Category Archives: NBA

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.

Continue reading Similarity Score Tool 2017

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.

Continue reading NBA Role Probability Model 2017

NBA Playoffs Conference Finals

Thankfully the Celtics and Wizards delivered us one 7-game series in what was an otherwise surprisingly non-competitive second round. John Wall and Isiah Thomas both had breath-taking moments, but in the end, it was the notoriously cold-blooded Kelly Olynyk who closed out the Wizards just as the model predicted (OK, maybe not that last part). In the Western Conference, the Warriors are clearly bored, and the Spurs took down the Rockets in one of the strangest, most anti-climactic elimination games I have ever seen. Let’s hope that James Harden gets his talents back from the aliens in time for next season. Continue reading NBA Playoffs Conference Finals

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,

Continue reading 2017 Peak NBA Statline Projection Model

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.

Continue reading Minnesota Timberwolves Draft History

2017 NBA Playoffs Simulation

According to our NBA playoff model, the probability of the Clippers winning their first round match up is 0.41. However, what happens if they do win their first round matchup – and what is the probability of the Clippers making it to the conference finals? Or winning the NBA championship? While we can assume that these values would be less than 0.41, the initial predictions alone do not supply us with the answer.
Continue reading 2017 NBA Playoffs Simulation

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