Around this time last year I started to get interested in the NBA draft. I don’t know why; I watch very little college basketball and know nothing about scouting. I guess it just seemed an interesting problem, and one that has become increasingly more important to the Celtics. I started out by trying to figure out the level of player that each pick could produce if a specific team always made the ideal pick.

From there, I worked on a Draft Pick Value Card similar to the well known ones that exist for the NFL. Shortly after I posted mine, ESPN analytics guru Kevin Pelton published his own version (Insider paywalled). The chart I created used minutes played, win shares, and awards (All Star, ROY, MVP) to build a relative trade value between picks. With another year in the bag it now looks like this:

Personally I think it’s a pretty good balance between how teams actually operate (they always overpay to move up so we should factor that in) and how players really perform. The biggest take-away from this exercise was that the value of picks from the mid-lottery through the top of the 2nd round is much flatter than people realize. No team is looking to trade the 6th pick for picks 29-31, but the historic value says it would be fair, even factoring in the increased value of getting performance from a single roster spot. Pelton’s board shows this in even more stark relief; he values those same four picks wrapping around the 1st and 2nd rounds as having the same value, in total, as the second pick in the draft.

The reason for this is that players hit and bust at a much higher rate than fans may realize. In the moment, we always think that a draft goes two deep or five deep or ten deep. Realistically, the league is pretty good at identifying the absolute best prospects, ok at figuring out players 2-5, and not particularly good at ranking players 6-45.

Evaluating Historical Draft Picks

With that in mind, this year I decided to look at some characteristics of players who rise and fall relative to their draft position and class. To do that, I had to define “risers” and “fallers.” For that problem I simply used the total win shares every player produced in their first five NBA seasons and re-ranked every draft from 1985-2012 for picks 1-54. I used five seasons because, under the current CBA, that’s how long a team has guaranteed control over a 1st round pick. With that information, I classified players into groups.

Stay Top: Players drafted in the top-3 who were top-3 performers for their class. A player drafted 1-3 can’t really “rise” but if you stay in that top-3 group it’s certainly a successful pick.

Risers: Players who performed at least 30 spots better than their draft slot, or moved up at least 350 points of value based on my draft value card.

Fallers: Players who dropped at least 20 spots, or players who dropped at least 10 spots that accounted for at least 350 points of draft value.

No NBA: Players who are not in one of the first three groups and never played in the NBA

Other: The rest. This could be anything from a successful pick at a slot where you expect success (the 5th pick being the 5th best player) to a low value pick like the 40th being unmemorable.

From the 1,432 picks I looked at, this model flagged 8.7% as particularly good picks (Stay Top and Risers) and 8.4% as particularly bad picks (Fallers). This formula is definitely not perfect. For example, it’s very difficult for the 4th pick to be flagged as good because they would have to be the best player in the class while the 3rd pick could be the third best player and be flagged. That being said, I’m just trying to define a population of players to investigate, and I think the number and type of players it flagged is generally good.

With the right data, these populations could be used to investigate the correlation between good/bad picks and any variable. For example we could look at how much vertical leap correlates to highly successful picks, but good luck finding vertical leap measurements for every drafted player. As a starting point, I decided to look at the correlation with draft age. I chose age because, over the years, multiple draft studies have identified age as either the most or second most important variable (after consensus “big board” ranking if you consider that a variable) in projecting NBA success for draft picks.

Results by Draft Pick Range

At the very top of the draft, age doesn’t really matter. As I found in creating the draft card, the league is pretty good at identifying the very best players in a class. From the top-3, 75% of picks will either be successful (stay in the top-3) or at least not unsuccessful (not doing poorly enough to be a “faller”). There are only 81 players in this group to begin with and when you break them down by age, no clear findings appear. Whatever age a consensus top prospect is, they’re all about equally likely to succeed.

The picks from outside the very top of the class through the middle of the 1st round do start to show a trend. Players in this range have been significantly more likely to be classified as “risers” if they’re 20 years old or younger, and somewhat more likely to be outright “fallers” if they’re 21 or older.

