The DC Times

A New Way to Look at the Cowboys, NFL, and Fantasy Football

By Jonathan Bales

Cowboys Analysis: Dez Bryant, Orlando Scandrick & Week 8 Primer

At DallasCowboys.com, I broke down why I like Orlando Scandrick:

Through Week 7, Scandrick has been targeted 41 times, a pretty high number for a cornerback playing so well. He’s yielded 26 receptions (63.4 percent) for 217 yards (5.29 yards per attempt or YPA). That efficiency is outstanding for any cornerback.

Actually, anything around 7.5 YPA or lower is great, and Scandrick has checked in below that in all but two games.

He turned in a decent performance against the Broncos (although you might say it was above average given the competition) and a slightly below-average game against the Redskins, but otherwise, Scandrick has been unbelievable. Even considering his work solely in the slot, Scandrick has allowed only 5.35 YPA.

One of the other cool ways to judge cornerbacks is by how many yards they allow on a per-route basis. That way, they’re actually rewarded for having good coverage and not getting targeted. Whereas a cornerback who gave up one completion of 15 yards in 100 snaps would be penalized in terms of YPA, he’d rank highly in yards per route (YPR).

Looking at how Scandrick compares to the Cowboys’ other cornerbacks and the NFL as a whole, we can start to visualize his dominance.

The top cornerback in the NFL in YPR is unsurprisingly Tampa Bay’s Darrelle Revis, according to Pro Football Focus. But not far behind him, ranking well within the top 10, is Scandrick. Cowboys cornerback Brandon Carr, who ranks in the top 20 in the NFL in YPR at 1.01, is performing better than he did in 2012. Still, at 0.73 YPR, Scandrick is over a quarter-yard better than the Cowboys’ “best” cornerback. He’s allowing well below half of Morris Claiborne’s 1.63 YPR.

At Dallas Morning News, I explained why I think Dez Bryant might eventually be better than Calvin Johnson:

A Different Look

One of the rebuttals I heard regarding the original Bryant vs. Johnson debate is that Bryant had a superior quarterback during his first three years in the NFL. That’s certainly true. And while you might think there’s no way to quantify that, we can look at market share—the percentage of their teams’ total passing yards and touchdowns that each receiver generated.

When we look at it through that lens, Johnson comes out on top.

Although the numbers are relatively close, Johnson had a higher percentage of his team’s yards (30.7 percent) and touchdowns (39.7 percent) through his first three seasons.

While this is certainly a positive for Johnson, there are a couple reasons I think it doesn’t matter as much as the original numbers. First, Johnson had way more targets in his first three years (382 versus 313 for Bryant). If we account for those numbers, the market share stats look very comparable.

Second, the total market share numbers reward Johnson for playing on a poor team. For example, he had 21 touchdowns in his first three seasons, while Bryant totaled 27. But Johnson’s market share of touchdowns was higher because the Lions as a team threw only 53 total touchdowns during that time, compared to 91 for the Cowboys.

Finally, there’s value in having the same market share with higher bulk stats. What’s more difficult: posting 10 touchdowns on a team that throws for 20, or 20 touchdowns on a team that has 40? The latter, for sure, but market sure doesn’t capture that.

We definitely need to examine quarterback quality when determining if Bryant’s first three seasons were indeed superior to Johnson’s, and market share is part of that. It certainly gives us a glimpse into just how poor Johnson’s team was, at least. But when you consider Bryant’s efficiency and bulk stats in combination with the market share numbers, there’s at least a semi-convincing argument to be made that he’s on the path to Johnson-esque greatness.

And at Bleacher Report, I posted a Week 8 primer:

Key Matchup to Watch: RT Doug Free vs. DE Willie Young

The Lions are loaded across the defensive line, with defensive ends Ezekiel Ansah and Willie Young out wide and defensive tackles Ndamukong Suh and Nick Fairley inside. Suh is the big name and the Lions’ top pass-rusher, but Young has been pretty effective as well.

Young checks in just below Suh in pressure rate, and he leads the Lions in quarterback hits. Pro Football Focus has tracked Young as lining up on the left side of Detroit’s defense on 70.8 percent of his pass snaps, so he’ll face off primarily against right tackle Doug Free.

