Limited commentary with this post, because it’s somewhat self-explanatory.  I decided to go back and calculate an on-going QB Rating for several prominent players to see how each would have looked, statistically, after every appearance made.  Obviously, this relates to Nick Foles.  Basically, I cannot think of a single QB who has had a start to his career anywhere near as good as Foles has (statistically) and then NOT gone on to have a good career.  However, I figured it would be instructive to look at some successful QBs and see how they progressed over time.  Below is the chart.

Before we get there, though, a PSA: The NFL QB Rating calculation, while useful, is very convoluted.  Almost nobody knows it or has even seen it, so for your education, here it is (from Wikipedia):

Fun stuff, eh?

Anyway, back to the subject.  Here’s the graph, I suggest you click to enlarge:

I only included the first 100 appearances (NOT starts) for each player.  Note that this is an ongoing calculation, so it becomes less susceptible to change with every appearance.  I’m hesitant to draw any conclusions from such a small sample size, but including other QBs (I originally included Vick, Brady, Brees, and Josh Freeman) makes the graph too hard to read.

In any case, I’m going to build the sample from here to see if we can divine a good idea of when, regarding Foles, we can exhale.  For now, just take note of how “poorly” each of the included QBs played to start their careers.  And because you knew I would, I included Foles below, along with Brady and Brees.  Notice that none of these guys had as high a QB rating after 14 appearances as Foles does.  Moreover, among these guys, Foles tracks most closely with…Tom Brady.   I know attempts would be a more helpful measure, but I don’t have data for that, so…this:

Update: Should have noted this originally, but the chart doesn’t account for the general offensive inflation the league has seen since guys like Peyton started their careers. Regardless, the adjustment would only involve shifting Foles’ line down slightly, and would’t significantly change how things look.  The trend, obviously, would remain the same.

Another game, another great statistical performance from Nick Foles.  This one was certainly luckier than some of his previous gems, but that’s hardly a good reason for writing it off completely.  While thinking through his performance, I decided to look at the topic referenced in the title to this post.  Before that, some quick notes on Foles:

– With each game, Foles’ sample size grows, and the odds of him being a “fluke” decline.  As promised, here’s his updated QB Rating by game chart:

– Eagles fans might not fully appreciate this, but having a QB whose “bad” games don’t involve a lot of turnovers is not a bad place to be.  McNabb never turned the ball over, but perhaps Vick has given you some perspective.  Most “bad” QB games are a lot uglier than we’ve seen from Foles.

– Going back to our strategic equation (E = R ((60 – T) / 60) + C), and applying it to yesterday’s game, we can see that Foles’ relatively underwhelming performance might have been the result of very rational decision-making.  When Seneca Wallace left the game, leaving Scott Tolzien to lead the Packers, the Relative Strength swung largely towards the Eagles.  As a result, they became the strong favorite, meaning they should be playing a LOW-variance game.  For Foles, this meant avoiding turnovers at all costs.  I don’t know whether this was actually the case or not (probably not), but Foles would have been completely justified in not throwing to WRs unless they were VERY open.  After grabbing a lead, it became somewhat clear that the best chance for the Packers to win lay in the Eagles turning the ball over.  If Foles/Kelly made a conscious decision to go with low-risk plays, then the result would look underwhelming but, in fact, be the right strategic call.

– Of course, Foles DID throw one ball into coverage, but it was on a deep throw.  While I have not confirmed on the replay, my initial take on the play was somewhat different from most others.  In my view, he did NOT throw “into double-coverage”.  He under-threw D-Jax, the result of which gave a second defender time to get into the play.  That seems like a slight difference, but it’s important.  There are two aspects to the play:

1) the decision to throw the ball (mental)

2) the actual throw (physical)

I think Foles got the first half 100% correct, he just messed up the execution.  In general, a slightly under thrown deep-ball is a lot more defensible than a decision to throw into actual double-coverage.

Ok, enough about Foles.  In thinking through the odds/fluke/sample-size piece, I decided it would be interesting to provide some context to the QB expectations discussion.  I did a little of this when I talked about McNabb’s HOF credentials, but today I’ll do it in more detail.

The overall point is that most fans (I think), overrate the consistency of “great” QBs and set their expectations for the position too high.  Great QBs have bad games, and as we’ll see (I hope), they happen more often than you’d think.

Setting it up

First we need to define our parameters.  I’m going to use QB Rating, with all relevant caveats acknowledged.  For our categories, I’m using the following:

Great: 105+

Good: 95-104

Decent: 85-94

Poor: 75-84

Bad: < 75

Below are charts for a selection of QBs.  I only included starts with at least 10 pass attempts.  Sorry for the blurriness, click for a clearer picture.

Take a good look, cause there’s a lot of good information in there.  Most important, of course, is the frequency of “bad” games (rating of worse than 75).

– Tom Brady has a “bad” game roughly 25% of the time.

– Drew Brees has a “bad” game nearly 1/3 of the time.

– Even Peyton Manning doesn’t crack a rating of 75 in almost 20% of his starts.

That doesn’t even include the incidence of “poor” games either.  When you add that in, you can start to see the point I’m trying to make.  Even the best QBs in the league have what fans would consider a bad game fairly often.  Now they do, obviously, provide a high rate of “great” games as well, that’s what makes them “great quarterbacks”.

