What Exactly Do We Know Part III: The Nightmare Edition

Categories: Decision Making, NFL
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Published on: October 28, 2014

This will be the final installment in my “What Exactly Do We Know” series. I think I’ve beat this horse enough that it’s about to fall over. But I need to talk about one final aspect of statistical reasoning and knowledge derived from data analysis. And this one is the creeping horror that should keep us all up at night. At the very least, this would keep me up at night if I were advising an actual NFL team. I’m going to being explaining the horror by having you imagine a job interview.

Actually, I’m going to ask you to imagine the lack of a job interview. How would you feel if you knew you were a top three candidate for a job, but the company called you one day saying “We don’t do interviews. We’ve already made our selection and we selected someone else because they had a higher college GPA.” When I ask my students to imagine this scenario, they say they’d be annoyed. They talk about how a particular score on a test doesn’t define them and if only they could get an interview they could prove their abilities and their worth.

However, if you’re trying to assess abilities and skills, evaluating on college GPA is actually the best way to get the skills and abilities you’re interested in. In fact, trying to assess abilities and skills with an unstructured conversation is one of the best ways to introduce unintended and significant bias into your decision making process. Most large, modern organizations don’t even use a conversation-style interview to assess skills anymore. Conversation-style interviews are done to only answer whether the person interviewing you could stand working with you for a day. But I digress.

I bring up job interviews because they are a fascinating point of the employee selection process. If used in the old let’s-chat-for-20-minutes way, the interviewer is unlikely to see the person’s worth with any form of accuracy. Which brings us back to football.

Football is an amazingly interesting game because of how interdependent all the action is. However, the interdependence leaves us with a fundamental problem. Can someone looking from outside the situation truly see what is actually happening in a quarterback-wide receiver connection? I looked in the published academic literature and couldn’t find the study that directly answers that question. I’m running the study in my lab right now, but I won’t have an answer for you for a long while. I haven’t looked at the data yet, but the tangentially related studies all seem to indicate that the answer is “No, we can’t see who is responsible for what when looking from the outside.” And, assuming I’m right, how then can we trust the opinion of any talent evaluator that doesn’t attempt to systematically control for such biases? Can even the most relevant talent evaluators, namely those that make personnel decisions for NFL teams, be trusted to make the right evaluation?

My top quarterback prospects from the 2014 draft were Nathan Scheelhaase and Keith Price. At the moment, neither of these players is on an NFL roster. The internet currently does not record what Scheelhaase is up to, but Prince is a quarterback – a backup for the Saskatchewan Roughriders in the Canadian Football League. So…what are we to make of this?

Keith Price 2013.jpg

Let’s say I’m right and we can’t trust talent evaluators that don’t use data to control the biases. This means that I can put out a list of prospect, those prospects can go out into the league and get evaluated. In the case of Price, he was evaluated by two of the best in the business – the Seahawks and Patriots.   Neither team desired his services which is how he ended up in Canada. But according to the theory we put out in the first paragraph, the fact that he didn’t get picked up doesn’t mean anything. We already believe that the evaluators can’t control a human bias. Hopefully you understand that this is a very advantageous rhetorical position to be in. How can you convince me that I’m wrong? What evidence would I accept if I won’t accept the pre-season evaluation of two teams who I have already stated I believe a two of the best in the league in evaluating talent?

The only evidence that the model will currently accept is on-field, regular season outcomes. And not only that, but I would need a lot of attempts to actually consider the notion I’m wrong. Which is why I would lose sleep if I worked for an NFL team. I’d have to trust the wings of Bayesian statistics to a degree I’ve never had to in the past. How terrifying would that be?

What Exactly Do You Know, Part II: The “Is He Your Cousin or Something?” Edition

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Published on: October 21, 2014

In last week’s post, I discussed the concept of what statistical inference actually tells you and how it’s boring and cumbersome to talk about it accurately, so analysts often shorten the conversation so they can actually talk with real people about something interesting. Today we take a slightly different tack regarding what exactly we know. Our example for this week is Minnesota Vikings quarterback Christian Ponder.

