As someone who’s worked for over 15-years at the intersection of athletics and data, the fusion of these domains are both my wildest dreams and my most extreme challenges. Here's a few thoughts.
Organizations around the globe recognize the emergence of data and its pivotal role in shaping data-driven decision making across all levels. The value proposition is well understood despite the challenges operationalizing data.
So what are the MANY challenges to actualizing and operationalizing data within sporting organizations?
Here’s a hand selected few:
- Organizational change is slow and doesn’t happen over night. As a rule of thumb, the larger the organization, the slower the change. Think of Steve Jobs’ quote “It’s more fun to be a pirate than to join the Navy.” Smaller vessels are more agile and quickly pivot.
2. Database centralization (and schema) is key. Your sample may not represent mine nor does your data structure match mine. Too often, data sources exist in silos, or worse, on individuals’ hard drives. Strong strategy around the data architecture is more important that many think.
3. Data literacy is (and has been) a valuable skillset. All the statistics and visualizations in the world still can be really confusing for key decision-makers.
Albert Einstein once said:
“Everyone is a genius. But if you judge a fish by its ability to climb a tree, it will live its whole life believing that it is stupid.”
Don’t conflate sport coaches for statisticians.
At the end of the day, Coaches coach. Quants compute. A handful can do both; but in the near distant future, data literacy will become just as important as your core discipline. Data literacy and sports are more complementary than oppose.
So where do coaches even begin to interface with the vast piles of numbers, statistics, and outputs various performance technologies export? What are the ways we can begin shaping a better data driven organization?
As my friend Eduardo Fiallos like to say, if we are compelled to become “data driven”, we should at least ensure the data has a driver’s license. That license is “context”.
Iterate. Fail-Fast. Learn. Re-Iterate.
In systems design (and start-ups) a fail-fast system is one which immediately reports conditions that lead to a failure. “That’s not a bug, it’s a feature” 🙃
Now think of athletics. Failures are RARELY reported (better yet, often concealed) which ultimately leads to slower learning and slower adaption.
Creating organizations that rally around data is an arduous task often marred with “over-promises” and “under-delivery”. The under-delivery aspect is short-sightedness to the long-term approach needed to factionalize data.
That’s my thoughts (for today) from operating in this hyper-niche space all of my professional career. We are standing at the metaphorical “Starting Line” of the sport data proliferation; and we have to choose the direction our organizational goes.
Evolve or Extinction 🦕
