ARCHIVE April 19, 2026
April 19, 2026 · NL East

San Francisco Giants vs Washington Nationals

April 19, 2026Nationals ParkCloudy 51°F · 8 mph, L To R
AWAY
San Francisco Giants
9-13
0
vs
HOME
Washington Nationals
10-12
3

Starting pitchers

AWAY · SF
Robbie Ray
Robbie Ray
IP 6
HOME · WSH
Andrew Alvarez
Andrew Alvarez
IP 4.1

Lineups

AWAY · SF
  1. 1 Willy Adames SS
  2. 2 Luis Arraez 2B
  3. 3 Matt Chapman 3B
  4. 4 Rafael Devers DH
  5. 5 Casey Schmitt 1B
  6. 6 Jung Hoo Lee RF
  7. 7 Heliot Ramos LF
  8. 8 Drew Gilbert CF
  9. 9 Patrick Bailey C
HOME · WSH
  1. 1 James Wood LF
  2. 2 Curtis Mead 1B
  3. 3 Brady House 3B
  4. 4 CJ Abrams SS
  5. 5 Jacob Young CF
  6. 6 Joey Wiemer RF
  7. 7 Luis García Jr. DH
  8. 8 Nasim Nuñez 2B
  9. 9 Keibert Ruiz C

Box score

  123456789 R
SF 000000000 0
WSH 000030000 3

Manager comparison

Both managers grade B- / C entering this matchup.

AWAY · SF
Tony Vitello
B- Lineup 0.8 R Bunts 0.7 R IBBs 1.4 R
HOME · WSH
Blake Butera
C Lineup 1.6 R Bunts 1.5 R IBBs 1.4 R

Recent form

AWAY · SF
1-8 L2 -25 run diff
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HOME · WSH
6-4 W3 +6 run diff
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Season series: 2-1 with SF listed first across 3 prior meetings.

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 · San Francisco Giants
  • Manager lineup cost +0.02 R/G Optimal arrangement projects 4.30 vs actual lineup 4.28
  • Player execution −4.28 R/G Players fell 4.28 short of the lineup's 4.28 projection (scored 0)
  • Game variance −4.30 R/G Total game outcome vs optimal expectation

Adames 0-for-5 batting 1st

HOME · Washington Nationals
  • Manager lineup cost +0.12 R/G Optimal arrangement projects 3.20 vs actual lineup 3.09
  • Player execution −0.09 R/G Players fell 0.09 short of the lineup's 3.09 projection (scored 3)
  • Game variance −0.20 R/G Total game outcome vs optimal expectation

Wood 0-for-3 batting 1st

COUNTERFACTUAL

Optimal lineups projected 4.3 – 3.2 — 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.