13 Days, 8,742 Edges: Mapping MLB Prop Market Mispricings
An analysis of every +EV betting opportunity our Monte Carlo model flagged between April 8 and April 20, 2026 — broken down by bet type, sportsbook, edge size, and odds tier. Covers 64 games, 460 players, and eight US sportsbooks.
Mongoose Bets Research
8,742
+EV MLB betting opportunities flagged in 13 days.
mongoosebets.com · April 8-20, 2026
TL;DR
- Our model flagged 8,742 +EV opportunities across 64 games and 460 players in a 13-day window — roughly 673 per day.
- Moneyline has the biggest per-bet edge (10.9% average) despite the smallest sample, while home run props produce the largest expected-value numbers because long odds amplify moderate edges.
- Doubles and total-bases props are the tightest markets: 2.9% average edge, rarely blowing past 10%. Books price these competently.
- About 34% of flagged edges land in the <2% noise band (below the model's own uncertainty); only ~12% clear the 10% threshold where edges become robust enough to bet confidently.
- Home-run longshots (+300 or longer) carry an average 59.9% EV — the odds tier where the biggest expected dollars live, because moderate edges compound against long payouts.
The dataset
Every morning at 6:30 AM ET, Mongoose Bets runs 2,500 Monte Carlo simulations per scheduled MLB game, ingesting confirmed or projected lineups, Statcast pitcher data, park factors, weather, and home-plate umpire tendencies. For every prop and game market on the slate, the sim produces a probability, which we compare against vig-stripped sportsbook odds across eight books — BetMGM, DraftKings, FanDuel, BetRivers, Fanatics, Bovada, Caesars, and BetUS. When the sim's probability materially exceeds the book's vig-free implied probability, we log the opportunity as +EV.
This analysis uses every +EV opportunity logged between April 8 and April 20, 2026, inclusive. The raw dataset: 8,742 bet-level rows spanning six markets (hits, home runs, doubles, RBI, total bases, moneyline), 64 distinct games, and 460 individual players. All edges are reported as percentage-point differences between sim probability and vig-free book probability.
Where the edges live — by bet type
Not all markets are equally mispriced. The highest-volume flagged markets are hits and home runs, which together account for 48% of all identified edges. Moneyline is the smallest sample but runs the largest average edge by a wide margin.
| Bet type | Bets | Avg edge | Median edge | Max edge | Avg EV |
|---|---|---|---|---|---|
| Hit (1+) | 2,133 | 5.95% | 4.46% | 36.32% | 9.78% |
| Home run | 2,042 | 5.95% | 3.80% | 37.43% | 59.74% |
| Double | 1,900 | 2.92% | 2.27% | 20.67% | 16.94% |
| RBI | 1,338 | 4.33% | 3.00% | 24.54% | 14.64% |
| Total bases (2+) | 1,193 | 2.94% | 2.30% | 16.23% | 8.98% |
| Moneyline | 136 | 10.90% | 8.79% | 27.11% | 23.80% |
Two things stand out. First, moneyline edges are roughly 2× larger than the richest prop market, which suggests books are less sharp on underdog game winners than on individual player props. Second, the doubles market is remarkably tight: with an average edge of just 2.92% and a max of 20.7%, doubles are the hardest-to-beat category in our sample. A plausible reason is that doubles are high-variance single-PA events and books rely heavily on pre-adjusted Statcast data for them.
By sportsbook — whose lines were softest
Aggregating every flagged edge against the offering sportsbook reveals a clear hierarchy. Caesars ran the tightest markets — an average edge of just 3.29% when they were the best available line. At the other end, BetUS produced a 10.88% average edge in the 62 samples where they posted the market's top price, though that sample is much smaller and should be treated with caution. Among the high-volume books, DraftKings averaged a slightly higher per-bet edge than BetMGM(5.13% vs 4.83%), despite BetMGM offering the best-of-market price more often.
