too small...
"A team source told SNY's Ralph Vacchiano that Oklahoma QB Kyler Murray is "probably a little too small" for the Giants.
The Giants "prefer to stick to the established measurables they have for a prototypical quarterback," Vacchiano notes. The Giants' organization emphasizes conventional wisdom and inside-the-box thinking, and they haven't started a quarterback that measures below 6-feet since 5-foot-11 Gary Wood went 0-6 in 1966. And "the philosophy hasn't changed all that much (in that over half-century timeframe)," Vacchiano confirms. 6-foot-3 Dwayne Haskins, 6-foot-4 Drew Lock, and 6-foot-5 Daniel Jones appear to be likelier candidates to succeed 6-foot-4 Eli Manning than 5-foot-9 Murray."
Source: SNY.com
Ultimately, I think both sides are right here, or at least there is room for both sides to be right - Gettleman (and the Giants, by extension) can simultaneously be open to some analytics (training/fitness/nutrition) and not pay enough attention (or flat out devalue) other analytics. It doesn't have to be binary, that someone is either a data junkie or a luddite, and the characterization as such definitely derailed this debate even when good points were being made.
The results seem to suggest that there is plenty of room for improvement, and some of Gettleman's personnel moves do appear at times to be grounded in the old school eyeball test more so than data. I think, as fans, we should hope that the front office continues to invest in the analytics side of the business. It doesn't have to replace the previous methodology; it can supplement it. More information is (almost) always a good thing.
Thanks GD for capturing where I was planning to go. I think NGD considers the training/fitness/nutrition stuff to be such a low bar as to not count. Whereas FMiC counts it.
If we want to have a reasonable debate on the subjust I think it behooves us to say...
"OTHER THAN FOR TRAINING/FITNESS/NUTRITION, AND SOME SIMPLE GAME THEORY DECISIONS LIKE WHEN TO GO FOR 2 OR GO FOR IT ON 4th DOWN, Gettleman's attitude towards technology/analytics is..."
My Opinion is:
Other than for training/fitness/nutrition and some simple game theory decisions like when to go for 2 or go for it on 4th down, Gettleman's attitude towards technology/analytics is that he doesn't believe in it, and has no intention of employing it for team building, deeper game theory decisions (i.e. play calling, matchups...), virtual reps, etc. I could be wrong, and he might surprise me by starting something, but I seriously doubt it. If he were interested I think he would have already started, and we would see some evidence of it.
In addition, from what I have read, the opinion above seems to be the general consensus from all parties.
1) Lack of contempt for others
2) Basic Change management skills and interpersonal relationship skills 101
3) Not mixing speculation and assertion when advocating adherence to fact based analysis
4) Humility
5) Listening
6) Effectively meeting people where they are
7) Asking the right questions
Model that
Or being just too ponderous...
But look at the forum if you will. In a place when someone can call someone stupid for what should be viewed objectively as valid disappointment and critique that same forum allows for absurdity.
I’m happy to communicate with those on their level and meet them as you’ve said.
Sometimes you meet in the mud. And if the preferred language is troll a learned gentleman can speak troll as well.
I actually asked you once maybe 10 years ago how you stayed level and your response was not all the time.
He who fights with monsters might take care lest he thereby become a monster. And if you gaze for long into an a
But look at the forum if you will. In a place when someone can call someone stupid for what should be viewed objectively as valid disappointment and critique that same forum allows for absurdity.
I’m happy to communicate with those on their level and meet them as you’ve said.
Sometimes you meet in the mud. And if the preferred language is troll a learned gentleman can speak troll as well.
I actually asked you once maybe 10 years ago how you stayed level and your response was not all the time.
He who fights with monsters might take care lest he thereby become a monster. And if you gaze for long into an an abyss, the abyss gazes also into you.
That note aside, I do agree with some good thoughts in your last post.
Regards, Yat
Thank you both very much for raising the bar of the discussion about the Giants' use - or rather probable lack of us - of modern analytics and data collection and mining in their overall operations.
One can easily imagine that it's been a sizable part of where the Giants have fallen behind their peers like the Eagles and Patriots over the past decade or two. Hopefully it's any area into which DG will at least consider strategic investigation.
I am also genuinely happy to hear that Gettleman seems open to Murray contrary to what the reporter might have said but the veracity of either thing is always up for debate.
And where I started on this thread is important to detail, Murray and this draft specifically is an interesting one. I think we all have our own analysis stories as it relates to any information and there is no substitutes for "seeing it all" which is why logging film hours is something that Gettlman has that I could never hope to match him on. But you also have to see what computers can do as logging an almost an infinite amount of more hours on an almost limitless amount of information. We just have to help them infer and connect in ways that they don't understand. In addition to computing hardware advances I think AI moving into the hands of practical business people vs. pure scientists is a big reason why we see it working a lot more.
