Minnesota Twins vs Cleveland Guardians
Starting pitchers
Lineups
- 1 Byron Buxton CF
- 2 Austin Martin LF
- 3 Ryan Jeffers C
- 4 Josh Bell DH
- 5 Luke Keaschall 2B
- 6 Brooks Lee SS
- 7 Victor Caratini 1B
- 8 Matt Wallner RF
- 9 Royce Lewis 3B
- 1 Steven Kwan LF
- 2 Angel Martínez RF
- 3 José Ramírez 3B
- 4 Rhys Hoskins 1B
- 5 David Fry DH
- 6 Travis Bazzana 2B
- 7 Brayan Rocchio SS
- 8 Austin Hedges C
- 9 Petey Halpin CF
Box score
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | R | |
|---|---|---|---|---|---|---|---|---|---|---|
| MIN | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 4 |
| CLE | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 6 |
Manager comparison
Both managers grade B / C+ entering this matchup.
Recent form
Season series: 0-1 with MIN listed first across 1 prior meeting.
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.
- Manager lineup cost −0.01 R/G Optimal arrangement projects 2.81 vs actual lineup 2.81
- Player execution +1.19 R/G Players exceeded the lineup's 2.81 projection by 1.19 (scored 4)
- Game variance +1.19 R/G Total game outcome vs optimal expectation
Lineup spots out of order vs season production
- Manager lineup cost +0.05 R/G Optimal arrangement projects 3.52 vs actual lineup 3.47
- Player execution +2.53 R/G Players exceeded the lineup's 3.47 projection by 2.53 (scored 6)
- Game variance +2.48 R/G Total game outcome vs optimal expectation
Martínez 0-for-4 batting 2nd
Optimal lineups projected 2.8 – 3.5 — actual was 4 – 6.
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Stephen Vogt (CLE) has run 29 distinct starting lineups across 30 games this season — that's 97% turnover game-to-game. Most managers stay below 14 for the same span.
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Derek Shelton (MIN) has run 30 distinct starting lineups across 30 games this season — that's 100% turnover game-to-game. Most managers stay below 14 for the same span.
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.