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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2018 NBA Finals
Through three rounds, we have gone 10/14 in series predictions. Two of these incorrect picks have come from series involving the Cavaliers. As we have seen in prior years, our models have a hard time assessing the Cleveland Cavaliers because they consistently under perform in the regular season. In the Championship round, our models are siding with the Golden State Warriors. Four of our five models picked the Warriors to win, and the average probability across the five predictions is 0.69. We will continue to update the table below following each game in the series!
2018 Peak NBA Statline Projection Model
Peak NBA Statline Projection (PNSP) is a model used to project NBA success for college and International basketball players. PNSP considers each player’s individual and team statistics, physical measurements, high school scouting ranking, and age/experience. The PNSP model returns a single rating value from 0 to 100, with higher values indicating a “better” NBA prospect. We provide a detailed article outlining how PNSP is formulated here, and PNSP rankings from previous years can be found here. Below are a few highlights from PNSP’s ratings for the 2018 NBA Draft Class.
Model 284 Podcast: NBA Draft Models
On this episode of the Model 284 Podcast, Sam and Marc are joined by fellow Model 284er Jack Werner to give some background on our NBA Draft models, discuss draft theory and positional value, and look a bit at the 2018 draft class.
Model 284 Podcast: Hockey Translation Factors and Peter Lindblad Interview
In this episode of the Model 284 podcast, we catch you up on everything going on at Model 284, breakdown our Hockey League Translations Model, and interview professional hockey player Peter Lindblad to discuss analytics in hockey, professional hockey in Europe, Sunday punting, and more.
Hammer The Over: NBA, NHL and MJ vs LeBron
Evan and Fred take on the NBA and NHL conference finals while Tom gallivants around Vegas for another weekend getaway. The guys complain about everyone talking MJ vs LeBron which leads directly into an MJ vs LeBron debate. Evan and Fred also both pitch their newest business ideas to any entrepreneurs who may be looking to invest.
Hockey League Translation Factors: Methodology
I. Introduction
If you didn’t know, there are a lot of people in the world—7.6 billion to be exact! There are also a lot of people that play hockey. As a result, there are a lot of hockey leagues in the world. Wow. Okay, moving on… The National Hockey League (NHL) is seen as the premier hockey league in the world, but players don’t start their hockey career in the NHL, and most never make it to the NHL. Some would argue that it is possible to have a successful and prosperous hockey career even if you never play in the NHL. In this article, I attempt to quantify the differences between these leagues; more specifically, translating individual player production from one league to the next. This would allow us to say things such as Tony Cameranesi registered 50 points in 50 games, or 1.0 point per game, in the NCAA, thus you would expect him to produce xx amount in the AHL, yy amount in the KHL, zz amount in the NHL, etc.
Continue reading Hockey League Translation Factors: Methodology
My Model Monday: Understanding the Impact of Hits in the NHL
In the sport of hockey, we often value players that are more physical, especially those that additionally produce points. In this week’s My Model Monday, I explored the importance of hits on NHL hockey games. Continue reading My Model Monday: Understanding the Impact of Hits in the NHL
Hammer The Over Sports, Denny’s and Vegas
The guys talk NFL Draft, Wolves and Wild making their early exits, drama in the NBA, and whether or not anyone cares about the World Cup with the US Team watching from their couches. Plus, Fred nearly gets murdered at a Denny’s and Evan recaps the bachelor party in Vegas.
2018 NBA Conference Finals
The probabilities for our second round predictions were generated from four unique statistical models that are built on team and player level data from the regular season. Our prediction for each series comes from computing the average of these four models. Keep in mind that teams who had significant injuries in the regular season may be undervalued by the models. Likewise, teams who have players that played through the regular season but are injured for the playoffs are likely overvalued. For more information on our modeling techniques check out our methodologies page. Continue reading 2018 NBA Conference Finals