31 Jul Understanding the Complicated Relationship Between Coaches and Analysts
The relationship between coaches and analysts is a complicated one. It also happens to be one of the most important relationships in an organization that is focused on using data to improve their performance and take their game to the next level. To help make sense of it, there are two things you need to understand: what’s normally broken in these relationships and what a healthy relationship looks like.
The Typical (Broken) Relationship
There are two big red flags that you see in the typical relationship between coaches and their analysts. If you’re finding yourself running into these, it’s time to take a step back and reevaluate the relationship.
Red Flag #1: Mismatched Expectations
When two people approach a problem, they can agree on the high-level goals and still be entirely misaligned on what they’re actually working towards. Of course, both the analysts and the coaches want to have the team perform better. But what does that really mean? Do the coach and the analyst both agree on the best way to improve the organization’s performance?
Unfortunately, all too often both the coach and the analyst don’t know the expectations of the other. This can cause quite a bit of tension since the analyst tries to give the coach something they don’t want, while the coach is expecting something from the analyst that they never put into words.
The root of this issue is that analysts and coaches are working from two different viewpoints. Analysts have an immense amount of context from living in the data, while coaches have context on what the organization as a whole needs. This leads to a world where the analyst just guesses what the coach wants, while the coach is frustrated with what they’re getting without being able to articulate why.
In a healthy relationship, what you’d be looking for is an analyst that can explain in detail what they’re planning on delivering. Then, at the same time, the coach would be able to give clear and detailed directions to the analyst on what they’re looking to get out of the analysis. The right way to do this isn’t to just tell each other what you’re planning and walking away, but instead to work together side-by-side in an iterative way. When this happens, you have a dynamic where the coach is invested in the analysis, while the analyst is getting value from the coach’s insights.
Red Flag #2: A Breakdown in Communication
The second big red flag is when there is a breakdown in communication. Oftentimes this is the result of the first red flag never being resolved, but it can occasionally be the end of a long road of mistakes, a lack of confidence in the analyst’s abilities, and an organization without a clear direction.
However, beyond these issues, the underlying issue is a lack of understanding of how the relationship between data analysts and coaches is set up. Oftentimes, organizations hire one data analyst to interpret the data being collected, with regular meetings set up to share their insights with the coach and management. This is a problematic setup since it relies as much on the interpersonal relationship between these two individuals as it does on their professional abilities. If there is a more mature data analysis operation in place, any interpersonal issues can be resolved without becoming structural data analysis bottlenecks.
This need for excellent communication was explained perfectly by Brian Colangelo, the former General Manager of the Toronto Raptors, when he said, “The single greatest thing I learned is that, as great as these new tools are, it can be very productive for you, but it can be equally destructive if it’s not communicated or messaged in the right way.”
What a Good Relationship Looks Like
Much like in an unhealthy relationship, a good relationship between coaches and data analysts has a few signals that you should be able to see. If you don’t have these in your relationship with your data analyst, it’s worth considering what changes need to be made.A good relationship between coaches & analysts has a few signals you should be able to see. Click To Tweet
Confidence in Measurement
The first thing you should notice is that you have complete confidence in what’s being tracked. This doesn’t just mean that you know things are being tracked, but that you know exactly what’s being measured, how it’s going to be used in the eventual analysis, and why it’s being tracked in the first place.
Going beyond just what’s being measured, you should also have complete visibility into what’s going on. That typically means dashboards that are a few clicks away, easily digestible charts and tables, and access from wherever you are. This relies on software being built that pulls together the key components to great measuring: the data being tracked and the questions being answered by that data.
Real World Meaning
The second big aspect of a healthy relationship between coaches and analysts is when the relationship goes beyond just plain numbers. Great analysis isn’t a long list of variables with the formulas used to analyze them alongside the data. Instead, great analysis is when an extensive data set is translated into real-world terms, in English. Technical jargon should be completely avoided unless it’s being discussed between analysts.
This gets to an underlying truth of what good data analysis is and why we all spend so much time digesting data. It’s because data isn’t valuable in and of itself. The millions of points of data that we all have access to are worthless without the right interpretation, without the right hypotheses, and without an experienced guiding hand transforming it into something that everyone can understand. When that happens, that interpreted data goes from something worthless to something priceless. But that value only accrues when real world meaning is layered on top.
Value For Everyone
The last signal that there’s a healthy relationship between analysts and coaches is that the entire organization can see it. When communication is clear and continuous, when the data is being interpreted using real world language, and when expectations are clear to both sides, things are dramatically different. Coaches perform better, analysts feel like they’re making a real difference, and the organization as a whole has a clear path towards improvement.
At the end of the day, both analysts and coaches have the same goal: creating athlete development programs that are world class. They want an athlete to walk in one door and be safely in the hands of a committed and passionate team. While that’s the common goal, reaching it requires athletes and coaches that work to deeply understand each other’s roles and what each side brings to the table. With that deep understanding then comes the ability to unlock the true potential of data and by extension, the true value of athletes.