in a mass of definitions, I had one question. How have any of these concepts been tested as to “falsification” in their usefulness in the NFL (or “truth” if you prefer)
if that is the point could someone explain to me what the process was and what did it reveal about any of those mass of “definitions”. If that is not the point, than would someone have some mercy (rarely found on bbi) and explain what am I to get from summer school. Thanks in advance
I’m not talking about the math, I’m talking about the linear, non linear, non parametric, what have you analysis that utilizes the math.
I mean, any optimization problem (which is any analytic tool), by definition, is trying to reduce some “error”/flaw. Mean squared error, etc. Even machine learning is optimized to reduce some frictional error/flaw term.
But we have to accept they are highly flawed and then we can have an honest discussion of how much can be learned, even with “big data”.
There are much more structured solutions.
Both are important and these problems can be solved. But ignoring especially building out an excellent data collection effort with hopefully human interaction and reinforcement learning you are putting itself at a disadvantage to those that have those systems and have had those systems. Because you can ramp up and simulate later but nothing beats people interacting with live outputs.
Every analytics method is highly flawed, which includes “intuition”, which is also a “nonlinear” analytics method.
That would be much better received than talking about “nonlinear”, or drones, or HIGGS-Boson particles.
A lot of people build models; there is absolutely nothing unique about that. Most people build shitty models, but doll it up in "nonlinear dynamics", or "stochastic processes", or some other term du jour.
People would be much better served understanding the intuition behind the models and explaining it in simple terms. But it requires a deeper understanding of all of these highly complex models and systems that a lot of people don't have. People can replicate this; understanding and being able to concisely convey that information without using any jargon should be the holy grail.
A lot of people build models; there is absolutely nothing unique about that. Most people build shitty models, but doll it up in "nonlinear dynamics", or "stochastic processes", or some other term du jour.
People would be much better served understanding the intuition behind the models and explaining it in simple terms. But it requires a deeper understanding of all of these highly complex models and systems that a lot of people don't have. People can replicate this; understanding and being able to concisely convey that information without using any jargon should be the holy grail.
That's a great post. Doing this also bridges the perception gap of oafy football people vs. brainiacs with fancy data.
It would avoid the pedantic arguments about 4 computer guys and shunning technology. The best speaker I've ever heard on analytics was breaking it down so a group of contractors and construction guys could understand it. Everyone learned a lot that day because fancy terms weren't taking away from the message.
Can more experts and structuring help, they sure can. But just because there is a lot of snake oil and buzzwords around doesn’t mean football isn’t a realistic analytical challenge to make big strides on today.
But if your point is it’s hard and complicated I don’t understand what so special about that point either. I wasn’t saying it wasn’t. Not sure who was.
There are people saying it can’t really be done effectively and I think that’s the inaccurate statement.
And there are people saying it can be potentially as bad to have a flawed system put in place than the coach intuitive system predominantly used today.
Anyone going up to an NFL team today and promising that they can improve their performance through analytics or can revamp their interpretation of data to achieve a specific goal are selling them snake oil.
And I really don't know why you're hung up on movement analysis.
Some people connect more with abstractions of successful advanced applications of the tech and some people don’t.
The benefits of slides and pictures are also incredibly helpful in understanding how these things work.
Psychological factors, even nutrition is making its way into these larger analysis as well. I think any flaws of a system are easily covered up by not making the system the decision point but something to learn from
until you actually trust it.
However, buying into the validity of the ultimate solution and trying to over structure something as a substitute for broad based tool and solution exploration could very well result in you building a lesser system.
I don't know about making promises, but I'm professionally famaliar with a few companies doing really interesting work with select NFL teams -- the most interesting and I think the most likely to bear fruit is telemetric analysis.
There's a body of data that shows pretty clearly that in-uniform timed speed in conjunction with lining up in the slot versus on the line, has a direct relationship to achieving the seperation rate that is most correlated with catch rate.
There are a bunch of these out there to uncover, that the tech is maturing to a place it can be useful.
On the line the chances of being deflected off optimal arrival point is higher. Even if the receiver has to swivel his head more or further to avoid an actual deflection.
How does that reality affect the use of drone based spatial analysis?
In world class sprinting one quarter turn of the head to see a rival lane costs enough to lose the race. So guys who glance at the whole field ahead wreck their own optimal speed versus a dummy who ploughs straight ahead but gets higher average speed to a straight first point of demarcation?
So Ron Dayne really was more desirable than Barry Sanders or Gayle Sayers who wasted drone time looking ahead? Can we get their gold jackets back?
Just a simple example of how many exogenous and non linear variables (a slight ankle sprain in the 3rd quarter that takes a play or two to shake off) remain between hypothesis and utility.
Where would you invest? One more scout so each has mess territory to cover thoroughly or an ex General Dynamics/Raytheon Researcher who worked on the drone module of the Aegis System?
I believe one of them is a former aerospace engineer too. I’d have to go back and check again to confirm though.
Ron Dayne vs. Gale Sayers? Sure.
Sterling Shepard vs. Golden Tate. More interesting.
On the field who's actually faster? Who can get closer to a seperation radius that benefits their catch rate off the line than in the slot? You can only play one in the slot, who is it going to be?
How is the decision being made today?
If the tech is 50K and requires only video analysis and no Bendix contractors, and the death of no scouts?
Add their enormous array of coaches, specialists ( pitch recognition, weight shift techniques, and enormous video capture and editing staff)strength and conditioning coaches and people who teach language, handling the media, getting credit, buying and renting ( they actually teach these things, on staff counseling and psychiatrists, physical therapists in many different injuries, getting their kids out of legal issues, green card, passport, family issues.
The Yankees ( and Dodgers and maybe Atlanta) dwarf the Patriots expenditure level. And they keep adding.
We many think Cashman is a PR front. He is a technology center manager.
If all the tape is preloaded and tagged in the DB, and it takes 3 hours to run the script, one more low cost data point.
If Tate has a sprained ankle, index that above new nifty, potentially irrelevant
catch radius/seperation rate measurement, in the decision tree ;)
DJ sees him getting up slowly from a prior play or notices he is taking in a lot of Gatorade on a hot day in the third quarter.
Analytics or instinct from years of playing football guides the answer?
My question has nothing to do with how to do the analysis. My question has to do with ROU up against other alternative wise uses of time and dollars.
My instinct is that what can be explored and what gets done will be quite different.
Look at the concerns about absorbing a new playback in time. There is an absorbtion rate during the season that hints at these insights needing to be both significant and pre season. ( or most promising...in data gathered pre draft).
Imo
Time to quit for this is beyond ROI OR ROU
Christian and I have both been focused on the leadership aspect of it, getting a strong CTO, because that is far more important than the number of coders.
Even systems to deduce learning the best combinations of learning styles and that’s a whole new world because we don’t know how many players might have done better with an alternative to memorizing a playbook.