ARCHIVE April 4, 2026
April 4, 2026 · NL West

New York Mets vs San Francisco Giants

April 4, 2026Oracle ParkPartly Cloudy 67°F · 10 mph, Out To CF
AWAY
New York Mets
5-4
9
vs

Starting pitchers

AWAY · NYM
Clay Holmes
Clay Holmes
IP 7
HOME · SF
Landen Roupp
Landen Roupp
IP 4.2

Lineups

AWAY · NYM
  1. 1 Francisco Lindor SS
  2. 2 Bo Bichette 3B
  3. 3 Jorge Polanco DH
  4. 4 Brett Baty RF
  5. 5 Mark Vientos 1B
  6. 6 Jared Young LF
  7. 7 Marcus Semien 2B
  8. 8 Carson Benge CF
  9. 9 Luis Torrens C
HOME · SF
  1. 1 Willy Adames SS
  2. 2 Rafael Devers DH
  3. 3 Heliot Ramos LF
  4. 4 Luis Arraez 2B
  5. 5 Matt Chapman 3B
  6. 6 Jung Hoo Lee RF
  7. 7 Harrison Bader CF
  8. 8 Patrick Bailey C
  9. 9 Jerar Encarnacion 1B

Box score

  123456789 R
NYM 030050100 9
SF 000000000 0

Manager comparison

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

AWAY · NYM
Carlos Mendoza
D+ Lineup 2.9 R Bunts 1.4 R IBBs 0.9 R
HOME · SF
Tony Vitello
B- Lineup 0.8 R Bunts 0.7 R IBBs 1.4 R

Recent form

AWAY · NYM
5-4 L1 +2 run diff
LW?WWLWLLW
HOME · SF
1-8 L2 -25 run diff
LLWLLLLL?L

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 Mets
  • Manager lineup cost +0.18 R/G Optimal arrangement projects 3.22 vs actual lineup 3.04
  • Player execution +5.96 R/G Players exceeded the lineup's 3.04 projection by 5.96 (scored 9)
  • Game variance +5.78 R/G Total game outcome vs optimal expectation

Lindor 0-for-5 batting 1st

HOME · San Francisco Giants
  • Manager lineup cost +0.04 R/G Optimal arrangement projects 2.40 vs actual lineup 2.36
  • Player execution −2.36 R/G Players fell 2.36 short of the lineup's 2.36 projection (scored 0)
  • Game variance −2.40 R/G Total game outcome vs optimal expectation

Adames 0-for-3 batting 1st

COUNTERFACTUAL

Optimal lineups projected 3.2 – 2.4 — actual was 9 – 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.