sensors
Article
Combining Radar and Optical Sensor Data to Measure Player
Value in Baseball
Glenn Healey


Citation: Healey, G. Combining
Radar and Optical Sensor Data to
Measure Player Value in Baseball.
Sensors 2021, 21, 64. https://
doi.org/10.3390/s21010064
Received: 11 December 2020
Accepted: 21 December 2020
Published: 24 December 2020
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4.0/).
Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92617, USA;
ghealey@uci.edu
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.
Keywords: Bayesian; baseball analytics; machine learning; radar; intrinsic values; forecasting; sensors;
batted ball; statistics; wOBA cube
1. Introduction
The expanded presence of sensor systems at sporting events has enhanced the enjoy-
ment of fans and supported a number of new applications [1–4]. Measuring skill on batted
balls is of fundamental importance in quantifying player value in baseball. Traditional
measures for batted-ball skill have been based on outcomes, but these measures have a low
repeatability due to the dependence of outcomes on variables such as the defense, the ball-
park dimensions, and the atmospheric conditions [5,6]. The Major League Baseball (MLB)
Statcast system [2] uses Doppler radar to measure parameters that include the initial speed
and direction of batted balls. These parameters can be used to compute batted-ball statistics
that are more repeatable than traditional statistics [7]. Research has shown that running
speed is an important determinant of batter performance that is not measured by the radar
sensor [8], but the Statcast system provides running speed data using stereoscopic optical
sensors. This data provides the opportunity to improve the capability of batted-ball models
by combining the radar measurements with the optical measurements. The objective of
this study is to determine whether combining running speed measurements with batted
ball measurements can be used to improve the accuracy of models for player performance.
Combining data from different sensors has been done successfully for numerous
applications [9–15]. In this work, we employ a Bayesian framework and machine learning
methods to build a model that combines radar batted ball data and optical running speed
data. The approach generalizes a previous method [7] that considered lower-dimensional
vectors consisting of only batted ball descriptors derived from a single sensor system. The
model uses a nonparametric kernel method [16] to estimate the probability densities in
Bayes law for vectors of radar and optical measurements acquired for over one hundred
thousand batted-ball observations. A cross-validation process is used to ﬁnd optimal
smoothing parameters for the density estimates. The model utilizes the weighted on base
average (wOBA) [17] linear weights model for run value. The result is the wOBA tesseract
which represents a batted-ball value as a continuous function of four variables generated
by the radar and optical sensors. Separate tesseracts are built to accommodate the effects
Sensors 2021, 21, 64. https://doi.org/10.3390/s21010064
https://www.mdpi.com/journal/sensors
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of batter handedness. We present visualizations obtained by taking slices through the
tesseracts to demonstrate properties of the model. We show that by including optical
measurements for running speed, the new model is signiﬁcantly more accurate than
previous models that only consider measurements for batted-ball parameters.
2. Radar and Optical Sensors
Beginning in 2017, the Statcast system employed radar along with optical stereo video
sensors to acquire data for each MLB game. The trajectories of pitched and batted balls
have been measured by Trackman’s phased-array Doppler radar component of Statcast. The
Trackman radar is situated behind home plate and operates in the X-band at approximately
10.5 GHz. This radar system approximates the path of each pitch using a nine-parameter
model defined by the pitch’s 3D acceleration which is assumed constant over the trajectory
and the 3D velocity and position at a specified point. The system also measures the pitch
spin rate from the distribution of Doppler shifts. In addition, the Trackman radar provides an
estimate of the initial speed s and the 3D direction of batted balls. The direction is described
by the vertical launch angle v, as shown in Figure 1, and the horizontal spray angle h, as
shown in Figure 2. The angle v takes on values from −90◦ (straight down) to +90◦ (straight
up) while the angle h takes on values from −45◦ (third base (3B) line) to +45◦ (first base (1B)
line) for balls in fair territory.
The Trackman radar is well suited for tracking the ball, but the Doppler shifts from
players are difﬁcult to discern from returns from clutter due to the players’ slower speeds.
