This is the 141st installment of The Labyrinthian, a series dedicated to exploring random fields of knowledge in order to give you unordinary theoretical, philosophical, strategic, and/or often rambling guidance on daily fantasy sports. Consult the introductory piece to the series for further explanation.

I’m from Texas, born and raised. Although my parents are originally from California, they’ve lived in Texas for, I don’t know, 40-ish years? Easily over half their lives. For people who aren’t native to the state, they’re about as Texan as two people can be. My mom somehow has a southern accent and irrationally hates Tony Romo. My dad talks about how great the state’s laws are. They’d be hilarious characters if they weren’t real people.

After graduating from Texas Christian University, I moved northeast to enroll in a Ph.D. program in English at Boston College. Ever since then, I’ve lived in Massachusetts, Washington, New Hampshire, Colorado, and Iowa — but not Texas. I’m not actively avoiding the state, but I’m unlikely to move back there, and I’ve been gone for so long that now it isn’t really home. It’s just where my parents live.

Visiting Texas is weird for me. I’m always happy to go there . . . and happier to leave. I feel unsettled there, especially in my childhood room, which is full of stuff that ostensibly is mine but doesn’t feel as if it belongs to me. Being in that room is like looking into a mirror and seeing a stranger who happens to look like my younger self. It’s uncanny, like living with the ghosts of long-gone lives.

Anyway, I was in Texas this past weekend. I arrived at the DFW International Airport on Thursday and departed on Monday. The plane for Iowa was leaving from gate D16, where there’s a musical labyrinth:

This interactive exhibit (called Circling) was created by Christopher Janney, an artist with degrees from Princeton and the Massachusetts Institute of Technology. Per Janney (via the DFW Airport website):

My thought was to create a game of some sort; a soothing quiet, contemplative game. I then considered labyrinths and their historical significance; they are used as walking for exercise, walking to rest, as in a calming meditation effect.

I was in a contemplative mood while waiting at my gate, so I walked the labyrinth and let my mind wander.

Here’s some of what I considered.

“Solvitur Ambulando”

Right now I’m making my way through about six books, give or take three. One of them is a random text called Cave Canem, a miscellany of Latin words and phrases. Like How the Wise Decide, it was stolen acquired cheaply at Barnes and Noble. This book has almost no utility whatsoever, but whenever I don’t feel like reading something on finance, fantasy sports, career success, ancient religion, mental fortitude, and adolescent wizards and witches I suppose this book suffices.

While in the labyrinth I thought of a particular phrase from the book (or one of the other Latin miscellanies I own): Solvitur ambulando (“It is solved by walking”). If you wanted to translate some of that phrase as “with walking” or “through walking,” I wouldn’t argue.

Let’s think about the implications of this phrase.

  1. If you have a problem, go for a walk, clear your head, and think of something else, and — as a wand with a wizard — the solution will present itself to you.
  2. There’s a beneficial correlation between exercise and mental capability.
  3. However long it takes for you to take a walk is the amount of time in which you should be able to come up with a solution to a problem.

No. 1 and No. 2 seem straightforward, especially No. 2, as FantasyLabs is run by basically a world-class athlete:

No. 3, however, deserves more attention, as it’s the least obvious and probably furthest-reaching implication.

Simplicity is Sophistication

People tend to think of simplicity as the antithesis of sophistication. If something is simple it’s often considered lowbrow. Perception, though, isn’t the same as reality. If something is simple that could mean it has been regularly refined till everything extraneous has been stripped away, leaving nothing but a product of pure efficiency: An object without friction.

Simplicity isn’t an insult. Simplicity is what you make of it.

For the Romans, solutions to problems were often simple. If a problem was complicated enough that a walk was needed to sort it out, the solution to that problem was nevertheless probably simple, having come in the course of the walk. When was the last time that you had a problem, went for a walk, and came up with a complicated yet effective solution?

Of course, this analysis is coming from someone who within his first month at Labs came up with something called the Simpleton’s NBA Player Model. In fact . . .

The Simpleton’s Model, Revisited

“Excuse me. Hi. Do you mind if I borrow that soap box? Thanks.”

Listen to me, all y’all. When you research with the Labs Tools, you’re not looking to construct the most complicated Player Models of all time. You of course should experiment widely and freely with the Models, but the point of experimentation is not to find a complicated solution to a hard problem but to understand the intricate dynamics of a system so a simple solution can emerge. To everything there is a season and all the rest of that Ecclesiastical mantra — if you look at the Model Previews on our Premium Content Portal, you’ll see that different factors warrant drastically different weights depending on the type of contests for which models are created — but basically the best models tend to weight heavily the factors proven to matter (in terms of Plus/Minus and ownership, which Pro subscribers can review in the DFS Ownership Dashboard).

Similarly, when trying to build dominant Labs trends with our Trends tool, you shouldn’t overfit and focus on noisy factors. You should use the factors most likely to produce representative matching cohorts. There are sport-to-sport differences, but Vegas data, Bargain Ratings, and a few other factors are almost always likely to matter regardless of whether you’re looking at NFL, NBA, or MLB.

I created the Simpleton’s Model not because I wanted to generate lots of lineups with it (via the Lineup Builder) but because I wanted to illustrate the sophistication of simplicity. The model weights only three factors:

  • Projected Plus/Minus (73 percent)
  • Projected Points Per Dollars (25 percent)
  • Bargain Rating (two percent)

Why did I choose to focus on these three factors? By experimenting with the models, I found that (given the minute-driven steadiness of basketball as well as its last-minute lineup-altering news cycle) the Labs player projections (which are constantly updated) are incredibly correlated with DFS success. I also found that, while our Plus/Minus metric is better than the standard pt/$ stat, the latter shouldn’t be ignored. And, finally, I gave Bargain Rating some weight because, all else equal, I’d rather roster players where they offer more value.

Bottom line: When you build models, focus on important data. Keep it simple.


If you’re thinking of a problem that can’t be solved during the course of a walk, you’re either focusing on the wrong problem or you’re not enough of a (DFS) contrarian.

Or you need to exercise more so you can take longer walks.


The Labyrinthian: 2017.46, 141

Previous installments can be accessed via my author page or the series archive.