ARCHIVE March 25, 2026
March 25, 2026 · NL West

New York Yankees vs San Francisco Giants

March 25, 2026Oracle ParkPartly Cloudy 63°F · 19 mph, Out To CF
AWAY
New York Yankees
1-0
7
vs

Starting pitchers

AWAY · NYY
Max Fried
Max Fried
IP 6.1
HOME · SF
Logan Webb
Logan Webb
IP 5

Lineups

AWAY · NYY
  1. 1 Trent Grisham CF
  2. 2 Aaron Judge RF
  3. 3 Cody Bellinger LF
  4. 4 Ben Rice 1B
  5. 5 Giancarlo Stanton DH
  6. 6 Jazz Chisholm Jr. 2B
  7. 7 José Caballero SS
  8. 8 Ryan McMahon 3B
  9. 9 Austin Wells C
HOME · SF
  1. 1 Luis Arraez 2B
  2. 2 Matt Chapman 3B
  3. 3 Rafael Devers DH
  4. 4 Willy Adames SS
  5. 5 Jung Hoo Lee RF
  6. 6 Heliot Ramos LF
  7. 7 Casey Schmitt 1B
  8. 8 Patrick Bailey C
  9. 9 Harrison Bader CF

Box score

  123456789 R
NYY 050020000 7
SF 000000000 0

Manager comparison

Both managers grade C+ / B- entering this matchup.

AWAY · NYY
Aaron Boone
C+ Lineup 1.5 R Bunts 0.0 R IBBs 1.1 R
HOME · SF
Tony Vitello
B- Lineup 0.8 R Bunts 0.7 R IBBs 1.4 R

Recent form

AWAY · NYY
7-3 L1 +26 run diff
LWLWWWWWLW
HOME · SF
1-8 L2 -25 run diff
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Tactical analysis

RunsLeft separates manager decisions from player execution from game variance. Each card below is the same three-number framework that drives the share cards on the SPA replay page.

AWAY · New York Yankees
  • Manager lineup cost −0.03 R/G Optimal arrangement projects 4.58 vs actual lineup 4.61
  • Player execution +2.39 R/G Players exceeded the lineup's 4.61 projection by 2.39 (scored 7)
  • Game variance +2.42 R/G Total game outcome vs optimal expectation

Judge 0-for-5 batting 2nd

HOME · San Francisco Giants
  • Manager lineup cost +0.03 R/G Optimal arrangement projects 2.74 vs actual lineup 2.72
  • Player execution −2.72 R/G Players fell 2.72 short of the lineup's 2.72 projection (scored 0)
  • Game variance −2.74 R/G Total game outcome vs optimal expectation

Chapman 0-for-3 batting 2nd

COUNTERFACTUAL

Optimal lineups projected 4.6 – 2.7 — actual was 7 – 0.

Keep reading

How RunsLeft analyzes games

Standard content on this page comes from the MLB Stats API. Tactical analysis comes from a per-game Monte Carlo: 10,000 simulated games against the opposing starter's ERA, both for the actual lineup the manager wrote and for the optimal arrangement of those nine players. The three numbers — manager cost, player execution, game variance — separate which part of the result was the lineup, the players, or the dice.

Read the full methodology.