For this reason, Statcast uses stereoscopic optical video from two arrays of cameras to track
the movement of players. These arrays are usually positioned in the stands on the third
base side of the ﬁeld and are time synchronized with the radar. This allows the movement
of defenders to be tracked which allows defensive skill to be quantiﬁed using measures
such as reaction time, route efﬁciency, and speed. The combined optical and radar sensors
can also be used to measure the time from batted ball contact until the batter reaches
ﬁrst base.
The success of a batter depends on both the quality of his batted ball contact as
measured by the (s, v, h) vectors as well as his running speed as measured by time to ﬁrst
data. In this study we use Statcast radar and optical measurements from every regular-
season MLB game during 2018. The data set includes (s, v, h) data for batted balls and
associated time to ﬁrst running speed measurements. For each batter with at least 20 ground
balls, we use the average of his three fastest times to ﬁrst to represent the batter’s time to
ﬁrst speed r. For switch-hitters who can bat both right and left-handed, a separate r value
is computed using their batted balls as a right-handed batter and as a left-handed batter.
Figure 1. Vertical angle v where v = 0◦ is parallel to the ground plane.
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Figure 2. Horizontal angle h in the plane of the playing ﬁeld where h = −45◦ is in the direction of
third base (3B), h = 0◦ is in the direction of second base (2B), h = 45◦ is in the direction of ﬁrst base
(1B); the three rays intersect at home plate.
3. Learning the Model from Sensor Data
3.1. Bayesian Approach
Let b be a d-dimensional vector that can include the (s, v, h) batted-ball parameters
and the r speed parameter. A batted ball can result in one of several outcomes Oj such as
an out or a home run. Bayes rule [18] can be used to compute the a posteriori probability
of an outcome Oj given b as
P(Oj|b) = p(b|Oj)P(Oj)
p(b)
(1)
where p(b) and p(b|Oj) are the probability densities for b and b given Oj respectively and
P(Oj) is the a priori probability of outcome Oj. We will derive a method that uses the
a posteriori probabilities P(Oj|b) to estimate the value of a batted ball given the vector b of
sensor measurements.
3.2. Estimating the Conditional Densities
In order to compute the a posteriori probabilities P(Oj|b) in Bayes rule we need to
estimate the densities p(b|Oj) and p(b). The conditional densities p(b|Oj) have a complex
dependence on the measurement vector b. An outcome Oj of a single, for example, can occur
for a slowly hit ground ball toward third base or a hard hit line drive to right ﬁeld. Therefore
we use a nonparametric technique known as kernel density estimation [19,20] to learn the
densities. In this approach, we use a set of n sensor vectors bi to construct an estimate for
p(b) according to
bp(b) = 1
n
n
∑
i=1
G(b − bi)
(2)
where G(·) is the Gaussian kernel
G(b) =
1
(2π)d/2|Σ|1/2 exp

−1
2bTΣ−1b

(3)
where Σ is a diagonal covariance matrix deﬁned by d parameters which determine the
amount of smoothing for each element of the b vector.
3.3. Optimizing the Smoothing Parameters
The d diagonal elements of the matrix Σ play an important role in determining the
accuracy of bp(b) in Equation (2) [18]. If these smoothing parameters are too small then bp(b)
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will be composed of spikes near the bi samples and if these parameters are too large then
the resulting bp(b) will be overly smooth. Cross-validation techniques have been developed
to optimize the smoothing parameters by maximizing the likelihood of a set of bi vectors
after building the estimate using other bi vectors [21]. An example of these techniques is
leave-one-out cross-validation [16] in which the likelihood of each sample is computed
after using the other samples to compute the kernel density estimate. We will take a similar
but more efﬁcient approach in this work to accommodate the size of our data set.
