Fresh week, fresh chaos. The slate’s wiped clean and the model’s recharged — fueled by Week 8 simulations, market swings, and a few late-night adjustments that definitely weren’t made after two cups of coffee. Below is the board, ranked by confidence (13 → 1), plus this week’s Underdog Pick and a 3-Leg Moneyline Parlay built purely on value and questionable self-control.

My picks are trending up. I am only a few points out of first place in my league. Stay with me folks, we are going to do this.

This week’s rankings blend model probabilities with real-world context — recent EPA swings, market corrections, and team momentum — to produce a truer reflection of confidence.

The simulations and market adjustment models are fully aligned heading into Week 6, giving a clear sense of which spreads carry real edge and which should be avoided. Below are the ATS confidence rankings (15–1) based solely on your document’s adjusted projections and summaries.

We’ve hit Week 5, and the simulations are now blending preseason expectations with in-season adjustments. That means the model is sharper, but I still keep my balance of 80% data and 20% instincts — factoring in efficiency trends, roster health, and locker room psychology.

Below are my ATS confidence picks for all 14 games this week, plus my favorite value moneyline parlay and the underdog to circle.

Three weeks into the season, we’re starting to see who’s for real and who’s headed for trouble. My process stays the same: lean on the simulation model (thousands of game iterations using minimax logic), then layer in real-world adjustments — offensive/defensive efficiency, injuries, roster stability, and the always underrated locker room toxicity factor.

For me, it’s still 80% data and 20% instincts. The sims narrow the board, but that last layer of context is what gives these picks real confidence.

Two weeks in, and we’re starting to see which teams are settling into form and which ones are still trying to figure out who they are. That’s why I keep leaning on my simulation model as the backbone of my pick strategy. It runs thousands of plays per matchup using minimax game theory, but I don’t stop there.

I layer in real-world adjustments based on Week 1 and Week 2 performance: efficiency metrics (EPA/play, yards per play), roster health, and even the “locker room toxicity” factor that doesn’t show up in spreadsheets but always shows up on the field. For me, it’s still that 80% data, 20% instincts mix that makes the difference.

Here’s how I see the Week 3 slate shaking out.

Week 1 of the NFL season gave us plenty of chaos — surprise performances, rookie jitters, and more than a few teams who already look like they need therapy sessions in the locker room. That’s why I take my simulation results and layer in real-world adjustments from the opening week.

My approach is still 80% data, 20% eye test. The data comes from a minimax game theory simulation that runs thousands of plays for each matchup. The eye test comes from me checking for roster health, coaching tendencies, and my favorite psychological factor: locker room toxicity. If a team looks fractured, I adjust their projection down regardless of how good the numbers look.

With that in mind, here’s how I’m attacking Week 2.

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.

College athletics was once defined by regional rivalries, underdog stories, and a sense that any program could rise to national prominence with the right mix of talent, grit, and coaching. Today, that balance is eroding. A small group of wealthy schools continues to surge ahead, while smaller programs struggle to remain relevant. The driving forces include massive media contracts, the transfer portal, and explosive NIL growth. These shifts reward the few at the expense of the many.

I’ve been working behind the scenes on a new way to analyze NFL games using game theory and data-driven strategy. Instead of just guessing, I’m applying the minimax principle — the idea that each coach calls plays to protect against the worst-case scenario — and combining it with EPA (Expected Points Added) from thousands of real plays (2022–2024).

This approach builds “matchup matrices” that show how offenses perform against specific defensive looks, then runs 2,000 full-game simulations to see how those tendencies scale up. The DAL vs PHI example? Both teams’ minimax move was to attack deep, but PHI’s deep passing proved far more consistent, leading to a 67% win probability in the sim.

In the mythology of the National Football League, nothing seems more important than winning the Super Bowl. A single championship can immortalize players and coaches. But from a business standpoint, a different truth emerges. The real winners—in terms of revenue and fan engagement—are often the franchises that consistently reach the postseason, even without frequent championships.

For small colleges, enrollment isn’t just a metric — it’s the scoreboard. When headcount drops, everything from tuition to team morale takes a hit. You can overhaul programs, rebrand your website, or hire a consultant. But one of the most direct and often overlooked strategies?

Athletics.

Not just as a recruiting tool — but as a full-on enrollment engine. If you're not leveraging sports strategically, you're missing one of the best opportunities to grow your campus and stabilize your bottom line.

I’ll be honest — when USC and UCLA announced they were heading to the Big Ten, my first thought wasn’t “tradition is dying.” It was:
“This is textbook game theory.”

College football has become a boardroom game. Universities are the players. The stakes? Media money, NIL power, and long-term survival.

We’re not just watching schools change conferences. We’re watching them make calculated, strategic moves in a system that now rewards consolidation over culture. And now that the Pac-12 has disbanded, it’s clear: the game isn’t regional anymore — it’s national, and it's ruthless.

If you told me five years ago that college athletes could finally get paid, I would’ve said “It’s about time.”

And I meant it. I believed in NIL. I supported the transfer portal. I still do — in theory.

But the way it’s playing out? It’s not what any of us hoped for.

If you’ve ever opened five apps just to find the one game you want, you already know:
Live sports streaming has taken over.

But behind the scenes, it’s not just a convenience war. It’s a money war. A strategy war. A who-blinks-first war. I’ve worked on the inside — from broadcast booths to streaming roadmaps — and I can tell you, this industry is rewriting the rules every season.

Let’s dig into what’s really going on under the hood.

Why is it so hard to watch the team you actually care about?

Sometimes it's blackouts, sometimes it’s a streaming service you didn’t know you needed. The answer behind it all? Sports media rights — the multi-billion dollar chessboard that decides who sees what, when, and where.

I’ve worked behind the scenes with broadcasters and streamers — places like Fox Sports, Stats Perform, and Amazon Prime Video. I've seen how complicated (and cutthroat) the world of media rights can be. Here's a real-world breakdown of how it works — and why it matters more than ever.

There’s something about game day energy that’s hard to explain but easy to feel.

The QB who’s bouncing on his toes. The wide receiver who won’t stop talking smack. The crowd, the music, the vibe—it all builds into something we call “momentum.” And whether you’re a player, a coach, or just a fan, you’ve seen it. Sometimes a team just looks ready.

Well, it turns out that feeling might be backed by science.