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.
Category Archives: Basketball
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,
NBA Playoff Betting Models
Throughout the 2017 NBA Playoffs, we have been tweeting spread and total predictions for some games (e.g., Warriors -11, Game Total 220). This article provides some context to the numbers – how accurate are they? How are they being created? How are they interpreted? Do we use a Gregg Popovich variable? You can find all the answers below. Continue reading NBA Playoff Betting Models
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.
NBA Playoffs Round 2
The Bulls gave the Celtics a scare, the Jazz closed out what could be the last stand of the Clippers big three, and the Warriors still look absolutely unbeatable (even with Mike Brown stumbling around on the sideline). Most importantly, our NBA model swept the first round – going 8 for 8 on series predictions.
Continue reading NBA Playoffs Round 2
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
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.
2017 NBA Playoff Bracket
Our NBA Playoff model calculates win probabilities for each series matchup based on each team’s regular season statistics. We used multiple regression techniques to develop a variety of models. We arrive at our consensus winners by taking the average prediction from a handful of our most successful models. For a more detailed breakdown of these NBA models, and to see our 2015 & 2016 brackets, check out our NBA Playoff Bracket Methodology. Our model has picked 78% of series winners correctly, along with 15 of the past 27 champions. In other words, on an average year, it gets 11.7 of the 15 series winners correct and has a 56% chance of getting the champion correct. Our predictions for the 2017 playoffs are shown below:
Continue reading 2017 NBA Playoff Bracket