A couple more notes:

– Tony Romo compares favorably too both Drew Brees and Tom Brady (commence vomiting now)

– FutureHallOfFamer Eli Manning’s starts have resulted in either “bad” or “poor” performances more than half the time (54%).  Let that sink in….if you randomly picked a game from Eli’s career, you’re more likely to get a poor/bad game than a decent/good/great game.  It’s not too late to kill his HOF candidacy…

So next time Nick Foles has a bad game (like that’s every going to happen again), remember this post.  Even the greatest QBs have bad games, and it happens more often than you’d think.

I was hoping to make this post about the equation I drew up earlier, particularly how to think about assimilating new information into the model.  Given this week’s Eagles scenario, though, I think it’ll be helpful to apply similar thinking to a real world example first, then go back to the model.

The “real world” example is, of course, Nick Foles.

His last start was terrible.  He looked completely overmatched and showed none of the strengths we had previously seen him use (pocket presence and accuracy in particular).  That’s not really up for debate.  However, there’s a BIG difference between knowing that information and using that information.  The issue is, what does this performance tell us about Nick Foles’ skill/ability in general?

To answer that with any confidence, we have to frame it correctly.  That means using ALL of our information, not just last game.  For example, here’s a chart that shows Nick Foles’ passer rating by game.  I’ve only included games in which he had at least 10 pass attempts.

What do we see?

– Well most shocking to me is that the Cowboys game wasn’t actually Foles’ worst game (by passer rating).

– The sample size (look at the X-axis labels) is still very small.  He’s only seen significant time in 10 games.  That means, regardless of what you think his performance to date says, you shouldn’t have that much confidence in it.

– When put in context, last week looks like an extreme outlier.  This is why it’s important to view everything together, rather than focus on one particular event.

Insert Passer Rating disclaimer here.

So we’ve got a general idea of the larger picture.  Now let’s take a sightly different approach.  First, let’s use what we KNEW about Foles BEFORE the Cowboys game to see just how likely the Cowboys result was.  Caution: Extremely over-simplified statistical analysis here.  It’s illustrative, not definitive.  There are definitely some more robust tools we can apply and some data adjustments we can make to increase confidence, but that’s a post for another day.  Apologies in advance to the statisticians out there.

Prior to that game, Foles career Passer Rating was 87.13.  Let’s assume, for a moment, that 87.13 is Foles’ “true” ability.  It probably isn’t (small sample), but IF IT IS, we want to know how likely the Cowboys game result was.  To do that, we’ll need not just his rating, but the standard deviation as well, which for Foles, was 27.03.

Given those to pieces of information, and assuming a Normal distribution (not necessarily a safe assumption) of potential outcomes, we can calculate what the odds were of Foles performing that badly.  Now, this doesn’t account for defensive strength, but I’m trying to keep things relatively simple.

In a Normal distribution, roughly 68% of the data will fall within one standard deviation of the mean.

Here, the mean is 87.13, so we would expect, if that’s Foles “true” ability, that 68% of his starts would result in a Passer Rating between 60 and 114 (rounded).

Now, Foles ended the Cowboys game with a Rating of 46.2, which is roughly 1.5 standard deviations (27.03) away from the mean (87.13).  1.5 is the Z-Score.

Cutting to the chase, that tells us that, given our assumptions, the odds of Foles performing that poorly against the Cowboys was just 6.5%.

We can’t stop there, of course.  Just getting the result isn’t enough.  We then have to go back and view the observed outcome in light of its probability and our assumptions.  Basically, there are two ways of viewing this:

– Foles performance was a result of random chance.  Given what we knew, he had a 6.5% chance of playing that poorly, and it just happened to hit.

OR

– Our initial assumption was wrong.  Foles’ “true” Rating is worse than 87, which means the likelihood of him playing that poorly was actually higher (potentially much higher) than 6.5%.  This one is attractive, because it let’s us increase the odds of occurrence for the event we witnessed.

I’m not yet ready to answer that question, but this is the real crux of the post, and the overall point I am trying to make.  If you actually want to KNOW what’s going on, you have to examine all of the information, and try to reconcile it.  Most fans, of course, have no interest in doing this.  They’ll trust their “gut”, which usually results in them using the most recent event and discarding almost everything else.  Think about the euphoria after the Bucs game.  If you go back to the chart at the top of the post, it’s clear that was an outlier as well, though it was part of the overall uptrend and not as serious as the Cowboys game.

A big part of objective analysis is accepting that there’s usually a real chance that you’re wrong.  In this example, we can view this from both sides.  Foles supporters, while pointing to the “bad luck” explanation, HAVE to accept that fact that the second explanation, “bad Foles”, may be true.  Similarly, Foles detractors can point to the “bad Foles” explanation and use the Cowboys game as proof that the Foles supporter were wrong.  However, they too must recognize the potential validity of the alternative explanation.  It’s possible (6.5% in our basic analysis), that it was simply a bad game.

Reconciling those two sides is mandatory for anyone try to learn the truth.  As I tried to explain in the short disclaimer above, the example I used was extremely simplistic.  It doesn’t account for things like quality of competition, potential for improvement, etc.   There’s also the Normal distribution assumption and the small sample issue.  There are things we can do to address a lot of those problems, I just didn’t have time to do it all for today’s post.

Going back to the larger topic of Information Assimilation, hopefully you can see how this type of analysis can be applied to the R value in our equation.  And for anyone still skeptical as to the applicability of that model, I give you this:

Look at the score by quarter and tell me E = R ((60 – T) / 60) + C isn’t important.