Greetings fellow bloggers and blog enthusiasts! My name is Matthew Lessard and I am a passionate baseball fanatic. I recently received my Bachelor of the Arts degree in Mathematics with Economics from Saint Anselm College in Manchester, New Hampshire. I have always had an affinity for number-crunching and mathematical concepts, hence why I majored in the analytical field that I did. However, with some influence from various media sources, such as the movie "Moneyball" and personal recommendations from friends and colleagues, I have decided to create this blog. The theme of this blog, which you may have inferred from the title and my previous remarks, is statistical analysis in baseball. I believe that baseball is largely a game of statistics. Through this belief, I acknowledge that a General Manager could effectively use analytics to determine the value of players and construct a winning team on a reasonable budget. I understand that critics of this point of view will argue that there are intangibles that either are very difficult to measure or flat out cannot be measured via statistics. I actually agree with this opposition to an extent. Factors such as intelligence, leadership, club house presence, and some others, I would place under this category of difficult to measure statistically. With that being said, I still stand strong with my opinion of baseball being a statistical game. Anyways, I hope that you enjoy my following post and any other posts to come.
*NOTE: All data used in the following charts
was taken from either baseball-reference.com or MLB.com. I am not claiming to be the owner of
this data. The only data that is
my own comes from the statistics that I developed. *
PREVIEW
Since
its creation, in 1845 by Alexander Cartwright, the modern game of baseball has
been a game of statistics. As the
game evolved over the years, so have the statistics that are used to determine
player production. Although, starting
in the 1970’s, the world of baseball statistics soared to new heights. These innovations in the realm of
baseball statistics can mainly be attributed to George William James (Bill) and
L. Robert Davids. James made his
contributions to the baseball statistical world via the publication of his
various books (SABR.com). Davids’
brainchild, SABR (Society for American Baseball Research), enables the
collaboration of intellectual, baseball oriented minds through conferences and
its website (SABR.com).
Implications of these statistical developments can be seen throughout
baseball, through the hiring of analytical positions. Perhaps the most renowned example of baseball analytics is
the story of “Moneyball” (Lewis).
This is the story behind the Oakland Athletics General Manager, Billy
Beane, assembling his 2001 club, after significant free agent losses in the
off-season and being under the constraints of a fettering budget. Through implementing several of Bill James’
ideas, Beane constructed a team that upon viewing the roster, one would pick to
be at the bottom of the league talent wise. However, the club managed to amass an incredible 102-60
record, trailing only the Mariners (116-46) for the best record in Major League
Baseball (MLB.com).
CURRENT DAY STATISTICS
In
the modern day, live ball baseball era, there is an innumerable amount of statistics,
with even more to come. Recent
innovations in the realm of baseball statistics include WAR (Wins Above
Replacement), Runs Created, BsR (Base Runs), and many others
(baseball-reference.com). However,
not all statistics prove to be as useful as others. In this section, I will describe some current statistics
that I believe to be important in evaluating a players worth. In addition, I will critique these statistics
and explain what I believe they are lacking.
·
OBP
(On Base Percentage)- On Base Percentage measures the frequency at which a
player reaches base. This is
calculated by adding a players hits, walks, and hit by pitches, then dividing
that sum by the sum of the players total at-bats, walks, sacrifice flies, and
hit by pitches (baseball-reference.com).
·
OPS
(On Base Plus Slugging Percentage)- On Base Plus Slugging Percentage sums the
players On Base and Slugging Percentages.
This statistic shows a combination of the frequency at which a player
reaches base and the amount of bases that a player gets per at-bat
(baseball-reference.com).
·
RISP
(Runners in Scoring Position)- Runners in Scoring Position is a statistic that
measures a player’s batting average when there are runners in scoring
position. This statistic is
measured by dividing a players number of hits he accumulates when there are
runners on 2nd and/or 3rd base, by the number of at-bats
he has when there are runners on 2nd and/or 3rd base
(baseball-reference.com).
·
Runs
(Runs Scored)- Runs Scored totals the amount of times that a player scores a run
(baseball-reference.com).
