2013 NFL Draft - Evaluating the Day 3 trades

Note: See Day 1 evaluation here and Day 2 here


Time to dig into the trades from Day 3 (rounds 4 through 7) of the NFL Draft. As with the previous two days, here is a graphical representation of the trades from Day 3 of the draft as calculated by the Sports + Numbers draft value chart (explained here).

 

1. What is Cleveland doing? – After praising the Browns yesterday for taking advantage of a team using the old chart, making the Dolphins believe they received value equal to a fifth rounder for receiver Davone Bess when the Sports + Numbers chart shows the Browns only parted with the value of a sixth round pick, these moves look questionable. These were straight-up this year for next year one round ahead picks, so why the discrepancy? In an effort to bring some standardization to the process, I calculate the discount rate on picks as an actual discount rate rather than the NFL standard of “one round per year.” The effective discount rate of NFL teams, as found by Cade Massey and Richard Thaler, is somewhere in the vicinity of 136%. If you leave everything equal and vary the discount rate, the Browns would have to be using an annual rate of 61% to make the Pittsburgh trade “fair” to both teams and 23% for the Indianapolis trade. 61% is a pretty insane discount rate (think what would be used if I wanted to do an NPV for a loan shark) while 23% is still on the high end. Given that the new front office smell is still floating around in Cleveland, the Browns brain trust may be assuming that they have the luxury of time and trading at a lower discount rate than the standard, very high, rate that the NFL uses. The other option here is that the Browns are not using any new chart at all and they simply valued Bess at a 5th round level yesterday. As for the later trade with the Colts, given that Indianapolis came up very Lucky (pun intended) and had an expected win-loss of 7-9 based on their point differential, the Browns may have tacked a few extra slots of mean reversion onto their analysis of where the pick would be in 2014. Time (and Andrew Luck) will tell if that is a wise strategy.
 
2. Not a lot of evidence for new chart use – These trades simply do not line up according to the Sports + Numbers chart (or any new version). While the premiums involved are small, there is scant evidence here for a change in mindset of NFL teams. 

 

3. Thoughts on the existing chart – Looking at the trades through the Jimmy Johnson chart, it remains clear, as it has been on other days in this draft, that a lot of teams use it as both a starting point and ending point for negotiations.

2013 NFL Draft - Evaluating the Day 2 trades

Note - See Day 1 evaluation here and Day 3 here

As with yesterday, here is a graphical representation of the trades from Day 2 of the draft as calculated by the Sports + Numbers draft value chart (explained here).

1. Smart teams trading with each other - I don't think it's a coincidence that the two trades that stand out as being very close to equal value are both between Green Bay and San Francisco. The only way that those go through is if both teams accept a different perspective  on the value of picks. The slight overcompensation goes to Green Bay as the team trading down.

2. Draft picks are made out of people - Two trades involved real, actual players in combination with draft picks. Miami-Cleveland, shown at far right of the graph, shows only the picks involved in the trade. Given the 4.5% value of the gap, this puts Davone Bess's value equivalent to the 182nd pick overall, midway through the 6th round. The Jets' trade for Chris Ivory, late of the New Orleans Saints, is much easier. The 106th pick, worth 10.9% of the first overall, was traded straight-up for Ivory. 

3. More evolution than revolution - As with yesterday, most of the trades conform to the existing "Jimmy Johnson" chart in use since the early 90s. 

4. Cleveland pulls a fast one - According to this chart Davone Bess is valued at 1.7%, but because the values decline so much faster on this chart that would be the 142nd pick. Given Miami's conformity to the existing chart in other trades, this represents a bonus to the Browns as they gave up what they consider to be worth a 6th rounder and the Dolphins believe they picked up a 5th rounder. The 106th pick, exchanged for Chris Ivory, is worth 2.7%.

2013 NFL Draft - Evaluating the Day 1 trades

Note - See Day 2 evaluation here and Day 3 here

A few quick-hit thoughts on the yesterday's trades during the first round. First, here's a graphical representation of how each team did. The units, as always, are percentages of the value of the first overall pick.

2013 first round trades in the NFL Draft according to the Sports + Numbers draft value chart

1. Don't trade with the Patriots - Just don't. You won't win the trade. New England absolutely mauled the Vikings with this trade, getting a bigger haul than the Cowboys or Rams despite trading 11 and 7 picks, respectively, behind them. The excess value received by New England is roughly equal to an early 3rd round pick.


2. A new way of looking at deals - It looks like things might be moving closer to the reality of what the players are actually worth as the Oakland-Miami deal represents pretty close to fair value according to the revised chart...

