The following bracket contains our consensus predictions for this year’s tournament. For each matchup, we considered predictions from all of our win probability and point spread models, as well as the results of a simulation of 60,000 tournaments.
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Category Archives: Basketball
March Madness – 1st Round Betting
The following tables display our predicted point spreads and point totals for all 2017 first round games, separated by region. The play-in teams are marked with an * and will be updated as needed. As an example of how to interpret the tables, in the second row of the East Region table, our models have Wisconsin (-2.8) beating Virginia Tech by 2.8 points, and Vegas has Wisconsin (-5.5) beating Virginia Tech by 5.5 points. Positive numbers for both spread columns indicate that Team2 is favored. Additionally, our models have the total combined points scored in the Wisconsin-Virginia Tech game as 144.1 while Vegas has 137.
For more on the historical performance of our point spread and point total models, click here.
March Madness – 1st Round Probabilities
The following tables display our predicted probabilities for all 2017 first round games, separated by region. The play-in teams are marked with an * and will be updated as needed. As an example of how to interpret the tables, in the first row of the East Region table, our models give Villanova a 97% chance of beating Mount Saint Mary’s (and, conversely, Mount Saint Mary’s a 3% chance of beating Villanova). We have more content on the way, including 1st round point spreads and totals, as well as a look at how our numbers see the entire tournament field.
For more on our methodology, click here.
Similarity Score Tool
Select any 2016 drafted NBA player from the dropdown menu to view their top 10 most similar college basketball players historically. Similarity Scores provide insight into how a player will translate to the NBA based on how similar college basketball players in production, physical measurements and experience have performed in the NBA. For a background on the calculation of the similarity scores, see here.
Similarity Score Methodology
Introduction
“Buddy Hield is the next Stephen Curry”
“Brandon Ingram is a poor man’s Kevin Durant”
“Andrew Wiggins’ upside is Carmelo Anthony, but his floor is James Posey”
So often when we talk about NBA players, we do it through comparisons to other players, and with good reason—comparisons are a good way to quickly convey lots of information about a player. For example, if I tell you that a player had a Box Plus/Minus of 7.8 last season, you might get a vague idea of how good he is. If I then tell you that player had 19.5 points per game, you might have a slightly better idea, but it’s still far from the full picture. But if I claim that this player is the next Chris Paul, it immediately brings to mind an idea of not only how good he is, but also his strengths, weaknesses, and overall playing style. Maybe your mind also queues up a mental highlight reel of Chris Paul-like plays, for good measure. Comparisons quickly give a complete picture of a player which would otherwise require taking the time to slowly digest each number in his stat line.
Also, they’re lots of fun!
We’ve created our own set of similarity scores to make comparisons using math for the purpose of prospecting players coming out of college. Our goal is to produce a useful complement to our PNSP model. Where our PNSP model answers the question, “How valuable will this player be?”, our similarity scores aim to answer the question, “Who will this player be like?”
For a glimpse of some player comparisons, you can check out Similarity Scores for 2016 NBA Draftees, here.
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2011 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 Peak NBA Statline Projection 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 2011 NBA Draft Class, as well as a full list of PNSP’s top 20 players of the class.
2016 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 Peak NBA Statline Projection 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 2016 NBA Draft Class, as well as a full list of PNSP’s top 20 players of the class.
Peak NBA Statline Projection Model Overview
Introduction
The following is a walk-through of our NBA Prospecting model called Peak NBA Statline Projection (PNSP). PNSP is a prospecting tool that synthesizes numerous variables for college basketball players to predict their NBA success. PNSP seeks to project peak potential success of a college basketball player in the NBA by returning a single rating value (ranging from 0 to 100) that is derived from all available information on a given player.
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March Madness 2016 Recap
The 2016 tournament had good spots and bad – most notably Michigan State, whom my models ranked as the 1st/2nd best team in the field, but lost in their first game. Thankfully, there were some bright spots as well, led by Villanova, who was picked to win it all in only 2.5% of brackets submitted to ESPN, but my models had right up there with Michigan State as the 1st/2nd best team in the field. The sections below go into detail on some of the bigger hits and misses from the tournament as a whole, as well as the results for a few individual models.
Continue reading March Madness 2016 Recap