On twitter the other day I noted that statistical analysis is the worst way to select draft picks, except all of the others. Actually though, best practices would be to combine scouting informaton and analytic analysis.
There are a number of things scouts and video reviewers are able to see that stats can not. For example, for lack of a better word, character is a tremendous variable in a young player's development. Some of that shows up through the stat sheet, poor decision makers will have worse shot selection and more turnovers, and hustle stats like boards and steals show hustle. But, many apects of ability and willingness to learn or put in gym time or deal with adversity are less easy to infer.
It is well known that player stats are less stable when they change teams, with different teammates around them they get different opportunities and fill different rolls. Taking situation into account can help adjust the numbers players put up, at least at the margins. (It is dangerous over imagine how a player would blossom in a different environment.
Essentially, the difficulty, I suspect, is not collecting the information but in weighing it properly and perhaps avoiding information cascades or falling for particular players early in the process. Since I am writing this as the Nets/Kidd meltdown is taking place, I should note that some teams seem to have less developed processes than others, or none, it would seem.
Following a clever idea by Layne Vashro and his Humble model, I tested actual draft place with my model both to see if it added independent value not seen by the stats and to see how it change the effects of some of the stats.
This is the second run of what I call the Scouting Informed Model, using the data used to train the P-AWS 2.0 model. It's also it is the first that applies the actual draft order to the 2014 draft class rather than using the Draft Express rankings as a proxy.
Essentially the model uses the historic value of the pick number balanced against the player's statistical projection to arrive at a new value. The good and bad of this method is that each front office is assumed to be equal and each draft class is assumed to be average.
I have also looked at Chad Ford's Draft Tier rankings, which potentially cold be better as they use a consensus of NBA team front offices and allow them to rank prospects on a historical scale, so there is not the same number of say first tier players every year. Unfortunately, there aren't enough years to properly test that theory yet.
Alternatively access to the major pieces of information historically rated in scouting, whether character issues or pick and roll defense would probably help the model more than an overall rating. With the scouting information deconstructed the more independently valuable pieces could be emphasized while those that show less independent value would be excluded.
But, even so, draft position in model testing retains some independent ability to improve the model's results. All things considered, this is likely to be the most accurate of my current models, though after rookie year my research indicates that draft position loses most of its predictive power once rookie playing time and production is considered.
To get to the actual players, here's how the first round looks. The table is ordered by draft order, with the Scouting Informed AWS Model (SI-AWS) score shown, SI-AWS rank and the Reach/Steal rating, which is simply the difference between where they were drafted and the model rank (including the knowledge of where they were ranked). Reaches are negative numbers representing players possibly drafted too high and Steals are positive are more likely to perform better than the typical result for their draft spot.
|Name||Team||LEAGUE||Drafted By||Draft Position||Scouting Informed
|Marcus Smart||Oklahoma State||NCAA||BOS||6||6.15||6||-|
|Elfrid Payton||La Lafayette||NCAA||ORL||10||5.26||15||-5|
|Dario Saric||Cibona Zagreb||Adriatic||PHI||12||5.36||14||-2|
|T.J. Warren||N.C. State||NCAA||PHO||14||4.88||20||-6|
|Adreian Payne||Michigan State||NCAA||ATL||15||3.35||39||-24|
|Gary Harris||Michigan State||NCAA||DEN||19||5.17||16||3|
|P.J. Hairston||North Carolina||NCAA||CHA||26||3.64||33||-7|
To look at some of the highlights
- The Scouting Informed Model gives considerable deference to the top picks, otherwise Wiggins might show up as more of a Reach.
- The biggest Steals in the first round go to Memphis for Jordan Adams, Houston for Clint Capela and San Antonio for Kyle Anderson. It's not a coincidence that these are three of the most analytically inclined front offices around.
- The biggest Reaches were C.J. Wilcox, Rodney Hood, Adriean Payne, Nik Stauskas and Zach LaVine.
- Rodney Hood is kind of the classic reach candidate because of his scoring and size for position
- Adriean Payne is one of the players the model has consistently disagreed with the scouting. I sort of suspect his game may translate better than the model projects, but at 15 I think there was more talent on the board than could justify grabbing him there.
- Stauskas and LaVine look too one dimensional to grab there.
In the second round I think of reaches as being something less of an issue, the model value between te 50th and 70th pick is less dramatic than the difference between the 5th and 25th. However, there are some potential steals that should be highlighted, basically getting a first round level prospect in the the second.
Not surprisingly the model had the most disagreement with the draft positions in the second round, one because the value of scouting information is so minor given the historic lack of value, especially in the last part of the 2nd. In addition, with the level of difference between prospects relatively small it is easy for team evaluation and the model's evaluation to diverge.
That said, there were some real value player on the board who I think have a chance to be the next Chandler Parsons or Paul Milsap type players.
|Name||Team||LEAGUE||Drafted By||Draft Position||Scouting Informed
|Cleanthony Early||Wichita State||NCAA||NYK||34||2.98||49||-15|
|Nikola Jokic||Mega Vizura||Adriatic||DEN||41||5.76||10||31|
|Walter Tavares||Gran Canaria||ACB||ATL||43||3.95||30||13|
|Markel Brown||Oklahoma State||NCAA||MIN||44||2.87||52||-8|
|Cameron Bairstow||New Mexico||NCAA||CHI||49||2.65||59||-10|
|Vasilije Micic||Mega Vizura||Adriatic||PHI||52||3.64||34||18|
|Nemanja Dangubic||Adriatic||Mega Vizura||PHI||54||1.92||79||-25|
|Xavier Thames||San Diego State||NCAA||TOR||59||2.24||74||-15|
Memphis and Denver probably have the steals of the draft, according to the model. Memphis has become one of the most analytical teams under John Hollinger, so their inclusion is no surprise. Their pick at number 35, Jarnell Stokes put up great numbers and is a physical load, which makes it surprising to me he was still around.
Denver picked up Nikola Jokic, a very young Center out of Mega Vizura. Jokic was specifically made for the P-AWS model as a passing big man with three point range. There are issues with lateral speed on defense, as well as a lot of development. But, Jokic is a promising young player, in fact the model likes him more than Denver's frist round pick Jusuf Nurkic, though Jokic is further away from having an impact. The influence of the Nugget's head European scout, Rafal Juc, can probably be seen in their selection of two promising young Euro centers.
Milwuakee gets a good overall grade. Parker was a good choice and I love the Damien Inglis pick, even if the fit behind Parker, the Greek Freak and Middleton isn't ideal. Johny O'Bryant isnt rated as a good prospect by the model, especially with players like Dinwiddle, Jerami Grant and Glenn Robinson still on the board.
Speaking of Robinson, I put it at essentially 50/50 as to whether he or Minnesota first round pick Zach LaVine turn into better players.