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 OkamotoBatting2-5, 1 HR, 4 RBI
- Dylan CeasePitching8.0 IP, 0 ER, 11 K
Giants- Heliot RamosBatting1-4
- Logan WebbPitching7.0 IP, 5 ER, 2 K
Scoring summary
- 9Springer homered to center (415 feet).10–0
- 9Guerrero Jr. homered to center (419 feet), Lukes scored.9–0
- 8Giménez reached on infield single to shortstop, Okamoto scored, Straw to second.7–0
- 8Straw grounded into fielder's choice to second shortstop, Varsho scored, Straw to first on error by first baseman Devers, Kirk out at second, Okamoto to third.6–0
- 1Okamoto homered to right (328 feet), Lukes scored, Springer scored and Varsho scored. Umpire review: call on the field was upheld.5–0
- 1Varsho singled to right, Clement scored, Springer to second, Lukes to third.1–0
The pick
Reconstructed from what was known before the game: the ratings at that point and the closing line.
Blue Jays 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
- TOR 53%
- ML TOR -125 / SF +104
- Pre-line forecast
- TOR 53%
- Elo + market-implied
- Elo ratings
- TOR 54%
- Ratings vs. market
- +0.6 pts
- TOR: Elo 53.7%, market 53.1%
- Fair odds
- TOR -113
- SF +113 · the forecast as odds, no margin
- Pregame projection
- TOR 3.6 – 3.4 SF
- Margin from the forecast, posted total 7
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 | 1478 | Elo entering the game; 1500 is league average |
| Home field | — | +18 | Worth 18 Elo in MLB |
| Starting pitchers | +17 | −1 | TOR: Dylan Cease: team allows 0.85 fewer runs than with its usual starter (≈10 weighted starts) · SF: Logan Webb: team allows 0.05 more runs than with its usual starter (≈10 weighted starts) |
| Elo ratings | TOR +26 Elo | TOR 53.7% | |
| Market-implied ratings | TOR 52.8% | Team strength as past closing lines priced it, same game context | |
| Pre-line forecast | TOR 52.9% | 15% Elo, 85% market-implied (log-odds blend) | |
| Forecast | TOR 53.1% | 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 |
|---|---|---|
| 28th of 304.0 | Runs scored | 4.123rd of 30 |
| 13th of 304.5 | Runs allowed | 4.819th of 30 |
| 22nd of 30−0.5 | Margin | −0.725th of 30 |
| 20th of 304–6 | Last 10 | 5–514th of 30 |
| 21st of 3019–24 | Road vs home | 19–2324th of 30 |
| 8th of 301502 | Opponent Elo | 15044th of 30 |
| 14th of 301504 | Elo rating | 147826th 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 | 44–48 | 42–49 |
| Last 10 | 3–7 | 6–4 |
| Favorite | 20–31 | 13–22 |
| Underdog | 24–17 | 29–27 |
| Overs–unders | 46–42–4 | 42–41–8 |
| Overs–unders, last 10 | 5–5 | 5–3–2 |
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 8–3 SF in their last 11 meetings; the forecast called 6 of 11.