The goal of this study was to determine which college basketball programs are the most efficient at developing their players to be selected in the NBA Draft. I gathered recruiting rankings and NBA Draft results in the “one and done” draft era (i.e. starting with the 2006 recruiting class and the 2007 NBA Draft) and assigned values for several criteria to determine how efficiently a player was developed in college for the draft.
The criteria used in calculating a development efficiency score included the player’s recruiting ranking*, number of years in school, and pick number in the draft in which they were selected. Aggregating the top 300 of each recruiting class, I calculated the cumulative percentage of players that ended up being drafted who were ranked at the given ranking or better. For example, every player who ranked #1 in their recruiting class was eventually drafted, so the cumulative percentage was 100% at this ranking. At #2, 11 out of 12 were eventually drafted, or about 92%. Cumulatively, the percentage drafted at #2 or better was 96%. The cumulative percentage at #300 or better was 12%, so to simplify my calculation for players outside the top 300 I set their cumulative percentage at 10%. In my formula, I used the complement (1 minus the cumulative percentage) to determine the efficiency parameter. This resulted in no credit for programs who developed a #1 recruit into an NBA Draft pick, as so far this has always happened; essentially the player was already going to be a pro regardless of college development so the program doesn’t get any development credit for these players.
I used a step-down parameter for years in school, where the less travel-time from stepping on campus to being drafted is valued the highest. 1 year in school is assigned a parameter of 1, 2 years .75, 3 years .5, and 4 years .25 (undrafted is 0). Finally, I assigned a percentile for the drafted position, where the top pick was assigned a parameter of 1 (60/60) and the last pick assigned 1.67% (1/60). I multiplied the 3 parameters together to give each player an efficiency score, which resulted in the highest score implying the greatest amount of development in college. In cases where a player transferred in college, his score was split proportionally to each school attended based on the percentage of total time in college spent at each school (e.g. 1 year at school A, then a redshirt and 1 playing year at school B resulted in 33.3% of the efficiency score for school A and 66.7% for school B). All player detail can be viewed on the Player List tab.
Finally, I summed the efficiency scores for all players and mapped them to the appropriate school in the Original Team Scores tab. The final results are available below.
*Using 247Sports rankings when applicable. In about a dozen cases, a player who should have had a top 300 ranking was omitted on 247, so I instead used his Rivals rank in these few cases. If a player was not in the top 300 on either site, he was assigned a rank of 301.
After receiving feedback on the original formula, I have added a Modified Team Scores tab to the sheets below with the following revision:
- Removed weighting for when in the draft a player was selected. The modification is now binary (0 for undrafted, 1 for drafted).
- Omitted all one and dones (i.e. set their scores to zero). It was suggested that if a player leaves after one year then he wasn’t actually being developed in college.
- Adjusted parameter for years in school to be tighter. Now the multipliers are as follows:
- .9 for 2 years
- .8 for 3 years
- .7 for 4 or more years