Christian Ponder close-up.jpg

If you’ve been reading for a while, you know that I was actually a fan of Ponder for a long time. Or, at the very least, I didn’t hate him with every fiber of my being like every other Vikings fan seemed to. I called him “not the problem in Minnesota” instead pointing to the largely ineffective receiving corps. I was talking to my neighbor before the season started. I said that Ponder is not the problem. We had this long and somewhat loud conversation about how I have to be wrong about him because everyone was giving up on Christian Ponder. Even Paul Allen – the radio play-by-play announcer for the Vikings – a guy who has never in his life given up on anyone in a purple jersey had given up on Christian Ponder. When I persisted that Ponder wasn’t the problem, my neighbor ended the conversation by saying, “You’re the only guy I know saying nice things about Ponder. Is he your cousin or something?” At the time the comment made me laugh. Then the Thursday night game against the Packers happened. I had to think more about this and examine what I know and what I don’t know about Christian Ponder in particular and the game of football in general.

So why was I so adamant that Ponder wasn’t the problem? Because, for all his faults, Ponder has one singular but important ability. He is rather accurate for an NFL quarterback. He’s not super-star Peyton Manning accurate, but he can get a football into a receiver’s hands slightly better than the average NFL quarterback. And why do I care so much about accuracy and nothing else? Because it’s the only quarterback ability I’ve found at the NFL level that will predict useful outcomes. Nothing else comes back predictive. Not a quantification of arm-strength, not Wonderlich scores, nothing at the combine, nothing but accuracy predicts NFL level outcomes.

And now we have another trap that analysts can fall into, a trap that is particularly present and meaningful for the NFL. I can’t find a predictive effect of my in-house metric that I think measures arm strength (let’s ignore the measurement point of “how do we know this thing is really arm strength” for now. It’s important but not where we’re going here). So I don’t find this effect. There are a couple possibilities why. The first possibility is the one that brings the page views and the loud conversations – that Arm Strength isn’t an important thing. However, another interpretation is that the lack of data at the NFL level makes finding the effect of arm strength insanely difficult.

Think about it like this. Imagine I told you that there was gold to be found in the body of water closest to you. To me that body of water is a river, so for the rest of this example I’ll be talking about a river. But maybe for you it’s a lake or an ocean or your friend’s bathtub. Whatever. You want to find this gold because you think having gold would be better than not having gold. So you go out and buy all the equipment necessary to pan for gold. You get the sorter pieces and the dirt sucker and everything else and you go stand in the river for a few hours and try to find this gold. Now, if you stood in the same spot panning for gold for four hours and didn’t find gold, would it be reasonable for anyone to assume that I’m wrong and that there is no gold in the river?

 

Crude Drawing of Where Gold is in a Fictional River
Crude Drawing of Where Gold is in a Fictional River

No, it would be ridiculous to say that. Maybe you were panning in the wrong spot. Maybe the screen you were using was too big and all the gold was little and slipping through. There could be many reasons why you didn’t find gold in the river.

Analytical findings are like gold. Just because you don’t find one, doesn’t mean that they aren’t there. This is a concept called “statistical power” and in the NFL it’s a huge problem. Our ability to find effects generally increases the more data we have. Think of it like this – more data makes our gold panning screens smaller. It allows us to find ever smaller nuggets of gold. In the NFL, the data is very sparse. There are only 32 teams playing 16 games each with maybe 30 passing attempts in each game. This pales in comparison to basketball’s 82 games and baseball’s 162. Compared to other sports, an effect in the NFL has to be fairly large before our screens will catch it. There is so little data coming from the NFL that it’s possible an arm-strength effect exists but there just isn’t enough data to find it.

So, after the Thursday night Ponder debacle, I went on a quest for more power. And in football, if you want more statistical power you need to look at the college level. With many many more teams we suddenly have a lot more power in our data set. I spent most of my summer calculating the same arm-strength metric for every NCAA FBS level quarterback and I ran the same model to see if arm-strength, along with accuracy, can predict useful quarterback outcomes. Low and behold, it does (said the amazed analyst and no one else). Ponder fairs very well on accuracy, but he suffers horribly on arm-strength. With this lesson learned, it’s time to quit dying trying to take the Ponder hill. Ponder is a problem for the Vikings offense. One of many, many problems.

What Exactly Do You Know?