Let σ be the d-dimensional vector of diagonal elements of Σ. We partition the n
measured bi vectors into an odd group and an even group depending on whether the
vector was acquired in a game starting on an odd or even day of the month. Let nv be
the smaller of the sizes of the two groups. The validation set SO is deﬁned as the ﬁrst nv
vectors bi from the odd group and the validation set SE is deﬁned as the ﬁrst nv vectors
bi from the even group. For set SO, we ﬁnd bp(b) using the n − nv vectors bi that are not
in SO as a function of the vector σ. The optimal σ for SO is deﬁned as the vector σ∗
O that
maximizes the pseudolikelihood [21,22] given by
σ∗
O = arg max
σ
∏
bi∈SO
bp(bi).
(4)
This process is repeated to ﬁnd the vector σ∗
E that maximizes the pseudolikelihood for SE.
The optimized smoothing vector σ∗ is found by averaging σ∗
O and σ∗
E .
3.4. Computing Batted Ball Values
Each a posteriori probability P(Oj|b) can be estimated using Bayes rule. The estimates
for the densities p(b) and p(b|Oj) in Equation (1) are generated using Equations (2) and (3)
where the model data for p(b) includes all n vectors bi and the model data for each p(b|Oj)
is deﬁned by the subset of the bi vectors with outcome Oj. We use the optimized σ∗
smoothing vector derived using the method in Section 3.3 for each case. The a priori
probabilities P(Oj) are estimated as nj/n where nj is the number of the n vectors bi with
outcome Oj. Using these estimates, P(Oj|b) is computed using Equation (1).
Many statistics such as batting average, on-base percentage, slugging average, and
on-base plus slugging have been deﬁned to quantify offensive value [23]. Each of these
statistics has certain deﬁciencies [17]. Batting average and on-base percentage, for example,
assume that all hits such as singles and doubles are equally valuable. Slugging average
overweights the value of extra-base hits (doubles, triples, home runs) compared to singles.
On-base plus slugging places too much value on slugging average relative to on-base
percentage. Weighted on base average (wOBA) [17] overcomes these deﬁciencies by
weighting each possible outcome according to its run value. This property has made
wOBA one of the most popular and useful offensive statistics [24].
Using wOBA each of the possible batted ball outcomes Oj can be assigned a numerical
value which allows the P(Oj|b) probabilities to be used to compute a single expected value
for b. This is implemented using wOBA by multiplying each outcome by its average run
value wj. Thus, we can represent the expected value of a batted ball as
wOBA(b) =
5
∑
j=0
wjP(Oj|b)
(5)
where O0 = out, O1 = single, O2 = double, O3 = triple, O4 = home run, and O5 = batter
reaches on error (ROE). The wj weights for MLB are compiled for each year at [25]. In this
project, we process 2018 data for which the weights are w0 = 0.000, w1 = 0.880, w2 = 1.247,
w3 = 1.578, w4 = 2.031, and w5 = 0.920.
If b is the three-dimensional vector b = (s, v, h) of batted-ball parameters, then the
wOBA(b) function in Equation (5) can be represented by the wOBA cube. If b is the
four-dimensional vector b = (s, v, h, r) of batted ball and running speed parameters, then
the wOBA(b) function in Equation (5) can be represented by the four-dimensional wOBA
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tesseract. We will provide examples of the wOBA cube in this section and will analyze the
wOBA tesseract in detail in Section 4.
Figures 3 and 4 examine one-dimensional slices through the wOBA cube. Figure 3
plots wOBA(b) for ground balls with a vertical angle of −5◦ that are hit at 85 and 93 miles
per hour. Minima in the two curves correspond to the typical position of inﬁelders with the
minima from left to right corresponding to the third baseman, shortstop, second baseman,
and ﬁrst baseman respectively. Over most horizontal angles, balls hit at 93 mph have a
higher value than balls hit at 85 mph since ground balls hit at a higher speed have a higher
probability of eluding a defender.
0
0.2
0.4
0.6
0.8
1
-40
-20
0
20
40
wOBA
h (degrees)
speed 93
speed 85
Figure 3. Weighted on base average (wOBA) for a batted ball with a vertical angle v of −5◦ for speed
s of 85 miles per hour and 93 miles per hour.
