How to Benefit from Randomness
About a year ago, I was chatting with Cal Spears about GrindersU and he asked why I play daily fantasy football and baseball, but not basketball. I’ve since started dabbling in NBA, but I’m just testing things out and it’s certainly not to the degree that I play the other sports.
My answer to him was that football and baseball are far more random than basketball on a nightly basis. It might sound pretty freaking dumb to purposely seek out randomness. We’re trying to make money predicting outcomes better than other players on DraftKings, so wouldn’t we want it to be as predictable as possible?
No, at least not in many situations.
Here’s the thing: almost all events that transpire in a fantasy sports league are zero-sum. You win, I lose. We don’t actually want to seek out the most predictability at all costs, but rather the greatest potential advantage over our competitors. When something is relatively easy to predict, such as NBA fantasy scoring, it becomes harder to create an advantage over others.
I’m not saying NBA isn’t beatable, because lots of players much smarter than myself are crushing it, but when something is more challenging to predict due to greater short-term volatility, such as MLB results from night to night, there’s potentially a larger advantage there.
How Randomness is Paradoxically Predictable
The idea here is that randomness is predictable. Naturally the very definition of ‘randomness’ makes that statement a bit paradoxical, but I’m talking more about the accrual of many random events. A baseball player’s batting average, for example, is highly volatile from day to day; it’s going to be next to impossible to predict how a player hits on a nightly basis. He could go 0-for-4 for 4-for-5; we just don’t know.
Over the course of an entire season, though, that player’s batting average – the result of the accumulation of many individual events (at-bats) that are filled with randomness – will come very close to stabilizing near his long-term average. Maybe he’ll hit .310 or maybe he’ll hit .290, but he’s not going to hit .500. At-bats aren’t random, but when dealing with the MLB player universe (as opposed to a professional vs. me or you), the individual performances are mostly random with bits of skill sprinkled in; the difference between a Hall-of-Fame player and a lifetime Triple-A player in terms of getting a hit is maybe four percentage points.
So we’re basically just dealing with a coin-flipping situation, except the coin is messed up and lands on heads roughly 30 percent of the time. Bad coin flippers can get heads 25 percent of the time and great ones 33 percent of the time. To determine the difference between good and bad coin flippers, we need a pretty high number of coin flips, right? Same for at-bats, same for passing efficiency, same for red zone touchdown percentage, etc.
How Randomness Is Exploitable
The primary way to exploit randomness is to understand that outlying performances are eventually going to regress toward the mean. If I flip heads 9 out of 10 times, you can bet your ass I’m going to see a lower rate of heads over my next 10 flips.
In that way, you can start to see how randomness can be very predictable. If we know the baseline, we know 1) where the randomness will eventually take us and 2) how quickly we are to get there. Compare coin-flipping to a highly skilled endeavor. Instead of understanding a generic baseline rate we can apply to everyone, we have to try to figure out each person’s lifetime average. So if coin-flipping were actually a skill, it would become more difficult to predict each flipper because we would need a large sample of coin flips to determine how skilled he might be.
Let’s take a football example. A highly touted rookie running back averages 3.8 YPC in his first season, dropping him in fantasy drafts. His team adds two big upgrades along the line, though, and we can now get the running back at a much cheaper price than we could when he was a rookie getting a ton of press. This is a situation in which we should be bullish on the running back because research suggests YPC is 1) extremely dependent on offensive line play and 2) very random. It fluctuates a lot from year to year, so we know it’s likely to regress toward the league mean of 4.2 YPC (or above it), especially since we’ve deemed the back a talented one.
So what we’re doing is searching for randomness, looking for outliers in either direction, and then either targeting/fading those outliers based on which direction they’re likely to regress.
One of the interesting things about football is that, unlike other major sports, it’s pretty random not only on the individual game level, but also the season level. With only 16 games, we often see talented players fail to live up to expectations and bums like Tim Hightower tear it up over the course of an entire year.
The second reason that randomness is exploitable is because fantasy football is a marketplace. It’s a game of competing minds, so you can capitalize off of the mistakes of others. Most owners – most people in general – are extremely susceptible to getting fooled by randomness and mistaking it for a signal.
Basically, they’re betting on coin flips as if each individual coin-flipper’s past coin flips were the result of his own skill and not chance. Fantasy football results aren’t completely random, of course, but they’re sure a lot more random than most want to believe. Most will act as though that running back with 4.65 speed who averaged 4.9 YPC in his rookie year will keep up that pace, or that the kicker who connected on 95 percent of his field goals will continue his accuracy, or that the small tight end who scored on 50 percent of his red zone targets will keep killing it to that extent in the red zone.
Sometimes people ask why I analyze big-picture stuff like red zone touchdown rate by size as opposed to looking at each individual player’s past success, and this is one of the major reasons; if you can figure out the overarching baseline rate at which we should expect certain stats from particular players, you can put yourself in the best position to 1) identify randomness, 2) (paradoxically) predict how “random” events will unfold, and 3) create the biggest potential edge over the field.