So, I was on my way to work when I thought about 2011-2012 and how almost every team seemed like a potential playoff contender. Unless you were the Browns, the "Any Given Sunday" philosophy could be posited on any team.
Flashforward to 2019 and it feels anything but. You have some teams tanking, like the Dolphins, but then teams like the Giants who are taking 3-4 years to turn around the ship.
I'm starting to believe parity is dead in the NFL due to certain teams having an advantageous upper hand in the analytics departments, especially with utilizing talent compared to their salary cap hit. Before, teams would go through the cycle of being good for a few years and then see a downturn. Now, teams have figured out how to stockpile talent cheaply while paying their perennials superstars (See Dallas Cowboys).
Am I going crazy or does this seem at least somewhat the case?
The more I think about the Giants, the more I'm starting to realize this current NFL does not allow for quick turnarounds like before. Ten years ago, any team with a few good drafts could rise to the top. Now, it feels if you've had few bad years of drafting followed by good ones, you're less likely to be able to get out of the dumpster fire of the NFL.
and walks were not considered important and they scoffed at OBP, for just about 20 years.
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PFF is a barometer to show just how dug in NoGainDayne is to his rant on analytics.
he claims the Giants don't have the right people to do analytical calculations, but in the next breath, he claims that PFF does have the right people with the right credentials.
So what does he do? He ignores the information about the Giants utilizing analytics for in-game decisions, yet regales the board about PFF doing things the right way. and stands by the ratings process.
Per my post you just don't really comprehend how these systems work or what their foundations are on.
You have where the Giants are.
1. No evidence of having anyone with advanced mathematics, computer science or software development skills.
2. Where some NFL teams are (some of these hires and Github libraries, the Giants don't have a github libarry)
3. Hybrid system like PFF where larger teams of people with the skills of #1 combine with a system like grading players
4. Purely quantitative system from converting film to physics equations or ZEBRA data
Now what you are failing to understand is a value based or outcome analysis systems could actually benefit from using data from kinds 3 and 4 because like i've said many times people and machines see different things and you can actually benefit from uniting the process and putting as much information of these different views in the hands of smart people.
Now you are trying to pretend like anything that touches subjective inputs is useless in machine learning? What dense person you are. Analyst estimates and price are valuable inputs in quantitative analysis and complex finance modeling EXACTLY because human input is layered in and it provides different signaling than pure data. Both are helpful.
2 sigma even got stopped from giving analysts surveys to basically layer in their personalities in addition to the data they provided to further signal off the estimates they gave.
You don't know anything about data analysis really yet you've also made yourself the arbiter of what is legitimate data and isn't? Give me a break, you know very little and continue to prove that
i think you can make your point without the derogatory tone. Subjective inputs on a play brings the fielding numbers like range factor to mind. I believe they were useful. And i think subjective determinations on a per play basis could be useful as well. Though they would need to be standardized. And yes integration with advanced next gen NFL metrics which are not subjective would seem to be a useful way forward.
It's not analytics, it's not real filmwork, it's not anything of value.
And it is telling that PFF is used by the majority of the teams (I believe 20+) for quantitative data.
How many use their ratings? Zero.
I know Sneakers understands this - others seemingly do not, but subjectivity isn't PFF's sin.
Combining subjectivity with unqualified reviewers, using a flawed methodology is their sin.
And it is a gigantic one. But the guy who supposedly knows analytics doesn't get that.
Do you understand they integrate subjective information?
Or are you disputing if they are useful in machine learning?
It isn't solely subjectivity in dispute. It is the combination with a flawed methodology and unqualified reviewers and operating with incomplete and incorrectly interpreted data.
Not only is the data better in doing price estimates, but the analysts are trained and considered experts in their field.
Now maybe you ignore points because you are too stupid to grasp them or you do it intentionally because you can't run roughshod over them in trying to praise PFF.
Neither one is a good look - but you do it over and over again.
Can you immediately become a price analyst by signing up on a website without any qualifications needed?
