Quantitative Projections from a Data-Driven Oddsmaker (MLB, NFL & NBA)
Welcome to the sports betting projection hub at MavenSports.io — a toolset built to mimic how a sharp oddsmaker prices games.
This isn’t a tip sheet. It’s the raw output of a custom, machine-learning oddsmaker model that simulates every MLB and NBA matchup to generate true win probabilities, fair odds, and edge values.
If you’re building your own model, looking to exploit inefficiencies, or just reverse-engineering the market, this is your data-native launchpad.
New to the site? Get to know MavenSports.io →
🧠 How This Oddsmaker Sports Betting Model Works
Our system is designed to simulate what a quantitative bookmaker would produce behind the curtain.
Inputs:
- Player & team-level performance data (historical and current)
- Advanced metrics: expected runs, shot quality, pace, etc.
- Market baselines: Pinnacle opening lines (overnight)
- Contextual modifiers: travel, back-to-backs, lineup news, injury impact
Outputs:
- Win Probabilities for each team
- Fair Odds derived from win% (using no-vig math)
- Edge % — how much value exists vs. the current market price
⚙️ Using the Data Like a Quant Oddsmaker
Treat this page as a modeling sandbox. Here’s how to extract maximum value:
- Compare Our Fair Odds to Market Lines: This is pure line evaluation — no guesswork.
- Look at the Edge Column:
- +3% or more = statistically relevant value
- 0–1% = market is too sharp — pass
- Blend It Into Your Workflow: Use it to validate your models, price shop, or flag suspicious line moves.
NFL Week 2 projections
COMING SOON
MLB Daily Projections 09/15
| away | home | starters | market ml | imp | edge% | total | ojuice | t_edge% |
| Athletics | Tampa Bay Rays | Jack Perkins / Griffin Jax | ATH +188 | ATH 33,4% / TBR 66,6% | ATH +2,5% / TBR -2,5% | 8,5 | 110 | -2,8 |
| Milwaukee Brewers | Pittsburgh Pirates | Jacob Misiorowski / Lake Bachar* | PIT +210 | MIL 68,5% / PIT 31,5% | MIL -10,9% / PIT +10,9% | 7,5 | -108 | 2,2 |
| Chicago White Sox | Cleveland Guardians | Davis Martin / Foster Griffin | CLE -125 | CHW 45,7% / CLE 54,3% | CHW -0,7% / CLE +0,7% | 8,5 | -102 | 6,4 |
| Los Angeles Dodgers | Cincinnati Reds | Yoshinobu Yamamoto / Rhett Lowder | CIN +199 | LAD 67,3% / CIN 32,7% | LAD -2,9% / CIN +2,9% | 8,5 | -104 | -2,1 |
| Philadelphia Phillies | Washington Nationals | Cristopher Sánchez / Jackson Kent | WSN +180 | PHI 65,2% / WSN 34,8% | PHI -1,6% / WSN +1,6% | 8,5 | 110 | 0,8 |
| Detroit Tigers | Toronto Blue Jays | Drew Anderson / Braydon Fisher* | DET +118 | DET 44,8% / TOR 55,2% | DET +3,0% / TOR -3,0% | 8,5 | -102 | 3,5 |
| Baltimore Orioles | New York Mets | Shane Baz / Sean Manaea | BAL +116 | BAL 45,2% / NYM 54,8% | BAL +1,1% / NYM -1,1% | 7,5 | -117 | 9,8 |
| New York Yankees | Minnesota Twins | Max Fried / Bailey Ober | MIN +154 | NYY 61,3% / MIN 38,7% | NYY -3,5% / MIN +3,5% | 7,5 | -118 | 7,9 |
| Atlanta Braves | Chicago Cubs | Martín Pérez / Kevin Gausman | CHC -134 | ATL 44,1% / CHC 55,9% | ATL -1,0% / CHC +1,0% | 8,5 | -106 | 2,1 |
| San Francisco Giants | St. Louis Cardinals | Blade Tidwell* / Andre Pallante | SFG +142 | SFG 40,3% / STL 59,7% | SFG +0,8% / STL -0,8% | 8,5 | 104 | -7,4 |
| Kansas City Royals | Houston Astros | Michael Wacha / Hunter Brown | HOU -159 | KCR 40,1% / HOU 59,9% | KCR -0,5% / HOU +0,5% | 7,5 | -115 | 0 |
| San Diego Padres | Colorado Rockies | Walker Buehler / Kyle Freeland | COL +170 | SDP 63,7% / COL 36,3% | SDP -8,0% / COL +8,0% | 11,5 | -104 | -10,4 |
| Seattle Mariners | Los Angeles Angels | Logan Gilbert / Ryan Johnson | LAA +148 | SEA 60,6% / LAA 39,4% | SEA -1,9% / LAA +1,9% | 8,5 | 113 | -7 |
| Miami Marlins | Arizona Diamondbacks | Janson Junk / Michael Soroka* | ARI -145 | MIA 41,8% / ARI 58,2% | MIA -0,5% / ARI +0,5% | 8,5 | -108 | -4,6 |
Other recommended US-friendly bookmakers:
Sportsbetting.ag. Everygame.eu, MyBookie
🧠 Behind the Model: From Market to Machine
We built this projection engine to mirror how modern oddsmakers think:
- Market as signal → sharp lines anchor the model
- Model as simulation → game dynamics drive probabilities
- Player input → Adjustments based on starting pitcher quality, lineup shifts, and real-world nuance
Our model is validated against closing line value and historical return on equity over multi-season datasets.
Got more questions? Check out our FAQ →
