10,000 Outcomes. One Clear Picture.
AI Predictions pairs a deep AI read on any matchup with the Monte Carlo block: the Betessi model’s margin, played out 10,000 times on the market’s current total, for every game the model priced that morning.
The Monte Carlo block
Run Monte Carlo on a game and read the distribution, not a guess: the model’s margin and score, where 80% of the simulated finals land, how often the home side wins outright, and the line the model implies against the market’s — with the edge in points.
- Model margin
- BUF by 4.2
- Model score
- BUF 25.9 – MIA 21.6
- 80% of finals
- BUF 14–38, MIA 10–33
- Home wins outright
- 37.8% of trials
- Implied line vs market
- MIA +4.2 vs market MIA +2.5 → edge 1.7 pts on BUF
10,000 trials, seeded by the inputs, so the same numbers give the same block. The margin is the Betessi model’s; the total is the market’s.
The day’s board
Pick a sport and a date and the board loads every game with its spread and total. Reported injuries are counted on the row, college sides carry their AP rank, and Game Lines or Player Props is one switch away.
A deep read on any matchup
Ask for an analysis and the AI reads the official lines, searches the latest news and injury reports, and applies your Advisor rules. It returns a pick at a price, its own projected score, the best price on the board, and whether it matches the published Betessi position — with the full reasoning and its sources.
“The number undersells the gap up front, and the market has moved toward Missouri since the open.”
Log positionIllustrative games and figures. In the app, every block is computed from that morning’s slate.
What is Monte Carlo Simulation?
A Monte Carlo simulation plays a game out many times from a few honest inputs and reads the spread of results, not a single guess. Ours takes the Betessi model’s margin for the game and how far real margins stray from it, lays that on the market’s total, and plays it out 10,000 times.
Play the Probabilities
No single outcome is promised. The useful part is the distribution — how often a side wins, and where most of the finals land — across a large number of runs.
Understand Implied Probability
Every price translates to a percentage (-110 implies 52.4%). A projection is only interesting where it disagrees with that number — and by how much.
Avoid the Outlier Trap
Blowouts and shootouts happen, but they sit in the tails. The 80% range shows where the heart of the distribution is — that is where a reading should start.
How to Use AI Predictions
Two readings of the same game, kept apart on purpose: the Betessi model’s simulation, and an AI’s deep read. When they agree, you see it; when they don’t, you see that too.
Pick the Game
Choose a sport and a date. The board loads every game with its spread and total, counts reported injuries on the row, and shows college sides with their AP rank.
Run the Monte Carlo
For any game the model priced that morning, the block returns its margin and score, the 80% range of finals, the home side’s win rate, and the implied line against the market’s.
Ask for the Deep Read
The AI’s analysis gives a pick at a price, its own projected score, the best price on the board, and whether it matches the published Betessi position. Full reasoning and sources are one tap away.
Log What You Take
Log a position from the read in one tap. It lands in My Positions and grades itself when the game goes final.
The Model’s Margin
The Betessi model’s expected margin for the game — the same model behind that morning’s Daily Picks — with a spread calibrated per sport.
The Market’s Total
The score is anchored to the books’ own total, with its variance taken from this season’s finals once there are enough of them. The block says which half is whose.
10,000 Seeded Trials
Each game is played out 10,000 times from those inputs. The trials are seeded, so the same inputs always return the same block.
Implied Line & Edge
The line the model’s margin implies, set against the market’s spread. The difference, in points and on which side, is the edge.