Correlations of this strength are fairly significant amongst varying metrics throughout baseball. Therefore, if a pitcher were to increase their Swing-Miss% from year to year, there is theoretically a ~61% chance that you would see a subsequent decrease in their xBA. The data shows that approximately 61% of the variance in a pitcher’s xBA could likely be explained by their present ability to force hitters to swing-and-miss more or less often. This will be an unconventional article that focuses on sabermetric research, as my ultimate goal is for each of you to learn more about the process of player evaluation rather than provide my own evaluations for you.Īs can be seen near the top of the chart above, the goodness of fit value for this relationship is a very feasible correlation of r 2 = 0.61, which can be roughly translated to a hypothesis of sort: Throughout this report, I am going to let the charts tell the majority of the story, while simply guiding you through the process of how to interpret the data that lies in front of you. It is to be noted that opaqueness of each point represents each pitcher’s actual BA, which can be contrasted to their xBA on the y-axis. Right off the bat, there are some interesting observations that can be made in regard to pitchers that you may either be high or low on for the 2020 season. The next plot below shows the correlation between Swing-Miss% and xBA for all qualified pitchers in 2019 (Min. This can be observed in the chart below, as the r 2 value for Swing-Miss% and BA is 0.41, compared to xBA having a significantly more correlated r 2 = 0.61. As a disclaimer, actual Batting Average (BA) was not used as the dependent variable in this analysis because of the weaker correlation existing between Swing-Miss% and BA. Knowing that an obvious relationship between these two variables exists on the field, I decided to dive a bit deeper in search of how strongly they were correlated. This section delves into the linear relationship between the number of swings-and-misses that a pitcher is able to induce and the expected batting average (xBA) that they possess. With a simple click of the mouse, interactive name labels can be toggled on each plot created with this tool for simplified empirical analyses instead of just staring at numbers all day. For this first rendition of my new Statcast Search Series, I take a statistical plunge into a few facets of Swing-Miss%, where I use the Statcast Search plot generator to highlight outlying qualified pitchers that may either be over-or-underperforming their Swing-Miss%. You can customize your search query to include any combination of variables available within the database.įor every active MLB player, there are datasets that exist for every game, every pitch, every outcome, and even every expected outcome. The creators of deserve an enormous amount of gratitude from every one of its users for providing endless data points for just about every variable that can possibly surface throughout a baseball game. In its entirety, the tool is the most comprehensive and advantageous source available for acquiring MLB data relevant to fit your custom analysis of a certain pool of players. This tool is published and maintained by Baseball Savant. Written by: Cory Ott ( us on Twitter! you hadn’t already discovered this vital resource, it is officially time to flip over the rock and evolve the methods by which you conduct player analyses by integrating the Statcast Search tool into your repertoire.
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