Baltimore Ravens


I suspect  to a first approximation almost no one other than Baltimore fans, such as Brian Burke, and this blog really believed that Baltimore had much of a chance(+). Well, I should mention Aaron Freeman of Falc Fans, who was rooting for Baltimore but still felt Denver would win. Looking, his article is no longer on the Falcfans site. Pity..

WP graph of Baltimore versus Denver. I tweeted that this graph was going to resemble a seismic chart of an earthquake. Not my work, just a screen shot off the excellent site Advanced NFL Stats.

WP graph of Baltimore versus Denver. I tweeted that this graph was going to resemble a seismic chart of an earthquake. Not my work, just a screen shot off the excellent site Advanced NFL Stats.

After a double overtime victory by 3 points, it’s awfully tempting to say, “I predicted this”, and if you look at the teams I’ve  favored, to this point* the streak of picks is 6-0. Let me point out though, that you can make a limiting assumption and from that assumption figure out how accurate I should have been. The limiting assumption is to assume the playoff model is 100% accurate** and see how well it predicted play. If the model is 100% accurate, the real results and the predicted results should merge.

I can tell you without adding up anything that only one of my favored picks had more than a 70% chance, and at least two were around 52-53%. So 6 times 70 percent is 4.2, and my model, in a perfect world, should have picked no more than 4 winners and 2 losers. A perfect model in a probabilistic world, where teams rarely have 65% chances to win, much less 100%, should be wrong sometimes. Instead, so far it’s on a 6-0 run. That means that luck is driving my success so far.

Is it possible, as I have argued, that strength of schedule is an under appreciated playoff stat, a playoff “Moneyball” stat, that teams that go through tough times are better than their offense and defensive stats suggest? It’s possible at this point. It’s also without question that I’ve been lucky in both the 2012 playoffs and the 2013 playoffs so far.

Potential Championship Scenarios:

 

Conference Championship Possibilities
Home Team Visiting Team Home Win Pct Est. Point Spread
NE BAL 0.523 0.7
HOU BAL 0.383 -3.5
ATL SF 0.306 -6.1
SF SEA 0.745 7.9

 

