FairPlay supports player props projections and expected value by ingesting live odds from 71 sportsbooks, measuring line dispersion on every game, and flagging props where its model disagrees with the market by more than the vig (the book's cut). The core insight: when books disagree on a game line, the player props tied to that game script (how the game plays out) carry more expected value opportunity.
How FairPlay supports player props projections and expected value
FairPlay ingests live odds from 71 sportsbooks, measures line dispersion (how much books disagree on the spread) on every game, and flags props where its model disagrees with the market by more than the vig (the book's cut). When books fight over a line, the props tied to that game script get messy — and messy lines mean edge.
Which games have the most book disagreement right now?
We pulled live home spreads across MLB and NFL from the same 2026-08-21 snapshot. MLB games show zero dispersion — every book posts the same home spread (±1.5) whether it is 1 book or 12 books ↗. Three games favor the road team at +1.5: Tigers at Royals, Guardians at Rockies, Cubs at Mariners ↗.
NFL tells a different story. MLB books (1–12 per game) agree; NFL books (71 per game) disagree. Jets @ Steelers spans 6 full points from -3.5 to +2.5. Commanders @ Lions spans 3 points. Packers @ Broncos spans 1.5 points even though both ends are heavy favorites ↗.
What props move most when books fight?
Expected value on a player prop exists when your projection beats the market line by more than the juice (the book's cut). In an efficient market (zero dispersion), the line is the truth — you need a genuinely better model. In a dispersed market, the line is the confusion. A model could exploit this by:
- Anchoring to game script. A 6-point spread range on Jets @ Steelers implies wildly different game scripts. One book sees a Steelers blowout; another sees a Jets cover. The quarterback passing yards, running back carries, and wide receiver targets all shift with each script.
- Pricing the disagreement. A model could simulate each script, weight by book probability, and output a distribution — not a single number. The expected value is the area under the curve where the prop line loses.
- Filtering by dispersion tier. A system could tag every game: tight (0–0.5 pts), moderate (0.5–1.5), wide (1.5–3), extreme (3+). Props on extreme-dispersion games would get priority in the feed ↗.
How do I check dispersion before I bet?
Consensus lines average books. A model could model the distribution. On a Chiefs -5.5 game where all 71 books agree ↗, the consensus is the line. A model could ask: what if Mahomes plays at 80%? What if the offensive line misses two starters? The projection shifts; the prop line does not. That gap is the edge.
On a Jets @ Steelers game where books span 6 points ↗, a model would not pick a side. It could weight each book's implied script by its historical accuracy on similar matchups. The resulting projection is a probability cloud — and the EV calculation integrates over that cloud.
Your one next step
Open the FairPlay feed, filter for "Extreme" dispersion tier, and bet the first QB passing yards prop with +EV > 3% → NFL picks filtered for extreme dispersion. Track closing line value (CLV — beating the closing line) by dispersion tier using the CLV guide. The track record tags every pick with its dispersion tier so you can audit the approach. Build your own projection logic in the workshop or sharpen your vig math with the vig explainer. The EV tool shows live dispersion for every game.
Where the data comes from
Every number above comes from the live game_odds table snapshot at 2026-08-21T07:07:00Z. MLB games show 1–12 books each; NFL games show 71 books each. College football games in the same feed show 38–71 books with their own dispersion patterns (UNC @ TCU: 0 dispersion at -7.5; San Jose State @ USC: 0 dispersion at -38.5) ↗.
The FairPlay engine ingests this feed continuously. You can see the live output on the NFL picks page and MLB picks page, filtered by EV threshold and dispersion tier.
What would change our mind
If Jets @ Steelers spread dispersion drops below 1 point across 71 books for three straight weeks, the edge is gone. Check the feed every Sunday — if the "Extreme" tier empties out, stop using this filter.
Bet responsibly — set limits, never chase losses.
Breakeven win % at common American odds
The win rate you need to break even at each price. Pick odds shorter than -150 and you must win >60% just to stay flat — a hurdle most casual handicappers never sustain.
Prop OVER hit rate vs line distance from median
Empirical hit rate of OVER bets as the prop line moves away from the player projection median, measured in standard deviations. A line set 1sd below the median hits ~84% of the time — but books price the juice to match.