2013 first round trades in the NFL Draft according to the existing "Jimmy Johnson" draft value chart

3. Long live the Jimmy Johnson chart - ...but there is still a lot of inertia around the classic chart spread around the league two decades ago. Only the Oak-Mia and Dal-SFO deals varied in any meaningful way. Everyone else - I'm looking at you St. Louis, Atlanta and Minnesota - paid out way too much value because their trade partners got them to deal on terms much more favorable to the team trading down.

4. Some additional reading - Both Chase Stuart at Football Perspective and I have put out analyses of which positions are successful (and which are not) in the first round. Two excellent reads if you have some time. Brian Burke from Advanced NFL Stats also had a post this week with some thoughts on economist Cade Massey's work on the draft (some with Richard Thaler) and the implications for NFL teams. If you enjoy the type of analysis on my site, go get much better versions of it at Brian's site and the links he cites.

Luck vs. Skill - Year over Year NFL Draft Performance

Note - I've gone back to this issue a couple times since this post and looked at whether there is any evidence for skill in sequential picks in a single draft and in picks made after trades up. Check them out via the embedded links.

Both Brian Burke and Chase Stuart recently wrote on the phenomenon of drafting in the NFL and the randomness that goes with it. Chase in particular wrote about the question of whether some teams are better than others at drafting.

At the recommendation of my friend Sarah, I recently read Michael Mauboussin's book The Success Equation: Untangling Skill and Luck in Business, Sports and Investing. One of the many interesting nuggets in the book was that fund managers demonstrably exceed the number of multi-year "streaks" of outperformance we would expect to see if performance were truly random (identified in a paper by Mauboussin's son). 

Based on the data posted by Chase here, I built a shoddy, homemade version of a Monte Carlo simulator and ran 2000 versions of the actual teams/seasons. With a few small changes (the '99-present Browns are a sad, weak imitation of the pre-95 version and I won't have them associated with each other), I ran the simulations with the actual teams and years played. This ensured that we maintain equivalency (e.g., the 2003 Houston Texans can have a maximum streak of 2 because it's only their 2nd season and so on) and it was a fun chance for me to hit F9 2000 times because the iterations were not working.

The results are below but I found two parts very interesting. 

First, the outperformance was never significant at a 90% level outside of the first year, meaning there are fewer streaks started than would be expected. These extra seasons of outperformance show up in the form of slightly longer streaks (actuals average 3.96 years while simulated streaks average 3.76).

Second, there are some terrible teams in the NFL. The underperformance is significant at the 90% level from seasons 6 through 13 - far more teams are bad at drafting longer than we would expect by chance alone. For those interested, these teams are the Falcons (season 10 in 1990), Colts (1984), Cowboys (1987), Raiders (2007) and Chargers (2000). 

The next level analysis here is to segment by team management so that different eras are recognized as discrete units rather than part of a larger whole. We should really see some differentiation there as "good" GMs stay employed and rack up streaks.

Leave the running back, take the quarterback (or center, or tackle)

For a number of reasons, drafting NFL players is a crapshoot. There are a wide variety of ways for a team to end up getting less than they bargained for – and less than the draft slot could have provided. A player could have a career ending injury before they even hit the field or they could even turn out to be not very good. I can’t help with those things.

What I can help with, however, is the third way a team could end up with a sub-optimal choice in the draft: ignorance of positional value.

Looking back over the past 19 drafts and using career value in terms of % of the salary cap, we can rank the actual value delivered and compare it to the draft slot where the player was selected. For example, Tim Couch was selected first overall by the Cleveland Browns in 1999 and delivered a career value of 18% (units are % of the salary cap to normalize across seasons). The actual most valuable player to date from that draft is Donovan McNabb with a career value of 98%. By those two numbers, Tim Couch’s selection would be rated as an unfavorable error of 80%. McNabb was drafted second and outperformed the actual second most valuable player in the draft (Champ Bailey with 76%) so he gets a favorable error of 22%.

Why should teams keep taking quarterbacks early?

Outcome of first round picks since 1994

Because that’s where the money is! Wait, that’s why people rob banks. Being by far the most valuable position on the field, QB is the position where even an average starter can justify the expectations of a first round pick. A starter at QB is worth the same as a pro bowl-caliber running back. Speaking of running backs, they perform… not well at this metric. Combining the tendency to outright disappoint and the tendency toward short careers leaves teams with an asset that underperforms the expectation for the draft slot.

What to do with the rest?

Note first of all that all errors here are negative. Because these are first round picks and this method is a zero sum game, if any fail to end up in the top 32 of their draft’s actual value they will result in a negative (unfavorable) error. While individual picks can be positive – if the player drafted 4th turns out to be the 2nd best in reality – the effect of 2nd-7th rounders outperforming drags down the first round numbers.

