Falcons at Saints: New Orleans Is the Pick, Not a Lock
Both frozen Week 4 forecasts pick New Orleans on Monday night, but 67.6% in the original and 53.0% in the rebuild describe very different levels of confidence.
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Both frozen Week 4 forecasts pick New Orleans on Monday night, but 67.6% in the original and 53.0% in the rebuild describe very different levels of confidence.
Detroit is 57.6% in the original Week 4 forecast and 53.2% in the rebuild, but the component models and separate point forecasts make Sunday night far less settled.
Both Week 4 forecasts picked Cleveland. The rebuilt point model nearly matched the three-point margin, but undershot the total. Grade the winner, spread and points, then look toward Sunday.
Cleveland is 51.9% in the rebuilt forecast versus 70.4% in the original. Thursday's prices favor Pittsburgh, while a falling total tests which saved estimates still apply.
The full five-model Week 4 board, before and after retraining: three changed winners, lower confidence, and the model disagreements behind both forecast versions.
A small dock for a sprawling blog: local search, section jumps, keyboard shortcuts, and a little animation to make the archive easier to explore.
Agentic development has become a project of its own. Agent Model Fit is where I'm working through how agents, models, and real problems fit together.
Atlanta dominated Green Bay 35–14. Every contributing model missed, and sharpening increased the scoring penalty. The preserved forecasts and 33-game record get graded.
All three models favor Green Bay, but Wednesday's -250 price sits between the raw and final probabilities. Penix returns as Week 3 tests the confidence adjustment.
Three models contribute again, six favorites exceed 90%, and three ensemble picks follow a single model. The complete September 22 Week 3 forecast.
The original Week 2 ensemble went 8–8. Its eleven 90%-plus favorites went 5–6, and sharpening again made the probability scores worse.
The corrected forecast picks the Giants at 92.3%, up from a 73.0% raw blend. Bayesian is back, Elo favors the Rams, and both versions stay in the record.
Buffalo beat Detroit 41–31. All three available models picked the winner, and sharpening improved the probability scores. The full record still favors the raw blend.
Buffalo gets 79.4% against Detroit, up from a 63.1% raw blend. Three forecasts favor the Bills, but Bayesian is excluded from this week's ensemble. Here is the pick and how either result will be graded.
Eleven Week 2 favorites clear 90%, but only two models contribute to the saved ensemble. The full board, the disagreements, and the confidence test ahead.
The frozen Week 1 board finished 9–7. Grading all 16 games shows that temperature scaling kept every pick and made the probability scores worse.
San Francisco beat Los Angeles 27–7 after my final forecast gave the 49ers just 6.4%. Grading the miss, the cost of extra confidence, and the first two games of the record.
The Rams get 93.6% in the saved Week 1 forecast. The raw blend is 74.5%. A Melbourne preview about model agreement, amplified confidence, and how to grade either result.
All three models picked Seattle, and Seattle won 13–10. Grading the frozen forecast shows what went right—and why one result cannot validate its confidence.
Seattle gets a 70.9% win probability in the season opener, but the model-by-model view is much closer. A Patriots–Seahawks deep dive with a link to the full Week 1 prediction board.
The 2026 opener arrives with a repaired three-model ensemble, five huge favorites, six disagreement games, and one important warning: confidence is not the same as calibration.
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.
Denver (7-2) is an 85.5% favorite at home against Las Vegas (2-6), but the models project only a 5.7-point win. Is DEN -9.0 too much?
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?
Step-by-step guide to configuring Ollama as a local AI provider for BrowserOS. Complete setup instructions including CORS configuration and model integration.
Week 8 brings unprecedented model agreement with 10 consensus picks—and one game that has the models fighting harder than any matchup this season.
Fighting with Sharp, pnpm, and Vercel for an hour. Here's what finally worked when image optimization kept failing.
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.
Exploring how our NFL prediction model understands each team through the lens of predicted vs actual performance. Some teams are exactly who the model thought they were. Others? Complete surprises.
A deep dive into Week 7 NFL predictions, analyzing where five different prediction models agree—and where they wildly disagree.
Using grok for sentiment analysis, straight from posts on X. Spoiler, it sucks.
Exploring baseball fan reactions to the innovative 'torpedo bat' in the 2025 MLB season, based on posts from X.
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Too lazy to get off the couch, so I’m using Grok and GitHub on my iPad to push posts to my site. Here’s the slacker’s guide.
Analyzing fan sentiment for WWE Raw on April 7, 2025, straight from posts on X.
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Analyzing fan sentiment for 2025 March Madness, straight from posts on X.
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Resuls, recap, and general sentiment of AEW Dynasty 2025, straight from posts on X.
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My website was down, no big deal. Deployed a Vercel template in like 5 minutes. Here’s the lazy tale thanks to Grok.
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Try a different word or a broader topic.