For me Murray passes the eyeball test completely and i'd love to have him but my personal analytics story tells me something about that. I took Russel Wilson off my draft board for the BBI mock, really I didn't even "scout" him because like many I saw too many QBs like Charlie Ward, Crouch or Troy Smith not really make it to the NFL. Mayfield I had some doubts about his maturity but loved him and thought he could be an NFL player, I didn't really want to argue with anyone last year (I know, shocking) but I did this sanity test for myself.
Since the famous 1998 here are the pro bowl hit rates (total drafted / players who made at least 1 pro bowl) less than or equal to the following heights for all 7 rounds
18.44% -- 77
16.55% -- 76
15.69% -- 75
12.77% -- 74
23.53% -- 73
33.33% -- 72
50.00% -- 71
and now just the first 3 rounds
35.63% -- 77
34.92% -- 76
31.82% -- 75
33.33% -- 74
44.44% -- 73
50.00% -- 72
100.00% -- 71
Weight is also pretty interesting, 7 rounds
18.50% -- 275
18.46% -- 250
17.68% -- 240
18.00% -- 230
14.93% -- 220
12.50% -- 210
3 rounds
35.00% -- 275
35.42% -- 250
34.48% -- 240
35.21% -- 230
37.50% -- 220
40.00% -- 210
Now this falls well short of a complete analysis and the sample sizes are dangerously small on the low end but it would seem to support the idea that if you can put up stats as a little guy and aren't depending on the option for those stats your chances of being good in the NFL are actually higher than the general population.
But there is another part of my analytics story, it wasn't just height that led me to knock Wilson, it was injuries. RG3 and to a lesser extent Vick made the Pro Bowl but wear and tear got to them. (Now it's quite annoying that I seem to see an overlap in enthusiastic Barkley supporters and rejection of shorter slighter players when RBs on the whole are more impacted by these hits but let's leave that to the side for now)
What kinds of models would you need to effectively evaluate this kind of information and not knee jerk to saying Murray is like Russel Wilson (which admittedly he looks like to me based on what I know of their mental makeups) or Vick
Probability of X number of games played
Probability of X number of pro bowls
You can use analytics of markers mental makeup as I discussed above, combined with stats to at least give you an interesting objective perspective not colored by your personal analytics story. These are easier forecasts because you can use binary values. You really need to see the probabilities distributions of being good vs. not playing anymore to have an informed discussion on this using many data sets I have no access to. I have little doubt that Ernie Adams has put these kinds of things in front of his decision makers for years and they have gotten better and better as the tech team has expanded with more skilled mathematicians and computer scientists.
But what do we really care about? Not these models per se, they are intermediate markers for harder questions like what will their stats be over the next 10 years in total? Where you can start looping in models like the IT factor one I discussed above. Like with Haskins, he doesn't do anything wrong, but it does make me super uncomfortable that he couldn't beat out J. T. Barrett. Again, we need to look at stats like number of years played and who else is on the roster with him to effectively measure these kinds of things. I was never ready to fault Mayfield for losing his competition with Mahomes. Jeez, talk about the wrong place at the wrong time.
Moral of the story is especially after the height / weigh in today Gettleman should be making these decisions with more information than just the traditional means and it is problematic that neither him nor the Mara's seem to care about this fact or remedying it ASAP.
Since 1998 here are the pro bowl hit rates (total drafted / players who made at least 1 pro bowl) less than or equal to the following hand sizes for all 7 rounds
18.82% -- 10.50
18.99% -- 10.25
18.29% -- 10.00
17.69% -- 9.75
19.79% -- 9.50
14.52% -- 9.25
3 rounds
34.38% -- 10.50
34.41% -- 10.25
33.73% -- 10.00
33.85% -- 9.75
38.30% -- 9.50
30.77% -- 9.25
Interesting to note Goff and Mahomes are both in that bottom bucket. I can slice and dice this data set pretty easily for QBs if people are curious about other things
It must be the sum (of player who have made 1 or more pro bowls) / sum (total of drafted players)
Otherwise the % would all be greater than 100%.
Personally, I'd suggest starting with a bunch of physical measurements and performance measurements and do a PCA (principal components analysis) with the data of say "multiple pro bowl selections" to steer me hopefully to the more significant data points.
Which I am certain some teams have done, and that's why the 40 yard dash remains a crucial parameter for some positions.
I'd say anecdotally that i'd overlook Mahomes but the idea is that the machine learning can account for that in the data of relative skill.
Perfect for what they did is incredibly qualitative. Why couldn't Haskins play like he did this year last year? Wouldn't that have been better?
That note aside, I do agree with some good thoughts in your last post.
And Bill, your insights here would be great, instead your posts are about regulating posting behavior. Just shouldn't have to be like that.
I'm hoping we see some better timeout usage this next year, and a more outside hope is that we bring in someone with the computer science / mathematical pedigree you need to start building these systems in earnest.
Otherwise I just think as fans we need to be talking to other fans about this and making sure if we have another bad season and/or are looking for a new GM our ownership cannot ignore the need to make a more progressive choice.