My model likes Seattle, which has the second best strength of schedule metric of all the playoff teams, but it absolutely loves San Francisco. It also likes Baltimore,  but not enough to say it has a free run throughout the playoffs. Like many modelers, I’m predicting that Atlanta and Seattle will be a close game.

~~~

+ I should also mention  that Bryan  Broaddus tweeted about a colleague of his who predicted a BAL victory.

* Sunday, January 13, 2013, about 10:00am.

** Such a limiting assumption is similar to assuming the NFL draft is rational; that the customers (NFL teams) have all the information they should, that they understand everything about the product they consume  (draft picks), and that their estimates of draft value thus form a normal distribution around the real value of draft picks, and that irrational exuberance, or trends, or GMs falling in love with players play no role in picking players. This, it turns out, makes model simulations much easier.

There were eight trades in the first day involving the first round of the 2012 NFL draft. Most of them involved small shifts in the primary pick, with third day picks added as additional compensation. The one outlying trade was that of the St Louis Rams and the Dallas Cowboys, which involved a substantial shift in  the #1 pick (from 6 to 14) and the secondary compensation was substantial. This high secondary compensation has led to criticism of the trade, most notably by Dan Graziano, whose argument, boiled to its essence, is that Dallas paid a 2 pick price for Morris Claiborne.

Counting  picks is a lousy method to judge trades. After all, Dallas paid a 4 pick price for Tony Dorsett. Was that trade twice as bad a trade as the Morris Claiborne trade?  The Fletcher Cox trade saw Philadelphia give up 3 picks for Fletcher Cox. Was that trade 50% worse than the Morris Claiborne trade?

In order to deal with the issues raised above, I will introduce a new analytic metric for analyzing trade risk, the risk ratio, which is the sum of the AV values of  the picks given, divided by the sum of the AV values of the picks received. For trades with a ratio of 1.0 or less, there is no risk at all. For trades with ratios approaching 2 or so, there is substantial risk. We are now aided in this kind of analysis by Pro Football Reference’s new average AV per draft pick chart. This is a superior tool to their old logarithmic fit, because while the data may be noisy, they avoid systematically overestimating the value of first round picks.

The eight first round trades of 2012, interpreted in terms of AV risk ratios.

The first thing to note about the 8  trades is that the risk ratio of 6 of them is approximately the same. There really is no difference, practically speaking, in the relative risk of the Trent Richardson  trade, or the Morris Claiborne trade,  or the Fletcher Cox trade. Of the two remaining trades, the Justin Blackmon trade was relatively risk free. Jacksonville assumed an extra value burden of 10% for moving up to draft the wide receiver. The other outlier, Harrison Smith, can be explained largely by the noisy data set and an unexpectedly high value of AV for draft pick 98. If you compensate by using 13 instead of 23 for pick #98, you get a risk ratio of approximately 1.48, more in line with the rest of the data sets.

Armed with this information, and picking on Morris Claiborne, how good does he  have to be for this trade to be break even? Well, if his career nets 54 AV, then the trade breaks even. If he has a HOF career (AV > 100), then Dallas wins big. The same applies to Trent Richardson. For the trade to break even, Trent has to net at least 64 AV throughout his career. Figuring out how much AV Doug Martin has to average is a little more complicated, since there were multiple picks on both sides, but Doug would carry his own weight if he gets 21*1.34 ≈ 28 AV.

Four historic trades and their associated risk ratios.

By historic measures, none of the 2012 first round trades were particularly risky. Looking at some trades that have played out in  the past, and one  that is still playing out, the diagram above shows the picks traded for Julio Jones, for Michael Vick, for Tony Dorsett, and also for Earl Campbell.

The Julio Jones trade has yet to play out, but Atlanta, more or less, assumed as much risk (93 AV) as they did for Michael Vick (94 AV), except for a #4 pick and a wide receiver. And although Michael is over 90 AV now, counting AV earned in Atlanta and Philadelphia, he didn’t earn the 90+ AV necessary to balance out the trade while in Atlanta.

Tony Dorsett, with his HOF career, paid off the 96 AV burden created by trading a 1st and three 2nd round choices for the #2 pick. Once again, the risk was high, the burden was considerable, but it gave value to Dallas in the end.

Perhaps the most interesting comparison is the assessment of the Earl Campbell trade. Just by the numbers, it was a bust. Jimmie Giles, the tight end that was part of the trade,  had a long and respectable career with Tampa Bay. That, along with the draft picks, set a bar so high that only the Ray Lewis’s of the world could possibly reach. And while Campbell was a top performer, his period of peak performance was short, perhaps 4   years. That said, I still wonder if Houston would still make the trade, if somehow someone could go back in to the past, with the understanding of what would happen into the relative future. Campbell’s peak was pretty phenomenal, and not entirely encompassed by a mere AV score.

After the Giants victory over the Packers, I finally got up the nerve to say what my system has been saying from the start, that my predictive system markedly favors the Giants throughout the entire playoffs.

