Colorado Rockies 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
Rockies- Ryan McMahonBatting1-3, 1 HR, 1 RBI
- Kyle FreelandPitching6.0 IP, 3 ER, 5 K
Giants- Heliot RamosBatting1-4, 1 HR, 1 RBI
- Justin VerlanderPitching6.1 IP, 2 ER, 4 K
Scoring summary
- 8Goodman singled to right, Beck scored, Goodman to second.4–3
- 8Beck hit a ground rule double, Doyle scored.3–3
- 7McMahon homered to center (400 feet).2–3
- 6Yastrzemski doubled to left, Flores scored.1–3
- 6Flores doubled to right, Adames scored.1–2
- 3Doyle singled to left, Trejo scored.1–1
- 1Ramos homered to center (430 feet).0–1
The pick
Reconstructed from what was known before the game: the ratings at that point and the closing line.
Giants 70%
Forecast Elo ratings alone
How often that comes true: MLB favorites the forecast gave 65–70% have won 69.7% of 518 graded games.
- Market
- SF 70%
- ML COL +215 / SF -265
- Pre-line forecast
- SF 67%
- Elo + market-implied
- Elo ratings
- SF 65%
- Ratings vs. market
- −4.5 pts
- SF: Elo 65.1%, market 69.6%
- Fair odds
- SF -229
- COL +229 · the forecast as odds, no margin
- Pregame projection
- COL 2.8 – 4.7 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 | COL | SF | Why |
|---|---|---|---|
| Team rating | 1412 | 1513 | Elo entering the game; 1500 is league average |
| Home field | — | +18 | Worth 18 Elo in MLB |
| Starting pitchers | −2 | −12 | COL: Kyle Freeland: team allows 0.10 more runs than with its usual starter (≈10 weighted starts) · SF: Justin Verlander: team allows 0.62 more runs than with its usual starter (≈10 weighted starts) |
| Elo ratings | SF +108 Elo | SF 65.1% | |
| Market-implied ratings | SF 66.8% | Team strength as past closing lines priced it, same game context | |
| Pre-line forecast | SF 66.5% | 15% Elo, 85% market-implied (log-odds blend) | |
| Forecast | SF 69.6% | 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.
| COL | Stat | SF |
|---|---|---|
| 30th of 303.2 | Runs scored | 4.612th of 30 |
| 29th of 305.8 | Runs allowed | 3.89th of 30 |
| 30th of 30−2.6 | Margin | +0.79th of 30 |
| 29th of 302–8 | Last 10 | 5–513th of 30 |
| 30th of 301–14 | Road vs home | 9–46th of 30 |
| 6th of 301511 | Opponent Elo | 15125th of 30 |
| 29th of 301412 | 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 |  COL |  SF |
|---|---|---|
| Run line | 12–18 | 15–16 |
| Last 10 | 5–5 | 3–7 |
| Favorite | 2–3 | 6–11 |
| Underdog | 10–15 | 9–5 |
| Overs–unders | 10–20 | 19–12 |
| Overs–unders, last 10 | 2–8 | 5–5 |
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
COL 7–19 SF in their last 26 meetings; the forecast called 16 of 26.