for per-executive numbers.

Rosternomics — quantifying front-office skill, attribution-clean. Follow @rosternomics on X. Decision-makers on record back to 1933; resource-controlled metrics (WAR & payroll) cover 1985–2026.
Data: FanGraphs (WAR), Baseball-Reference & the Chadwick Bureau register (rosters, IDs), the Lahman Database, Baseball America prospect rankings (via The Baseball Cube), and public payroll records. Transaction and trade data was obtained free of charge from and is copyrighted by Retrosheet. Executive birth/death data via Wikidata (CC0); bios are original. Emoji graphics by OpenMoji (CC BY-SA 4.0).
All figures are franchise-level outcomes credited to the decision-maker in the acquisition year — not solely attributable to one executive.