This is probably the most important finding from the exercise. Picks near the top of the draft are valuable but also challenging. The reason age is found to be such an important factor in larger draft studies is that players in this pick range produce the majority of the total value in drafts and success in these picks is correlated to age.

The rest of the 1st round and the top of the 2nd round shifts back to a point where age doesn’t factor much. One thing to note here is that my method for flagging players made it much easier for a player picked 21-30 to be classified a “riser” than in any other range. This wasn’t intended, it’s just the nature of how the draft value chart assigns points; I would say it’s a weakness in the particular formula I used. You’ll also see that starting around the 31st pick there are no more “fallers.” A single draft simply doesn’t produce more than 30 players who make an impact on the league, so it’s hard to call any pick from here down a notable failure. A pick can be bad, but the assumed value just isn’t there to single it out.

In this range of picks there’s a lot of volatility but it’s not particularly associated to age. If there were still high schoolers in the draft I might suggest taking risks on them but there are tiny samples in that group and it’s moot, regardless. This is the range where I would say the strategy is that, as usual, if someone stands out in your prospect rankings you should grab them, otherwise you can probably draft for need and ignore age.

At the very end of the draft, the tables turn. Older players really stand out here. There are a few huge hits down at the bottom of the draft, but these are mostly players drafted 50th who ended up being the 15th best player in their class. It makes some conceptual sense that older players would thrive here. Realistically, the 200th best player in the NBA isn’t that much more talented than the 400th best. Opportunity divides them as much as anything. No one is going to commit a lot of playing time to the 45th pick so if that player gets a chance, they have to produce right away or they’ll never get another opportunity. An older player seems better suited to that environment, so maybe it’s as simple as that.

Application of Findings

I draw an arbitrary line in the charts between the 3rd and 4th picks but in 2016 it seems like the consensus only goes two deep at the top so the 3rd pick is probably better placed with the second group than the first. If you do that, the Celtics have two picks where youth should be a major consideration. Especially with the third pick, I find it hard to justify selecting 22 year olds Kris Dunn or Buddy Hield. Even at the 16th pick I would suggest that a team needs to have a player rated significantly higher than all other options if they’re going to take someone over 20 years old. If you think that Denzel Valentine is the 8th best player in the class and the next best available player is 14th on your board then you take Valentine at 16; if there’s a 20 year old even close in ranking, I would suggest taking the kid.

One thing to note is that players under 19 have not performed all that well in the 4-20 range, but again the sample is tiny. There simply weren’t enough very young players taken between when Kevin Garnett took the cap off prep-to-pros and when David Stern put it back on. I would not downgrade Dragan Bender, who is only 18, because I think the case for “under-21 is good” is much stronger than the one for “under-19 is bad.”

The picks at 23, 31, and 35 may come down to “stashability” more than anything else (if they aren’t traded) but if the roster crunch were not a factor I would say you no longer have to strongly consider age in this range. If there’s a 22 year old who the scouting team loves, you should grab them.

At the very end of the draft you’re again left considering roster management but if you want to find a useful role player, you may as well just take someone old and ready to contribute. If you think you can carve out minutes for your 45th pick it may as well be Malcolm Brogdon or Gary Payton II.

As I said at the top, I don’t watch college basketball (except the tournament and a few UConn games). I have no history in scouting even if I did. My general way for ranking draft prospects is to compile a few big boards, balance that with as many analytics projections as I can, and then put the players into tiers. With age as a major concern for the Celtics’ high draft picks, my own personal Completely Uniformed Big Board looks something like this:

You can follow me @dangercart but my feed tends to get pretty random in the offseason. If I can find a way to mine the data, I would like to use this same model to work out some other variables like college PER or BPM, or combine measurables. I don’t have much hope for actually doing that though, because the data is difficult to collect. The data used for this post can be downloaded via Dropbox. It was sourced from Basketball-Reference with player ages from Draft Express.