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Week 13 brings the biggest betting edge of the season (MIN @ SEA, +28.4%), three Thursday games, and a Rams team ready to destroy Carolina. Plus: 7 consensus picks and why the models love underdogs this week.
Breaking down how our ensemble model went 10-4, XGBoost surged to 85.7%, and every single consensus pick hit. Plus: the four upsets that kept things interesting.
Week 12 brings the biggest value opportunity of the season (NE @ CIN), five true toss-ups including Thursday's BUF @ HOU, and Seattle traveling to Tennessee as 87.9% favorites—the most confident pick we've made all year.
Week 10 features three brazen road favorites above 75% confidence, a Thursday night trap game, and DET @ WAS—the most confident pick of the week that somehow carries the worst betting value.
The models face-planted on game prediction (50% accuracy) and barely stayed above .500 on spreads (57.1% ATS). Also: Chicago and Cincinnati decided gravity was optional.
Week 9 trims the chaos: 5 games have complete model agreement, while IND@PIT and KC@BUF headline as coin-flip shootouts with market edges hiding in plain sight.
Baltimore travels to Miami in a battle of 1-4 disappointments. The models are fighting again, Vegas sees a blowout, and our predictions suggest massive betting value—or a trap.
The models promised consensus. Week 8 delivered carnage. Five high-confidence predictions failed, XGBoost emerged as the lone survivor, and the season's most divisive game taught us a brutal lesson about overconfidence.
The Bears-Ravens matchup is the most divisive game of the 2025 season. Why do prediction models disagree so dramatically, and what does it teach us about football analytics?
Week 8 brings unprecedented model agreement with 10 consensus picks—and one game that has the models fighting harder than any matchup this season.
Week 7 delivered a masterclass in the power of consensus—and a humbling reminder that even 87% confidence can't predict Thursday Night Football chaos.
A deep dive into Week 7 NFL predictions, analyzing where five different prediction models agree—and where they wildly disagree.