Week 8 Autopsy: When Consensus Wasn’t Enough

Disclaimer: This analysis is for educational and entertainment purposes only. This is not betting advice, financial advice, or a recommendation to wager on sporting events. Always gamble responsibly.

The Promise vs. The Reality

Week 8 was supposed to be different.

After the chaos of Week 7, where Thursday Night Football gave us a 64-point thriller and upsets ran rampant, our five prediction models came together with unprecedented unity. Ten consensus games. The highest agreement rate of the 2025 season. We predicted the “Consensus Week”—a slate where certainty would finally prevail.

Week 8 had other plans.

Final Score:

• Ensemble: 8/13 (61.5%) — Down 11.8% from Week 7
• Consensus Games: 8/10 (80%) — Strong when united
• High-Confidence Misses: 5 — Every prediction between 57-68% confidence failed,
• The War Game: Ravens 30, Bears 16 — The season’s most divisive prediction went to the minority

This wasn’t just a bad week. This was a systematic failure of confidence. A reminder that in the NFL, certainty is a myth.

The Consensus Held (Mostly)

Let’s start with the good news: When all five models agreed, they went 8-for-10 (80%).

That’s actually excellent performance. When ELO, Logistic Regression, XGBoost, Bayesian, and Ensemble all see the same winner, they’re right 4 out of 5 times. The problem? Only 10 of 13 games had consensus.

Consensus Wins ✅

The TEN @ IND slam dunk delivered exactly as advertised. We called it the week’s most confident prediction at 92.1%, and the Colts crushed Tennessee 38-14. This was the model performance we expected across the board.

Consensus Failures ❌

But then there were the two consensus games where we got it wrong:

1. MIA @ ATL: Atlanta 68.5% → Miami Won 34-10

Every single model picked Atlanta. The ensemble had 68.5% confidence. The Falcons were at home. The fundamentals said Atlanta.

Miami said “not today” and hung 34 points in a dominant road performance. This wasn’t close—it was a 24-point blowout in the wrong direction.

2. GB @ PIT: Pittsburgh 61.7% → Green Bay Won 35-25

All five models gave Pittsburgh the edge at home. The Steelers had been solid. The Packers traveled to a tough environment.

Green Bay dropped 35 points and controlled the game. Another consensus prediction failed.

The Lesson: Even 60%+ confidence with universal model agreement doesn’t guarantee anything. Consensus reduces uncertainty—it doesn’t eliminate it.

The War Game: When Minority Rules

Remember the most divisive game of the 2025 season?

CHI @ BAL: 55.4% prediction spread—the widest disagreement we’d seen all year. The models went to war:

• ELO: Ravens 70.5%
• Logistic Regression: Bears 83.5%
• XGBoost: Bears 51.0%
• Bayesian: Ravens 71.9%
• Ensemble: Bears 58.5%

Three models picked Chicago. Two picked Baltimore. The ensemble sided with the Bears at 58.5% confidence.

Final Score: Ravens 30, Bears 16

The minority was right. ELO and Bayesian—the conservative, historically-grounded models—correctly identified that Baltimore’s home field advantage and fundamental quality would overcome Chicago’s recent hot streak.

What Went Wrong?

In our deep-dive analysis, we laid out the tension:

• Logistic Regression (83.5% Bears) weighted Chicago’s 3-0 streak heavily and saw Baltimore’s offensive collapse (13 points in two home games) as decisive.
• ELO and Bayesian (70%+ Ravens) trusted Baltimore’s historical quality, home field advantage, and the Bears’ negative point differential.

The traditionalists won. Recent form didn’t matter—Baltimore came off the bye week and executed. Chicago’s luck ran out.

The brutal lesson: When models disagree this dramatically, it’s not noise. It’s genuine uncertainty. The 58.5% ensemble prediction essentially said “this is a coin flip.” We should have listened to the uncertainty, not the majority.

Thursday Night Chaos Strikes Again

MIN @ LAC: Vikings 57.4% → Chargers Won 37-10

After Week 7’s Pittsburgh-Cincinnati 64-point thriller taught us about Thursday night volatility, we approached this game with caution. The models were mildly divided (15.9% spread), and we predicted Minnesota by 1.3 points.

