SABR Seminar: Quantifying In-Game Command: Observed Data & Intended Location

Teams and analysts have been trying to find a way to quantify command for decades. These processes have included measuring from the middle of home plate, from where the catcher setup, to using walk rate, edge percentage and manual tagging at both the college and professional level. Metrics have been created to measure how command works with called strikes in and out of the zone, but nothing has ever truly captured how well a pitcher can command his pitches during a game. With the advent of new data available, we can now know the intended location of each pitch. The combination of knowing the plate location height and side of a pitcher’s intended location and the observed location allow for new metrics to be added for each pitch, such as pitch miss distance and pitch miss direction.

This presentation will explore how definitive location data opens up a new sector of command metrics that can be developed. From a game perspective we can look at Execution Rate, Sequential Command Grades, and overall Command Scores. We’ll also take a look at the relationship with ballistic pitch and hit data regarding how well pitchers can not only throw a pitch where they wanted to, but does it have the spin and movement profile that is expected. For example, seeing how a pitcher’s breaking ball movement and spin profile might change across the plate will provide explanation and new analysis for player development. From a game level, looking at command grades for a starting pitcher after having a low score compared to league average, yet taking a no-hitter into the 6th inning gives us new questions to ask about the connection between “Stuff” and actually hitting spots.

Defining in game command with precise data gives the industry an opportunity to grade, improve, teach, and scout pitchers in a unique way. From identifying a pitcher’s command margin of error based off of their current arsenal, to leveraging seam-shifted wake models to better build and simulate an optimal sequence, we’ll explore an untapped data avenue that is ready to be further researched.

Ryan Reinsel is Vice President of Innovation at BaseballCloud and the creative mind behind the baseball industry’s newest data visualization software, BallR and PitchR. Equipped with a Fine Arts degree and a passion for technology, Ryan is a proven innovator at creating contextual applications to drive player development forward at the professional and collegiate level. Prior to BaseballCloud, Ryan held positions in multiple sectors of baseball including a role in Research and Player Development for the Chicago Cubs at their Arizona complex.

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