Tennessee Titans

The first round is over and in terms of predicting winners, not my best (by my count, 1-2-1, as we had Jax and Bills in a de facto tie). I was pleased that the model got Rams and Atlanta correct, and the Sunday games all came down to the wire. One or two plays and my formal results would have been impressive. Still, back to the predictions for this week.

To add some spice, we will predict results for New Orleans normally, and also as if Drew Brees is elite. Values in parentheses are the elite numbers. With elite status or no, Minnesota is still favored in this data set.

The only home team not favored is Philadelphia. We discussed this in part in this article.

Second Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Philadelphia Eagles Atlanta Falcons -0.878 0.294 -6.5
Minnesota Vikings New Orleans Saints 1.231 (0.484) 0.774 (0.619) 9.1 (3.6)
New England Patriots Tennessee Titans 1.674 0.842 12.4
Pittsburgh Steelers Jacksonville Jaguars 1.915 0.872 14

Determining how to assess draft trades in the NFL is not hard (see here, here, and here). Ever since Pro Football Reference went through the trouble of determining what average AV can be assigned to a draft slot, it’s merely a matter of counting. The technique has some variance, as the draft slot of a future pick is not known. Even so, with a bit of conservative extrapolation, you can still get a feel for the overall cost of a trade.


First, the numbers:


The AV costs of the 2016 Rams Titans trade.
Rams Titans Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
1 51 15 28
113 14 43 24
177 5 45 25
76 17
(20) 29
(84) 13
Total 70 Total 136
66 1.94


In the data above, we assume that the Rams will improve 5 slots in draft placement, so that the first and third they sent to the Titans would be picks 20 and 84. If the Titans end up 18th or 23rd, it’s notable that the difference in value at this point is less than the point-to-point deviation, so that kind of change won’t affect the calculation much. Pro Football Reference’s raw data are moderately noisy.

The Rams total investment is 136 AV, roughly equal to the career value of John Elway. That’s not entirely accurate, as the Rams actually received three picks in return, and if the other two return 19, then the player they pick at #1, to return the value of the investment, only has to yield 117 AV.Now, 117 points is about mid in between Phillip Rivers and Aaron Rogers in value.

Update: Johnny Unitas, at 114, is a closer comparable.

In terms of risk, the trade is riskier than the Eli Manning trade, and less risky than the RG III trade or the Earl Campbell trade. For 9 more AV than the RG III trade, they received 24 more AV in return.

Best of luck to the Rams. I hope their picks work out well for them.

The recent success of DeMarco Murray has energized the Dallas fan base. Felix Jones is being spoken of as if he’s some kind of leftover (I know, a 5.1 YPC over a career is such a drag), and people are taking Murray’s 6.7 YPA for granted. That wasn’t the thing that got me in the fan circles. It’s that Julius Jones was becoming a whipping boy again, the source of every running back sin there is, and so I wanted to build some tools to help analyze Julius’s career, and at the same time, look at Marion Barber III’s numbers, since these two are historically linked.

We’ll start with this database, and a bit of sql, something to let us find running plays. The sql is:

select down, togo, description from nfl_pbp where season = 2007 and gameid LIKE "%DAL%" and description like "%J.Jones%" and not description LIKE '%pass%' and not description LIKE '%PENALTY on DAL%' and not description like '%kick%' and not description LIKE '%sacked%'

It’s not perfect. I’m not picking up plays where a QB is sacked and the RB recovers the ball. A better bit of SQL might help, but that’s a place to start. We bury this SQL into a program that then parses the description string for the statement “for X yards”, or alternatively, “for no gain”, and adds them all up. From this, we could calculate yards per carry, but more importantly, we’ll calculate run success and we’ll also calculate something I’m going to call a failure rate.

