PropPrizm Insights

De-vig the line, compare it to your number, and price the difference. A worked example from -115 to +EV, why +EV bets lose constantly, and why CLV tells you the truth years before your record does.

A starter's first pass through a lineup flatters him, the third may never happen, and treating all six innings as the same performance is what turns a reasonable strikeout estimate into a bad one.

Temperature gets credited for swings it cannot produce, wind gets ignored when it matters most, and domes get read as a boost when what they really change is variance. Here is what the physics supports.

A starter projects for 6.1 strikeouts at noon and 7.0 once the official card drops, with no injury and no news in between. Here are the three things a posted lineup settles that a projected one only guesses at.

Most strikeout projections start with the pitcher and the lineup. But there's a third person crouched behind the plate quietly moving called strikes around — and the gap between the best and worst receiver can be nearly a full strikeout for the same arm.

Two hitters can post the same exit velocity and get completely different results. Barrel rate is the stat that separates them — it combines how hard and at what angle a ball is hit into the contact that actually produces damage.

The getaway-day fatigue story is one of baseball's most repeated rules of thumb. In a fair sample the effect is real but tiny — far too small to explain any single quiet afternoon. Here's why the reliable signal isn't fatigue at all, it's who's actually in the lineup.

A model that says 60% is making a promise about the long run, not a call you can grade on one night. Here's the difference between being accurate and being honest, and why every projection gets checked against the box score.

Two starters with identical K/9 can sit a full strikeout apart in a fair projection. The difference is workload: expected batters faced moves single-game strikeout numbers more than raw rate stats do.

A four-game tear makes everyone want a piece of a hitter, but a week of results tells you almost nothing about tomorrow. Here's why quality of contact stabilizes in a quarter of the sample batting average needs, and how the gap between the two is where the real signal lives.

A hitter batting .224 with a .271 expected average isn't slumping — the results just haven't caught up to the contact. Here's what xBA and xwOBA actually measure, how to read the gap against batting average, and where the expected numbers mislead you.

A tired bullpen forces a starter deeper, a fresh one yanks him early, and the market is slow to price either. Here's what the new bullpen freshness chip on PropPrizm tracks, why it moves K projections, and how to use it tonight.

Two pitchers with the same K/9 can have wildly different K projections depending on what pitches they throw and which lineup they face. Here's how to read pitch mix the way the model does, and why it beats season-long K rate every time.

Barrel%, hard-hit rate, and pull/fly-ball profile predict HR probability far better than season HR totals or slugging percentage. Here's what to look at — and what to ignore — when sizing up a home run prop.

Most people project strikeouts from pitcher rate and lineup K%. The third variable — who's calling balls and strikes — quietly swings projections by a full strikeout, and the market is slow to price it in.

Two pitchers with identical K/9. Two batters with identical SLG. But one pair plays in Coors Field and the other in Oracle Park — and the math on their projections should look completely different. Here's why park factors quietly decide more prop outcomes than most people realize.

Strikeout projections are where the PropPrizm model is strongest. Here's what to look for — matchup K%, park SO factor, umpire zone tendencies, and how to cross-reference the projection with the matchup card to find the best spots.