The Chargers had other ideas. They dominated 37-10 in a game that wasn’t even close.

Thursday Night Football Record This Season: 0/2 for ensemble predictions.

The pattern is clear: Thursday games defy modeling. Short rest, weird matchups, unpredictable execution. Until further notice, treat TNF as a chaos variable.

The Five Horsemen: When Confidence Betrayed Us

Here’s where Week 8 really went off the rails. Five games where the ensemble had decent-to-strong confidence (57-68%)—and all five predictions were wrong:

Every. Single. One. Failed.

This is statistically improbable. These weren’t coin-flip games—these were predictions with 57-68% confidence. At those levels, you expect to hit 3-4 out of 5. We hit zero.

The NYJ @ CIN Thriller

Special mention to Jets @ Bengals, which gave us the week’s wildest finish: 39-38.

The models predicted Cincinnati 58.5%, with a total of 40.4 points. The actual game delivered:

• 77 total points (36.6 points over prediction!)
• A 1-point margin
• Back-and-forth scoring that defied defensive expectations

This was the Week 8 version of Week 7’s Thursday night chaos—a game where the fundamentals said “low-scoring defensive battle” and reality said “hold my beer.”

Model Performance: XGBoost Emerges from the Ashes

While the ensemble crashed and burned, individual model performance tells a fascinating story:

XGBoost: The Unlikely Hero

XGBoost went 10-for-13, the best performance of any model in Week 8. This is the same model that was middle-of-the-pack in Week 7 (10/15, 66.7%).

What changed?

• It got CHI @ BAL right by being uncertain (51% Bears) while Logistic was wildly overconfident (83.5%)
• It avoided the worst upsets by having more modest confidence levels
• Its pattern recognition handled the week’s chaos better than linear models

The irony: XGBoost was the “honest uncertainty” model in Week 8’s predictions. When everyone else screamed confidence, XGBoost whispered “maybe.” That humility saved it.

Ensemble: The Catastrophic Fall

The ensemble—designed to combine model strengths and reduce bias—had its worst week yet: 8/13 (61.5%), down 11.8% from Week 7.

This is what happens when you average confident wrong predictions. The ensemble’s diplomatic approach works when models are calibrated. But when multiple models are systematically overconfident (looking at you, Logistic Regression), averaging doesn’t save you—it just splits the difference on failure.

ELO: The Wounded Champion

ELO, which dominated Week 7 at 80%, fell to 69.2% in Week 8. The culprit? It got burned on the consensus upsets (MIA @ ATL, GB @ PIT) where its traditional home-field and rating-based predictions failed.

But ELO got CHI @ BAL right—picking the minority (Ravens 70.5%) and trusting fundamentals over recent form. That’s the ELO philosophy in action: slow, steady, historically grounded.

Scoring Predictions: The Good, The Bad, The NYJ @ CIN

Scoring predictions were a mixed bag, ranging from perfect to catastrophically wrong.

The Perfect Prediction 🎯

CHI @ BAL: Predicted 46.0 → Actual 46

Zero error. Absolutely perfect. The same game where we got the winner wrong, we nailed the total to the exact point. The models correctly identified a relatively low-scoring, defensive-oriented game.

The irony is delicious.

The Honorable Mentions

• CLE @ NE: Predicted 45.8 → Actual 45 (-0.8 error)
• MIN @ LAC: Predicted 45.8 → Actual 47 (+1.2 error)
• MIA @ ATL: Predicted 45.9 → Actual 44 (-1.9 error)

These games stayed within 2 points of predictions. Solid performance on totals even when we missed the winner.

The Catastrophic Misses

1. NYJ @ CIN: Predicted 40.4 → Actual 77 (+36.6 error)

The week’s worst miss by a country mile. We predicted a low-scoring defensive game. We got a 77-point track meet. This was off by nearly an entire second game’s worth of points.

2. DAL @ DEN: Predicted 46.4 → Actual 68 (+21.6 error)

Another shootout we didn’t see coming. Denver dropped 44 points, Dallas kept pace with 24, and the total blew past our projection by 21.6 points.

3. TB @ NO: Predicted 46.9 → Actual 26 (-20.9 error)

The opposite problem: we predicted an offensive game and got a defensive slugfest. Tampa Bay won 23-3, and the total fell 20.9 points short.