Figure 4 plots wOBA(b) for balls hit in the air with a vertical angle of v = +16◦ at the
same two speeds. Minima in these curves correspond to the typical position of outﬁelders
with the minima near −20◦, 0◦, and 20◦ corresponding to the left ﬁelder, center ﬁelder,
and right ﬁelder respectively. For this vertical angle, balls hit in the direction of an outﬁelder
have a higher value for a speed of 85 mph because these balls often fall in front of the
outﬁelder for hits while balls hit at 93 mph more frequently carry to the outﬁelder for outs.
For both the ground balls and ﬂy balls, the largest wOBA values occur for balls hit near the
foul lines (|h| = 45◦) which often result in extra-base hits instead of singles.
Fielder positioning is dependent on whether a batter is right-handed or left-handed.
For this reason, we partition the measured b vectors by batter handedness and learn two
separate wOBA(b) functions: wOBAl(b) for left-handed batters and wOBAr(b) for right-
handed batters. As an example, Figure 5 plots wOBAl(b) and wOBAr(b) as a function
of the horizontal angle h for a batted ball with a vertical angle v of −5◦ and a speed s of
93 miles per hour. Each curve has four minima which correspond to the typical location of
the four inﬁelders. Each of these typical locations is shifted a few degrees to the left for
right-handed batters due to ﬁelder positioning. The value of wOBAl(b) or wOBAr(b) will
be referred to as the intrinsic value of the batted ball.
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0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
-40
-20
0
20
40
wOBA
h (degrees)
speed 93
speed 85
Figure 4. Weighted on base average (wOBA) for a batted ball with a vertical angle v of 16◦ for speed
s of 85 miles per hour and 93 miles per hour.
0
0.2
0.4
0.6
0.8
1
1.2
-40
-20
0
20
40
wOBA
h (degrees)
LHB
RHB
Figure 5. Weighted on base average (wOBA) for a batted ball with a vertical angle v of −5◦ and a
speed s of 93 miles per hour for left-handed batters (LHB) and right-handed batters (RHB).
Sensors 2021, 21, 64
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3.5. Player Statistics
A player’s performance on batted balls is measured by statistics that are compiled
over a period of time. Each batted ball can be assigned the weight wj based on its outcome
as described in Section 3.4. This outcome-based value depends on variables such as the
defense, the atmospheric conditions, the ballpark dimensions, and random noise which
are independent of batter skill. Let O denote the average of a player’s outcome-based
values on batted balls over a period of time. The statistic O is also known as wOBA on
contact or wOBAcon. A player’s intrinsic values are based on parameters (s, v, h, r) that
a player has direct control over. The average of these intrinsic values over time has been
shown to have a signiﬁcantly higher degree of repeatability than the average O of the
outcome-based values [7]. We refer to the average of a batter’s intrinsic values computed
using the three-dimensional vector b = (s, v, h) of batted-ball parameters as I3 and we refer
to the average of a batter’s intrinsic values using the four-dimensional vector b = (s, v, h, r)
that also includes his time to ﬁrst estimate r as I4.
4. wOBA Tesseract
In previous work [8] we showed that players who outperform their I3 wOBAcon
estimate tend to be faster runners, and many players who underperform their I3 are slower
runners. This motivates augmenting the wOBA cube with batter running speed to generate
the wOBA tesseract.
4.1. Time to First Measurements
The Statcast system generates multiple measurements of running speed. Statcast
measures sprint speed, which is derived from a runner’s fastest one second window on
individual plays, and time to ﬁrst which measures the time from batted ball contact to
when the batter touches ﬁrst base. For our application we use time to ﬁrst, which includes
factors such as a batter’s time to recover from the swing and start initial acceleration which
affects his ability to beat out a hit.
As described in Section 2, we deﬁne the running speed parameter r for batters with
at least 20 ground balls as the average of the player’s three fastest measured times to ﬁrst.
