Earlier this week over at ESPN Insider I took a look at the average career value produced by each of the top 14 NBA draft picks (i.e., the lottery picks).

You have to be an Insider in order to read that piece, so let me briefly summarize what I did in order to get a career value for each player:

1. A player’s season value is equal to his regular season win shares plus his postseason win shares.
2. The player’s season values are ordered from best (highest) to worst (lowest).
3. The player’s career value is equal to 100 percent of his best season, plus 95 percent of his second-best season, plus 90 percent of his third-best season, etc.

In the Insider column I went on to give the average value for each of the lottery picks as well as the top three players and bottom three players for each slot.

In today’s post I’m going to expand on that idea a bit by building a model to figure out the expected value for each lottery pick*, then use that model to find some of the best and worst draft picks since the ABA-NBA merger in 1976.

* From here on out I will use the term “lottery pick” to refer to a top 14 pick even though the draft lottery did not start until the 1985 NBA Draft, not to mention that not all top 14 picks were technically lottery picks.

I decided to keep things relatively simple and build a model to predict a player’s career value based on his overall pick number. Using data from 1977 through 1994, I came up with the following model:

value = 61.07 – 14.17 × ln(pick)

For example, the expected career value of the fifth overall pick is:

value = 61.07 – 14.17 × ln(5) = 38.3

Here is the expected value chart for all of the lottery picks:

* The difference between the expected value of the pick and the expected value of the pick immediately preceding it.

The rest of this post will mostly be a data dump, but I’ll add some explanations when necessary.

First, here are the top 10 lottery picks since the ABA-NBA merger based on career value:

* The difference between the player’s actual career value and his career value based on where he was picked.

The next table shows the players since the merger who exceeded their expectations by the greatest amount:

The differences above are absolute differences. Here’s a similar table using percentage differences instead:

* The percentage difference between the player’s actual career value and his expected career value based on where he was picked.

For example, as the 13th overall pick Karl Malone would have been expected to finish with a career value of 24.7 but actually finished with a career value 466% higher than that ((140.0 − 24.7) ÷ 24.7).

Now here are the players since the merger who missed their expectation by the greatest amount*:

* I excluded active players from this table.

Finally, here are the 10 players who came closest to hitting their career expectation:

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