All posts by Marc Richards

2020 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 for the 2020 NBA Draft Class, and a listing of each player’s PNSP.

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2019 NHL Draft Prospect Sentiment Analysis

Continuing our analysis of 2019 NHL Draft Prospects, this article looks at Scouting Reports and tweets on 2019 NHL Draft eligible prospects. In hopes of capturing some additional information outside of the numbers as seen by the “public” scouts. If you follow our NBA Draft content, you are already familiar with our sentiment analysis on 2019 NBA Draft Prospects. For those that are unaware, sentiment is a form of NLP (Natural Language Processing) or more formally defined as “the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc., is positive, negative, or neutral.” Using Professor Bing Liu’s graded sentiment dictionary, we can analyze text to and identify who is more positively written about. For more information, please read our My Model Monday series article.

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2019 NHL Draft Role Probability Model

Our NHL Draft Role Probability Model uses prospects’ statistical production, physical measurements, league prominence, and team statistics to predict the likelihood that a players assume a specific NHL Role (i.e., First Line / Top Pair, Second Line / 2nd pair defensemen, 3rd Line / 3rd Pair, Fourth Line / 7th defensemen, Two-way player, and Non-NHL player). This Model expands on our NHL Translated PPG model in order to account for variables outside of Points Per Game, such as size, scouting ranks, caliber of leagues played in, etc. Continue reading 2019 NHL Draft Role Probability Model

2019 NHL Draft Prospects Translated PPG

This Friday, June 21st, 217 NHL prospects will be drafted with the hopes of becoming the next Sidney Crosby, Jamie Benn, or Evgeny Kuznetsov. Teams are faced with the difficult task of projecting young adults to the NHL, with some of those players coming from leagues that don’t even track second assists, and others coming from the highest levels of hockey. Analysts, Scouts, GMs, etc. will scour eliteprospects and hockeydb, checking out how many points a prospect produced in each league, and then try to use their small DB of memory to remember what NHL players also played in that league, and how they performed at the NHL level. Instead of using our 0.1 GB RAM human brains, we will use our 16GB RAM MacBook Pro to reference historical data necessary to translate point production across leagues for 2019 NHL Draft Eligible prospects.

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2019 NBA Draft Prospect Sentiment Analysis

If you are a frequent reader of Model 284, you can recall we did a sentiment analysis on 2018 NBA Draft Prospects based on scouting reports at The Stepian and NBADraft.com. For those that are unaware, sentiment is a form of NLP (Natural Language Processing) or more formally defined as “the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc., is positive, negative, or neutral.” Using Professor Bing Liu’s graded sentiment dictionary, we can analyze text to and identify who is more positively written about. For more information, please read our My Model Monday series article.

Continue reading 2019 NBA Draft Prospect Sentiment Analysis

2019 Similarity Score Tool

Select any 2019 NBA Draft prospect from the drop-down menu below to view their top 10 most similar college basketball players, as measured by our Similarity Score model. Similarity Scores provide insight into how a player will translate to the NBA based on how their historical comparisons have performed in the NBA. This model considers a player’s college production, physical measurements, and age/experience to generate their most similar historical players. For more background on the calculation of the similarity scores, see this article. 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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2019 NBA Role Probability Model

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 2019 prospect landing in a given role in the NBA.
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2019 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 for the 2019 NBA Draft Class, and a listing of each player’s PNSP.

Continue reading 2019 Peak NBA Statline Projection Model