NBA Role Probability Model is used to predict the likelihood that a given college basketball player becomes an NBA All-Star, Starter, Bench player, or does not make the NBA. The model uses individual college basketball season-long box score statistics, team-level statistics (e.g. strength of schedule), physical measurements, high school scouting ranking, position, and age/experience to predict the probability of each NBA role. For more detail on this model, see here. In the following table, you can find our predicted probabilities for the 2016 NBA Draft prospects landing in each category:
Continue reading NBA Role Probability Model 2016
With the 28th overall pick in the 2016 NBA Draft, the Sacramento Kings selected Skal Labissiere, who perfectly fit the bill of a modern-day NBA big man: nearly 7’0″ and roughly 215 lbs, armed with a 7’3″ wingspan and a smooth jumper. However, Labissiere’s production in his one season with Kentucky was extremely minimal. Though he was Draft Express’s preseason number 1 overall pick, he averaged just 6.6 points per game, 3.0 rebounds per game, and 1.6 blocks per game while playing a measly 15.8 minutes per game. Given this production, Labissiere seemingly didn’t warrant any draft pick at all. But not only was he drafted, he went in the first round. Why? Potential. The idea was that Labissiere could develop his tantalizing tools and become the all-star caliber player many thought he would be prior to his time at Kentucky. While that would have been a great outcome, it was still more likely that Skal would not reach that all-star potential at all. With the combination of this potential and an unproven track record, it seemed that Labissiere’s role in the NBA would be either be an all-star or a bench warmer—or maybe even out of the league! Contrast Labissiere with Frank Kaminsky, who earned the Wooden Award in his senior season at Wisconsin. Most did not envision Kaminsky as an all-star, but rather a 4th, 5th or 6th man in the NBA. He had more polish than Labissiere, but a lower ceiling. These two seven-footers had very different profiles coming out of college.
In order to capture the likelihood that players like Skal Labissiere become NBA all-stars and players like Frank Kaminsky become NBA starters, we have created an NBA Role Probability Model that seeks to predict what role an NBA prospect will play in the NBA. Adding this to our previous prospecting work, we now have three components to help evaluate NBA prospects:
(1) PNSP, which answers the question, “How valuable will a player be?”
(2) Similarity Scores, which tell us about playing style by comparison to similar players
(3) NBA role probability model, which answers, “what roles might this player fill in the NBA?”
For a glimpse of how the 2016 draft class scored on this model, check out NBA Role Probabilities for 2016 NBA Draftees here.
Continue reading NBA Role Probability Model Methodology
Previous installments in this series:
The Twins play their first game on Monday, so it’s time to wrap up our Twins rotation preview. On Thursday, it was announced that Adalberto Mejia will be the team’s fifth starter coming out of camp. Mejia is an exciting young prospect, but he has only pitched 2.1 innings of Major League ball. That’s not a lot of available PITCHf/x data. Rather than subjecting you to a deep dive into each of his 41 pitches for the Twins last year, we’re going a different route for our last preview.
Continue reading Twins Rotation Preview: Tyler Duffey and Jose Berrios
Previous installments in this series:
We’ve reached the last lock in the Twins rotation: Phil Hughes. Next week, we’ll start breaking down options for the fifth starter position.1 But before we get there, let’s break down Hughes’s game.
Continue reading Twins Rotation Preview: Phil Hughes
Previous installments in this series:
This week, we continue our Twins rotation preview by taking a look at Hector Santiago. Acquired from the Angels midseason last year, he figures to be a fixture in the Twins rotation for the 2017 season. It’s a good week to brush up on your Santiago knowledge, because you’ll probably be able to see him pitch in a meaningful game within the next couple of days! He’s on the Puerto Rican roster for the World Baseball Classic, and they’re playing three games in a row: they face Venezuela today, Mexico tomorrow, and Italy on Sunday.
Continue reading Twins Rotation Preview: Hector Santiago
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.
Continue reading Similarity Score Tool
“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.
Continue reading Similarity Score Methodology
This week, we continue our series on the Twins starting staff by taking a look at Kyle Gibson. For the series introduction and the first installment on Ervin Santana, look here.
Gibson’s 2016 season was a bit of a step backward; from 2015 to 2016, his ERA jumped from 3.84 to 5.07, and his FIP increased from 3.96 to 4.70. Nevertheless, Gibson is a lock for the 2017 rotation, and will try to bounce back. Let’s take a look at the tools he’ll use to do it.
Continue reading Twins Rotation Preview: Kyle Gibson
Last week, I introduced the Pitcher Count Predictivity Score, a new stat to measure pitch mixing. PCPS is a very general, simplistic measure. It has to be! There are so many different types of pitchers, with such diverse arsenals of pitches, that a stat must be very broad to apply to all of them. This broadness is an asset, in that it allows us to compare very different pitchers on a similar scale, but it also means that PCPS doesn’t tell the whole story about a pitcher.
Continue reading Twins Rotation Preview: Ervin Santana
Select any pitcher from the dropdown menu to view their Pitcher Count Predictivity Score and their offspeed percentage by count. The data is from the 2016 season, and contains all pitchers who threw at least 1,500 pitches. For a background on PCPS, see this article .
Continue reading Pitcher Count Predictivity Viewer