The NBA Role Probability Model predicts the likelihood that a given college basketball player becomes an All-Star, starter, bench player, or does not make it in the NBA. The model considers individual box score statistics, team-level statistics (e.g. strength of schedule), physical measurements, high school scouting rank, position, and age/experience to predict the probability of a player landing each NBA role. For more detail on how this model is formulated, see this article. The Role Probability model is one of three pieces that we use to evaluate the NBA potential of college and international players, with the other two being PNSP and Similarity Scores. In the table below, you can find the model’s predicted probabilities for each 2019 prospect landing in a given role in the NBA.
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Category Archives: NBA Role Probability Model
2018 NBA Role Probability Model
The NBA Role Probability Model predicts the likelihood that a given college basketball player becomes an All-Star, starter, bench player, or does not make it in the NBA. The model considers individual box score statistics, team-level statistics (e.g. strength of schedule), physical measurements, high school scouting rank, position, and age/experience to predict the probability of a player landing each NBA role. For more detail on how this model is formulated, see this article. The Role Probability model is one of three pieces that we use to evaluate the NBA potential of college and international players, with the other two being PNSP and Similarity Scores. In the table below, you can find the model’s predicted probabilities for each 2018 prospect landing in a given role in the NBA.
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NBA Role Probability Model 2017
The NBA Role Probability Model predicts the likelihood that a given college basketball player becomes an NBA All-Star, starter, bench player, or does not make the NBA. The model uses individual college basketball season-long box score statistics, team-level statistics (e.g. strength of schedule), physical measurements, high school scouting ranking, position, and age/experience to predict the probability of each NBA role. For more detail on this model, see here. This model is one of three pieces that we use to evaluate the NBA potential of college players, with the other two being PNSP and Similarity Scores. In the table outlined below, you can find our predicted probabilities of the 2017 NBA Draft prospects landing in each category.
NBA Role Probability Model 2016
NBA Role Probability Model is used to predict the likelihood that a given college basketball player becomes an NBA All-Star, Starter, Bench player, or does not make the NBA. The model uses individual college basketball season-long box score statistics, team-level statistics (e.g. strength of schedule), physical measurements, high school scouting ranking, position, and age/experience to predict the probability of each NBA role. For more detail on this model, see here. In the following table, you can find our predicted probabilities for the 2016 NBA Draft prospects landing in each category:
Continue reading NBA Role Probability Model 2016
NBA Role Probability Model Methodology
With the 28th overall pick in the 2016 NBA Draft, the Sacramento Kings selected Skal Labissiere, who perfectly fit the bill of a modern-day NBA big man: nearly 7’0″ and roughly 215 lbs, armed with a 7’3″ wingspan and a smooth jumper. However, Labissiere’s production in his one season with Kentucky was extremely minimal. Though he was Draft Express’s preseason number 1 overall pick, he averaged just 6.6 points per game, 3.0 rebounds per game, and 1.6 blocks per game while playing a measly 15.8 minutes per game. Given this production, Labissiere seemingly didn’t warrant any draft pick at all. But not only was he drafted, he went in the first round. Why? Potential. The idea was that Labissiere could develop his tantalizing tools and become the all-star caliber player many thought he would be prior to his time at Kentucky. While that would have been a great outcome, it was still more likely that Skal would not reach that all-star potential at all. With the combination of this potential and an unproven track record, it seemed that Labissiere’s role in the NBA would be either be an all-star or a bench warmer—or maybe even out of the league! Contrast Labissiere with Frank Kaminsky, who earned the Wooden Award in his senior season at Wisconsin. Most did not envision Kaminsky as an all-star, but rather a 4th, 5th or 6th man in the NBA. He had more polish than Labissiere, but a lower ceiling. These two seven-footers had very different profiles coming out of college.
In order to capture the likelihood that players like Skal Labissiere become NBA all-stars and players like Frank Kaminsky become NBA starters, we have created an NBA Role Probability Model that seeks to predict what role an NBA prospect will play in the NBA. Adding this to our previous prospecting work, we now have three components to help evaluate NBA prospects:
(1) PNSP, which answers the question, “How valuable will a player be?”
(2) Similarity Scores, which tell us about playing style by comparison to similar players
(3) NBA role probability model, which answers, “what roles might this player fill in the NBA?”
For a glimpse of how the 2016 draft class scored on this model, check out NBA Role Probabilities for 2016 NBA Draftees here.