Free has improved significantly over last year, allowing pressure on just 3.6 percent of his snaps (compared to 6.1 percent in 2012). If Free can contain Young on his own, the Cowboys will be in a better position to help their interior linemen face off against one of the league’s premiere defensive tackle duos.

By Jonathan Bales

Running the Numbers: A Week 7 Cowboys-Eagles Prediction

At DallasCowboys.com, I used player projections to predict a final score in Week 7:

A Look at Tony Romo

As usual, I’ll be using the rotoViz GLSP model to project Cowboys and Eagles players based on their comps – similar players facing comparable defenses. Here are Romo’s 25 closest comps versus the Eagles. Note that “heart” has already been factored in.

The app combs past data for quarterbacks with similar stats to Romo, so it’s no surprise to see a lot of the same names on the list: Matt Ryan (5), Peyton Manning (3) and, of course, Romo himself (4). The average stat line for these comps is 25-of-40 for 296 yards, 1.96 touchdowns, and 0.92 interceptions.

If we look at just how Romo has performed against defenses similar to Philly’s, the average line is 27-of-45 for 339 yards, 2.25 touchdowns, and 1.50 interceptions. That sort of game is probably about equal to the first line because, although Romo himself has more yards and touchdowns than all of his comps, he also has significantly more picks. That suggests we could be in for a bit of a rollercoaster ride on Sunday, which might or might not be a good thing.

Another piece of evidence that suggests a potentially volatile game from Romo is that many of his comps have performed close to the extremes – either a dominant performance or a really poor one. That’s reflected in Romo’s touchdown probability (based on the comps).

Of Romo’s 25 closest comps for this game, 40 percent have thrown either three or four touchdowns. That’s a high number. On the flip side, 44 percent have tossed either zero or one touchdown. While Romo’s average line is pretty standard or just slightly better than normal, the numbers suggest he’s set up for an outlying performance – either four-touchdown dominance or an ugly 250/1/3 sort of line.

Head over to DallasCowboys.com for the final score.

By Jonathan Bales

Running the Numbers: On the Difference Between Film Study, Analytics

At DallasCowboys.com, I took a look at Kyle Wilber’s future in Dallas. In that post, I went off on a tangent about what I believe are the primary problems with “blind” film study:

“Turn on the tape.”

We hear that a lot, right? Why has George Selvie gone sack-less the past couple of games? Just turn on the tape. Why isn’t Tony Romo throwing downfield? Just turn on the tape. What’s Kyle Wilber’s long-term outlook in the NFL? Well, all you gotta do is turn on the tape.

And there’s no doubt that film study is an important part of football, whether it’s game-planning for opponents or scouting NFL rookies.

But as it stands right now, there are a few problems with blind film study.

First, we really have no consensus on “how” to watch film. Two very well-trained scouts can watch the same tape and come away with two very different opinions. We see that all the time in the draft, and it can be problematic.

Second, there’s not always much relevant film to study. Is it really all that helpful to watch a dozen games of a small-school college prospect competing against players half his size? How about a player who was known to be competing through an injury for an entire year? What about a guy like Wilber who is now a couple of years removed from his college days without much NFL experience? Or players making position switches?

When it comes down to it, film isn’t standardized. We can’t just “turn on the tape” and trust what we see because there are just too many variables for the results to be extremely meaningful. And even when everything is as systemized as it can get, different eyes see different things.

That brings us to the third problem with the current state of film study, and the most damning: It’s not falsifiable. That’s a major, major issue because a lack of falsifiability is a telltale sign of something being unscientific, and thus incapable of improvement.

How do you falsify a scout’s claim that a prospect “plays with heart,” “displays savvy,” or “has great hips”? You can’t. Not without analytics.

Look, scouts are really good at what they do. To watch film and accurately grade individual prospects in an environment as chaotic as the football field is awesome. But we need to accept their opinions in spite of the fact that they can’t be falsified. They can’t be improved upon.

Analytics, on the other hand, is built upon a scientific foundation. Through an evolutionary process, bad stats can become good stats. Statisticians are refining their formulas and models all the time, using data to create more accurate forecasts.

But how can bad film study become useful? How can one scout piggyback on the work of another? As it stands right now, that can’t be done. Each scout needs to learn the nuances of watching film, which requires countless hours of dedication. Even then, there’s probably a fairly low ceiling on what he can provide since there’s no scientific foundation on which he can build. Worse, his opinions must be accepted on faith.

