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St. Louis Cardinals @ San Francisco Giants

MLB · 2026-09-08 09:10 KST · Scheduled
SLCAwaySt. Louis Cardinals56%
@
SFGHomeSan Francisco Giants44%
Model PickSt. Louis Cardinals 56%Market edge — disclosed after closing odds are collectedEvidence 12 structured · 1 qualitative
AI Summary grounded in real data · zero hallucination

We recommend the St. Louis Cardinals with a 56% model probability, though the edge against the market is uncalculated. The primary driver for this pick is the advantage in '팀 장타율(SLG)', while the main swing risks stem from the opponent's '홈 어드밴티지' and our deficit in '최근 10경기 폼'. While we acknowledge that our model does not beat the market in the long run, we present this breakdown with complete transparency.

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Logistic-regression statistical model — predicts St. Louis Cardinals vs San Francisco Giants by accumulating the log-odds contributions of 12 proven structured metrics. Only structured metrics enter the probability; qualitative and folklore factors get a label only. Predictions are sealed with SHA-256 before the game.
Model factor contribution How the 44% home win was derived
Home Advantage+2.5
Pythagorean Expectation-1.9
Season Win%-1.6
Last 10 Games Form-2.5
Home/Away Split-0.6
Team OBP-1.3
Team SLG+1.5
Team ERA-0.6
Team WHIP-0.2
Starting Pitcher Matchup-0.9
Baseline 50% → final home win44.3%
Each factor's log-odds contribution accumulated · only structured metrics reflected (qualitative/folklore excluded) · each %p converts log-odds to probability, so it's non-linear — a simple sum may not exactly equal (final − baseline)
Structured metric analysis 12 metrics · fully disclosed
MetricAnalysis (home vs away)Source
Home AdvantageSan Francisco Giants at home — MLB historical home win rate ~.535 (ballpark familiarity, no travel)MLB StatsAPI
Pythagorean ExpectationHome 44.7% (603 RS/678 RA) vs Away 49.4% (657/666) — strength from runs scored/allowedMLB StatsAPI
Season Win%Home 59-85 (.410) vs Away 72-72 (.500)MLB StatsAPI
Last 10 Games FormHome 4-6 vs Away 6-4 · Home L1MLB StatsAPI
Home/Away SplitHome team home 33-36 · away team away 38-34MLB StatsAPI
Team OBPHome .308 vs Away .317 — foundation of scoringMLB StatsAPI
Team SLGHome .407 vs Away .387 (OPS .715/.704)MLB StatsAPI
Team ERAHome 4.39 vs Away 4.32 — lower is betterMLB StatsAPI
Team WHIPHome 1.36 vs Away 1.35 — baserunners allowed per inning (lower is better)MLB StatsAPI
Starting Pitcher MatchupHome Logan Webb (ERA 4.21) vs Away Michael McGreevy (ERA 3.97)MLB StatsAPI
Hitting Detail (BABIP, K, BB)Home BABIP .290 · K 1156 · BB 397 / Away BABIP .285 · K 1137 · BB 456 (BABIP is luck-driven → not factored into probability)MLB StatsAPI
Pitching Detail (K/BB, HR9)Home K/BB 2.03 · HR9 0.98 / Away K/BB 2.32 · HR9 1.01MLB StatsAPI
Qualitative & folklore factors 1 factors · fully disclosed
Qualitative · Webb vs. Cardinals
Giants' starter Logan Webb has historically performed exceptionally well against the Cardinals, suggesting he either exploits their hitters' weaknesses effectively or his specific pitch mix is particularly potent against them.
Basis: LLM · news · Logan Webb against the Cardinals
12 source headlines (auto-collected · basis disclosed)
  • St. Louis Cardinals at San Francisco Giants Preview - 09/07/2026
  • How to watch the St. Louis Cardinals vs San Francisco Giants: Live stream info, schedule, preview
  • St. Louis Cardinals at San Francisco Giants - Where to Watch, Stream Info, TV Channel, and Live Updates (September 07, 2026)
  • St. Louis Cardinals vs. San Francisco Giants - Live Score - September 07, 2026
  • Logan Webb against the Cardinals
  • Ball 2 confirmed after ABS challenge
  • Josuar Gonzalez's four-hit game
  • Breaking down Rafael Devers' home run
Frequently asked questions This game's prediction summary
St. Louis Cardinals vs San Francisco Giants — who's more likely to win?
SealedSlate's logistic-regression statistical model gives St. Louis Cardinals a 56% chance to win (Home 44% · Away 56%). MLB · 2026-09-08 09:10 KST.
How was the 44.3% home-win probability calculated?
Starting from a 50% baseline, we additively accumulate the log-odds contributions of 12 proven structured statistical metrics to reach the final 44.3%. Each factor's %p contribution is fully disclosed on the page; qualitative and folklore factors are not reflected in the probability.
Can this prediction be altered later?
No. Every prediction is sealed with a SHA-256 hash before the game starts and permanently recorded in the track record, making after-the-fact changes impossible. SealedSlate runs a transparent ledger that never inflates the hit rate and leaves missed predictions exactly as they were.
We don't compete on hit rate — every factor is fully disclosed and labeled. Only 📊 structured stats feed the probability (each factor's contribution shown in %p); ⚔️🔮 are context to weigh. Predictions are locked and sealed before the game — verify the hits directly on the track record.