For switch-hitters a separate r value is computed for plate appearances as a right-handed
and as a left-handed batter. All other things being equal, we would expect left-handed
batters to have smaller r values because they start closer to ﬁrst base. For the 2018 season,
the average r value over 207 qualifying left-handed batters was 4.245 s and the average
r value over 319 qualifying right-handed batters was 4.305 s. Tables 1 and 2 present the
left-handed and right-handed batters with the fastest r values for 2018. Figure 6 plots
wOBA as a function of r for right-handed and left-handed batters for all batted balls with
a vertical angle of less than 10 degrees in 2018. These are ground balls for which the r
value is most relevant. We see that there is a strong dependence of batted ball value on
running speed as wOBA decreases as r increases. We also see that right-handed batters
have a higher wOBA for a given r since a higher fraction of ground balls from RHB are hit
to the left side of the inﬁeld which requires a longer throw to ﬁrst base.
Table 1. Fastest time to ﬁrst (r) for left-handed batters (LHB) in seconds, 2018.
LHB
Time to First (r)
Dee Gordon
3.807
Billy Hamilton
3.814
Roman Quinn
3.824
Magneuris Sierra
3.836
Cody Bellinger
3.879
Jack Shuck
3.882
Brett Gardener
3.909
Mallex Smith
3.929
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Table 2. Fastest time to ﬁrst (r) for right-handed batters (RHB) in seconds, 2018.
RHB
Time to First (r)
Delino DeShields
3.855
Dansby Swanson
3.884
Trea Turner
3.896
Jose Altuve
3.896
Harrison Bader
3.899
Starling Marte
3.904
Scott Kingery
3.923
Adam Engel
3.929
3.8
3.9
4
4.1
4.2
4.3
4.4
4.5
4.6
4.7
Time to First (seconds)
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
wOBA
RHB
LHB
Figure 6. Weighted on base average (wOBA) versus time to ﬁrst (r) in seconds over all batted balls
with a vertical angle v < 10◦ for right-handed batters (RHB) and left-handed batters (LHB) in 2018.
4.2. Tesseract Examples
The wOBA tesseract deﬁnes the mapping from (s, v, h, r) to intrinsic value. A separate
wOBA tesseract was generated for right-handed and left-handed batters by applying the
process described in Section 3 to 63,301 batted ball and time to ﬁrst measurements for
right-handed batters and 44,247 measurements for left-handed batters acquired during the
2018 MLB regular season. Figures 7 and 8 provide examples of slices through the tesseract.
-50
-40
-30
-20
-10
0
10
20
30
40
50
Horizontal Angle (degrees)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
wOBA
Time to First 4.0 seconds
Time to First 4.4 seconds
Figure 7. Weighted on base average (wOBA) for right-handed batter (RHB) batted balls with a speed
s of 87 miles per hour and a vertical angle v of −9◦ for two time to ﬁrst (r) values.
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-40
-30
-20
-10
0
10
20
30
40
Horizontal Angle (degrees)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
wOBA
Time to First 4.0 seconds
Time to First 4.4 seconds
Figure 8. Weighted on base average (wOBA) for left-handed batter (LHB) batted balls with a speed s
of 97 miles per hour and a vertical angle v of −12◦ for two time to ﬁrst (r) values.
Figure 7 plots wOBA(b) for right-handed batters for two different values of r as a
function of the horizontal spray angle h with the initial batted ball speed and vertical
launch angle ﬁxed at s = 87 mph and v = −9◦. The red curve corresponds to a faster than
average time of r = 4.0 seconds and the black curve corresponds a slower than average
time of r = 4.4 seconds. The four minima in the curves correspond to the typical position
of the four inﬁelders against right-handed batters. Near these minima we have a ground
ball hit directly at an inﬁelder and the wOBA values are similar for the different values of r.
As we move away from the minima we see that a faster runner (red curve) tends to produce
a higher wOBA. We see that the largest wOBA values are observed for ground balls hit
near the ﬁrst base line as this horizontal angle is often undefended against right-handed
batters and balls down the line may go for extra bases.