Meanwhile, analytics continue to evolve. The scientific nature of stat analysis – the way in which it can advance – makes it scalable in a way that traditional analytics-deprived scouting is not. If the Cowboys are truly a team built upon valuing “the process” over the outcome, analytics absolutely must be embraced.

By Jonathan Bales

Tony Romo, Randomness, and a Week 6 Pick

At DallasCowboys.com, I posted an article on Tony Romo and randomness:

Tony Romo’s Week 5 performance was one of the all-time great games by any quarterback. Since 1970, only 13 quarterbacks have ever thrown for 500 yards in a single game. Every single one of them threw at least 45 passes, except Romo. With just 36 attempts, he recorded perhaps the greatest combination of bulk stats and passing efficiency that the NFL has ever seen. It was a truly jaw-dropping performance.

But how likely is it that Romo can carry that sensational play into Week 6? Probably not as likely as you might think. That has nothing to do with Romo and everything to do with how we perceive randomness.

Most sports are filled with randomness, football even more so than others. A lot of weird things can happen when you have an odd-shaped ball and 22 men colliding into one another.

But our brains are hardwired to detect patterns, even when there’s nothing there. That’s why we perceive in-game momentum when, for the most part, it doesn’t exist. It’s also why we perceive players or teams as “getting hot” when that’s typically not the case.

To give you an idea of how this can happen, I created a random number generator to simulate Romo’s potential touchdowns in a game. The simple generator was built to randomly provide a number – either one, two or three. I ran the simulation 160 times to simulate 10 full NFL seasons.

If we were to assume that Romo has an even chance to throw either one, two or three touchdowns in every game he plays, we could expect results relatively similar to this over the course of 10 seasons. The more games Romo plays, the closer his stats would get to reflecting the “real” Romo, which would of course be an even distribution of one, two and three-touchdown games.

But in the short-term, we can see lots of weird things. I broke down my simulation into 10 distinct seasons, and here’s the touchdown distribution in one of them:

1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1.

That’s 16 straight games without a single three-touchdown performance, even though we know that, in this simulation, a three-touchdown game is just as likely as a one or two-touchdown outing. We also see five straight games with one touchdown, even though there was just a 33.33 percent chance of that happening each time.

In that particular “season,” Romo threw 22 touchdowns. In the average season, though, we’d expect 32 touchdowns from Romo. So that’s one heck of an “underachieving” year, despite the fact that we know the results were completely random.

But imagine if we saw this sort of outcome in real life. If Romo came out and threw one touchdown (or even none) in five straight games, we’d say he’s playing without confidence. We’d say he’s lost his ability to lead the team. We’d say Jerry Jones made a huge mistake in re-signing him to a huge contract extension.

We’d come up with any and every narrative that it would take to explain the patterns we’re built to detect. Everything, of course, except that it just kind of happened and, maybe, there’s not a great reason behind it.

None of this is to say that football is completely random. There are lots of non-random factors that can affect an individual or team’s performance, such as confidence, for example. But looking at the big picture, the results we see aren’t too far from what we’d expect out of a totally random sequence.

That doesn’t make the game unpredictable, though. Actually, random outcomes can paradoxically become extremely predictable over large samples. In my example, we know beyond a shadow of a doubt that if we could simulate one million games, we’d expect Romo’s percentage of one, two and three-touchdown contests to all come very close to 33.3 percent.

Let’s look at Romo’s potential performance in Week 6. Even though we don’t have an overly firm grasp on how many yards he’ll compile, I bet you’d be willing to wager a pretty penny that he won’t repeat last week’s stats, right?

Far more difficult than making predictions in random environments is trying to discern between actual non-random play – performances that are caused by repeatable factors – and randomness. Certainly Romo’s Week 5 performance could be the start of a hot streak for him. But how could we possibly know that? How could we tell if he’s ready to break out or if Week 5 was just a fluky outlier when, in the short-term, it’s so difficult to predict performances?

In short, if we know we can see very strange outcomes that seemingly contradict the numbers in the short-term, should we really be using short-term results as the sole basis for our predictions?

