This is a companion article to my piece about draft steals, found here. However, instead of discussing players who fall in the draft, I will be taking a look at players who teams pick ahead of where they were projected. These “stretches” are the opposite of “falls” in terms of fan reaction: fans love to see a team snag a player who was projected much higher, but generally dislike when a player is taken ahead of projections. They view this as a “reach”, a player who could have been had later in the draft, but was picked instead of more talented prospects.

Reaches are seen quite frequently as flaws of the front office: an obsession with foreign born players, or with seniors who can shoot, or an avoidance of better prospects who might have attitude issues. All these and more are brought up as to why teams pick certain players ahead of projections. Fans will rarely sit back and say, “Huh, our GM must have just really liked this guy”. To be honest, I don’t necessarily blame them: NBA fans who follow the draft and college basketball often get attached to prospects who they like, and are frustrated when those players are passed over for guys who they don’t approve of as much. I became curious about how often teams make good picks with these reaches, especially when compared to picking players who fall in the draft.

The methodology will be similar to the previous article. Draft Express’s mock draft will be used as a guideline for where players were predicted to be drafted, and then juxtaposed to the position where they were actually picked. Prospects had to have been selected at least nine spots higher than their projection to be considered, and were chosen from the 2008-2013 drafts. Being a “good pick” is based on how much value the players have provided at the actual spot they were drafted, matched against a rough estimate of what a player in that area of the draft usually contributes to a team. For example, Tyler Hansbrough was drafted at 13, moving up from his projection at 22. He was a rotation player for a few years, but never a particularly good one, and is not signed to a team for the 2016-2017 season. Wayne Ellington, picked at 28 in the same draft, has had a similar career to Hansbrough, but because he was drafted 15 spots later is considered a good pick while Tyler was not. With all that said, here are the results.

Again, similarly to the number of players who fell drastically through the draft, the amount of “reaches” per year has slowed. The 2008 and 2009 draft each had eight reaches, while each draft since has only had five. I believe this decline partially relates to an increase in scouting and statistic modelling, with teams coming to closer evaluations on players than they were 10-15 years ago. Another potential reason there are fewer stretches is that GMs are drafting more safely, going for a solid single or double instead of swinging for the fences and potentially whiffing. Even if they really like a player who might have slipped through the scouting or analytics cracks, or just have decided that the prospect in question defies those measurements, General Managers are probably cautious of taking a player ahead of his predicted range. This is because they face more criticism for a prospect not turning out if it is someone they reached up to take, rather than someone who fell to them– who by the conventional wisdom was a smart pick.

Therefore, prospect reaches, especially farther up in the draft, signify that the front office is extremely confident in the prospect in question. And this table shows that their faith was rewarded at a relatively high rate. The average draft pick for these 36 players was 26.7, or very late in the first round. Considering the entire sample, 16 have provided good value for their positions, and two have embarked on their NBA careers so recently that their value is yet unknown. Discounting those two players, 16/36 is a 44% success rate, which is simply terrific for mostly late 1^st round and early 2^nd round picks. If teams always thought they had a near 50% chance of picking up a useful (or better) player with picks of this caliber, they would be far more valuable than they currently are.

Interestingly, the success rate for players drafted in the 1^st round was not higher than those who were merely selected earlier in the 2^nd round. Ten out of 23 players drafted in the first round were good picks—actually a little lower than the rate for the entire sample. However, while the rates are virtually identical, this actually means that the reaches were somewhat less effective. After all, the general rate of success for draft picks increases closer to the top of the draft, and while 43% is still impressive for the mid-end of the 1^st round, it is not nearly as remarkable as it is in the 2^nd.

Differences did appear, however, when looking at just how big a reach teams made when drafting. Teams who stretched 20+ spots to select the player of their choice made a smart decisions two out of six times, but that is a very small sample. When players were picked 15+ ahead of projections, only 4/15 produced good value. So, similarly to players who fell quite far in the draft, significant prospect reaches turned out well much less frequently than those with more moderate differences between their projected and actual pick. This makes sense. The farther from general consensus a team/general manager is on a prospect, the more likely they are to be incorrect. While teams should be confident enough in their knowledge to select players somewhat ahead of projections, they definitely need to be more wary the greater the gap is.

So far, I have found that both “steals” and “reaches” actually tend to pay off more frequently than the average draft pick. The first article helped validate the internet draft community while this one demonstrated that front offices and general managers do, despite claims to the contrary, know what they are doing. The next question, after sorting through all this, is why exactly things happen as they do. Do players who fall far in the draft fail because of their own issues (which is why teams pass on them), or do they crumble because of the drops themselves? Similarly, are players more likely to prosper simply because teams reached on them—they have greater support through ups and downs due to the reflection they cast on the organization—or due to the real success of individual scouts and analytic models? In the third (and final article) of this series, I will discuss these (and other) questions. Hopefully some reasonable answers will result.