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.
All posts by Marc Richards
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.
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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.
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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.
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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
2017-18 NHL Playoff Probabilities and Elo Ratings
Below is a table of our 2017-18 NHL Playoff Simulation and Elo Ratings. Rankings are based on Probability of Winning the Conference Championships. Tune in to this post for updates to these figures throughout the rest of the 2017-18 NHL Playoffs.
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2017-18 NHL Playoff Bracket
Below is our Model 284 consensus bracket for the 2017-18 NHL Playoffs as well as some Model Factoids. As you will see from our first round Predictions and playoff simulation results, we do not necessarily pick the model’s predicted winner for every single game, but use all available information (e.g. injuries, areas model might be lacking, etc.) to make the best prediction on each series.
My Model Monday: NBA Draft Scouting Text Analysis
With March Madness just wrapping up, the natural next step in the basketball calendar is to turn to the NBA Draft. At Model 284, we have created a number of different models projecting college basketball players in the NBA; these include: our Peak NBA Statline Projection (PNSP) which attempts to predict overall NBA ability on a scale of 0-100, Similarity Scores that capture style of play, and lastly, a Role Probability Model which puts a probability that each player ends up in a certain role in the NBA (All-Star, Starter/Sixth Man, Bench, or out of the league). One idea which has come up repeatedly in our draft analysis is incorporating scouting or subjective analysis into our draft models. Rather than using a rubric of someone’s scouting grades, I wanted to do a text analysis of scouting reports already written. Since DraftExpress recently folded and moved to the dark side with ESPN Insider (probably a smart business decision), I used NBADraft.net. Continue reading My Model Monday: NBA Draft Scouting Text Analysis
Model 284 Podcast: Final Four and Coach Koz Interview
On the fourth episode of the Model 284 Podcast, we dig into our model’s predictions for the final four and we are joined by St. Olaf Men’s Basketball Coach Dan Kosmoski to talk all things basketball, including his perspective on how the game has changed over the years, his coaching strategies, and more.
Listen on iTunes here: https://itunes.apple.com/us/podcast/model-284-podcast/id1356851364?mt=2
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Model 284 Podcast: Sweet 16 Matchups
In the third episode of the Model 284 Podcast, Sam Walczak and Marc Richards recap this past weekend of madness, go through our models’ predictions for the Sweet 16 matchups, and cover our probabilities for each team moving forward in the tournament.
Listen on iTunes here: https://itunes.apple.com/us/podcast/model-284-podcast/id1356851364?mt=2
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