Nikola Jokić, the Human Outlier: How One Big Man Broke the Inverted Pick-Pair Chart

“This above all: To thine own self be true.”
– William Shakespeare, Hamlet

Nikola Jokić: The Statistical Outlier Hiding in Plain Sight

I spend most of my professional life knee-deep in charts, scatter plots, and spreadsheets. If you give me a pile of marketing analytics or lead-generation funnel data, I’ll be looking for (and hopefully finding) the patterns — the anomalies that tell you what’s working, what’s broken, and what’s hiding behind the noise. So when I take time for my guilty pleasure, writing, I tend to TRY to avoid datasets and patterns, and leave that to badasses like Ryan Blackburn and Andy Bailey.

I say all that. I do try to avoid the data-geek in my writing. Until Adam Mares (and Co.) messes it all up for me…

When I spied this lovely and trending chart from ALLCITY_NBA showing the most efficient inverted pick-and-roll combinations in the NBA this past season — ball handling big on the left, screening wing on the right — my brain went into work mode, though their graphic calls it out pretty readily.

On a quick glance, it’s just a pretty little cluster of data points. Zoom in, and it’s like any other scatter plot: mostly evenly distributed, one or two random outliers doing their own thing, and one giant… cluster? You probably see that one giant Serbian cluster, just glancing at the graphic above. A face that seems to be set on repeat: Nikola Jokić.

Jokić’s name popped up again. And again.
And again. Six times out of ten, again.
Even in a small data set, you realize Joker is not one dot in the cloud — he’s practically his own constellation. He isn’t just a player on a chart. He is the chart. The rest of the league is just scattered data points around him.

This is the point where my inner analyst and my inner basketball fan started high-fiving.
In my line of work, when you see a dataset where one something keeps showing up over and over, you don’t call it coincidence. You call it statistical significance.
In basketball terms, you call it being a demigod. The demigod of the inverted pick-and-roll.

Inverted Pick-and-Roll 101

For those not marinated in basketball terminology, a “pick-and-roll” is when a big guy sets a screen for a smaller, quicker ball handler. They force the defender into a bad decision:

• Go over the screen and risk getting burned to the hoop
• Go under the screen and risk giving up an open jumper

The inverted pick-and-roll flips that on its head, and makes it even tougher to defend. The ball handler is the big — in this case, Jokić — and the screener is the smaller player.
Why? Because if you switch that play, you’ve just handed the 6’11”, 284-pound, passing savant the basketball against a point guard who’s 8 inches shorter and 60 pounds lighter. No switch? No problem. Joker and Jamal, or MPJ, or Peyton, or Ruus, or Christian, or Julian made opponents pay and pay for the play, no matter what decision they made. But the constant? The Serbian mastermind doling out his own masterclass. Over and over. It’s all in the data.

It’s like challenging a chess grandmaster to a match and then starting on your side with only three pawns.

How the Switch Creates Two Easy Reads

The inverted pick-and-roll gives the offense two painfully simple decisions:

1. Mismatch in the Post – Switch the screen and Jokić gets the ball near the paint against a helpless smaller defender.
2. Wide-Open Shooter – Help down on Jokić, and he’ll pass over the top to an open shooter, often one of Denver’s many three-point snipers.

Either way, you’re cooked like burek.

When a Pattern Becomes Proof

Now, back to the chart.
Every other inverted ball-handler/screener pair shows up once. There isn’t another name on the list, big or wing, that shows up twice.

Jokić? The man is everywhere.

It’s like looking at a customer-satisfaction report where one product gets five-star reviews from five different market segments, while everything else is hovering around an ambivalent three stars. At that point, it’s not luck — it’s the product. Or in this case, the player.

Data, but Make It Basketball

This is why analytics people tend to love sports, and why sports people (often, and quietly) love analytics. The data confirms what your eyes already told you. Jokić’s fingerprints are all over the Nuggets’ most efficient plays because the offense is built on his Swiss-army-knife skillset:

• He can post up like a (the) Dream.
• He can shoot the damned lights out.
• He can pass like a point guard from foot further up.
• He makes the right read faster than you can say, “Wha?”.

The numbers don’t lie, but in this case, they also don’t surprise.
They just give you the receipts.

Why This Matters

For Denver fans, it’s validation.
For opponents, it’s a warning.
For closet analysts like me, it’s a reminder that patterns — whether they’re in a marketing funnel or an NBA offensive scheme — are worth noticing when they repeat this often.

Data and charts don’t care about hype or narrative. They just show you where the magic is and isn’t happening. And right now, the magic is happening wherever — and whenever — Nikola Jokić touches the basketball. You don’t like it? Don’t argue with me, go fight the MATH.