Methodology | How Mongoose Bets Simulates MLB Games
The math behind the line.
Mongoose Bets runs 2,500 Monte Carlo simulations per MLB game to expose where sportsbook lines disagree with the model. No tipster picks. No black-box scores. We show our work.
What is Monte Carlo simulation?
Monte Carlo simulation is a way to estimate the probability of an outcome by playing it out thousands of times with realistic randomness. Instead of asking "will the Yankees win tonight?" a Monte Carlo model asks "if we played this game 2,500 times under tonight's exact conditions, how often would the Yankees win?"
Baseball is a near-perfect fit for this approach. Every plate appearance is a discrete event with a measurable probability distribution (this batter against this pitcher, with this defense, in this park, in this weather). Stack 9 innings of those events, run the whole game 2,500 times, and you get a real probability distribution — not a single guess.
What the model ingests, every game
Each of the 2,500 simulations per game runs batter-by-batter, inning-by-inning, with these inputs feeding every plate-appearance probability:
Pitcher quality & pitch mix
ERA, WHIP, K/9, BB/9, HR/9, handedness splits, recent form.
Batter splits & BvP history
Season stats, vs LHP/RHP splits, career line against the opposing pitcher (sample-weighted).
Park factors
Coors Field plays nothing like Oracle Park. HR rate, hit rate, and run scoring all park-adjusted.
Weather (live forecast at first pitch)
Wind speed, direction, temperature, humidity. A 15 mph wind blowing out at Wrigley changes everything.
Home plate umpire
Strike zone size, K%, BB%, runs/game tendency — built into game-level scoring expectations.
Bullpen fatigue & quality
Pitches thrown last 3 days, leverage availability, reliever quality once the starter exits.
From probability to edge
Once 2,500 sims are done, every prop and every game line gets a model probability. We compare that against the sportsbook's vig-free implied probability — the line stripped of the book's juice. The gap is the edge.
Worked example
Sportsbook prices Aaron Judge to hit a home run at +320. Strip the vig — that's an implied probability of about ~24%.
Our 2,500 sims tonight (Yankee Stadium, 78°F, 12 mph wind blowing out to right, opposing starter Mongoose has him batting against) say Judge homers in 38% of them.
Edge = 38% − 24% = +14 points of EV. That's a real value bet at +320.
The ranks every prop and game line by this same edge math, every day, across DraftKings, FanDuel, BetMGM, and other major books. Kelly Criterion sizing is also computed so you can manage the edge against your bankroll.
Galaxy Insight: see the disagreement
Most analytics tools collapse a sim to a single number ("Yankees 63% to win") and call it a day. We plot all 2,500 sim outcomes as points on the joint score distribution — the Sim Outcome Galaxy. You can literally see where the cluster of likely outcomes lands relative to the book's lines.
When the cluster sits firmly to one side of the total runs line, that's the edge made visible. No formula necessary — the picture is the math.
Calibration & trust
A model that says "65% likely" should produce that outcome ~65% of the time over a large sample. That property is called calibration — and it's the single best test of whether a sim model is honest.
Most tipster services and "AI projection" tools won't show you their calibration. They'll show you a record of wins and losses (which can be cherry-picked) but never a calibration plot (which can't be).
Coming soon
Live calibration dashboard
We're publishing a public calibration leaderboard: rolling Brier scores, log loss, and reliability curves on every market we project. When our sim says 65% and the team wins 64% of the time, we want you to see it. When we miss, we want you to see that too.
Until that page ships, judge us on the math: every edge on the site is shown with the sim probability, the book's implied probability, and the vig-free fair odds. Nothing is hidden.
What Mongoose is not
We're not a tipster.
We don't sell picks at $49 a pop. We show the math behind every edge so you can decide.
We're not a black box.
Every prop has its sim probability, the book's implied probability, and the resulting EV visible on the page.
We're not a multi-sport tool.
MLB only. Baseball depth (lineup-aware sims, weather, ballpark, umpires, bullpen state) beats sport breadth.
We're not a sportsbook.
We tell you where the edge is. You place the bet on whichever book is offering the best line.
Common questions
What is Monte Carlo simulation in sports betting?+
Monte Carlo simulation estimates the probability of an outcome by playing it out thousands of times with realistic randomness. In MLB betting, we simulate every game 2,500 times using real lineups, pitcher matchups, weather, ballpark factors, and umpire tendencies — then aggregate the results into win probabilities, predicted scores, and player prop projections.
How does Mongoose Bets find +EV bets?+
After simulating each game 2,500 times, we compare the sim-derived probability of every market (moneyline, total, player prop) against the sportsbook's vig-free implied probability. When the sim probability is meaningfully higher than the book's, that's a positive expected-value bet — a market mispricing the true likelihood. The Best Bets page ranks these every day.
Why does Mongoose simulate 2,500 times per game (and not more)?+
2,500 simulations is the point at which game-level probability estimates stabilize within roughly ±1%. More sims yield diminishing returns on accuracy and burn compute time that's better spent updating the model when lineups change. We re-run after confirmed lineups post (typically 1-2 hours before first pitch) for maximum accuracy.
How is Mongoose different from tipster sites and DFS optimizers?+
Tipsters sell you a pick (the conclusion) without showing the math. DFS optimizers simulate contest lineups, not game outcomes from first principles. Mongoose simulates the actual baseball game using real player data, then exposes the probability distributions, fair odds, and edges so you can see exactly why a bet has value.
What does "edge" mean in MLB betting?+
Edge is the difference between the true probability of an outcome and the probability the sportsbook is implying with its odds (after stripping out the vig). A bet with a +5% edge means the model believes the outcome is 5 percentage points more likely than the book is pricing — which, over a long sample, would yield a profit at that odds.
Does Mongoose Bets work in every state?+
Mongoose Bets is an analytics platform — we don't take bets ourselves. Anyone in the U.S. (21+) can read our edges. Whether you can act on them depends on whether legal sports betting is available in your state.
Original research & comparisons
Research · April 20, 2026
13 Days, 8,742 Edges: Mapping MLB Prop Market Mispricings
Every +EV opportunity our model flagged in the first three weeks of the 2026 season — broken down by bet type, sportsbook, edge size, and odds tier. 64 games, 460 players, eight sportsbooks.
Comparison
Mongoose Bets vs Action Network
Honest head-to-head with Action Network (and Sports Insights + Bet Labs) — pricing, methodology, MLB depth, where each wins. Both tools have legitimate strengths; the comparison doesn't pretend otherwise.
Comparison
Mongoose Bets vs OddsJam
Free sim-based vs $200/month market-based +EV tools. Top-down vs bottom-up methodology. Includes the honest limit that OddsJam is structurally better at short-term closing-line value.
Comparison
Mongoose Bets vs Unabated
Sharp-book-weighted Unabated Line vs Monte Carlo sim. The market-truth tool wins on standard-market closing-line value; Mongoose catches edges where Pinnacle doesn't post.
Alternatives
Free Alternatives to MLB Tipster Services
Why the picks-for-subscription model usually fails the math test — and how to evaluate any betting-picks product honestly, including Mongoose itself.
Mongoose Bets is for entertainment and educational purposes only. Not professional betting advice. Must be 21+ to wager on sports. Gambling Problem? Call 1-800-522-4700.