Figure 8 plots wOBA(b) for left-handed batters for two different values of r as a
function of the horizontal spray angle h with the initial batted ball speed and vertical
launch angle ﬁxed at s = 97 mph and v = −12◦. The red curve corresponds to a faster than
average time of r = 4.0 seconds and the black curve corresponds a slower than average
time of r = 4.4 seconds. The four minima in the curves correspond to the typical position
of the four inﬁelders against left-handed batters. We see that the minima are shifted to the
right compared to the minima for right-handed batters shown in Figure 7. Near three of
these minima the wOBA values are similar for the different values of r. For a ground ball
hit directly at the third baseman near h = −28◦, a faster runner enjoys an advantage since
the third baseman will often be playing shallower to defend against a bunt for the faster
runner and a 97 mph ground ball has a better chance of resulting in a hit. As we move
away from the minima we see that a faster runner (red curve) tends to produce a higher
wOBA. We see that the largest wOBA values are observed for ground balls hit near the
third base line as this horizontal angle is often undefended against left-handed batters and
balls down the line may go for extra bases.
4.3. Comparing I3 and I4
We computed the I3 (wOBA cube) and I4 (wOBA tesseract) estimates of wOBAcon
for all batters in 2018 with at least 250 balls in play. Table 3 is a list of the I3 leaders. These
batters are known for their high quality of contact. Table 4 is a list of the I4 leaders which
factors running speed in addition to quality of contact into the value of each batted ball.
We see that several of the slower runners (Gallo, Martinez, Judge, Goldschmidt) have a
lower I4 than I3 while several of the faster runners (Trout, Story, Yelich, Betts) have a higher
I4 than I3. The value of I4 − I3 depends on both the batter’s running speed parameter r and
his particular collection of batted balls.
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Table 3. Weighted on base average (wOBA) cube (I3) leaders for 2018.
Batter
I3
Joey Gallo
0.597
Aaron Judge
0.544
Julio Martinez
0.544
Mike Trout
0.541
Paul Goldschmidt
0.531
Matt Carpenter
0.527
Giancarlo Stanton
0.524
Christian Yelich
0.522
Table 4. Weighted on base average (wOBA) tesseract (I4) leaders for 2018, difference between wOBA
cube and wOBA tesseract values (I4 − I3), and time to ﬁrst (r) in seconds.
Batter
I4
I4 − I3
Time to First (r)
Joey Gallo
0.589
−0.008
4.319
Mike Trout
0.542
+0.001
4.062
Julio Martinez
0.535
−0.009
4.340
Aaron Judge
0.534
−0.010
4.487
Trevor Story
0.529
+0.015
3.955
Christian Yelich
0.527
+0.005
4.080
Mookie Betts
0.526
+0.007
4.055
Paul Goldschmidt
0.522
−0.009
4.309
Table 5 is a list of the batters with the highest I4 − I3 for 2018. These are the batters that
would be expected to have the largest gain in wOBAcon due to their running speed given
their collection of batted balls. We see that all of these players have better than average
values of the running speed parameter r. Note that for switch hitters two values (L/R) of r
are used.
Table 5. Largest differences between weighted on base average (wOBA) cube and wOBA tesseract
values (I4 − I3) for 2018 and time to ﬁrst (r) in seconds; two r values are given for switch-hitters.
Batter
I4 − I3
Time to First (r)
Cody Bellinger
0.025
3.879
Ozzie Albies
0.022
3.936/3.942
Niko Goodrum
0.019
4.08/4.022
Rougned Odor
0.018
3.984
Dansby Swanson
0.018
3.884
Odubel Herrera
0.017
3.969
Scott Kingery
0.017
3.923
Brandon Nimmo
0.017
4.113
Table 6 is a list of the batters with the lowest I4 − I3 for 2018. These are the batters
that would be expected to have the largest loss in wOBAcon due to their running speed
parameter r given their collection of batted balls. We see that all of these players have
worse than average values of r.