4. WAS @ KC: Predicted 55.3 → Actual 35 (-20.3 error)

We called this Monday night’s “offensive showcase” with the week’s highest predicted total. Kansas City won 28-7 in a dominant but low-scoring performance. Off by 20.3 points.

What Week 8 Taught Us

1. Consensus ≠ Certainty

Yes, consensus games went 8/10 (80%). That’s good! But the two failures (MIA @ ATL, GB @ PIT) were both high-confidence predictions. When all five models agree and still get it wrong, it’s a reminder that even statistical unity doesn’t eliminate uncertainty.

The math: 80% success rate means 1 in 5 consensus picks will fail. With 10 consensus games, we expect 2 failures. We got exactly 2. The models weren’t wrong—probability did its thing.

2. Overconfidence Is the Enemy

All five of the week’s upsets came from predictions with 57-68% confidence. These weren’t supposed to be coin flips—they were supposed to be “likely” outcomes.

The lesson from CHI @ BAL: The Logistic model’s 83.5% confidence was absurd given the underlying uncertainty. When a model screams certainty in the face of conflicting signals, distrust it.

XGBoost won Week 8 by being humble. Its 51% on CHI @ BAL essentially said “I don’t know,” which was more honest than Logistic’s screaming confidence.

3. Recent Form vs. Historical Quality: It Depends

Chicago rode a 3-0 hot streak. Baltimore had scored 13 points in two home games. The recent-form models (Logistic, XGBoost) picked the Bears. The historical models (ELO, Bayesian) picked the Ravens.

The Ravens won 30-16.

But this doesn’t mean “always trust history.” Week 7 saw plenty of recent-form predictions hit. The real lesson? Context matters. Baltimore came off a bye week. Their offensive struggles were partly due to injuries, not systematic failure. The traditionalist models correctly identified the bounce-back potential.

4. Thursday Night Football Is Chaos

TNF Ensemble Record: 0-2

Stop trying to predict Thursday games with normal models. Short rest, weird scheduling, unpredictable execution—it’s a different sport. Until proven otherwise, treat TNF as a “stay away” situation for confident predictions.

5. Model Diversity Is Critical

If we’d only run the Ensemble, we’d have gone 8/13 (61.5%). By running five different models, we learned:

• XGBoost (10/13) crushed it with pattern recognition
• ELO got the war game right by trusting fundamentals
• Logistic was dangerously overconfident on multiple games
• Bayesian improved 9.2% from Week 7 by being appropriately uncertain

No single model is perfect. The value is in seeing where they agree (consensus) and where they violently disagree (CHI @ BAL).

The Complete Week 8 Results

Here’s every game, prediction, and outcome:

Final Tally:

• ✅ 8 Correct Predictions
• ❌ 5 Wrong Predictions
• Consensus: 8/10 (80%)
• Disagreement Games: 0/3 (0%)

Looking Ahead: Week 9 and Beyond

Week 8 was humbling. After the chaos of Week 7, we thought consensus would save us. Instead, we learned that confidence is a trap.

Key Takeaways for Week 9:

1. Trust XGBoost’s pattern recognition — It just won Week 8 by being appropriately uncertain
2. Be skeptical of Logistic’s extreme confidence — 83.5% on CHI @ BAL was overconfident nonsense
3. Respect consensus, but don’t worship it — 8/10 is great, but that means 1-2 failures are expected
4. Avoid Thursday night predictions — Or at least approach with maximum uncertainty
5. When models war (55%+ disagreement), treat it as a coin flip — CHI @ BAL taught us that lesson the hard way

The models will recalibrate. The ensemble will adjust. And we’ll be back next week with fresh predictions, armed with the brutal lessons of Week 8.

Want More Deep Dives?

Check out our full analysis of the CHI @ BAL war game—we broke down why the models disagreed so dramatically and what it teaches us about prediction science.

For Week 8 predictions and methodology, see the original predictions post.

Got questions about the models or want to see the raw data? Hit me up on X @Cresencio.

Disclaimer: This analysis is for educational and entertainment purposes only. All predictions are based on statistical models and historical data. Past performance does not guarantee future results. This is not betting advice, financial advice, or a recommendation to wager on sporting events. Please gamble responsibly and within your means.