·
RBI
(Run Batted In)- Run Batted In measures the total amount of runs that a player
drives in (baseball-reference.com).
It is blatantly obvious that the only way
to win a baseball games is to score more runs than your opponent. The concept of scoring runs is the
basis of how I discern which statistics are the most important. The five statistics above all have
relatively significant contributions to evaluating a players ability to produce
runs. The two more rudimentary
statistics above are the measures of Runs and Run Batted In. Runs measures the amount that a player
scores, which obviously is important because a manager would much rather prefer
to have a player who scores many runs than a player who does not. Run Batted In measures the amount of
runs a player knocks in, which is important because like the scenario with
Runs, a manager would prefer a player who drives in many runs to a player who
does not. Now, I want to address
my problems with these statistics.
Unless the Run or RBI was generated from a homerun, the player was dependent
upon others for their increase in these tallies. A player needs his teammates to reach base in order to drive
them in and a player needs to have the players behind him hit or walk in order
to score runs. This sense of
dependency for these statistics makes me consider them like assists in
hockey. There could be a player
who reaches base a significant amount of times, however if the players before
him and after him in the lineup do not reach base, his Runs and RBI totals will
not be great.
Now, addressing the statistic of Runners
in Scoring Position. As stated
above, this measures a player’s batting average with Runners on 2nd
and/or 3rd base. I
believe that this is a more important statistic than regular batting average
because it evaluates a hitter’s ability to get hits when they truly matter. There could be a player with a batting
average above .300, however that batter could also have a RISP of .100. A player with these kinds of averages
is not as valuable to a team, as a player that has a batting average of .250
and RISP of .400. However, similar
to the Run and RBI stats, RISP is dependent upon the other players in the
lineup. Also, not all players
receive similar amounts of opportunities for hitting with runners in scoring
position. This discrepancy between
the numbers of at-bats with runners in scoring position amongst players makes
it hard to consider RISP a prominent stat in baseball.
The data in the proceeding chart is based
on the final statistics from the 2013 MLB season. I took six dominant offensive players from the Cardinals and
Red Sox, the two most winning teams and World Series competitors during the
2013 season. Then, I took six
dominant offensive players from teams that did not qualify for the 2013
playoffs and compared the two sets of players.
Name
|
BA
|
BA w/
RISP
|
Matt
Holliday (STL)
|
0.300
|
0.390
|
David
Ortiz (BOS)
|
0.309
|
0.315
|
Shane
Victorino (BOS)
|
0.294
|
0.315
|
Allen
Craig (STL)
|
0.315
|
0.454
|
Matt
Carpenter (STL)
|
0.318
|
0.388
|
Jacoby
Ellsbury (BOS)
|
0.298
|
0.304
|
Giancarlo
Stanton (MIA)
|
0.249
|
0.226
|
Eric
Hosmer (KC)
|
0.302
|
0.262
|
Jose
Bautista (TOR)
|
0.259
|
0.298
|
Edwin
Encarnacion (TOR)
|
0.272
|
0.300
|
Bryce
Harper (WSH)
|
0.274
|
0.230
|
Chase
Utley (PHI)
|
0.284
|
0.330
|
First, consider the data for the St.
Louis and Boston players. All of
these players had BA w/RISPs that were higher than their BAs. Next, consider the non-playoff team
players. Three of the six players
on the list have BA w/RISPs that were lower than their BAs (These players are
outline in red). This shows me that winning might have some correlation to the BA
w/RISP of your players.
Another piece of data that strengthens my
argument that RISP is important to winning is based upon the production of the
St. Louis Cardinals during the 2013 season. The Cardinals set the all-time season high for RISP.
The highest
average since 1974, the first year of reliable RISP stats, by a team with
runners in scoring position was .311 by Detroit in 2007. The Cardinals
shattered that number by going 447 for 1,355, or .330. They did this in a
season when averages across baseball with runners in scoring position were at a
low for the past decade (“RISP-ect! Cardinals shatter the all-time clutch
hitting record”).
