ARCHIVE March 30, 2026
March 30, 2026 · NL Cent

New York Mets vs St. Louis Cardinals

March 30, 2026Busch StadiumClear 84°F · 14 mph, Out To LF
AWAY
New York Mets
3-1
4
vs
HOME
St. Louis Cardinals
2-2
2

Starting pitchers

AWAY · NYM
Clay Holmes
Clay Holmes
IP 5.2
HOME · STL
Kyle Leahy
Kyle Leahy
IP 5

Lineups

AWAY · NYM
  1. 1 Francisco Lindor SS
  2. 2 Juan Soto LF
  3. 3 Bo Bichette 3B
  4. 4 Jorge Polanco DH
  5. 5 Brett Baty RF
  6. 6 Jared Young 1B
  7. 7 Marcus Semien 2B
  8. 8 Carson Benge CF
  9. 9 Francisco Alvarez C
HOME · STL
  1. 1 JJ Wetherholt 2B
  2. 2 Iván Herrera DH
  3. 3 Alec Burleson 1B
  4. 4 Masyn Winn SS
  5. 5 Nolan Gorman 3B
  6. 6 Jordan Walker RF
  7. 7 Nathan Church LF
  8. 8 Pedro Pagés C
  9. 9 Victor Scott CF

Box score

  123456789 R
NYM 100012000 4
STL 100001000 2

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 · STL
Oliver Marmol
B- Lineup 1.2 R Bunts 0.7 R IBBs 0.3 R

Recent form

AWAY · NYM
5-4 L1 +2 run diff
LW?WWLWLLW
HOME · STL
7-2 W1 +13 run diff
WL?WLWWWWW

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 −0.12 R/G Optimal arrangement projects 4.91 vs actual lineup 5.04
  • Player execution −1.04 R/G Players fell 1.04 short of the lineup's 5.04 projection (scored 4)
  • Game variance −0.91 R/G Total game outcome vs optimal expectation

Benge 2-for-4 from the 8-hole

HOME · St. Louis Cardinals
  • Manager lineup cost −0.01 R/G Optimal arrangement projects 2.43 vs actual lineup 2.44
  • Player execution −0.44 R/G Players fell 0.44 short of the lineup's 2.44 projection (scored 2)
  • Game variance −0.43 R/G Total game outcome vs optimal expectation

Wetherholt 0-for-3 batting 1st

COUNTERFACTUAL

Optimal lineups projected 4.9 – 2.4 — actual was 4 – 2.

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.