The 10th Annual Sloan Sports Analytics Conference recently wrapped up, and it couldn’t have come at a more interesting time of convergence of new analytics findings and proliferation of analysis tools. The panels with the typical star power provided the usual slate of memorable quotes, but for me, the most interesting part was picking up on the trends that are emerging across different sports and organizations, and how analytics is an increasingly large umbrella of services. Here are some of my favorite observations I took away:

· Cloud computing is starting to skyrocket among even the smallest of analytics groups. Nearly every analyst I talked to mentioned either Amazon Web Services or Google Cloud Platform as their go-to resource for larger-scale database and computing power. This is a pretty sizable departure from older setups, where database management and IT procurement was someone’s full-time job and capital resources were required to build up an organization’s data capabilities. Now, analysts need only the most cursory training to spin up as much storage and computing power as they need, which will be a lot as tracking data continues to make its way into every sport. With procurement no longer an issue, the analysts and data scientists have become even more of the focal point of analytics groups since they can effectively provide their own infrastructure. …

Models are all the rage right now, and why wouldn’t they be? Their applications and capabilities seem to double every year, the toolboxes keep growing, and you can’t throw a rock without hitting 10 articles written about their use cases every week. It’s inevitable, then, that clients will start to ask for more and more models, since they’re hearing all about them. Along with that comes a tendency to ask for the model first, and a deeper examination of the business question they’re trying to answer later, when it should be the other way around. …

I moved into the utility space after several years in the field as an energy engineer. The most rewarding part of having been in both worlds is trying to do everything I was doing before, except for 5 million customers at once. Ten years ago, that would have been unthinkable, but thanks to some timely investments in data capabilities, a single analyst can get more information than they might know what to do with.

It has been really great seeing utilities have such a high level of interest in building out their data warehousing capabilities over the last couple of years. The level of commitment to doing the smart stuff has never been higher: collect and store all of our smart meter data, de-silo all of our databases, and put some cloud services and analytics on top of all of it. By now, most places have a good idea of what they should be doing, but the biggest hurdle I’ve seen to committing to boosting their data capabilities has been a shortage of concrete use cases for all of these upgrades. I understand the hesitation: new data warehouses aren’t cheap, and if department heads are going to request funding to pay for these upgrades, they need to have some expectation of what their ROI will be. …

When I interviewed at Slalom, I talked about my work experience for maybe 10% of the time and my hobbies 90% of the time. This wasn’t because Slalom was that interested in my interests outside of data science, but because my hobbies are what got me started in data science in the first place: sports analytics. Like most people that get into sports analytics, I was drawn into the field because it was a way for the analytically-minded to better understand the games they were already watching. Sports analytics are a way to explore the same questions fans and coaches alike are asking: who do I think will win, and why? Who are the best players in the world right now? …