Want to Know How to Win $1 Million Next NFL Season? This Is How They Did It Last Year

Want to know how to win a million dollars over the next NFL season?

I started with the simplest question I could think of:

How did people actually do it last year?

Not what the optimal lineup theoretically should have looked like.

Not which DFS strategy sounds smartest on Twitter.

Not which player had the highest projection on Sunday morning.

I went through the winning DraftKings Millionaire Maker lineups from last season and looked at the actual structures that produced the million-dollar result.

Which quarterbacks got there.

Which players they were paired with.

Where the cheap salary came from.

Which expensive players were worth paying for.

How many true ceiling scores a lineup actually needed.

And, perhaps most importantly, which patterns kept showing up.

The first thing I learned is that there is a very real score you are chasing.

The Number: About 235

I like starting with the winning score because it defines the entire problem.

The average Millionaire Maker winning lineup last season scored 229.14 DraftKings points.

My working target is about 235 points.

There were three weeks where the winner blew through that number with scores of 262.10, 281.68 and 263.86, but those were the exceptions rather than the baseline.

So when I build a lineup, I am not asking whether nine players individually look like good plays.

I am asking whether I can see a realistic path to roughly 235.

DraftKings gives me $50,000 to fill nine roster spots.

At 235 points, I need approximately 4.7 fantasy points for every $1,000 of salary across the lineup.

Obviously, every player does not need to hit exactly 4.7x.

That is not how winning lineups work.

A $9,000 superstar might only return 30 points.

That is only about 3.3x salary.

But if those 30 points happen to be one of the highest scores available at the position that week, I may still need them.

The trick is finding enough value elsewhere to make the mathematics work.

And when I looked at the winners, one structure appeared over and over.

Quarterback Is Rarely an Isolated Decision

If there is one thing I would not ignore when building Millionaire Maker lineups, it is correlation.

Touchdowns create fantasy points.

And high-scoring games create lots of touchdowns.

That sounds obvious, but the winning lineups demonstrate just how important it is.

Last season, the winning quarterback exceeded 30 DraftKings points eight times.

Those quarterbacks were:

But the more interesting question is what the winning lineup did around them.

Week 4: Justin Fields + George Pickens

Justin Fields scored 35.98.

George Pickens scored 20.30.

That is the basic stack.

Quarterback produces through the air, receiver captures part of that same production, and I get two fantasy results from the same game outcome.

If Pittsburgh's passing game exceeds expectation, I am not betting on two unrelated things happening.

I am betting on one football event creating two useful fantasy scores.

That is the point of correlation.

Sometimes the Winning Stack Comes From the Other Team

Week 5 is one of my favorite examples because it shows why I think people sometimes define "stacking" too narrowly.

Lamar Jackson scored 37.42.

But the winning lineup did not need a Baltimore receiver attached to him.

Instead, it had:

Ja'Marr Chase — 44.30

and

Tee Higgins — 29.30

from Cincinnati.

Think about what that lineup was really betting on.

It was betting on the game.

If Cincinnati scores enough to force Baltimore to remain aggressive, Lamar keeps throwing and running.

If Lamar and Baltimore score enough, Cincinnati has to keep attacking.

The game environment becomes the stack.

That is an important distinction.

I do not necessarily need my quarterback's receiver if I can capture the same shootout through the opposing offense.

And that week, the game delivered exactly what the lineup needed.

Week 11: Jared Goff + Amon-Ra + Jameson Williams

This is closer to what people picture when they think about a classic DFS stack.

Jared Goff scored 37.58.

Amon-Ra St. Brown scored 41.70.

Jameson Williams scored 25.60.

Now I have three roster spots benefiting from the same fundamental bet:

Detroit's passing offense is going to smash.

And it did.

That combination produced nearly 105 DraftKings points from three players.

That is almost half of my 235-point target from one offensive thesis.

This is why I think stacking gets misunderstood when people describe it simply as "play your quarterback with his receiver."

The real purpose is not checking a correlation box.

The purpose is concentrating a lineup around a game outcome that, if I am right, can move several roster spots toward their ceilings at the same time.

Week 12: Tua + Waddle + Achane

Tua Tagovailoa scored 31.48.

Jaylen Waddle scored 31.40.

De'Von Achane scored 20.50.

Again, three players.

One offensive environment.

But notice something else.

Achane is a running back.

That did not prevent him from being positively connected to the quarterback because Miami can generate offense through multiple channels.

I think this is another mistake people make when treating lineup construction too mechanically.

Correlation is not a rulebook.

It is an attempt to understand how football events interact.

If Miami scores 35 points, there may be room for Tua, Waddle and Achane to all matter.

The actual winning lineup demonstrated exactly that.

Week 14: Josh Allen + Khalil Shakir + Puka Nacua

Josh Allen scored 54.88 fantasy points.

That alone can fundamentally change a DFS tournament.

Khalil Shakir scored 24.60.

And Puka Nacua, from the opposing side, scored 44.80.

Again, the lineup did not simply say:

"Josh Allen is good."

