ARCHIVE March 31, 2026
March 31, 2026 · NL Cent

New York Mets vs St. Louis Cardinals

March 31, 2026Busch StadiumPartly Cloudy 80°F · 10 mph, Out To LF
AWAY
New York Mets
3-2
0
vs
HOME
St. Louis Cardinals
3-2
3

Starting pitchers

AWAY · NYM
Kodai Senga
Kodai Senga
IP 6
HOME · STL
Andre Pallante
Andre Pallante
IP 5

Lineups

AWAY · NYM
  1. 1 Francisco Lindor SS
  2. 2 Juan Soto LF
  3. 3 Bo Bichette 3B
  4. 4 Luis Robert CF
  5. 5 Jared Young 1B
  6. 6 Mark Vientos DH
  7. 7 Marcus Semien 2B
  8. 8 Carson Benge RF
  9. 9 Luis Torrens C
HOME · STL
  1. 1 JJ Wetherholt 2B
  2. 2 Iván Herrera C
  3. 3 Alec Burleson 1B
  4. 4 Masyn Winn SS
  5. 5 Nolan Gorman DH
  6. 6 Jordan Walker RF
  7. 7 Nathan Church LF
  8. 8 Ramón Urías 3B
  9. 9 Victor Scott CF

Box score

  123456789 R
NYM 000000000 0
STL 002000100 3

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 −0.03 R/G Optimal arrangement projects 3.92 vs actual lineup 3.95
  • Player execution −3.95 R/G Players fell 3.95 short of the lineup's 3.95 projection (scored 0)
  • Game variance −3.92 R/G Total game outcome vs optimal expectation

Lindor 0-for-3 batting 1st

HOME · St. Louis Cardinals
  • Manager lineup cost +0.04 R/G Optimal arrangement projects 4.82 vs actual lineup 4.78
  • Player execution −1.78 R/G Players fell 1.78 short of the lineup's 4.78 projection (scored 3)
  • Game variance −1.82 R/G Total game outcome vs optimal expectation

Urías HR from the 8-hole

COUNTERFACTUAL

Optimal lineups projected 3.9 – 4.8 — actual was 0 – 3.

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.