| Sportsbook | Times best price | Avg edge | Avg EV |
|---|---|---|---|
| BetMGM | 2,356 | 4.83% | 18.82% |
| DraftKings | 2,048 | 5.13% | 29.61% |
| FanDuel | 1,628 | 4.66% | 29.65% |
| BetRivers | 1,118 | 3.63% | 17.23% |
| Fanatics | 861 | 4.72% | 20.12% |
| Bovada | 430 | 4.78% | 28.95% |
| Caesars | 218 | 3.29% | 19.80% |
| BetUS | 62 | 10.88% | 22.12% |
Edge distribution — noise vs actionable
The raw count of +EV opportunities is misleading without context. A sim uncertainty of roughly 1-2 percentage points on any single prop means edges under 2% are inside the noise band — a sample of those bets would underperform the point estimate. Only 33.7% of flagged edges clear 5%, and just 12.1% clear 10% — the threshold where we consider the edge robust enough to resist sim error.
| Edge bucket | Bets | Share | Read |
|---|---|---|---|
| < 2% | 3,011 | 34.4% | Noise band — skip |
| 2-5% | 2,792 | 31.9% | Marginal — price-shop |
| 5-10% | 1,886 | 21.6% | Actionable core |
| 10-15% | 627 | 7.2% | High-conviction |
| 15-20% | 279 | 3.2% | Investigate before betting |
| 20%+ | 147 | 1.7% | Likely stale line or injury |
The practical implication is that the headline "8,742 +EV opportunities" collapses to roughly 1,053 high-conviction bets (10%+ edge, ~12% of the total). Over 13 days that's about 81 actionable edges per day — still a large menu, but nowhere near the raw count.
Where the expected value lives — the longshot premium
Edge size in percentage points tells only half the story. Expected value — edge multiplied by the payout — is what determines long-run bankroll growth. Grouping flagged edges by odds tier shows a consistent pattern: long odds amplify moderate edges into outsized EV.
| Market | Odds tier | Bets | Avg edge | Avg EV |
|---|---|---|---|---|
| Home run | Longshot (+300+) | 2,033 | 5.95% | 59.9% |
| Moneyline | Plus-money underdog | 81 | 11.50% | 27.8% |
| RBI | Longshot (+300+) | 146 | 4.58% | 19.5% |
| Double | Longshot (+300+) | 1,887 | 2.93% | 17.0% |
| Hit | Favorite (-110 or shorter) | 1,914 | 5.93% | 9.6% |
| Hit | Near-even | 62 | 7.77% | 15.2% |
Home-run props at +300 or longer carry an average 59.9% EV — by far the richest tier in the dataset, because even a modest 6% edge against a +500 line produces an EV north of 40%. Plus-money moneyline underdogs run the second-richest EV tier at 27.8%. Short-priced favorite hits, conversely, average just 9.6% EV despite a comparable edge — the payout doesn't amplify the edge enough to compete with longshot structures.
What this doesn't prove — honest limits
Three things are worth stating plainly before anyone treats these numbers as betting advice.
- Flagged edge ≠ true mispricing. Every number here is the gap between our sim's probability estimate and the book's vig-free implied probability. A gap exists because one of us is wrong. Long-run profitability depends on our model being right on average, which is a separate question from how many edges we find.
- The sim drifted mid-period. Between April 16 and April 19 our internal calibration showed the sim was over-predicting run totals by roughly 1.8 runs per game — a period where flagged edges on total-type bets (hits, HRs, RBI, total bases) inflated. We corrected this on April 19-20 (logged in our methodologyand append-only tuning log). If you want a conservative read of the data, haircut the mid-April numbers ~1.5 percentage points.
- 13 days is a small sample. For market-level conclusions (e.g., "DraftKings is softer than BetMGM"), this sample is directionally informative but not statistically definitive. A full season of data would be required to make strong claims about any single book's pricing accuracy.
Three takeaways
Filter out the noise band.
Edges under 2% are below the sim's own uncertainty. A ruthless bettor cuts the flagged pool by 34% before anything else.
Longshot HRs and underdog MLs carry the expected value.
If you optimize for expected dollar growth rather than hit rate, weight the bankroll toward the high-EV tiers — +300-and-longer HR props and plus-money moneylines — even though most individual bets in those tiers lose.
Watch your own model drift.
The biggest risk to any sim-edge strategy isn't a sharper book — it's your own model miscalibrating and inflating every number. Sim-vs-actual daily backtesting isn't optional; it's the feedback loop that keeps the edges honest.