QBs may be roughly split between those two underperform and those who either meet expectations (the best you can do at the first pick) or exceed them, but they have a huge standard deviation that speaks to the boom or bust nature of high QB picks. Players like Tim Couch, Akili Smith and Cade McNown highlight the risks – and that’s just from the 1999 first round.

Offensive linemen turn out to be the most reliable bets in the first round, with relatively low errors and standard deviations. Receivers and running backs, on the other hand, represent the highest errors but have a glimmer of hope in their standard deviations. Despite the average error there are still numerous examples of receivers and running backs who work out in the league.

NFL Draft Trade Machine

Welcome to the Sports + Numbers NFL Draft Trade Machine, a handy resource to see what teams can do with the assets they have and to evaluate trades as they happen. While I am biased toward my own value chart, I included several others so that individual users can select the one they prefer or look at the differences.

Make edits to the blue-shaded cells to customize for any scenario you want to look at and see where it lands on the graph (with actual 2012 trades overlaid as black diamonds).




I've given in to my recent fixation on the NFL draft and decided to focus exclusively on it for the next few weeks. For those of you just stopping by for the first time, check out a few of my NFL-themed posts:

• Draft-themed
• A summary of the various draft value charts 
• Detailed introduction of my own draft value chart and analysis of whether it fits the results on the field
• Analysis of whether the NFL combine helps or hurts accuracy of draft picks
• The top ten drafts of the salary cap era (split into 10-6 and 5-1)
• Other NFL
• A look at injury rates for running QBs and non-running QBs
• Two articles about how frustrating forecasts are as a Browns fan (actually analyses of future power rankings and beginning-of-season forecasts)

Additional notes on the trade machine:

• Sources for each chart
• Existing Chart: Here's a version at ESPN
• Sports + Numbers 
• Football Perspective
• Harvard Sports Analysis Collective
• Discount Rate - Cade Massey and Richard Thaler's paper, The Loser's Curse, provides the 136% number
• Mean Reversion - I've found this to be roughly 58% in the last 20 years
• 2012 trade data
  

Why doesn't paying win like it used to?

For a while in the late 1990s it seemed like the final standings in baseball were a foregone conclusion. The relationship between payroll and winning topped out in 1998 and 1999 with a correlation of .68 and 0.71. In 2000 and 2001 the relationship dropped to .32 and .31 as the Moneyball A’s racked up impressive win totals on a low budget. While this is a topic that has been addressed at length, I would like to add one more dimension to the discussion: locking in free agents.

Note that correlations differ somewhat throughout - whether calculated on opening day salary or end of year and all related variations. I am going by Dave Studeman's via the next link below.

Free agency only arrived in baseball in the 1970s so data on this phenomenon are already constrained. The collusion of the owners in the late 80s further clouded the issue and drove correlation down to extremely low levels. Dave Studeman at Hardball Times has the explanation:

In the first few years of free agency—the latter half of the 1970s—teams did take advantage of new opportunities by signing top talent to big bucks. It's no coincidence that this period coincided with the Steinbrenner Yankees' return to glory and the introduction of two bottom-dwelling, low-pay expansion teams (the Mariners and Blue Jays). These developments exacerbated the differences between the have's and have-not's.

Beginning around 1980, however, the picture changed as young, lower-paid talent began to make an impact on the pennant races. Players such as Eddie Murray and Cal Ripken in Baltimore, Rickey Henderson in Oakland and George Brett in Kansas City changed their team's fortunes before changing their payrolls. The Mets developed a gaggle of phenomenal, "cheap" young talent. This influx of top young talent helped change the picture in the early part of the decade. At the same time, bad contracts started appearing. The Angels became the first team known for its bloated, underperforming contracts.

Something else happened in the 1980s: collusion. In 1985, 1986 and 1987, free agents such as Andre Dawson, Tim Raines, Jack Morris and many others found no market for their services. It turns out that commissioner Peter Ueberroth had convinced major league owners that they should work together to refuse expensive, long-term contracts. The owners reportedly established standards of no more than three years for position players and two years for pitchers. As a result, average payroll actually declined in 1987.

The impact on the economics of winning was stark, and the correlation between wins and payroll reached two of their lowest points in 1986 and 1987 (0.17 and 0.15, respectively). Money was losing its power and competitive balance seemed possible. Trouble was, this was illegal. In three different cases, arbitrators ruled that the owners had colluded and eventually ordered them to pay damages.

What changed?

What I’m interest in is why the post-collusion, post-strike period of high correlation broke down. After the A’s (and others) pushed correlations down in 2000 and 2001, they popped back up into the 0.50 range by 2004 and hovered there for a while. The last several years, however, have drifted down to a 0.18 correlation in 2012.