It sounds like it's going to be very effective at identifying outliers. This goes back in all scientific studies. It's always the law of the bell-shaped curve. You're looking for the mean, but there are people who are on the far edges of those bell-shaped curves, and if they read that study and follow its advice, it's actually not going to help them very much. It seems like this would be very good at saying, hey, you're not responding the way you should be or the way most people are. There's something different about you. I could potentially start identifying what actually works for them. It's that you're Canadian, but that's always

it. Yeah, I think that's the advantage. You think about what happens in a study, and you think about the type of results that you see, and you see a scatter plot, and you see a straight line that goes through the scatter plot. If you're on that straight line, that's great. With these more complex models, it doesn't have to be a straight line. It can be a wavy line dependent on where you are on different features. In that wavy line can come very, very close to your particular specifics as an athlete, and that's where these things really shine. I think it's super important, obviously, because we are quite different as individuals. We have different muscle fiber types. We have different anatomies, different height, different weight. There's all of these things that come into play in determining what the optimal action for an athlete is. I think there's huge advantage in individualizing things that come from this approach.

Moving on, I know nothing about programming Trevor. Do you? You're a computer guy. It used to be. You have a video game system at your house. It's closer to computer programming than I have. How does somebody, I don't even know what language is this? Is this C++? I'll pull that one out of thin

air. The easiest way to get involved in this is definitely through Python. Python is the machine learning language. Dijer of the day, if you are interested in machine learning, then you're definitely going to find it easier with Python. Just because there's a whole lot of libraries that have already been written around machine learning, it really is dead easy. In 20 lines of code, you can build a model if you're using Python. Just because a lot of smart people have already written all the hard code and you just have to import it and plug and play. Definitely recommend getting started with Python. Once you pass the basic levels of learning how to structure code within Python and those basic things, it's not a big leap to start playing with machine learning. As I said, you put a line of code in saying what you want the layer of the network, how many nodes you want it to have, and you put a layer in saying what the output looks like. It really does the rest and it's really then just a matter of trying different things and fiddling and saying what scores well and what doesn't score well. It's kind of fun.

So what you're saying is I should just watch a YouTube video.

I didn't take that route.

I bought some books and things but I'm sure in this day

and I just probably some pretty good ones out there.