Toronto Blue Jays at San Francisco Giants, MLB forecast
Win probability
Starts from the pregame forecast, then moves with the score and the time left after each inning. Above the line favors SF.
Game center
From the ESPN box score.
Top performers
Blue Jays- Kazuma OkamotoBatting1-4, 1 HR, 1 RBI
- Kevin GausmanPitching5.1 IP, 4 ER, 8 K
Giants- Heliot RamosBatting3-5, 2 HR, 5 RBI
- Landen RouppPitching8.0 IP, 1 ER, 5 K
Scoring summary
- 8Ramos homered to left center (444 feet), Eldridge scored.1–10
- 6Ramos homered to right (349 feet), Adames scored and Bericoto scored.1–8
- 6Bericoto singled to right, Devers scored and Lee scored, Adames to third.1–5
- 6Okamoto homered to left center (412 feet).1–3
- 5Bericoto stole home, Arraez stole second.0–3
- 5Bericoto stole home, Arraez stole second.0–3
- 4Adames grounded into double play, shortstop to second to first, Devers scored, Lee out at second.0–2
- 1Ramos tripled to center, Ramos scored on throwing error by shortstop Giménez.0–1
The pick
Reconstructed from what was known before the game: the ratings at that point and the closing line.
Blue Jays 51%
Forecast Elo ratings alone
How often that comes true: MLB favorites the forecast gave 50–55% have won 52.7% of 2,871 graded games.
- Market
- TOR 51%
- ML TOR -114 / SF -105
- Pre-line forecast
- TOR 50%
- Elo + market-implied
- Elo ratings
- TOR 53%
- Ratings vs. market
- +1.9 pts
- TOR: Elo 52.9%, market 51.0%
- Fair odds
- TOR -104
- SF +104 · the forecast as odds, no margin
- Pregame projection
- TOR 3.8 – 3.7 SF
- Margin from the forecast, posted total 7.5
How the number is built
Elo inputs in rating points (higher favors that team; 25–28 Elo is about a point of spread in football and basketball), then how the pieces combine.
| Factor | TOR | SF | Why |
|---|---|---|---|
| Team rating | 1504 | 1477 | Elo entering the game; 1500 is league average |
| Home field | — | +18 | Worth 18 Elo in MLB |
| Starting pitchers | −7 | −18 | TOR: Kevin Gausman: team allows 0.35 more runs than with its usual starter (≈10 weighted starts) · SF: Landen Roupp: team allows 0.89 more runs than with its usual starter (≈10 weighted starts) |
| Elo ratings | TOR +20 Elo | TOR 52.9% | |
| Market-implied ratings | SF 50.2% | Team strength as past closing lines priced it, same game context | |
| Pre-line forecast | TOR 50.2% | 15% Elo, 85% market-implied (log-odds blend) | |
| Forecast | TOR 51.0% | Follows the market (vig removed): in testing, adding the ratings made it less accurate | |
Tale of the tape
This season coming into the game, regular season, each number with its league rank (the longer the bar, the higher). Home and road: the host's record at home against the visitor's on the road. Opponent Elo: how strong the teams each has played are, 1st the toughest.
| TOR | Stat | SF |
|---|---|---|
| 29th of 304.0 | Runs scored | 4.123rd of 30 |
| 12th of 304.4 | Runs allowed | 4.820th of 30 |
| 21st of 30−0.5 | Margin | −0.727th of 30 |
| 25th of 303–7 | Last 10 | 4–619th of 30 |
| 21st of 3018–23 | Road vs home | 18–2225th of 30 |
| 7th of 301502 | Opponent Elo | 15054th of 30 |
| 11th of 301504 | Elo rating | 147727th of 30 |
Rating history
Each team's Elo over the last two seasons. Hover or drag to compare on any date.
Recent form
Last five results, and whether the forecast hit.
Against the line
How each team has done against the closing spread and total, coming into this game.
| This season |  TOR |  SF |
|---|---|---|
| Run line | 43–47 | 41–48 |
| Last 10 | 2–8 | 5–5 |
| Favorite | 20–31 | 12–21 |
| Underdog | 23–16 | 29–27 |
| Overs–unders | 44–42–4 | 40–41–8 |
| Overs–unders, last 10 | 5–5 | 4–4–2 |
Against each game's closing line. Books set every line knowing these records: across 3,901 past games, the team with the better record against the spread so far that season covered 51.2%, short of the 52.4% a standard −110 bet needs, and over/under streaks held 48.0% of the time. They describe the past.
Head to head
TOR 7–2 SF in their last 9 meetings; the forecast called 5 of 9.