After Game 3 · NBA Finals 2026

Spurs 1–2 Deficit
Bayesian Series Analysis

A three-hierarchy Bayesian analysis of San Antonio's playoff and regular-season statistics against the Knicks — updated after the Spurs' Game 3 road win at Madison Square Garden.

by Charlie of Mr. Danoff's Teaching Laboratory · Last Updated June 10th, 2026

Plain English

The Spurs' Game 3 victory (115-111 away) dramatically shifted their comeback prospects from near-zero to realistic, with the model showing:

If they maintain Game 3 performance: 62.1% [2.8%, 99.8%]
If they regress to Finals average (G1-G3): 4.9% [0.0%, 27.4%]
Midpoint scenario: 42.0% [0.9%, 92.7%]

The remaining schedule still runs two of the next three games in New York, so the away win probability is the critical number — and Game 3 showed they can get it.

Game 3 Recap

115-111
Final Score
Away
Location
MSG
Venue

What Changed from Games 1-2 → Game 3

28
Assists
↑ from 19/22
8
Turnovers
↓ from 13/16
46%
FG%
↑ from 41% avg
48
Rebounds
≈ same
3.5
Ast/TO ratio
↑ from 1.4

Bayesian Model: Comeback Probability by Scenario

Remaining schedule: G4 Away · G5 Home · G6 Away · G7 Home — NYK needs 2 more wins, SAS needs 3

Scenario P(win home) P(win away) P(comeback) 90% CI
Finals avg (G1-G3) baseline 14.6% 17.3% 4.9% [0.0%, 27.4%]
Game 3 level last game 66.6% 70.1% 62.1% [2.8%, 99.8%]
Midpoint improvement scenario 52.5% 56.1% 42.0% [0.9%, 92.7%]
Other playoff win avg ceiling 87.4% 89.5% 89.6% [47.2%, 100.0%]

Probabilities are posterior medians from the v3 model (3-hierarchy partial pooling, 10,000 MCMC draws). Run spurs_bayesian_model_v3.py after Game 4 for updated numbers.

Posterior Distribution of Comeback Probability

Simulated from 5,000 browser-side draws using beta-distributed posteriors. The Game 3 and midpoint scenarios now overlap significantly — the Spurs' Game 3 performance was close to their historical winning profile, not an outlier.

Finals avg (G1-G3) Game 3 level / midpoint Other playoff win avg

What Drives Spurs Wins

Observed averages from SportRadar box scores — descriptive statistics, not model coefficients. The logistic regression assigns highest posterior confidence to assists and turnovers as predictors of winning.

27
Assists
playoff win avg
19/22 G1-G2 · 28 G3 · ↑ target met
48%
FG%
playoff win avg
36/47% G1-G2 · 46% G3 · ↑ trending
50
Rebounds
playoff win avg
60/53 G1-G2 · 48 G3 · roughly on par
12
Turnovers
playoff win avg
13/16 G1-G2 · 8 G3 · ↓ target met

Model v3

Three-hierarchy Bayesian hierarchical logistic regression with partial pooling across groups: Group 0 = NYK Finals (n=3); Group 1 = Pre-Finals SAS vs NYK matchups — two regular season games and the NBA Cup Final (n=3); Group 2 = All other 2026 SAS playoff games (n=18).

Fit via adaptive Metropolis-Hastings, 10,000 post-warmup draws. Predictors: FG%, rebounds, assists, steals, blocks, turnovers, home/away indicator — all standardised. The third hierarchy adds matchup-specific information the earlier model lacked.