In my first post, I aim to shelter NBA fans from the dangers of bad draft takes. If you want to see an example of this in the wild, just search your favorite team and “draft” on Twitter and gasp at the ridiculous optimism, pessimism, and trade suggestions that would make the involved GMs laugh. There are a few violations that seem ubiquitous. I discuss their alternative below.
The most common violation usually stems from finding the all time greatest (or worst) player at a team’s draft position and using that as a driving argument. The typical take goes something like this: “We’re picking at 5 this year. You know who went 5 in 2003? Dwyane Wade.” This is all kinds of wrong. By focusing only on one of the greatest players to ever be selected at that position, the other 32 players selected in that spot are being totally disregarded. Choosing the best or worst player misses the overall trend. To get a more accurate depiction of the pick’s value, the aggregate draft history must be taken into account.
Another common mistake comes from thinking that box scores show the whole story on a player. Trae Young had an incredible statistical season, but I do not think he is the plug-and-play scorer that the Twitterverse believes him to be. His usage rate and turnovers are a bit of a red flag, and his measurables are less than idea for the switch-heavy defensive rotations in the modern NBA. Additionally, his strengths might not be areas of need for every team in the draft. Teams like Dallas and New York that have already spent a lottery pick on a point guard will more than likely pass on his skill set. To see what kind of value a draft pick might have beyond the box score, it would be useful to look at advanced metric outputs historically in that slot. Interested in a rim protector? Try looking at Total Rebound % or Block % for Power Forwards and Centers. A 3-and-D wing? Take a look at True Shooting % and Defensive Win Shares.
Additionally, many of the hypothetical trades posited on Twitter are not only unlikely but also impossible. There are salary considerations that each team must reach in order for any trade to be allowed. Additionally, the statistical contributions going out must typically align with those coming in. The ESPN Trade Machine tests simulated trades for their validity and likelihood and is a good starting point for any hypothetical trade.
In order to see the value of a draft pick at the aggregate level, I built this tool to show the various advanced metric contributions for all first round draft picks since 1985 (the start of the lottery) for each of their first 5 NBA seasons. This tool shows the overall trend and also shows the clear outliers for every pick. In the default setting, it seems as though Value Over Replacement tails off after pick 10. Do point guards’ Turnover % go down over time? Can you still find a defensive stopper in picks 15-30? Use this tool to fine tune all your NBA draft hypotheticals and have the numbers to back it up.
Click here for a full screen version.
All data provided by Basketball Reference.