The Four MLB Markets

PropBetEdge models four player prop markets for MLB. Each has a different volatility profile and different analytical approach.

Highest Variance

Home Run Props

Binary (HR or no HR). Lambda typically 0.15–0.45. Biggest odds, most variance. Best modeled with barrel%, xSLG, and pitcher EV allowed.

Medium Variance

Total Bases

Sum of bases. Lines at 1.5 or 2.5. Smoother distribution — more games hit than HR. Best for consistent edge.

Pitcher-Driven

Strikeout Props

How many Ks does the starter get? Driven by pitcher stuff + opposing lineup K rate + umpire. K-Score summarizes it.

Lowest Variance

Hits Props

Does the batter get at least one hit? Or OVER/UNDER a hits line. Smoothest distribution. Best for conservative modeling.

Home Run Props: The Framework

HR props are the signature market on PropBetEdge because the Poisson model is most powerful when the event has a clean binary structure. Either the player hits one or they don't.

The Key Inputs

MetricWhat to Look ForWhy It Matters
xSLG.500+ elite, .450+ above avgBest predictor of true power output
Barrel%12%+ strong, 18%+ eliteMost predictive contact quality metric
Exit Velocity92+ mph solid, 96+ eliteRaw power — harder = further
Pitcher EV AllowedBelow avg = favorableDoes the pitcher give up hard contact?
Park Factor100 = neutral, 120+ = hitter-friendlyCoors adds significant HR probability
Wind10+ mph out to CF/RF/LFWind out boosts HR probability 10-30%
PlatoonBatter vs. opposite-hand pitcherStandard platoon advantage
L7 xSLGRecent form vs. season averageHot streaks are partially predictive
The edge signal: When PropBetEdge shows model odds of +280 and DraftKings is offering +340, the book is overvaluing the OVER — the market implied probability (22.7%) is below our model probability (26.3%). That gap is +EV at roughly +$11 per $100 bet.

How to Read the HR Output

// PropBetEdge HR output for Aaron Judge player_name: "Aaron Judge" market: "hr" line: 0.5 direction: "OVER" expected_stat: 0.38 ← lambda (expected HRs per game) implied_prob: 0.314 ← 31.4% — our model probability odds_fmt: "+210" ← our fair-value odds book_odds: "+280" ← DK/FD best available // Book > model = book paying MORE than fair value = +EV

Strikeout Props: The K-Score System

Strikeout props are our other signature market. Unlike HR props (batter-driven), K props are primarily pitcher-driven. The model combines six inputs into a single K-Score.

K-Score Components

  1. 30%

    Swinging Strike Rate (SwStr%)

    The single strongest K predictor. 15%+ is elite. Every percentage point above average adds meaningful K probability.

  2. 20%

    CSW% (Called + Whiff Rate)

    Called strikes plus whiffs divided by total pitches. More comprehensive than SwStr% — captures borderline pitches that generate called K3s.

  3. 20%

    Opposing Lineup K Rate

    How often does this lineup strike out? Against a high-K lineup, even a mediocre pitcher's K upside increases significantly.

  4. 15%

    Stuff Quality / Velocity

    Fastball velocity tier, pitch mix complexity, and Stuff+ where available. Better stuff = more swing-and-miss potential.

  5. 10%

    Umpire K Tendency

    Some umpires call significantly more/fewer strikeouts than average. A K-friendly umpire can add 0.5–1 K to a starter's expected output.

  6. 5%

    Recent Form (L5 Starts)

    K/9 and stuff trends over the last 5 starts. A slight recency adjustment to capture current form vs. season baseline.

Weather: The Most Underrated Factor

Sportsbooks update HR prop lines for starting pitchers but often lag on weather adjustments — especially wind direction and speed. PropBetEdge pulls real-time weather for every ballpark at each model run.

ConditionEffect on HRsHow to Trade It
Wind 10+ mph Out (to CF)+15–30% HR probabilityTarget OVER HR for batters hitting to CF
Wind 10+ mph In (from CF)−15–25% HR probabilityConsider UNDER HR or avoid
Temp 85°F++5–10% HR probabilityHot, humid air travels further
Temp below 50°F−5–10% HR probabilityCold, dense air suppresses carry
Rain delay / domeVaries / neutralCheck /mlb/weather endpoint

The Daily Research Workflow

  1. 1

    Check /mlb/odds/model/top at 10AM CT

    After lineups are mostly confirmed. This is the model's top OVER value plays for the day, sorted by expected stat.

  2. 2

    Cross-reference with /mlb/weather/all

    Any parks with significant wind out to the power alleys? That's a multiplier on HR probability — especially for high-barrel% batters.

  3. 3

    Check K-Score for each starting pitcher

    Any pitchers with K-Score 75+? Those are your K prop targets. Cross with the opposing lineup's K rate.

  4. 4

    Check umpires via /mlb/umpires

    K-friendly umpires behind the plate can add 0.5–1 strikeout to a starter's expected output. Worth adjusting your line expectations.

  5. 5

    Compare model odds to book odds

    Is the book paying more than fair value? That gap is your edge. Bet where the book is mispriced — not where the pick feels best.

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