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Model vs Market Today

Today's games where our model and the betting market are furthest apart on who wins, across all sports.

9 games where the model and market disagree today
NFL5:00 PM
TEN at BAL
Model favors TEN
TEN win chance
40%model15%market
25-point disagreement
ModelMarket (no vig)
NFL5:00 PM
NYJ at CHI
Model favors NYJ
NYJ win chance
58%model36%market
22-point disagreement
ModelMarket (no vig)
NFL8:05 PM
MIA at MIN
Model favors MIA
MIA win chance
31%model19%market
12-point disagreement
ModelMarket (no vig)
NFL5:00 PM
DAL at HOU
Model favors HOU
HOU win chance
67%model57%market
10-point disagreement
ModelMarket (no vig)
NFL8:25 PM
KC at LV
Model favors LV
LV win chance
44%model34%market
10-point disagreement
ModelMarket (no vig)
NFL5:00 PM
ARI at NYG
Model favors NYG
NYG win chance
55%model45%market
10-point disagreement
ModelMarket (no vig)
NFL8:25 PM
DEN at SF
Model favors DEN
DEN win chance
48%model41%market
7-point disagreement
ModelMarket (no vig)
NFL5:00 PM
LAR at PHI
Model favors PHI
PHI win chance
44%model37%market
7-point disagreement
ModelMarket (no vig)
NFL5:00 PM
NE at BUF
Model favors NE
NE win chance
32%model27%market
5-point disagreement
ModelMarket (no vig)

How we measure a disagreement

For every game our model gives each team a win probability. The betting market gives one too, hidden inside the odds. When the two numbers are far apart, that's a disagreement. Say the model makes the home team 60% to win and the market works out to 52%. That's an 8-point disagreement, and it's how we rank games on this page.

We take the sportsbook's margin (the “vig”) out of the odds first, so we compare the model with a fair market number, not a price padded in the book's favor. The implied probability explainer walks through that math with a worked example.

What a disagreement does and doesn't tell you

It tells you the model reads the game differently from the market. It doesn't tell you who wins. A team at 60% still loses 4 games in 10, and the market is often right when the two split. We publish how the model's numbers hold up on the accuracy page, down to which probability ranges it over- and under-shoots. The methodology page shows the factors behind every number, so you can check the reasoning yourself.

Model outputs are for informational purposes only. Past performance does not guarantee future results.

See our model accuracy page for transparent performance data.