It effectively said:

This game is going to produce enough offense that I want exposure to both sides of it.

Allen gives me the quarterback ceiling.

Shakir captures some of Buffalo's passing production.

Puka captures the other team's response.

And when the game explodes, all three bets can hit together.

That is what I am trying to manufacture.

Not nine independent good plays.

A lineup in which several players can be proven correct by the same underlying event.

Week 15: Goff Goes Back to the Same Well

Jared Goff scored 44.06.

Amon-Ra St. Brown scored 41.30.

Sam LaPorta scored 21.10.

The interesting thing is that Goff and Amon-Ra had already appeared together in a million-dollar winning lineup.

That matters to me.

Because one of the recurring ideas in the data is that players and structures can remain viable even after they have already delivered.

DFS players are often obsessed with being early.

I am increasingly interested in not being too early to leave.

Week 17: Baker + Bucky + Evans

Baker Mayfield scored 37.56.

Bucky Irving scored 26.00.

Mike Evans scored 29.70.

Quarterback.

Running back.

Wide receiver.

Same offense.

Again, the winning lineup was not afraid to concentrate around an offense capable of materially outperforming expectation.

And the player names matter because several of them show up more than once across the season.

Bucky Irving appears on three winning Millionaire Maker lineups.

That is not something I want to dismiss as random noise without looking more closely.

Sometimes the Cheap Quarterback Is the Entire Key

This is probably where Millionaire Maker construction gets most interesting.

I know I need stars.

The problem is that stars are expensive.

So one of the cleanest ways to change the entire lineup is finding a quarterback who costs far less than his possible production implies.

I think of it as:

Cheat at quarterback so I can buy ceiling somewhere else.

Last season gave several examples.

Week 2: Daniel Jones + Malik Nabers

Daniel Jones scored 18.32.

Malik Nabers scored 31.70.

Jones was not the star of the lineup.

That is exactly the point.

The quarterback position provided enough production at the right salary while Nabers supplied the premium score.

I do not need my cheap quarterback to be QB1 overall.

I need him to avoid killing the lineup while freeing salary that produces more points elsewhere.

Week 3: Sam Darnold + Justin Jefferson

Darnold scored 23.24.

Justin Jefferson scored 20.10.

Again, this is not an outrageous combined score.

But salary matters.

If that pairing costs materially less than an elite-quarterback stack, the remaining money can be pushed into positions where the slate contains bigger differences between the cheap and expensive options.

That is why I do not evaluate stacks solely by raw points.

I evaluate the points relative to what I paid for them.

Week 9: Geno Smith + Jaxon Smith-Njigba

This is where the concept really hits.

Geno Smith scored 28.12.

Jaxon Smith-Njigba scored 40.00.

That is 68.12 points from the pairing.

When a lower-cost quarterback stack produces something close to 70 DraftKings points, the rest of the lineup suddenly has enormous flexibility.

That is the type of outcome capable of creating a million-dollar roster.

The Five-Times-Salary Test

When I am considering a cheaper quarterback stack, I want the combination to have a legitimate path to dramatically outperforming its salary.

A rough framework I use is:

It is not an iron law.

It is a way of forcing myself to ask whether the stack's ceiling actually justifies using two or three roster spots.

This is also why I like cheap quarterback/tight end combinations.

If I can access quarterback correlation without paying premium WR1 pricing, I preserve more salary for running backs and receivers with legitimate 30- and 40-point ceilings.

The Weird Ones Matter Too

Not every million-dollar lineup fits neatly into a strategy article.

And I think it is important to admit that.

Week 16 is the obvious example.

Michael Penix scored only 7.38 fantasy points.

The Falcons defense scored 25.00.

And that combination still appeared on the million-dollar winner.

That should prevent me from turning historical patterns into commandments.

There are weeks where the rest of the lineup is so strong that one weak score survives.

There are weeks where a strange construction wins because the slate itself produces strange outcomes.

The goal is not to pretend every winner was perfectly optimized.

The goal is to understand which patterns happened often enough that I want to incorporate them into how I think.

Running Back + Defense Showed Up More Than Once

Another construction that appeared repeatedly was pairing a running back with his own defense.

Week 6:

Sean Tucker — 37.20

Tampa Bay defense — 17.00

Week 8:

James Cook — 31.30

Buffalo defense — 9.00

Week 11:

De'Von Achane — 20.50

Miami defense — 6.00

Why can this work?

Because the game script can help both sides of the pairing.

If my defense forces turnovers, creates short fields and helps its team build a lead, the offense may become more run-heavy.

If my running back benefits from positive game script and scores touchdowns while the opposing offense is forced into predictable passing situations, the defense gets more opportunities for sacks and turnovers.

Again, I am trying to find players whose paths to success are not entirely independent.

The Players Who Kept Appearing

One of the most useful things I found was how frequently some players appeared on winning teams.

At running back:

At wide receiver:

That is useful because it pushes against another DFS instinct:

"He already had his big game. I missed it."

Maybe.

But sometimes the information that made him a good play has not disappeared.