And I hope we were both wrong about Daniel Jones...
Can you immediately become a price analyst by signing up on a website without any qualifications needed?
this sounds so much like the model employed by Stats Inc 30 years ago. They hired barely qualified people, and by hired I mean didn't pay, to sit in the press box and do advanced charting of baseball games. My buddy would call me from the press box in the Big O as he charted games and then I would get calls from some bean counter there asking me who called me. But it's the same model. The point is, I think though, is that they are doing it, and if they modify and refine their practices they will become a big player in something that is inevitable.
Strategic moves are transparent when they happen. A player's responsibility is apparent. You don't have an endless array of formations. You don't have audibles.
A guy thows a ball, another one attempts to hit it and fielders attempt to catch it.
Data is much more straightforward and crisper. It is a matter of charting things, not making significant subjective decision.
Strategic moves are transparent when they happen. A player's responsibility is apparent. You don't have an endless array of formations. You don't have audibles.
A guy thows a ball, another one attempts to hit it and fielders attempt to catch it.
Data is much more straightforward and crisper. It is a matter of charting things, not making significant subjective decision.
Oh yes this is totally true. Baseball is essentially a game of binaries. But I would say two things. 1) Just because football isn't binary like baseball doesn't mean charting every play isn't useful and won't eventually yield a system or series of metrics that are better than what we have today, because what we have today is essentially garbage. 2) Baseball moved onto more complicated and subjective metrics such as range factor and other fielding metrics. Those were considered useful enough that the entire sabermetrics community accepted the conclusion that Derek Jeter was a lousy shortstop. That was based entirely on subjective inputs on a per play basis and it was accepted as truth. And I would bet the inputs were done by people no more qualified than the folks PFF employs. All this is to say that PFF may or may not be doing a good job, I just havent done the deep dive, but it could turn into something useful, even essential. I wouldn't dismiss their effort out of hand. Analytics needs to revolutionize football. Better data, better metrics, better thinking. And right now this is one of the only games in town.
Building a data driven org is not about following a punch card, it's not about having staff, it's not about "doing analytics."
The primary questions I ask when building a program 1) are you willing to fail 2) are you willing to hurt feelings.
If an org isn't willing to do those two things game over.
The second series is 1) are willing to let analysis guide all investment 2) are you willing to let data guide all operations. Again if the answer is no game over.
I've followed football keenly the last 10 or so years from a data perspective -- my feelings on the brand names are well known in the data field. Scoring player performance will take the next step in predictive motion analysis (play execution). That will be a step up from predictive scenario analysis (play calls) which isn't impossible now and is much more interesting than situational analysis (more on this later). And much easier is resource allocation analysis.
Doing some of this and calling it a day is a false sense of security and frankly more dangerous than doing nothing. If you follow a chart of what to do in scenario X, but don't know what to do against Y, you might as well go with your gut.
I'm thoroughly unimpressed by the Giants going for 2 points or passing more. That's kindergarten. They need to understand what plays are likely to succeed based on their roster. Following a punch card is "doing" analytics.
Same goes for investment. Like I posted above, I'd love to spar with the analyst who said Manning, Solder, Jenkings, and frankly Barkle fit a value-based investment system.
Employing thoughtful analysis then needs to be backed by results. If your models predict success, and you suck, self assess.
A ball travels a specific distance at a specific velocity. A batter pulls a curveball a specific % of the time. The right term is "charted", because a baseball play can be easily placed into a chart and categorized.
PFF's player ratings don't do that. They are trying to ascertain success or failure without knowing the assignment or the objective. They aren't charting plays - they are grading them - and that's where the significant difference comes into play
A ball travels a specific distance at a specific velocity. A batter pulls a curveball a specific % of the time. The right term is "charted", because a baseball play can be easily placed into a chart and categorized.
PFF's player ratings don't do that. They are trying to ascertain success or failure without knowing the assignment or the objective. They aren't charting plays - they are grading them - and that's where the significant difference comes into play
gotcha.. yes that does seem like a weird methodology.
George Box