Trying to predict Romo’s exact touchdown total is kind of like predicting short-term fluctuations in stock price. Over the course of a few hours, a stock will bob and weave in a way that has very, very little to do with its true value. The same is true of Romo (or any player’s) output; it’s extremely random over the course of a game or even a handful of games, but over the course a season, that changes.

This isn’t baseball where guys play every day and are placed in individual situations, making it more likely for them to truly be hot or cold. Football teams play once a week and the game is far, far less standardized than baseball. There are no binary forms of measurement (like hitter vs. pitcher) that would allow for more confidence in our ability to label potentially streaky play.

This concept is why I don’t weigh the most recent player performances heavier than more dated stats. When I’m using past stats to project Romo’s play, for example, I won’t count the Broncos game any more than the Week 1 Giants game.

Head over to the team site for my Week 6 projections and pick.

By Jonathan Bales

Running the Numbers: How much should the Cowboys target Dez?

At DallasCowboys.com, I suggested the Cowboys need to target Dez Bryant a whole lot more.

Here’s how Bryant’s targets/game have increased over his career.

Pretty obvious trend. Despite the jump in targets, though, most people seem to agree that the Cowboys need to feed Bryant the ball even more. Even when he’s double-teamed, it seems like Bryant offers a positive expectation when he’s targeted.

Since 2010, here’s the frequency with which Bryant has been targeted per game.

The distribution here unsurprisingly resembles a bell curve. In the majority of his games, Bryant sees somewhere between four and nine targets. With those looks, he’s been able to produce at an amazingly efficient rate.

Take a look at Tony Romo’s passer rating when throwing to Bryant over the past three seasons.

This is one of the most incredible charts you’ll see on any receiver; over the past three seasons, the lowest passer rating Romo has recorded when targeting Bryant was 110.8! The Romo-to-Bryant connection that year still ranked eighth-best in the NFL. Last season, the duo was third. In 2013, they’re second behind Denver’s Peyton Manning to Demaryius Thomas.

By Jonathan Bales

Denver’s Offensive Dominance Visualized

At DallasCowboys.com, I posted some visualizations to put Peyton Manning and the Broncos’ dominance in perspective:

Using expected points, we can see if the Broncos have been lucky at all in regards to their league-leading 179 points. Guess what? They haven’t. Here are the nine teams with at least 100 expected points.

With 176.5 expected points, Denver’s output has really been what they’ve deserved given how well they’ve played. Their expectation is nearly 30 percent higher than the league’s second-place offense. It’s really just incredible.

And how about Mr. Manning himself? His level of dominance has been unprecedented.

All of the following charts will rank the top-10 quarterbacks in each category. Manning ranks so far ahead of the other passers that you don’t even need to know the numbers; you can just immediately see the effect. First, let’s look touchdown rate, the percentage of throws resulting in a touchdown.

Passer rating:

And finally, adjusted yards per attempt, a stat that accounts for both touchdowns and interceptions.

It’s truly amazing that a player can stand out so far from his peers in a league comprised of the best of the best. If the Cowboys are going to win on Sunday, they not only need to contain the best quarterback of all-time, but they need to do it at a time when he just might be playing the best football anyone has ever played.

By Jonathan Bales

Tony Romo’s Conservative Play & What You Need to Know in Week 5

At DallasCowboys.com, I explained why Tony Romo isn’t playing aggressively enough to win:

Tony Romo isn’t throwing enough interceptions. Could that seemingly absurd statement actually have some merit?

Through four games, Romo has compiled a 105.0 passer rating and 72.4 percent completion rate, both the highest marks in his entire career. There’s also this:

With just one pick through the first quarter of the season, Romo’s interception rate (0.7 percent) is at an all-time low. Moreover, that lone Week 1 pick was really the result of a blown route by Terrance Williams.

Romo’s low interception total is good in and of itself, obviously, since interceptions are strongly correlated with losing. But we can’t examine Romo’s interception rate in isolation.

The truth is that, while interceptions aren’t beneficial, the style of play that leads to interceptions can be advantageous. Let me repeat that: The style of play that leads to interceptions can be advantageous. When Romo takes more chances, the Cowboys have the potential to be a more efficient offense. Take a look at his career yards per attempt (YPA).

Resembles the interception graph, huh? As Romo’s interception rate has increased, so has his YPA. The more chances he takes, the greater the probability of 1) enhancing offensive efficiency, and 2) throwing interceptions. One is good and one is bad. So what’s a quarterback to do?

