ARCHIVE April 4, 2026
April 4, 2026 · NL Cent

Baltimore Orioles vs Pittsburgh Pirates

April 4, 2026PNC ParkPartly Cloudy 82°F · 15 mph, Out To LF
AWAY
Baltimore Orioles
3-5
2
vs
HOME
Pittsburgh Pirates
5-3
3

Starting pitchers

AWAY · BAL
Shane Baz
Shane Baz
IP 5.2
HOME · PIT
Carmen Mlodzinski
Carmen Mlodzinski
IP 4.2

Lineups

AWAY · BAL
  1. 1 Taylor Ward DH
  2. 2 Gunnar Henderson SS
  3. 3 Pete Alonso 1B
  4. 4 Adley Rutschman C
  5. 5 Dylan Beavers LF
  6. 6 Coby Mayo 3B
  7. 7 Leody Taveras CF
  8. 8 Colton Cowser RF
  9. 9 Blaze Alexander 2B
HOME · PIT
  1. 1 Oneil Cruz CF
  2. 2 Brandon Lowe 2B
  3. 3 Bryan Reynolds LF
  4. 4 Ryan O'Hearn RF
  5. 5 Marcell Ozuna DH
  6. 6 Konnor Griffin SS
  7. 7 Spencer Horwitz 1B
  8. 8 Nick Gonzales 3B
  9. 9 Joey Bart C

Box score

  123456789 R
BAL 000200000 2
PIT 000100011 3

Manager comparison

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

AWAY · BAL
Craig Albernaz
C+ Lineup 1.2 R Bunts 0.3 R IBBs 1.8 R
HOME · PIT
Don Kelly
C- Lineup 2.3 R Bunts 0.3 R IBBs 0.8 R

Recent form

AWAY · BAL
3-7 L2 -25 run diff
LLWWLLLLWL
HOME · PIT
5-5 W2 +1 run diff
WWLWWWLLLL

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 · Baltimore Orioles
  • Manager lineup cost −0.05 R/G Optimal arrangement projects 4.16 vs actual lineup 4.21
  • Player execution −2.21 R/G Players fell 2.21 short of the lineup's 4.21 projection (scored 2)
  • Game variance −2.16 R/G Total game outcome vs optimal expectation

Ward 0-for-4 batting 1st

HOME · Pittsburgh Pirates
  • Manager lineup cost +0.04 R/G Optimal arrangement projects 4.12 vs actual lineup 4.08
  • Player execution −1.08 R/G Players fell 1.08 short of the lineup's 4.08 projection (scored 3)
  • Game variance −1.12 R/G Total game outcome vs optimal expectation

Lowe 0-for-4 batting 2nd

COUNTERFACTUAL

Optimal lineups projected 4.2 – 4.1 — actual was 2 – 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.