When I woke up this morning, I saw that Dak Prescott, the star quarterback for the Dallas Cowboys, signed a new contract, making him the highest-paid player in NFL history. His new four-year, $240 million deal makes him the league’s first $60 million per year player.
If you are curious, here is a list of the highest-paid NFL players in 2024.
Each of the 32 teams has an active roster of 53 players. That is 1,696 active roster players. Add the practice squad and account for injuries, and in a typical season, you end up with about 2,100 players per season.
Now that the NFL Football season is officially underway, I thought it would be interesting to look at each position’s composite player.
As you might expect, different sports have a different ratio of ethnicities, builds, and features. The same is true for different positions on a football team. For example, you might expect more Pacific Islanders in Rugby or Asians in Badminton. You expect NBA players to be taller, swimmers to have longer arms, and football players to have more muscle.
Here is a visualization that shows what happens when you average the top players’ faces in various positions.
Composites are interesting.
While you may be thinking, “This player must be unstoppable,”... statistically, he’s average.
The “composite” NFL player would be the 848th-best player in the league. He’s not a starter, and he plays on an average team.
We found the same thing with our trading bots. The ones that made it through most filters weren’t star performers. They were the average bots that did enough not to fail (but failed to make the list as top performers in any of the categories). The survivors were generalists, not specialists.
In reality, you need both.
In an ideal world with no roster limits, you’d want the perfect lineup for each granular situation. You’d want to evaluate players on how they perform under pressure, on different downs, against other players, and with various schemes.
On a related but slightly different note, I recently read a post called “Why Generalists Own the Future.” It says that, in the age of AI, it’s better to know a little about a lot than a lot about a little. But part of that rationale is that it is easy to find or create digital specialists to do the things people used to do.
That’s what technology lets you do with algorithms. You can have a library of systems that communicate with each other ... and you don’t even have to pay their salary (but you will need data scientists, researchers, machines, data, alternative data, electricity, disaster recovery, and a testing platform).
You won’t find exceptional specialists if your focus is on generalized safety. Generalists are great, but you also have to be able to respond to specific conditions.