There needs to be some sort of balance, through which Romo (and the offensive play-calling) remains aggressive without unnecessarily increasing risk. The Cowboys need to take their shots downfield while still maintaining a certain level of safety.

It doesn’t have to be all-or-nothing. We don’t have to see either “Romo the Gunslinger” or “Romo the Checkdown Monster.” How about a little bit of both?

And at Bleacher Report, I posted a little Week 5 preview:

Matchup to Watch: Linebackers vs. Tight End Julius Thomas

Julius Thomas has come out of nowhere with 18 receptions for 237 yards and four touchdowns through the Broncos’ first four games. He’s been Manning’s go-to guy at times, especially in the red zone, when defenses are so focused on the outside receivers that they leave the middle of the field open.

As mentioned, the Cowboys have traditionally struggled with tight ends. That was particularly true on Sunday; linebackers Sean Lee and Bruce Carter were horrific in coverage, combining to give up 14 completions on 16 attempts, including an unreal 192 yards and three touchdowns.

In most cases, Lee will be the man on Thomas. Of Antonio Gates’ 11 targets against the Cowboys in Week 4, six of them came on Lee, three on Carter and two on safety Barry Church.

To stop Thomas over the middle, it might be helpful to understand how Denver is utilizing him and how he’s performing in certain situations. So here is Thomas by the numbers…

  • 1: Dropped passes.

Thomas has dropped only one of his 19 catchable targets.

  • 40.7: Percentage of routes run from the slot, according to Pro Football Focus (subscription required).

Believe it or not, this number is about average for today’s tight ends. Gates is slightly higher at 55.1 percent, and Cowboys tight end Jason Witten checks in at 39.3 percent.

  • 2.10: Yards per route.

Yards per route is one of my favorite stats because it judges receivers on every snap, not just those on which they’re targeted, meaning it penalizes for a failure to get open. At 2.10 yards per route, Thomas has been the fourth-most efficient tight end in the NFL through four weeks. Witten ranks 22nd at 1.37 yards per route.

By Jonathan Bales

Cowboys vs. Chargers: Stat Projections, Final Score

At DallasCowboys.com, I ran through the numbers to project the skill position players for both teams and predicted a final score:

Let’s take a look at Tony Romo’s comps for this game. These are the quarterbacks who most closely resemble Romo’s stats over the past year (with his 2013 numbers getting the most weight).

Before even aggregating those stats, we see some big-time numbers for Romo. Part of that is because he had a quality game last week, but a bigger part is the ineptitude of the Chargers’ pass defense. Quarterbacks with similar numbers to Romo, which you can see includes Romo himself four times and Peyton Manning four times, have dominated defenses that have posted numbers similar to those from San Diego.

If his comps are any indication, take a look at Romo’s chances of throwing for X number of yards.

These numbers are drastically different from what we saw in the first three weeks. Of Romo’s 25 comps, only eight (32 percent) have thrown for under 250 yards. But what’s really amazing is the ceiling production; an incredible 40 percent of Romo’s comps topped 351 yards passing against Chargers-like defenses.

The model also gives Romo nearly a coin flip’s chance of throwing for more than 300 yards. In comparison, it predicted just a 12 percent chance of crossing 300 yards against the Chiefs!

Final Player Projections for Cowboys

Using the same methodology, here are the final stat projections for the main skill players in this contest.

  • QB Tony Romo: 25-for-40 for 304 yards (7.6 YPA), 2.24 touchdowns, 1.16 interceptions

In addition to the high yardage projection, Romo is also projected to throw 2.24 touchdowns. That’s a really high number, actually, and a great sign for Dallas. Notice that the numbers are somewhat dependent on a lot of attempts, meaning opposing quarterbacks have thrown a lot on pass defenses as poor as San Diego’s, which makes sense.

Also notice that Romo is projected to throw 1.16 interceptions, a high number. Again, that’s probably the result of an expected jump in attempts.

Final score prediction is right here.

