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How it works

SALCI is a strikeout prediction model for MLB starting pitchers. This page explains what the model does, how to read the outputs, and how we track accuracy.

What SALCI predicts

For each starting pitcher, SALCI estimates the distribution of strikeouts they'll record in their next start. It combines four factors:

  • Stuff — pitch quality and whiff potential from Statcast.
  • Matchup — the opponent lineup's strikeout vulnerability.
  • Workload — recent batters faced + projected innings.
  • Location — command and called-strike rate.

Those inputs roll into a single SALCI score (0–100) and a grade (S through F). The model also runs a Monte Carlo simulation of the pitcher's likely K distribution, from which we report a floor, an expected count, and a ceiling.

Reading a pitcher card

SALCI score & grade

Overall model confidence. S = elite K profile; F = poor. Higher grades have historically hit their floor more often.

K floor

The K count the simulation reaches or exceeds 70% of the time. A conservative estimate of the pitcher's downside.

Expected Ks

The median K count from the simulation. The model's best single-number guess.

K ceiling

The 75th-percentile K count. The pitcher's plausible upside on a good day.

Book line

The sportsbook's main strikeout total for the pitcher. Shown for context only; we do not advise betting.

Alt line

A non-main K threshold the book offers at different prices (e.g. Over 4.5 vs Over 6.5).

Floor bets

A floor bettakes the Over on a pitcher's strikeout total when a sportsbook offers a line at or below SALCI's projected floor — the K count the model is roughly 70% confident the pitcher reaches. When the book prices a low alternate line at or under that floor, the model reads the Over as having a margin of safety.

Raw

Every pitcher whose floor is offered as a real alternate-K line at the book today — no further filter.

Calibrated

The +EV subset: the season-to-date hit rate at the pitcher's grade, multiplied by the payout, exceeds the stake. Needs ≥5 prior settled starts at that grade.

Parlays

Every Raw pitcher combined into at least one 2-leg parlay across different games. Shown for context, not as advice.

A risk gate excludes lines priced more expensively than −350, so the record reflects prices we'd realistically take. Outcomes are settled against the official MLB line score and published in the Track Record.

Ceiling bets — under reconstruction

The ceiling bet (the Under-side mirror of the floor) is paused while we rebuild it. Our own track record flagged it: the Under fired exactly on the weak starters where the sportsbook's number is sharper than the model's, so we were betting against the better line and the ROI went deeply negative.

We'd rather pull a losing play than keep it on the board. We're testing the opposite side of those same spots and will only bring a ceiling bet back once it proves out — measured by closing-line value, not a hot streak. Floor bets and hitter props are unaffected.

How we track accuracy

Every starter we score is logged in SALCI History with the predicted floor, expected count, book line, and ultimately the actual K count from the game log. The accuracy chart on that page shows hit-rate by grade and by date, so the model is auditable in public.

Day-to-day predictions are stored before first pitch. The accuracy tally updates after the game ends and the K count is confirmed against MLB's official line score.

Track record & ROI

The Track Record shows how the bets we actually post have performed — this is separate from model accuracy above. It spans floor bets (pitcher K Over) and our recommended pitcher-K and hitter anytime-hit plays. (Ceiling bets are paused — see above.)

Flat-stake ROI

Every settled bet risks one unit. ROI = net profit ÷ total staked. American-odds payouts: a +120 win returns 1.2u, a −150 win returns 0.67u, a loss is −1u.

Settled bets only

Pending, push, and void bets are excluded. A bet counts once its game's K or hit outcome is final per MLB's official line score.

Honest filter

Floor bets exclude lines juicier than −350 and book-mismatched alt lines (e.g. two-way players' batter props), so the number reflects bets we'd realistically place — not inflated edge.

Sample size shown

Each bet type displays its bet count. Small samples swing hard; always read ROI next to N.

Past performance does not predict future results. Variance dominates small samples, the model is wrong some of the time, and hot or cold stretches are expected. We publish the full record so it's auditable — not as a promise of returns.

What this isn't

SALCI is a prediction model. It is not a betting service or financial advice. Sportsbook lines are displayed for context because they're the reference market most readers use to gauge a pitcher's expected K count.

If a sportsbook line sits well below the model's expectation, that's information about the gap between model and market — not a recommendation. The model is wrong sometimes, the market is wrong sometimes, and variance dominates any small sample. Treat the numbers as analysis, not advice.

We do not place wagers and do not benefit from any user's wagering decisions. If you choose to bet, do so legally in your jurisdiction and within your means.

For entertainment and informational purposes only — not financial, investment, or betting advice, and not a guarantee of profit. Must be 21+ (or the legal age in your jurisdiction). If you or someone you know has a gambling problem, call 1-800-GAMBLER.

Data sources

  • MLB Stats API — schedules, lineups, box scores, post-game line scores.
  • Baseball Savant (Statcast) — pitch-level metrics for stuff, whiff, and command inputs.
  • odds-api.io — sportsbook main lines and game totals (context).
  • the-odds-api.com — alt-line ladders for the same context.

Questions or feedback? Reach out via the contact links on the home page. SALCI is built and maintained as an open accuracy experiment — methodology may evolve as we learn more.