Category Archives: Basketball

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 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 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.

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Game Predictions | March Madness 2019

The tables below provide our predictions for all games in the 2019 NCAA Tournament. The tables will be updated after each round of the tournament, with new games/predictions added as they become available. A brief description of how to interpret the predictions is provided above each table, and a summary of the historical accuracy of each model is provided at the end of the post.
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Checking in on the Model 284 March Madness pool

The first week of March Madness is in the books, and so far the tournament has been more predictable than usual. Five teams seeded 11 or higher made it past the first round, but only one of those—Oregon—made it through to the Sweet 16. They, along with five-seed Auburn, were the only teams to advance through the first two rounds who weren’t seeded four or lower.

All this chalk means the brackets in our Model 284 pool are in pretty good shape. None of you has lost your champion or runner-up yet, a remarkable occurrence given that thirteen different teams were finals picks of one bracket or another. Fifty-eight of your seventy-two entries haven’t lost a single Final 4 team. Thirty-one of you could still have a perfect Elite 8 as well. Last year at this stage, the leader had 470 points. This year, in a pool of almost the same size, thirty people have a higher score than that. And it’s not just our pool who has had it good; for the very first time, someone in the NCAA Bracket Challenge picked the first two rounds perfectly.

So long story short, your brackets look pretty good. All of you correctly picked all of the one- and two-seeds, save for the brackets “JGR Official” and “Official 2”, which officially got too cute by picking Montana over Michigan. Liberty, Iowa, and Baylor’s victories threw you off the most, but even still, each of those teams were picked by around a quarter of you.

Your Sweet 16 picks were equally impressive. Everybody picked Duke to make it to the second weekend, though UCF came dangerously close to proving you all wrong. Save for one, each given Sweet 16 team was picked more frequently than the three others vying for its spot. The exception was 12-seed Oregon, and even then, they were picked by more of you than 4-seed Kansas State and almost as many as 5-seed Wisconsin, Oregon’s two lower-seeded competitors.

Looking ahead to the rest of the tournament, it’s anybody’s game. With everybody’s finals picks still in the running, the rest of the tournament will be an exciting watch! Good luck to all of you.

Model 284 Bracket | March Madness 2019

Below is our Model 284 consensus bracket for the 2019 NCAA Tournament. As you will see from our Game-by-Game Predictions and Tournament Simulation results, our bracket does not necessarily advance our model’s predicted winner for every single game. Rather, we use a combination of (1) our model’s individual game predictions, (2) our tournament simulation results, (3) injuries / other factors not captured by our models, and (4) consideration of public picks – to make sure we are differentiating our bracket enough from the most popular choices. For those interested, here is a bracket filled out purely using model predictions. Continue reading Model 284 Bracket | March Madness 2019

Tournament Simulation | March Madness 2019

The article below shows how our models view this year’s tournament field from a simulation perspective. Each table shows the % chance that each team advances to a given round of the tournament (e.g., in the first table, Duke has a 97% chance of advancing to the 2nd round). These figures are calculated based on 60,000 simulations1 of this year’s tournament, which were performed using win probability predictions from our models. Note that play-in game teams are subject to change and are denoted with an *. Continue reading Tournament Simulation | March Madness 2019