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- Willy AdamesBatting2-3, 2 HR, 2 RBI
- Logan WebbPitching6.0 IP, 4 ER, 1 K
Blue Jays- Tyler HeinemanBatting2-3, 1 HR, 3 RBI
- Eric LauerPitching6.0 IP, 2 ER, 7 K
Scoring summary
- 8Heineman homered to right (357 feet), Straw scored.3–6
- 7Adames homered to left (386 feet).3–4
- 6Heineman doubled to center, Wagner scored.2–4
- 6Wagner doubled to right, Barger scored and Clement scored.2–3
- 6Clement singled to left, Bichette scored, Barger to third.2–1
- 6Ramos hit sacrifice fly to right, Matos scored.2–0
- 5Adames homered to left center (391 feet).1–0
The pick
Reconstructed from what was known before the game: the ratings at that point and the closing line.
Giants 53%
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
- SF 53%
- ML SF -125 / TOR +105
- Pre-line forecast
- SF 55%
- Elo + market-implied
- Elo ratings
- SF 44%
- Ratings vs. market
- −8.8 pts
- SF: Elo 44.4%, market 53.2%
- Fair odds
- SF -114
- TOR +114 · the forecast as odds, no margin
- Pregame projection
- SF 3.9 – 3.6 TOR
- 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 | SF | TOR | Why |
|---|---|---|---|
| Team rating | 1501 | 1515 | Elo entering the game; 1500 is league average |
| Home field | — | +18 | Worth 18 Elo in MLB |
| Starting pitchers | +5 | +11 | SF: Logan Webb: team allows 0.23 fewer runs than with its usual starter (≈10 weighted starts) · TOR: Eric Lauer: team allows 0.57 fewer runs than with its usual starter (≈8 weighted starts) |
| Elo ratings | TOR +39 Elo | TOR 55.6% | |
| Market-implied ratings | SF 56.6% | Team strength as past closing lines priced it, same game context | |
| Pre-line forecast | SF 54.8% | 15% Elo, 85% market-implied (log-odds blend) | |
| Forecast | SF 53.2% | 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 |
|---|---|---|
| 23rd of 304.1 | Runs scored | 4.611th of 30 |
| 7th of 303.9 | Runs allowed | 4.419th of 30 |
| 16th of 30+0.1 | Margin | +0.214th of 30 |
| 13th of 305–5 | Last 10 | 7–33rd of 30 |
| 12th of 3024–26 | Road vs home | 33–163rd of 30 |
| 23rd of 301497 | Opponent Elo | 149918th of 30 |
| 19th of 301501 | Elo rating | 151512th 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 | 41–57 | 56–41 |
| Last 10 | 4–6 | 3–7 |
| Favorite | 16–42 | 22–25 |
| Underdog | 25–15 | 33–16 |
| Overs–unders | 45–53 | 53–44 |
| Overs–unders, last 10 | 3–7 | 3–7 |
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 2–5 TOR in their last 7 meetings; the forecast called 4 of 7.