Week 1 NFL Picks: Using Game Theory to Outsmart the Spread

Every year, my friends and I play in a “pick ’em” league. It’s simple: you pick every NFL game against the spread, then assign confidence points (16 down to 1). If your pick hits, you earn that many points. Bragging rights and season-long glory are on the line.

This season, I’ve added a twist: I’m using a minimax game theory simulation (yes, the same kind you see in AI research) to model offensive/defensive strategies and simulate 2,000 games per matchup. It helps cut through noise, find the best ATS edges, and spot value on moneylines and totals. For anyone who wants to dive deeper, here’s the full simulation explanation.

🧠 Expert Sanity Check

Simulations are powerful tools, but football isn’t played in spreadsheets. The numbers give me a strong baseline — about 80% of my decision-making comes from data — but there’s still that critical 20% that comes from the “eye test.” I always reevaluate what the model spits out by considering things like injuries, coaching tendencies, roster depth, and intangibles. One of the biggest “off the spreadsheet” factors for me is locker room toxicity. Teams with fractured locker rooms or shaky leadership often underperform, no matter what the numbers say. That psychological factor can flip a spread that looks safe on paper.

The simulations this week give me confidence in teams like Cincinnati and Philadelphia, who not only show up as safe picks statistically but also pass the eye test when you look at their roster stability and offseason continuity. Miami shows up as the sharp underdog in the model, and when I look closer, I see a team with the tools to surprise despite being overlooked by the market. Seattle, on the other hand, is a reminder of why I never go 100% with the data — the model leans their way, but roster health questions and locker room variables make me cautious.

The numbers help narrow the board, but that final gut check keeps me grounded. For me, it’s always 80% data, 20% instincts.

🔒 Against the Spread (ATS) Picks with Confidence

Here’s how I’m ranking Week 1, from most confident (16) to least (1).

Confidence Game Pick ATS Why

16 DAL @ PHI PHI -7.5 Over 82% cover probability, largest model edge of the week.

15 CIN @ CLE CIN -5.5 Bengals clear the spread in 8 of 10 sims.

14 NYG @ WAS WAS -6.5 Double-digit edge vs market, 80% cover rate.

13 ARI @ NO ARI -6.5 Cards lean strongly; nearly 78% cover probability.

12 SF @ SEA SEA -2.5 Seahawks cover in three-quarters of sims.

11 TEN @ DEN DEN -7.5 Titans starting fresh at QB, Denver stability wins out.

10 MIA @ IND MIA +1.5 Market favors Indy, but sims flip it: 71% Dolphins ATS.

9 CAR @ JAX JAX -3.0 Jaguars cover 70% of the time.

8 KC @ LAC KC -3.0 Chiefs cover two-thirds of sims, even in Brazil opener.

7 PIT @ NYJ PIT -2.5 Modest but clear lean toward Steelers.

6 HOU vs LAR HOU +2.5 Rams projected to cover ~65% but injuries will hold them back.

5 TB @ ATL TB -2.5 Slight model edge to Tampa.

4 BAL @ BUF BUF -1.5 Bills cover ~62% of sims, not flashy but reliable.

3 GB vs DET DET +2.5 Model likes Lions’ upgrades; 62% cover rate.

2 MIN @ CHI MIN -1.5 Thin margin, but Vikings lean ATS.

1 LV @ NE LV +2.5 Carroll/Geno will make a big splash but this is the closest matchup on the board.

📊 Best ATS Parlays

If you want to go beyond the league picks:

• Top ATS 3-Leg (highest conviction):

  • CIN -5.5, SF -2.5, JAX -3.0

• Underdog ATS Parlay:

  • DET +2.5, HOU +2.5, LV +2.5 (closest thing to a live-dog trio this week)

💰 Moneyline Parlays for Value

Favorites are often fairly priced, so the best profit angles are on live underdogs:

• Value 3-Leg ML (balance risk/reward):

  • DET ML + MIA ML + BAL ML

  • Hit rate ~10%; would need +1000 or better payout.

• High-Risk, High-Reward ML:

  • DET ML + MIA ML + SEA ML

  • Hit rate ~6%; payout must be +1500 or better to make sense.

⚖️ Totals (Over/Under)

No strong edges in Week 1. The model projections lined up closely with market totals across the board (BUF–BAL, CIN–CLE, KC–LAC, JAX–CAR). Best to sit these out.

👉 That’s the full Week 1 card. If you want to see the deep-dive methodology, here’s the link to the simulation writeup.