Categories: General Info, Statistics
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Published on: October 14, 2014

(This post was inspired, in part, by a post Matt Waldman posted on his website a few days ago. If you haven’t already, go check out his site. He does an amazing job over there. The post in question is titled “Deny Emotion and You Only See a Fraction of the Game.”)

The establishment in sports has, for a while now, been telling outsider-sports-analytics types that one of the main barriers to widespread acceptance of analytics rests on the ability of the quants to communicate with the non-quants in ways that the non-quants can understand. I’ve covered the problems of communicating information both to quants and to non-quants in the past. But Matt’s post about emotion made me realize something more.

His general point was about the role emotion plays in sports performance. Specifically, he was contemplating whether or not momentum exists in sports. He cites conversations with analytics experts on the subject of momentum. If you’re familiar with the argument, most analytics experts conclude momentum does not exist. This is a fairly standard finding across many sports – basketball’s hot hand being another classic example. Matt’s reaction to that conclusion is also fairly standard among non-analysts. His argument is that emotion, and the effects of emotion on situations in sports are obviously occurring. Anyone who denies that is missing a huge portion of the signal inherent in the game. At one point he suggests that analysts have possibly never put themselves in physically dangerous situations and felt the impact of emotion first hand.

So, two things with that summation, both having to do with communication. First, assuming experiences that someone has or hasn’t had is a raw nerve for me. The stereotype of the milquetoast academic who simulates experiences rather than having actual experiences looms large over me. I’m guessing it has something to do with my father telling me to put down the video games and go spend some time working on my grandfather’s farm. But, I know the comment wasn’t directed at me and it wasn’t malicious anyway, so let’s put aside any irritation that might shut our brains off. In fact, we need to keep our brains on if we’re going to truly examine the point Matt is trying to get at.

Analysts (myself included) are often guilty of a particular linguistic shorthand. Our job is to find predictive effects. Does changing the way a request is phrased reliably change donations to charity? If I know about your height, can I make an educated guess about your weight? That sort of thing. We can become so familiar, so practiced in that job that, when we talk to other people, we tend to shorten the description of what we’re talking about. When my neighbor asks me what I know about momentum in sports, I say “I’m trying to find out if momentum exists” and he gets excited and engages with the conversation. The problem for analysts, though, is that this gets the conversation off on a disingenuous foot. Because we’re not truly trying to find out if momentum exists. We’re trying to find out if the predictive effect of momentum on some other variable, like scoring, exists. But we repeat the phrasing about studying the existence of momentum so much that we can forget that other people see that collection of words as having a different meaning.

And when we analyze the results in sports, the results a pretty clear. The effect of momentum on any variable we look at is unpredictable. The same is true in the academic literature as well. We cannot predict the effect of emotion on motivation with any reliability. So, I agree that we should probably stop saying that momentum doesn’t exist. The subjective experience of the emotion of the game is a real thing that people feel.  But I will hold fast to the notion that predicting what will initiate a change in momentum and how a change in momentum will impact athletic performance is an unpredictable enterprise.

NCAA Quarterbacks: 2015 Draft Class

The quarterback situation for the 2015 draft class is looking very murky.  The 2012 draft class was a very unique class in that the highly talented players all managed to get drafted highly and to generate the needed playing time to demonstrate that talent.  I don’t see that happening with the 2015 draft class currently.  I see the 2015 class looking a lot more like the 2013 draft class.  Guys that can play exist in the pool, but who knows if they will get the playing time they need.

Revisiting the quarterbacks I’m watching section from earlier this year, I’m still high on Rakeem Cato.  I saw one feature story on him, but it had to do more with his background than with his ability as a passer.  Hopefully he gets some more attention for the latter.

One player we should keep a close eye on is Bo Wallace at Ole Miss.  I didn’t have him on my original list this year, but he’s having a very good season, both in terms of accuracy and in down the field throws.  That second part is important because he did not have a good season throwing down field last year.  Good to see him demonstrate that he actually has that ability.

Last but not least, someone to keep an eye on is Conner Halliday at Washington State.  Not saying I’m overly excited about him, but he does have something worth looking at.  I don’t know where he’ll end the season in my projections, but his current performance is at least elevating him from where he was.

Last but not least, looking far into the future, is anybody looking at this sophomore from Middle Tennessee?  His name is Austin Grammer.  A name you might want to get to know.

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