Combining Radar and Optical Sensor Data to Measure Player Value in Baseball

Abstract

Evaluating a player’s talent level based on batted balls is one of the most important and difficult tasks facing baseball analysts. An array of sensors has been installed in Major League Baseball stadiums that capture seven terabytes of data during each game. These data increase interest among spectators, but also can be used to quantify the performances of players on the field. The weighted on base average cube model has been used to generate reliable estimates of batter performance using measured batted-ball parameters, but research has shown that running speed is also a determinant of batted-ball performance. In this work, we used machine learning methods to combine a three-dimensional batted-ball vector measured by Doppler radar with running speed measurements generated by stereoscopic optical sensors. We show that this process leads to an improved model for the batted-ball performances of players.

Measurement Space Partitioning for Estimation and Prediction

“…Variation in the outfield geometry across stadiums [9] and variation in the ambient weather conditions [8] can affect the value of a ball hit in the air. The batter's running speed [10] plays a role in determining batted ball value especially for balls hit on the ground. The quality of defenders can also affect the value of a batted ball hit to a given region of the field.…”

“…These variables cause a player's battedball performance to have a low correlation across samples [5] [6]. Batted ball results are also biased by variables such as the atmospheric conditions [7] [8], the ballpark geometry [9], and the batter's running speed [10]. For these reasons, the prediction of batted ball results is considered the most difficult aspect of forecasting player performance [11] [12].…”

“…Several ML methods have used sensor data to quantify player performance on batted balls. These methods include a technique [18] that combines knearest neighbors with a generalized linear model as well as techniques [10] [19] that use kernel density estimates within a Bayesian framework. These measures reduce the impact of random variation compared to the naive estimate, but have the disadvantage that they are optimized for modeling performance on observed data rather than for predicting performance on unobserved data.…”

“…Batted balls that follow similar trajectories can have different outcomes due to differences in outfield geometry from ballpark to ballpark [9]. A player's running speed [10] and variables that include the height of the infield grass and the composition of the infield surface [4] can affect the value of batted balls hit on the ground. The fate of batted balls also depends on the quality of the defenders in the field.…”