The vast majority of scientific research has indicated that people with growth mindsets tend to be higher-achieving than those with fixed mindsets. Growth-oriented people are the ones who are more interested in furthering their education, or picking up a new hobby, or getting out of their comfort zones to experience new things.
Yesterday, I had a nice chat with a member of the hockey media whom I respect a great deal for his habit of “seeking truth from facts” despite often sitting on panels where his co-hosts did not share the same attitude. He reached out to me with questions regarding something I previously published on Hockey-Graphs, and we spent about 20 minutes exchanging information – something I enjoy doing anytime with people who like to think the game.
At one point, his line of questioning turned to the specifics of the work I was doing, some of which I wasn’t really keen on discussing. So I told him:
“Look, what I do for our staff is pretty simple. I do things either to save time, or to reduce guesswork. That’s all there is to it.”
Earlier this week, I read an interesting story by Frank Seravalli on TSN.ca on Mike Babcock, Phil Kessel and the Toronto Maple Leafs, which is a good read for anything interested in how coaches think. For me, it also illustrate another way analytics could be employed to make a coach’s life a lot easier and take out some of the guesswork inherent in the job.
It was not too surprising to hear that Babcock was already thinking about how to get the most out of his team this season while on vacation, but I am very curious about his thought process behind how best to replace Phil Kessel. But before we start thinking about how to replace Phil Kessel (or his production in aggregate), we need to start thinking about how we are to measure a Phil Kessel’s offensive contribution.
As I alluded to in my previous post, the choice of words is very important when selling ideas to a coaching staff. Semantics lets us see the same idea from different angles, and can be a very powerful way to alter our understanding of a subject matter.
Recently, I’ve began to refer to hockey analytics tools (possession metrics, Player Usage Charts, HERO Charts, dCorsi, etc.) as technology, which has allowed me to relate better with those less well-versed on the matter and have all sorts of interesting discussions with people who otherwise wouldn’t give advanced stats the time of day.
Recently, I’ve received some unsolicited emails from some very smart young people about working in analytics for a hockey team. There are definitely people more qualified that they could’ve tracked down, but most of them are not allowed to talk about their jobs, so I guess they were stuck with me.
It felt a little bit strange corresponding with these mathematics or engineering students, because theoretically, their backgrounds are a lot more suited to this line of work than mine (I graduated in Marketing). I apparently passed Calculus II 10 years ago (I barely remember taking it). I’m a mediocre programmer. And I don’t even work in the NHL.
But there are still a few thoughts I could share.