San Francisco Giants at Toronto Blue Jays, 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 TOR.
Game center
From the ESPN box score.
Top performers
Giants- Heliot RamosBatting2-4
- Tristan BeckPitching4.1 IP, 0 ER, 2 K
Blue Jays- George SpringerBatting3-4
- Chris BassittPitching6.1 IP, 0 ER, 5 K
Scoring summary
- 2Lukes singled to right, Wagner scored.0–4
- 2Wagner doubled to right, Kirk scored and Loperfido scored.0–3
- 2Loperfido doubled to right, Barger scored, Kirk to third.0–1
The pick
Reconstructed from what was known before the game: the ratings at that point and the closing line.
Blue Jays 54%
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 54%
- ML SF +110 / TOR -130
- Pre-line forecast
- TOR 59%
- Elo + market-implied
- Elo ratings
- TOR 56%
- Ratings vs. market
- +1.8 pts
- TOR: Elo 56.1%, market 54.3%
- Fair odds
- TOR -119
- SF +119 · the forecast as odds, no margin
- Pregame projection
- SF 4.1 – 4.4 TOR
- Margin from the forecast, posted total 8.5
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 | SF | TOR | Why |
|---|---|---|---|
| Team rating | 1503 | 1513 | Elo entering the game; 1500 is league average |
| Home field | — | +18 | Worth 18 Elo in MLB |
| Starting pitchers | −25 | −10 | SF: Justin Verlander: team allows 1.25 more runs than with its usual starter (≈10 weighted starts) · TOR: Chris Bassitt: team allows 0.52 more runs than with its usual starter (≈10 weighted starts) |
| Elo ratings | TOR +43 Elo | TOR 56.1% | |
| Market-implied ratings | TOR 59.3% | Team strength as past closing lines priced it, same game context | |
| Pre-line forecast | TOR 58.9% | 15% Elo, 85% market-implied (log-odds blend) | |
| Forecast | TOR 54.3% | 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.
| SF | Stat | TOR |
|---|---|---|
| 22nd of 304.1 | Runs scored | 4.610th of 30 |
| 8th of 303.9 | Runs allowed | 4.422nd of 30 |
| 15th of 30+0.2 | Margin | +0.214th of 30 |
| 5th of 306–4 | Last 10 | 7–33rd of 30 |
| 11th of 3024–25 | Road vs home | 32–163rd of 30 |
| 23rd of 301497 | Opponent Elo | 149918th of 30 |
| 18th of 301503 | Elo rating | 151313th 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 |  SF |  TOR |
|---|---|---|
| Run line | 41–56 | 55–41 |
| Last 10 | 5–5 | 3–7 |
| Favorite | 16–42 | 21–25 |
| Underdog | 25–14 | 33–16 |
| Overs–unders | 45–52 | 53–43 |
| Overs–unders, last 10 | 4–6 | 4–6 |
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
SF 2–4 TOR in their last 6 meetings; the forecast called 3 of 6.