For our purposes, a failure rate is the number of plays that gained 2 yards or less, divided by the total number of running attempts, multiplied by 100. The purpose of the failure rate is to investigate whether Julius, in 2007, became the master of the 1 and 2 yard run. One common fan conception of his style of play in his last year in Dallas is that “he had plenty of long runs but had so many 1 and 2 yards runs as to be useless.” I wish to investigate that.


In late 2007 I had grown interested in what positions were drafted where, and posted some results on a football forum, derived from the draft data on drafthistory.com. It’s 2010 and I recalled that there was a result that didn’t get published on the forum — or maybe it did, it’s been a while –a ranking of teams by the number of total draft picks they had. In this study, we’ll be considering the period  from 1994 to 2010, as that 1994 is the beginning of the seven round draft. Data again come from the pages of drafthistory.com.

To  note, 1994 to 2010 is a 17 season span, in which 272 regular season games were being played.

We’ll pick four teams out of the 32, and consider their records in that span of time.

New England Patriots: 180-92, 12 seasons 10 wins or more (8 consecutive), 15 seasons 8-8 or more.

Tennessee Titans: 143-129, 6 seasons 10 wins or more, 10 seasons 8-8 or more.

Green Bay Packers: 170-102, 11 seasons 10 wins or more, 15 seasons 8-8 or better.

Philadelphia Eagles: 154-116-2, 10 seasons 10 wins or more, 12 seasons 8 wins or better.

What do these four teams have in common? They accumulate draft choices, and they do so better than almost all teams in football. When I recharted the same data above in terms of teams (as opposed to positions), those four were at the top 5 of the chart, and Pittsburgh wasn’t very far behind.

Old chart. Now obsolete.

New chart, ordered by draft picks per year, and with win-loss-tie data

Now, there are some winning teams down at the bottom. The Vikings and Giants come to mind. But not so many, and if you look at teams that are noted to be consistent winners, they all seem clustered at  the top.

What other trends appear in this chart?

  • The four teams don’t care much for #1 draft choices.
  • The four teams care a fair amount about 2nd and 3rd round draft choices.
  • The four teams care a lot about late round draft choices (181 or lower).

What’s the advantage in second and third rounders? Cost, for one, and successful draft picks in these rounds are potential starters. They make the backbone of teams, if not the preponderance of All-Pros.

These kinds of teams pay a lot of attention to the seventh rounders. You can often get a 7th as a throw in in a trade. It’s the “additional value” you want whenever you swap players back and forth. Seventh rounders supply depth, supply bodies for special teams, and occasionally yield a starter or perhaps an All-Pro. Bill Parcells tended, in these kinds of picks, to look for people with raw skills and prototypical size and then give them time to develop. The kid with great measurables and a coaching deficit is a much better risk at 7th than higher rounds. Put simply, sheer numbers count.

Note: Updating this post as I’m getting better info. Please be patient.

A new sheet has been added above, sorted by picks per year. This more accurately reflects the drafting habits of teams that didn’t have a 17 year history during the period in question. 1994 was picked as a start because that’s when the 7 round draft began.

Trends to notice. Teams 1-10 have 6 winning teams, and 4 losing teams. Teams 11-20 have 6 winning teams, and 4 losing teams. So it’s entirely possible to win, and win a lot, in the middle “third” or so. The bottom 12 have a success rate of 4 winners to 8 losers. It’s not the best place to be.

Ranking draft position by total wins, and then adjusting the Ravens for their 15 seasons, results in a top 10 list as so:

  1. Patriots. 1st in draft.
  2. Steelers. 8th in draft
  3. Colts. 18th in draft.
  4. Packers. 3rd in draft.
  5. Broncos. 17th in draft
  6. Eagles. 5th in draft
  7. Vikings. 28th in draft.
  8. Cowboys. 16th in draft.
  9. Ravens. 21st in draft.
  10. Giants. 27th in draft.

The Giants beat out the Titans for the 10th slot by half a game.

Update: on this blog in this article, we show a statistical correlation between winning and draft picks/year.