As iterated above, the Cardinals were tied for the league lead in wins
and made it all the way to the World Series during the 2013 campaign. I understand that many baseball
statisticians deny the existence of the term “clutch” in baseball. RISP is very volatile as well, for it can vary greatly from season to season for a given team. However, after seeing this I
acknowledge the possibility that “clutch” could have a place in baseball.
The last two statistics listed above, On
Base Percentage and On Base Plus Slugging, have significant value in
determining a players independent offensive value. Unlike the other three statistics, OBP and OPS generally are
dependent upon the single player. However,
there are situations that might skew the independence of these statistics, such
that another player in the line-up has an impact on them. For example, an unprotected hitter
batting 4th in a line-up might see worse pitches than the 5th hitter
because the opposing team would much rather pitch around the 4th
hitter and have him walk, than have him make solid contact. This scenario could also be viewed in a
contrary way, such that the 3rd hitter, who precedes the talented
clean-up hitter receives better pitches because the opposing team wants to
pitch for contact with him and to try to get an out. In this situation, the hitters surrounding an upper-echelon
player receive better pitches and therefore would have more at-bats conducive
to higher OBP and OPS. Although,
these situations that decrease the independency of OBP and OPS are primarily
situational and hard to incorporate in the determination of the stats. Another concern I have pertaining
to these two statistics stems from another situational mindset. Both OBP and OPS include walks as a
part of them. Walks can be very
important to a team’s success, but only in certain situations. In some scenarios, the team might need
the hitter to take a chance and swing away instead of walking. For example, say a team has their 4th
hitter, typically a team’s best hitter, up at-bat. Also say there is a decent drop off in talent going from the
4th hitter to the 5th hitter. Assume that there are men on 2nd and 3rd
base, with two outs in the inning.
In this scenario, it is more valuable to have the 4th hitter
swing away. This is mainly because
the 4th has a better chance of getting a hit and driving in the runs than the 5th
hitter, who would come to the plate with a walk. I understand there is a situation in which the hitter may
receive no hittable pitches and that is an exception. However, the aforementioned example is just one of many
situations that swinging may be more valuable than walking. The last issue with OBP and OPS is that
they do not take into consideration scoring. Getting on base is very important, however the only way to
win a baseball game is by scoring more runs than your opponent.
MY STATISTICS
In this section, I will discuss the
statistics that I have been working on.
I also will perform calculations to display their value.
·
Offensive
Team Contribution Rating (OTCR)- The Offensive Team Contribution Rating
measures a player’s ability to knock in other runners, ability to get on base,
and ability to move base runners. The abilities to move and score base runners
I view as assists because you directly are helping your team advance on the
base paths and most of the time score.
The stats that I included to cover these criteria are Sacrifices and
Runs Batted In. The second aspect that I desired to cover in the creation of
OTCR is OBP because I wanted to include the hitter’s ability to get on base and
give his teammates the opportunity to score him. I divided the sum of RBI and
SAC by the at-bats to get the frequency of the hitters scoring or moving base
runners. Then I add the players
OBP to obtain the OTCR. All of the
constituent statistics of the OTCR (except OBP) are values that are dependent
upon the other players in the line-up.
As elaborated on in the “Current Day Statistics” portion, another player
in the line-up can affect the OBP of a player, however this dependency on other
players is situational and difficult to incorporate in the calculation.
The Equation for OTCR is as follows:
((RBI + SAC) / (At-Bats)) + OBP
The Proceeding data is based upon the
2013 MLB Regular Season statistics.
The ranks are based upon final batting average, going in descending
order. The data includes OTCR in
the last column.