Going all the way?

The deal, of course, is a heavily favored team can lose. A team seeded 1 or 2 and favored by 70% in every game only has a 34% chance of making it through 3 games. The nature of the playoffs make it difficult for any team, even a really good team, to win it all.

That said, the Giants are favored by 75% over the San Francisco 49ers. The only advantage the 49ers hold is home field advantage. The Giants have to be considered a playoff experienced team, and they have a massive strength of schedule advantage, the same advantage that will give them precendence over either New England or Baltimore. If you choose to treat the Giants as having no playoff experience, that lowers their odds to win to a mere 58%.

Favored in the Conference Championship Round:

Giants over 49ers: 75%
NE over Ravens: 59%

Favored in the Super Bowl:

Giants over NE: 66%
Giants over Ravens: 64%
NE over 49ers: 64%
Ravens over 49ers: 65%

Odds of winning the Super Bowl:

Giants: 49%
NE: 24%
Ravens: 18%
49ers: 9%

For contrast, we’ll calculate the Pythagorean odds for these teams as well, ignoring the effects of strength of schedule, and playoff experience.

49ers over Giants: 86%

NE over Ravens: 61%

49ers over NE: 61%

And the 49ers are favored to win the Super Bowl, via Pythagoreans, by 52%.

Of course, if you’re taking these kinds of offensive metrics seriously, please note the odds of the Giants having made it this far was only 7.4% (Originally calculated as 5.4%). Consider those odds, please, before writing my little predictive system off.

The wins by Houston and New Orleans ensure that the #3 NFC and AFC seeds will be playing the #2 seeds, and that the #1 seeds will be playing the winner of the #4-#5 game. For now we’ll simply ask: if a team has playoff experience, but a rookie quarterback, does the rookie negate that experience advantage? Houston certainly looked good in their game.

Odds:

In San Francisco-New Orleans, the Saints have the advantage of playoff experience, but San Francisco has home field and a tough schedule. My code suggests the odds in this game are 50-50. In Baltimore-Houston, Baltimore has all three advantages, and is favored to the tune of a 81% chance to win.

From the context of a former head coach, “More than a Game” talks about the NFL as a business: what the NFL was, what the NFL is, and where is NFL is headed.  It talks about the draft, about talent evaluation, about the salary cap and its implications, about why there aren’t old fashioned fullbacks anymore. It is an especially useful book in the context of the current negotiations, as it was written with the potential lockout in mind.

I’ll pull out some quotes, in the hopes of giving you a feel for the material.

With real free agency and the salary cap, as stipulated in the 1993 collective-bargaining agreement, both the rules of how to build a team and the job of the builders changed dramatically. Suddenly, the balance of power between owners and players shifted. It wasn’t enough to draft the right players. Teams now had to figure out how to retain those players.

The stockpiling of proven talent – think of the Steelers’ trio of All-Pro linebackers in the seventies, or the 49ers’ pair of future Hall  of Fame quarterbacks in the late eighties and early nineties-proved impossible in the new era.

In other words, the talent rich teams from the 70s through the 90s are things of the past. Billick and his coauthor, Michael MacCambridge, go on to emphasize the title of this particular chapter, “Don’t Fall in Love With Your Own Players”.

Later in the chapter on draft philosophies. titled “There are no certainties”, you get

The Cowboys, with the development of their computerized scouting system of the sixties, relied on “measurables”  (height, weight, speed, arm strength, shuttle speed and so forth). The 49ers rose up against Dallas at the end of  the seventies by being more interested in the intangible elements that couldn’t be fed into a computer….The Patriots  understood before most teams that in the transient modern age, it was more important than ever that the parts fit, and that players drafted or signed through free agency be a particularly good fit for the style of offense or defense a team favored.

And in this chapter, Billick builds his case by example after example. There is extensive discussions of the drama around the Raven’s first round draft pick  in 2003, about all the issues behind choosing Kyle Boller, and the final selection of Terrell Suggs. The chapter ends with a particularly provocative bit of text.

We got Suggs that draft because we and other teams have learned what to value and what to disregard. In general, teams know what sort of running backs excel in pro football. They’ve gotten very good at developing a profile of the sort of talent it takes to be a successful offensive tackle (the top two selected in the 2008 draft, Boise State’s Ryan Clady to Denver and Virginia’s Branden Albert to Kansas City, started every game of their rookie seasons). As a group, we are smarter and more sophisticated about nearly every position on the field – with notable exception of the most important position, quarterback.

So in short, a NFL must read, for the personal side of the NFL. Topics also include what coaches go through, how the league has changed in character as the new breed of owners has emerged, and other components of the business of the NFL. It’s more of a “GM” view of the process than a line coach view, but without a peer in this day and age.

This content is a personal interpretation of elements of Rex Ryan’s book on the 46. The diagrams come from scans from the book.

(more…)

Follow

Get every new post delivered to your Inbox.

Join 244 other followers