So you may not know this about me but I was a finalist for head of analytics roles for three major sports franchises in two different leagues.

Why so surprising? I list zero sports analytics experience on my resume (tho technically not true–as a select few know)

Why did I get the opps? Good plans and adaptability.

Why did I lose? 1) Midwest degree and didn’t work in big tech; 2) Knew the sport too well. (What I was told, btw)

PS-The third was a much better candidate IMO.

Let’s just say things have worked out–so no sympathy needed.

Barring some major life changes, this isn’t going to be happening so I thought I’d drop some notes.

#1: Organizations might be worth a billion dollars, but they are not big tech. They are not infused with the same resources that are going into data collection and analysis. Some are great, but none at that level. It’s more like various stages of Fortune 500s.

#2: In the first month you will uncover insights so important to share but they won’t be ready. It’ll take a year before you can guide them to ask that question you already have the answer to. So stay patient.

#3: Don’t get too caught up in the precision of your models or tools. Fractions of a percent don’t matter. A human just can't act on 0.2%. PS: If 0.2% matters you need to share your standard error.

#4: If you could draw a curve of how the organization will adopt analytics its going to be negatively skewed (even more than my example). Get Ws. Focus on those early adopters.

#5: You are going to have to repeat yourself. You are going to have to repeat yourself.

You… get my point. Teach them. Get buy-in. And lead people to the next question.

#6: Remember everyone cares a lot. They want to know you care too.

#7: Pick topics with constraints. Why are 4th down models so hot with some teams? Constraints to a single down.

#8: Set communication rules. Know the rules for engagement. The last thing you want to do is step on toes when you are making progress.

#9: Work those relationships all the time. Ask questions. Seek to understand. Get in the film room and just listen.

#10: Don’t complicate things by choosing washy metrics. Pick metrics that are easy for your audience to understand.

#11: Never, ever say that fitting a model is like watching every single play that went into the data.

The model does account for all plays and lots of context but it's not the same.

Your goal is to gain trust, not showboat.

#12: If you use a complicated metric, get into the weeds and pull the film on the specific plays. Hypothetical: Down 5, 4th and 3 and 52 to GL with 5 minutes left. Go pull the plays.

Yes, it’s not comprehensive but at least it looks like diligence.

#13: It’s really easy to create ad-hoc models that do some of the job.

The real challenge is in scaling the model and keeping it in a production state. Production means no loose ends and lots of time.

#14: Remember that the stats inform tactics, not technique. They help guide human decision making.

#15: The products you are creating are not static–they will constantly need to be tuned. Stay data-agile.

#16: Value yourself appropriately. The business is built on low-paid analyst roles because everyone wants in. Know your worth and hold to it.

Very little of this was about analytics. Because like every other business there are lots of great, dedicated people who can do the work. The hard part is navigating relationships–which some call politics. It’s not politics, it’s just about establishing trust–which takes time

Here’s the (not so) funny thing. All of this is the same across almost every industry and organization. Swap some scenarios and it’s basically the same.

This list is by no means exhaustive–but it’s a good starting point for something.

Now that this exaltation has come to a close hopefully it is of good use.