A guide for ESPN"s NBA draft projection model

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While the rest of the world is focused on the NBA Finals, general managers across the league are also preparing for the 2016 NBA Draft, which takes place on June 23 in Brooklyn. Debates over Ben Simmons and Brandon Ingram are likely emerging in the Philadelphia 76ers war room, while the Boston Celtics front office is contemplating its best options with the No. 3 overall pick.

In preparation for the draft, ESPNs Analytics Team has dusted off its NBA Draft Projection model, which debuted last year on FiveThirtyEight.com. This model is designed to project the NBA success of college prospects early in their careers, or, more precisely, it is projecting a players statistical plus/minus (SPM) in years two through five in the league.

That time frame was chosen because it reflects the number of years a first-round pick is under team control, and does not penalize players for poor rookie season, which are often outliers for a variety of reasons (drafted to a bad team, needs time to develop, etc.).

The models main inputs are college statistics (adjusted for pace and level of competition faced), Chad Fords Top 100 Prospect Rankings and player information such as age, height, weight and position. It uses information from the 2001 to 2011 draft classes to predict SPM for players in later classes, with steps taken to adjust for the players that barely saw the court or never played in the NBA.

Like many models, a prospects scouts rank is by far the most important variable when predicting NBA success, but by adding other variables the model reduces the uncertainty in projecting SPM by about 10 percent compared to using Chad Fords Scouts rankings alone. Aside from scouts rankings, demographic factors such as age, height and BMI had some of the biggest impacts.

Not surprisingly, younger prospects generally achieved higher SPM in their first five seasons, but that is largely a function of the draft; players usually enter the draft when they believe they will be drafted, so the top players will generally enter at a young age.

Among the 14 opponent-adjusted college statistics included in the model, offensive rebounding percentage has the biggest impact on the projections for big guys and steal percent is important for guards. Its likely these stats are capturing a level of athleticism transferrable to the NBA and may be able to isolate a players skill in that area of the game. Three-point rate (percentage of shots taken behind the arc) also proved valuable, which could be a sign of the changing nature of todays game.

Bringing all of these variables together into a random forest survival model produces two main outputs. The first is a players draft grade, which inputs a players average SPM projection on a 0-to-100 scale. The players with the highest draft grades are most likely to be successful in the NBA but may not necessarily have the highest ceiling.

All-Star PctBust PctBrandon Ingram90.525%26%Kris Dunn88.918%26%Jakob Poeltl88.812%16%Ben Simmons88.225%35%Marquese Chriss88.221%31%

To understand the risk and reward of each prospect, a players SPM projection is also broken out into a players chance to play at the level of an All-Star, starter, bench player or bust early in his career. Based on this methodology, depending on the number of college prospects in the top 100 each year, there are expected to be about 2.5 All-Stars, seven starters, 30 bench players and 40 busts per college entry class.

Its important to note that this is measuring the percentage chance each player reaches these levels in his first five seasons. For example, Kyle Lowry made the All-Star team in each of the last two seasons, but he did not reach that level until his ninth season.

Jakob Poeltl is a great example of the value of both outputs. With the third-highest draft grade and lowest bust potential of any college prospect in this draft, Poeltl provides a safe option for a team looking for a big man. If that team has a top pick, however, they are likely looking for a superstar, and he does not provide the same upside as a number of other big men such as Simmons and Marquese Chriss. Teams are constantly weighing the risk and reward of each prospect, and these contrasting outputs should supplement the conversations already ongoing in NBA front offices.

The next and most natural question is how the model has done in predicting past classes. No model will be ever be 100 percent correct, but this one has proven to be effective and well calibrated. Since draft classes from 2001 to 2011 were used to train the model, we can look at the 2012 to 2015 classes to see how it played out.

Anthony Davis39%Nerlens Noel34%Marcus Smart32%Dion Waiters30%Thomas Robinson29%Otto Porter29%Karl Anthony-Towns27%Victor Oladipo24%Joel Embiid24%Andrew Wiggins20%*Pct chance to play at All-Star Level entering draft

Among the top 10 most-likely players to play at an All-Star level in the last four draft classes, there were certainly hits and misses. Some of the misses were a product of injuries and others were a result of the overall uncertainty surrounding the draft. Nonetheless, among this group, about three players would be expected to play at an All-Star level in their first five seasons, and given the experience of each player, that projection may not be too far off.

Looking only at the 2015 draft class, five of the top eight projected college players made the First- or Second-Team All-Rookie teams (seven of 10 spots were college players), with the top projected prospect, Karl-Anthony Towns, winning NBA Rookie of the Year.

Now, no algorithm or general manager will ever be able to perfectly forecast the NBA Draft. Every year there will be examples of players that slipped through the cracks and other seemingly safe picks not working out. One thing that the model does not explicitly measure (though it is accounted for in the scouts" rankings) is a players drive, leadership and intangibles.

Injuries are also another factor that cannot be forecast beyond accounting for a players BMI. Based on what can be measured, however, the model is a valuable and accurate tool to help sort out the players at the top of the draft and identify sleepers in the 2016 draft class.

Source: http://espn.go.com/blog/statsinfo/post/_/id/119567/a-guide-for-espns-nba-draft-projection-model