PITCHf/x Summit 2010
Sportvision and Major League Baseball Advanced Media 2010 PITCHf/x Summit!
Infield Defense with FIELDf/x John Walsh
Using Velocity Components to Evaluate Pitch Effectiveness Matt Lentzner/Mike Fast
Using standard PITCHf/x data, how can you compare a fastball to a slider? You can’t. How can you explain how a fastball, which compared to a no‐spin pitch, moves a lot but is easier to hit than a slider, which moves very little (compared to a no‐spin pitch)? The answer is that movement doesn't mean anything by itself. The important factor is how fast the ball is moving in the batter’s frame of reference, and that is affected by where the pitch is released and where it ends up. In this presentation Mike and Matt will show why these velocities matter, how to get them and what this means going forward.
PITCHf/x Application in Player Development and Evaluation Glenn Schoenhals
The authors will describe their experience from December 2009 through July 2010 utilizing PITCHf/x in conjunction with video in an indoor setting. The site was primarily used for teaching and evaluating pitchers aged 8‐18 and included several professionals. The trial was to establish how the technology was combined in essentially a ‘laboratory’ setting. The conclusion is that the application promises to benefit both students and instructors in the science and art of baseball pitching.
Okajima’s Mystery Pitch Matt Lentzner
Hideki Okajima, oddball reliever for the Red Sox, throws an off‐speed pitch that some call a ‘rainbow curve’. It's no curveball though. What is it and how does it fit into the pitching model (the pitching peanut)?
Leaving the No‐Spin Zone Alan Nathan
Some experiments are described that lead to a model for oblique ball‐bat collisions as it relates to both the spin and the angles of the batted ball. An attempt will be made to use the model to extract impact information from HITf/x data.
From Raw Data to Analytical Database Peter Jensen
The raw FIELDf/x data that Sportvision has generously provided to us is only in Beta form. To become a useful database it is necessary to define the questions for which FIELDf/x may be able to provide answers, proof the data to remove errors and inconsistencies, and organize the data so that it can be easily queried for multiple relevant questions. This presentation will be a walkthrough of one possible solution to this problem.
Using FIELDf/x to Assess Fielders’ Routes to Fly Balls Dave Allen
Dave uses the FIELDf/x data to reconstruct fielders’ routes to a fly ball when they attempt to make a catch. He measures the distance traveled compared to the shortest distance possible, time it took the fielder to get there, time it took the fielder to initially break towards the ball, initial fielder break direction versus optimal direction towards landing point and time it took the ball to reach the fielder. Ultimately, he addresses how feasible it is to use this information to evaluate fielders’ ability to field fly balls.
Measuring Base Running with FIELDf/x Mike Fast
This presentation is an examination of base runners in the FIELDf/x data set utilizing measurements of speed, acceleration, reaction time, size of leads off the bases, pickoff plays and routes around the base paths.
Speed/Jumps Jeremy Greenhouse
As HITf/x and PITCHf/x data has allowed analysts to separate the processes of hitting and pitching from the results, FIELDf/x allows us to do the same with fielding and base running. Jeremy explains how he used FIELDf/x data’s time and distance coordinates to model safe/out probabilities of fly balls and stolen bases during every given time interval. He then takes a look at some notable plays.
Where Fielders Field: Spatial and Time Considerations Matt Thomas
Continued application of close‐range photogrammetry through high‐resolution digital photography to baseball is revealing hitherto unseen patterns of fielding in the game. Matt examines these patterns and where data permits, factors time into this examination. After reviewing general trends he notes specific achievements and then speculates on whether any of this freshly quantified insight tells us what makes for good (and not so good) fielding.
SCOUTf/x Max Marchi
This presentation evaluates players’ tools with PITCHf/x, HITf/x, and FIELDf/x.
True Defensive Range (TDR): Getting out of the Zone Greg Rybarczyk
Greg intends to display detailed tracking of the 25 batted balls in the released data that were hit in the air to the outfield. Presented data will include the relative positions of the outfielders and the ball from the time the ball leaves the bat until the time it is retrieved by the fielder. Using the essential elements of this data (fielder starting position, ball hang time and landing point), he outlines the fundamentals of a new outfield defensive metric, called ‘True Defensive Range’ or TDR, which should provide more accurate player defensive ratings with a smaller required sample size than current metrics. Full realization of this metric will require establishment of baseline values using the full data set. TDR for infielders will employ a similar method, but it will not be covered during this presentation.