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Table 6. Smallest differences between weighted on base average (wOBA) cube and wOBA tesseract
values (I4 − I3) for 2018 and time to ﬁrst (r) in seconds; two r values are given for switch-hitters.
Batter
I4 − I3
Time to First (r)
Yasmani Grandal
−0.035
4.663/4.966
Victor Martinez
−0.034
4.634/4.965
Kendrys Morales
−0.031
4.788/4.816
Justin Bour
−0.029
4.498
Chris Davis
−0.027
4.491
Albert Pujols
−0.025
4.839
Yangervis Solarte
−0.022
4.556/4.649
Joey Votto
−0.022
4.575
4.4. Variance Reduction
Differences between a batter’s observed wOBAcon O and his I3 are due to several
factors including running speed, susceptibility to shifts, the ballpark, the weather, and
random noise. By developing the I4 statistic we improve the accuracy of the estimate by
explicitly modeling the dependence of each batted ball on the running speed parameter r.
Table 7 is a list of the batters with at least 250 batted balls with the highest O − I3.
We see that each of these batters had a faster than average running speed r. In addition,
several of these batters, such as Carlos Gonzalez and Trevor Story in Colorado, beneﬁted
from their home ballparks [6]. We see that in each case the use of the wOBA tesseract to
generate I4 improved the accuracy of the model as O − I4 is less than O − I3.
Table 7. Largest differences between observed weighted on base average (wOBA) on contact (O) and
wOBA cube values (O − I3) for 2018; differences between O and wOBA tesseract values (O − I4);
and time to ﬁrst (r) in seconds; two r values are given for switch-hitters.
Batter
O − I3
O − I4
Time to First (r)
Carlos Gonzalez
0.063
0.054
4.150
Ronald Acuna
0.051
0.039
3.945
Mallex Smith
0.050
0.039
3.929
Brandon Nimmo
0.049
0.033
4.113
Chris Taylor
0.048
0.039
4.017
Trevor Story
0.045
0.030
3.955
Eddie Rosario
0.045
0.029
3.969
Yoan Moncada
0.045
0.029
4.094/4.175
Table 8 is a list of the batters with at least 250 batted balls with the lowest O − I3. We see
that each of these batters had a slower than average running speed r except Joe Panik who
was slightly better than average. Several of these players (Morales, Moreland, Calhoun,
Martinez, Carpenter) were shifted on during a large fraction of their plate appearances.
We see that in each case the use of the wOBA tesseract to generate I4 improved the accuracy
of the model as |O − I4| is less than |O − I3|.
If we consider all of the players with at least 250 batted balls in 2018, the R-squared
for the set of points (O, I3) is 0.79 and the R-squared for the set of points (O, I4) is 0.85.
Therefore, the model that includes running speed using the r parameter has increased
accuracy for representing a batter’s wOBAcon. We therefore expect that I4 is a better
estimate of wOBAcon skill and provides more value for projection [7].
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Table 8. Smallest differences between observed weighted on base average (wOBA) on contact (O)
and wOBA cube values (O − I3) for 2018; differences between O and wOBA tesseract values (O − I4);
and time to ﬁrst (r) in seconds; two r values are given for switch-hitters.
Batter
O − I3
O − I4
Time to First (r)
Kendrys Morales
−0.064
−0.033
4.788/4.816
Mitch Moreland
−0.063
−0.052
4.262
Kole Calhoun
−0.058
−0.045
4.315
Nelson Cruz
−0.055
−0.049
4.395
Albert Pujols
−0.054
−0.029
4.839
Victor Martinez
−0.052
−0.018
4.634/4.965
Matt Carpenter
−0.048
−0.037
4.281
Joe Panik
−0.047
−0.046
4.241
5. Discussion
Player valuation is a critical task for professional baseball teams that operate in
an environment where player contracts are frequently worth tens of millions of dollars.
Many statistics have been developed to quantify the offensive value of players. During
the twentieth century these statistics, for example batting average, on base average, and
slugging percentage, were based on outcomes such as whether the offensive player got
a hit or made an out [23]. These outcomes, however, depend on many variables that are
beyond the control of the offensive player such as the opponent ﬁelders, the ballpark
dimensions, and the weather. This dependence reduces the reliability of these statistics.
