Category Archives: NBA Draft

Prospect Profile: Josh Jackson

Leading up to the 2017 NBA Draft, we will be diving into what our Draft Models tell us about this year’s top prospects. Our Draft Models include the PNSP Model, NBA Role Probability Model, and Similarity Scores which each provide unique ways of evaluating college prospects. Our Prospect Profiles will look at which stats positively/negatively affect NBA projections, unique data points from a player’s stats, and relevant comparisons to current NBA players. You can find links to all of our Prospect Profiles in the header menu above (NBA –> NBA Draft –> Prospect Profiles). In this article, we take a look at KU Freshman Josh Jackson.

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Prospect Profile: Lonzo Ball

Leading up to the 2017 NBA Draft, we will be diving into what our Draft Models tell us about this year’s top prospects. Our NBA Draft Models include our PNSP Model, NBA Role Probability Model, and Similarity Scores which each provide unique ways of evaluating college prospects. Our Prospect Profiles look at which stats positively/negatively affect NBA projections, unique data points from a player’s stats, and relevant comparisons to current NBA players. You can find links to all of our Prospect Profiles in the header menu above (NBA –> NBA Draft –> Prospect Profiles). In this article, we explore one of the more highly debated draft prospects, Lonzo Ball.

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Prospect Profile: Markelle Fultz

Leading up to the 2017 NBA Draft, we will be diving into what our models tell us about this year’s top prospects. These Prospect Profiles will look at which stats positively/negatively affect NBA projections, unique data points from a player’s stats, and relevant comparisons to current NBA players. You can find links to all of our Prospect Profiles in the header menu above (NBA –> NBA Draft –> Prospect Profiles).

In this article we dive into the consensus top ranked prospect Markelle Fultz from the perspective of our Draft Models. Our Draft Models include our PNSP Model, NBA Role Probability Model, and Similarity Scores which each provide unique ways of evaluating college prospects.

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Similarity Score Tool 2017

Select any 2017 drafted NBA player from the dropdown menu to view their top 10 most similar college basketball players historically. Similarity Scores provide insight into how a player will translate to the NBA based on how similar college basketball players in production, physical measurements and experience have performed in the NBA. For a background on the calculation of the similarity scores, 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 NBA Role Probability Model.

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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.

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2017 Peak NBA Statline Projection Model

Peak NBA Statline Projection (PNSP) is a model used to project NBA success for college basketball players based upon their individual and team college basketball statistics, physical measurements, high school scouting rankings, and college basketball experience. The PNSP model returns a single rating value from 0 to 100. A higher rating value indicates a “better” NBA prospect. We provide a more detailed article outlining how PNSP is formulated here. Below are a few highlights of PNSP’s ratings for the 2017 NBA Draft Class,

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Minnesota Timberwolves Draft History

Unfortunately for Timberwolves fans in the past decade, the most exciting part of the season has consistently been the NBA Draft. And for yet another offseason, this is the case. Although the NBA draft provides excitement in the possibility of landing the next Steph Curry, Tim Duncan, or Chris Bosh, the reality in Minnesota has been grabbing point guards that cannot shoot, 25-year olds such as Wesley Johnson, and black holes such as Derrick Williams and Shabazz Muhammad. While the last few years have presented promise by way of Karl-Anthony Towns, Andrew Wiggins, and Zach LaVine, the Wolves’ drafts have yet to bear fruit in the form of a playoff appearance. So let’s take a walk down memory lane and relive the agonizing decisions made throughout the McHale and Kahn eras through the lens of Model 284’s main Peak NBA Statline Projection (PNSP) draft model, NBA Role Probability, and Similarity Score Models.

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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:
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