Do Not Be Afraid of a Player Who Just Won Someone a Million Dollars

Lower-tier players sometimes remain undervalued for more than one week.

Their role changes faster than their salary.

Their volume changes faster than the market adjusts.

Or the underlying matchup remains favorable.

Last season I saw this with players such as:

Bucky Irving

Jonathan Taylor

Chuba Hubbard

and

Pat Freiermuth

That makes me interested in what I think of as two-game winning windows.

If a player just smashed, I do not automatically cross him off because the field saw it too.

I ask:

What actually changed?

Did his snap share increase?

Did his touches increase?

Did an injury open a larger role?

Did the offense start using him differently?

Is the salary still behind the information?

Because sometimes last week's winning play is still this week's winning play.

You Cannot Win Without Impact Scores

This may be the most important construction lesson in the entire dataset.

To get above 200 fantasy points, I generally need multiple players producing genuinely elite scores.

A lineup of nine players scoring 20 points sounds beautiful.

It also almost never happens.

The back end of the player pool makes that construction unrealistic.

So I am not really trying to build nine good scores.

I am trying to find impact scores.

Thirty.

Thirty-five.

Forty.

Fifty.

Last season's winners repeatedly had them.

Josh Allen at 54.88.

Ja'Marr Chase at 44.30.

Puka Nacua at 44.80.

Amon-Ra at 41.70 and 41.30.

JSN at 40.00.

Sean Tucker at 37.20.

Goff at 44.06.

Those are the performances that fundamentally alter the leaderboard.

If I want 235 points, I probably need at least three players capable of giving me something in that neighborhood.

That changes the way I view safe plays.

A player projected for 16 points who almost never gets beyond 22 may be perfectly useful in cash games.

He may also be exactly the wrong player for a tournament where first place requires several extreme outcomes.

Paying for a Stud Is Not Automatically Bad Value

There is another DFS cliché I think deserves more scrutiny:

"You need 4x or 5x from every player."

Not necessarily.

Suppose an expensive running back scores 31 points.

Maybe he only gives me 3.3x salary.

But if 31 is the highest running back score on the slate and the next realistic option scored 19, I just purchased a 12-point positional advantage.

That can be more valuable than finding 6x from a cheap player whose score is easily replaced.

The lineup is a portfolio.

I care about the relationship between all nine scores, not whether every individual player passes the same value formula.

Where I Actually Want to Be Contrarian

I do not want to fade the highest projected players simply because they are popular.

I want to understand where popularity exceeds the certainty of the projection.

Those are different things.

If a $5,000 player is going to be heavily owned, perhaps there is another option at $4,800 or $5,200 with a similar projection and a much lower roster percentage.

That is interesting.

But I do not want to sacrifice 10 projected points simply to brag that my player was 3% owned.

The objective is not uniqueness.

The objective is useful uniqueness.

That is something the Battle Royale data taught me too.

Being different everywhere is not automatically an advantage.

I want to choose where my lineup disagrees with the market.

What I Think the Million-Dollar Lineups Are Actually Telling Me

If I reduce all of this to the patterns I want to carry into the next season, my list is surprisingly short.

I want to build toward roughly 235 points.

I want several players with legitimate 30-plus-point ceilings.

I want quarterback correlation whenever the game environment supports it.

I am willing to use the opposing offense as part of that correlation.

I want to identify cheap quarterbacks who allow me to buy elite scoring elsewhere.

I want to exploit players whose roles improved faster than their salaries.

I do not automatically abandon a player just because he smashed the week before.

I want to consider running back/defense correlation when the game script makes sense.

And I want to concentrate my lineup around a small number of football outcomes rather than asking nine unrelated predictions to all hit independently.

That last point may be the most important.

The best Millionaire Maker lineups did not simply contain nine good players.

They often told a story.

Detroit's passing game explodes.

Buffalo and Los Angeles shoot out.

Miami scores enough for Tua, Waddle and Achane.

Tampa Bay's offense carries the slate.

If that story comes true, several parts of my lineup become right at the same time.

That is leverage I can actually understand.

The Honest Part

There is one limitation I do not want to hide.

I am looking at the winners.

That is survivorship data.

I can tell you what the million-dollar lineups looked like.

I cannot tell you from the winners alone how profitable each strategy was across every lineup entered.

To do that properly, I would want the complete field.

Ownership.

Salary combinations.

Duplication.

Stack frequency.

Player exposures.

How often each archetype was entered.

How often it reached the top 1%.

How much expected value each construction generated.

Without that denominator, I am describing patterns rather than proving causality.

But the patterns are still useful.

Because when I look at the people who actually got the million dollars, I see the same concepts showing up repeatedly:

Impact scores.

Correlation.

Game environments.

Value at quarterback.

Salary allocation.

Players whose role was bigger than their price.

And sometimes, simply having the conviction to return to a good player after everyone else assumed the opportunity had passed.

That is what I am going to study heading into the next NFL season.

Because if the question is:

How do I win a million dollars?

The most logical place for me to start is with the people who already did it.