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.
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.
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 .
On Sunday, the NFL ended its season with the Super Bowl. As soon as tomorrow, pitchers and catchers will report to Florida or Arizona to begin spring training. And in Minneapolis as I type this, it’s a balmy 42 degrees. I have no doubt that we haven’t seen the last of this Minnesota winter, but nonetheless, I’ll take it as a sign that it’s time to turn our attention to baseball. So let’s talk about pitchers.
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.
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.
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.
Continue reading Peak NBA Statline Projection Model Overview