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I've spent a lot of time over the last few days reading about a relatively new statistic called "quality of pitch" (QOP), which assigns a numerical value to each pitch a pitcher throws. The values can then averaged together to come up with a pitchers average quality of pitch (QOPA) or you can look at a quality of pitch set of values (QOPV) as another tool to measure the performance of a pitcher. The purpose of this post is to provide a simple overview of this data as it may be referenced in future articles. Background QOP was first publicly introduced in March 2015 by Jason Wilson and Wayne Greiner. Since then it has been written in various publications such as "Baseball America", the "Fangraphs", and by Yahoo Sports! columnist Jeff Passan among others. Meanwhile, Wilson and Greiner have presented their findings at the 2015 SABR Analytics Conference. In short, this statistic was introduced and quickly regarded as a good tool to measures a pitchers performance in a way the baseball community has not previously done before. Computation QOP is computed by integrating velocity (MPH), pitch location, and pitch movement. Pitch movement is defined as the vertical break, horizontal break, breaking distance, and/or rise. These variables are put together and assigned a number 0 - 10, where 0 is a very poor pitch and 10 is an excellent pitch. The MLB average QOP is 4.5 and median is 5. Here is an example of QOP being used. Validation Wilson and Greiner have measured QOP against ERA, FIP, and SIERA which all produced a strong, negative correlation. That is, the better the QOP the lower the ERA/FIP/SIERA. Furthermore, a search of the top 10 2017 QOPA leaders for pitchers who threw 1,000 or more pitches provides you with a list of some of the more effective pitchers in baseball. Limitations As with all stats, QOP has its limitations. From a mathematical perspective anytime we are averaging numbers together the data can be skewed by outliers, and QOP is no exception to this rule. To help minimize the effect of outliers Wilson and Greiner have created a guide to determine the margin of error depending on the sample size. From a baseball perspective, QOP doesn't take into account of a pitcher who misses his spots. That is, if the catcher calls for a fastball high and inside but the pitcher throws it low and outside he could still get a high QOP score despite completely missing his spot. If technology exists for the location and break of each ball to be tracked, then I would like to see something developed that also accounts for the movement of the catcher's glove. Author's Conclusion Again, this post was solely meant to introduce you to this stat without diving into specifics on Twins pitchers. Personally, I look forward to using this stat and wouldn't be surprised if we start seeing it more and more in future posts by me or any other Twins Daily writer. Despite its limitations, I think it provides fans with a different, more insightful perspective than the traditional pitching stats (W/L, ERA, WHIP, etc.), especially when coupled with other SABR pitching stats. I also wonder how well this stat can be used to predict future outcomes. I look at the list above and a couple names surprised me, but specifically Joe Biagini who was also a top 10 QOPA guy in 2016 under the same criteria. A quick look at his fangraphs page shows that he hasn't been great in 162.0 big league innings. Is this the sign of a good pitcher who has just had some bad luck early in his career? Or is he the poster child for how finding the average QOP can, at times, be a misleading statistic? What do you guys think about this stat? Is this something you would look forward to seeing in future articles? What are your thoughts in the curious case of Joe Biagini?