Draft Picks – An Undervalued Asset

The NFL draft is tomorrow! In a recent post I talked about how the Browns trading back was probably the best strategy and the one that a new analytically-driven front office would probably pursue. I am going to do a few things with the formula developed on this blog, then I am going to expand on this concept and try to get an understanding on how many “valuable” players each team can expect and their relative value. Both the analysis that I did and the blog that was referenced use approximate value from pro-football-reference.com. Its important to note that I used the formula in the blog’s chart so my numbers will be slightly different due to rounding. Below are the draft picks followed by the value over an average draft pick. This means a value of 500 is 5 times more valuable than the average draft pick. The picks that are in the future are unknown in that we don’t know how these teams will finish in 2017 and 2018 and thus which draft picks they are giving up. What is shown is the range of those values. I sampled from a uniform distribution and the array represented is the range of percentiles from 0 to 100 in increments of 10. So they represent the [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100] percentiles where 0 and 100 represent the last pick in the round and the first pick in the round respectively. So if the Eagles finish last, the Browns get the 1st overall pick and thus the highest value for that pick.

  • Rams get (from Titans):
    • 2016 1st round (1 overall) – 497
    • 2016 4th round (113 overall) – 92
    • 2016 6th round (117 overall) – 54
  • Titans get (from Rams):
    • 2016 1st round (15 overall) – 265
    • 2016 2nd round (43 overall) – 175
    • 2016 2nd round (45 overall) – 171
    • 2016 3rd round (76 overall) – 126
    • 2017 1st round – [ 200, 209, 218, 229, 242, 257, 274, 297, 327, 375, 497]
    • 2017 3rd round – [ 106, 109, 112, 115, 118, 121, 125, 128, 132, 136, 140]
  • Eagles get (from Browns):
    • 2016 1st round (2 overall) – 437
    • 2017 4th round – [ 82, 84, 86, 88, 90, 93, 95, 98, 100, 103, 105]
  • Browns get (from Eagles):
    • 2016 1st round (8 overall) – 319
    • 2016 3rd round (77 overall) – 125
    • 2016 4th round (100 overall) – 103
    • 2017 1st round – [ 200, 209, 218, 229, 242, 257, 274, 297, 327, 376, 497]
    • 2018 2nd round – [ 141, 145, 150, 155, 159, 165, 170, 176, 183, 190, 198]

So which team made out the best? There are a few ways to look at this.

Trade Value Chart

From the data we gained from the Harvard sports blog we can look at total team value and value per pick. Below you can see that in general the Titans and Browns are going to add a lot of team value with their trade partners. On a per-pick basis things seem to favor the team that traded up. This is a flawed metric because it biases towards higher picks, but is interesting because the team may be adding value if they subscribe to the fact that top-tier talent is only found in the first round. Some additional explanations could be that the team feels the draft is top-heavy in talent, the team doesn’t have faith in its scouting departments ability to assess talent properly in later rounds, these quarterbacks are slam-dunk prospects, etc.

table

One Player Away

There is another way to look at this. What if the Eagles and the Rams believe they are one player away from contending? If this is the case then you may be able to justify the move in that it gives the team the ability to win now. Under this assumption, you would expect the approximate value of the team to be relatively high for the past year. In the following analysis I wrote a web crawler to gather the roster information for every team from 2005 to 2015 off of pro-football-reference.com. I then took the average approximate value for players that played 10 or more games for every team-year. The histogram of the average team AV can be found below:

team_avg_av

In general this is just an OK way to measure good vs bad. I wont ride or die on this metric to appropriately predict record. New England’s 15-1 team is at the top and Detroit’s 0-16 is at the bottom so it also isn’t terrible. Special note: San Francisco went from 3rd overall in 2013 to last in 2015. The talent dump on that team over the last few years has been insane. So where do the teams in this trade fit in?

  • Rams: 4.05 (23rd in 2015)
  • Titans: 3.65 (31st in 2015)
  • Eagles: 3.96 (26th in 2015)
  • Browns: 3.69 (29th in 2015)

In general this doesn’t represent teams that are one person away from contention as they all fall relatively low on the distribution of team-AV. So what would happen if these teams got an extremely exceptional first year starting quarterbacks (not likely, but what if). This charts shows AV by position from 2005-2015.

valuebyposition

So this means that an extremely high valued QB would be worth around 15 AV points in 2016. This is a good bit higher than the AV of other positions. So if the Eagles and Rams got great quarterbacks in this trade, what would their average team AV turn into?

  • Rams: 4.40 (~16th place in 2015)
  • Eagles: 4.30 (~ 20th place in 2015)

So basically these teams might get close to average in total team AV next year with extremely high quality quarterback play. For any quarterback. Not just a rookie. This suggests that the teams may have issues that can not be fixed with solely elite quarterback play.

Is the Trade Worth the Quarterback?

Lets play a scenario game to emphasize the importance of the draft picks over the top overall pick. I created a Bayesian hierarchical linear regression model to predict the expected AV points added per game played by draft pick and draft position. The cliff-notes on the model is that the slope and intercept of the line for expected AV versus draft position is unique by position, but is assumed to come from some underlying distribution. The reason I noted that it was Bayesian is the fact that I can sample from the underlying distribution of the slope and the intercept. Which means I can create simulations from the posterior distributions. To summarize this we are going to look at three charts. The first chart represents the histogram of the expected AV of every player per game since 2005. So I took the sum of a player’s AV and divided by games played. I multiplied this out to get an expected value for a full season (16 games). The second chart is some summary statistics. The last chart is the expected number of players each team could add through the traded picks at different cutoff criteria.

Screenshot from 2016-04-28 02:40:03

table2

table3

This is where it gets pretty interesting. It looks like the Eagles and Rams actually maximized their likelihood of drafting an extreme outlier. This makes sense intuitively as well due to the fact that most of the talent (and quarterbacks) are usually found at the top of the draft, both of which cause greater AV scores. The interesting thing about the chart is really in the > 6 AV range. These are highly exceptional players (in the top 80th percentile). In a million simulations The Browns net out 1.52 more expected high level contributors and the Titans net out 1.79 more expected high level contributors. To put this in perspective, some of the players that netted out between 6 and 8 AV are: B.J. Raji, Jordan Mathews, Andre Ellington, Dominique Rodgers-Cromartie, Greg Olsen, Akeem Ayers, and Allen Hurns.

In Conclusion

I think in general the Browns and Titans got the better ends of these deals. I think there is clear evidence here that if you are one player away (preferably a quarterback) from contention the trade can sometimes make sense. For these quarterbacks I don’t really see it, but I could be wrong. This post does a great job comparing these quarterbacks to other quarterbacks that have come out of the draft. We also saw that all four of these teams seem to be more than a single player away from being a contender. When this is the situation, we have seen that having more picks is much more valuable for the franchise.

This post doesn’t even go into the benefits of having rookies based on the rookie pay-scale. So imagine more rookie contributors to your team and what that means for cost savings to sign top free agents and lock up top home-grown talent. I might have to do that analysis in a future post.

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