The use of outcomes has also made it difﬁcult to separate the impact of the key components
that contribute to offensive value: batting skill and running speed. There have been
some attempts to isolate the contributions of these components. For example, researchers
have attempted to quantify running speed by using metrics like the Bill James speed
score [26] which is based on factors that include an offensive player’s number of triples
and stolen base attempts. But such a measure depends on factors besides running speed
namely a player’s power-hitting ability and how often his team’s manager calls for stolen
base attempts.
Starting with the PITCHf/x system [27], sensors have been available in all MLB
ballparks to recover the 3D trajectory of pitched balls since 2008. The collection of sensors
has evolved and expanded and the current system, Statcast [2], consists of multiple sensor
types that collect seven terabytes of data during each MLB game. Large sets of sensor
data provide beneﬁts for measurement especially in the ability to reduce the variance of
estimators [28]. In addition, sensor data has enabled the discovery and measurement of
new skills. Pitch trajectory data, for example, uncovered the large role that a catcher plays
in determining the probability that a pitch is called a strike. This led to the quantiﬁcation
of a new skill called pitch framing [29] that is highly valued in the sport. Sensor data
has also led to advances in the quantiﬁcation of defense [30] and pitch sequencing [31].
The measurement of batted ball vectors has enabled the calculation of batting statistics
that are more reliable than statistics that depend on outcomes [7]. The ability to measure
running speed enables new insights into how different skill components affect offensive
performance. New sensor systems [32] are becoming available that measure biomechanical
data for batters and pitchers which will increase understanding of how players achieve
given levels of performance [33]. These measurements can also be used to improve the
level of detail of models for predicting the result of matchups [34,35].
The ability to derive models from large sets of sensor data has been enhanced by recent
advances in machine learning methods [36–38]. The discrete nature of baseball makes its
analysis highly amenable to these methods [39]. For many applications [40,41] the use of
nonparametric models enables the recovery of functions with a complex dependence on a
set of variables. In this work, we use nonparametric density estimates [16] in a Bayesian
framework [18] to model a player’s offensive performance using batted ball vectors and
Sensors 2021, 21, 64
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running speed measurements generated by radar and optical sensors. We show that by
applying machine learning methods to a large set of measurements acquired by multiple
sensors we obtain a model with significant advantages over previous models for representing
a player’s offensive performance.
6. Conclusions
Analytical models in baseball have proven valuable for applications involving strat-
egy [17,31,34,35], player development [33], and player evaluation [42,43]. We have com-
bined data acquired by radar and optical sensors to generalize the 3D wOBA cube to the
4D wOBA tesseract. The new model accounts for the impact of batter running speed and
is signiﬁcantly more accurate than previous models. Thus, the use of multiple sensors
enables the generation of a model that is more accurate than the model that is obtained
by using either sensor in isolation. This accuracy enables the computation of offensive
statistics that more reliably assess talent level on batted balls and support more accurate
projections of future performance. This approach also allows separation of the impact of
batted-ball skill and running speed in offensive value. An important advantage of this
separation is that each skill can be regressed and projected using individual reliability
and aging curves before conversion to projected offensive value during forecasting [44].
The wOBA tesseract also has the potential to improve defensive metrics by quantifying the
relationship between the batter’s running speed and the difﬁculty of a play. We have shown
that the wOBA tesseract enables visualizations that provide insights into the mapping
between batted-ball and running speed parameters and intrinsic value. The process of
combining sensor data and machine learning techniques to generate new statistics can be
readily adapted to support other areas of sports analytics.
Funding: This research received no external funding.
Acknowledgments: I thank Travis Petersen at MLB Advanced Media for providing Statcast data
that was used in this study.
Conﬂicts of Interest: The author declares no conﬂict of interest.
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Combining Radar and Optical Sensor Data to Measure Player Value in Baseball.
Healey, Glenn