By Jonathan Bales

Stat Projections, Final Score Prediction for Cowboys vs. Rams

At DallasCowboys.com, I projected the primary skill players and predicted a score for Dallas vs. St. Louis in Week 3:

Tony Romo’s Comps vs. St. Louis

As I did last week, I’ll examine player comps – similar players versus comparable defenses – to project the most important guys. And when we look for players with similar recent stats to Romo playing against defenses comparable to that of the Rams, this is what we get:

The average line for those guys is 24-for-37 (64.5 percent) for 282 yards (7.62 YPA), 1.92 touchdowns, and 0.64 interceptions, significantly better than last week.

We can break down the comps further to estimate Romo’s probability of achieving certain levels of success. Here are the touchdowns.

Unlike last week, Romo’s most likely outcome is two touchdown passes. There’s probably around a three-in-five chance that he tosses either one or two. He’s also got nearly a one-in-four chance to throw either none or at least four.

The Other Guys

Using the same methodology, let’s take a look at the average line for the Cowboys’ other skill players:

  • RB DeMarco Murray:65 rushing yards, 0.48 rushing touchdowns, 3.4 receptions for 29 yards, 0.12 receiving touchdowns

I’ve been high on Murray all year, so I think there’s a really good chance that he turns things around. He’s contributing quite a bit as a receiver, and I think he’ll get going on the ground this week against the Rams.

As a side note, I’ve heard some talk about Murray underperforming because he’s a “straight-line runner.” I think we all already knew that, right? It doesn’t take a scout to see that Murray doesn’t juke many defenders. But you know who else is a straight-line runner? Jamaal Charles. And Chris Johnson. And even Adrian Peterson, to a degree.

Not every back is LeSean McCoy. Size and speed matter most for backs, and Murray has that. He’ll be fine.

By Jonathan Bales

Cowboys Analysis: Report Card and Play Breakdowns

At ABC, I broke down two plays from the Cowboys’ Week 2 loss in Kansas City:

A Big Third Down

Although Monte Kiffin had his defense playing well on Sunday, they stumbled out of the gates, digging themselves an early hole by allowing a touchdown on the first drive. The ‘Boys had Kansas City in a difficult spot, facing a third-and-15 at the Cowboys’ 35-yard line.

In that situation, I think Kiffin was more concerned with making sure the Chiefs didn’t advance the ball at all than ensuring they didn’t secure a big play. That’s understandable given the field position; if the Cowboys could force an incompletion or even get a sack, Kansas City would be forced to either attempt a long field goal or punt.

So Kiffin brought the dogs, lining up six defenders at the line with a soft look behind it.

At the snap, Orlando Scandrick rushed off of the edge and Sean Lee dropped into coverage, meaning the ‘Boys had five defenders coming after Smith and six in the back end. The secondary played off, seemingly content to give up any underneath completions.

Smith had time to throw the ball, so he hung onto it instead of taking the sure thing underneath to set up a closer field goal try. The problem for Dallas was that, once the receivers got downfield, the back six were out of position to corral the scrambling Smith. He took off down the sideline, diving for a first down that ultimately led to the first of only three total touchdowns for both teams in the game.

This is the problem with continually playing man coverage against a mobile passer. In such a close game, it’s pretty evident that Dallas would have won had they contained Smith as a runner. Kiffin probably felt as though Smith is accurate enough to consistently pick apart zone coverage if given enough time, but it might have been smarter to utilize zone blitzes if he wanted to send pressure. That way, the Cowboys could have forced Smith out of the pocket, yet still have defenders playing underneath to stop him on the ground.

And at Bleacher Report, I graded each position.

Quarterback

Just as was the case in Week 1, Romo turned in a poor performance from an efficiency standpoint. He averaged 7.1 YPA—up from 5.4 YPA in Week 1—but it seems like Romo is throwing the ball scared right now.

It’s pretty apparent he’s placed an emphasis on minimizing his turnovers, which he’s done really well, but it’s come at the expense of some big plays. In Romo’s defense, he had some passes dropped, including a big one down the sideline to Bryant late in the contest.

Still, Romo didn’t lead the team at the end of the game. I’m as big of a Romo fan as any, but why wasn’t the team in a hurry-up mode for much of the fourth quarter when they were losing?

Down by four points with just a few minutes remaining, the Cowboys showed no urgency. Yeah, they had enough time to score on that drive, but what happens if you don’t score a touchdown right away? The Cowboys didn’t, and they had no time left to come back after kicking off.

Grade: C-