Note: SH + SF = SAC
Rk
|
Name
|
AB
|
RBI
|
OBP
|
SH
|
SF
|
OTCR
|
1
|
Miguel Cabrera
|
555
|
137
|
0.442
|
0
|
2
|
0.692
|
2
|
Michael Cuddyer
|
489
|
84
|
0.389
|
0
|
3
|
0.567
|
3
|
Joe Mauer
|
445
|
47
|
0.404
|
0
|
2
|
0.514
|
4
|
Mike Trout
|
589
|
97
|
0.432
|
0
|
8
|
0.610
|
5
|
Chris Johnson
|
514
|
68
|
0.358
|
0
|
2
|
0.494
|
6
|
Freddie Freeman
|
551
|
109
|
0.396
|
0
|
5
|
0.603
|
7
|
Yadier Molina
|
505
|
80
|
0.359
|
0
|
3
|
0.523
|
8
|
Matt Carpenter
|
626
|
78
|
0.392
|
3
|
7
|
0.533
|
9
|
Jayson Werth
|
462
|
82
|
0.398
|
0
|
5
|
0.586
|
10
|
Andrew McCutchen
|
583
|
84
|
0.404
|
0
|
4
|
0.555
|
11
|
Adrian Beltre
|
631
|
92
|
0.371
|
0
|
2
|
0.520
|
12
|
Allen Craig
|
508
|
97
|
0.373
|
0
|
5
|
0.574
|
13
|
Robinson Cano
|
605
|
107
|
0.383
|
0
|
5
|
0.568
|
14
|
Troy Tulowitzki
|
446
|
82
|
0.391
|
0
|
5
|
0.586
|
15
|
David Ortiz
|
518
|
103
|
0.395
|
0
|
5
|
0.603
|
16
|
Joey Votto
|
581
|
73
|
0.435
|
0
|
6
|
0.571
|
17
|
Torii Hunter
|
606
|
84
|
0.334
|
3
|
10
|
0.494
|
18
|
Daniel Nava
|
458
|
66
|
0.385
|
4
|
8
|
0.555
|
19
|
Paul Goldschmidt
|
602
|
125
|
0.401
|
0
|
5
|
0.617
|
20
|
Eric Hosmer
|
623
|
79
|
0.353
|
1
|
4
|
0.488
|
21
|
Josh Donaldson
|
579
|
93
|
0.384
|
1
|
6
|
0.557
|
22
|
Victor Martinez
|
605
|
83
|
0.355
|
0
|
8
|
0.505
|
23
|
Dustin Pedroia
|
641
|
84
|
0.372
|
0
|
7
|
0.514
|
24
|
Matt Holliday
|
520
|
94
|
0.389
|
0
|
4
|
0.577
|
25
|
James Loney
|
549
|
75
|
0.348
|
1
|
4
|
0.494
|
26
|
Jacoby Ellsbury
|
577
|
53
|
0.355
|
1
|
2
|
0.452
|
27
|
Howie Kendrick
|
478
|
54
|
0.335
|
3
|
3
|
0.461
|
28
|
Marco Scutaro
|
488
|
31
|
0.357
|
9
|
3
|
0.445
|
29
|
Carlos Beltran
|
554
|
84
|
0.339
|
1
|
6
|
0.503
|
30
|
Buster Posey
|
520
|
72
|
0.371
|
0
|
7
|
0.523
|
31
|
Jean Segura
|
588
|
49
|
0.329
|
2
|
2
|
0.419
|
32
|
Shane Victorino
|
477
|
61
|
0.351
|
10
|
2
|
0.504
|
33
|
Adrian Gonzalez
|
583
|
100
|
0.342
|
0
|
10
|
0.531
|
34
|
Salvador Perez
|
496
|
79
|
0.323
|
0
|
5
|
0.492
|
35
|
Marlon Byrd
|
532
|
88
|
0.336
|
1
|
7
|
0.516
|
36
|
Jed Lowrie
|
603
|
75
|
0.344
|
3
|
4
|
0.480
|
37
|
Brandon Belt
|
509
|
67
|
0.360
|
1
|
3
|
0.499
|
38
|
Billy Butler
|
582
|
82
|
0.374
|
0
|
4
|
0.522
|
39
|
Adam Lind
|
465
|
67
|
0.357
|
0
|
4
|
0.510
|
40
|
Nori Aoki
|
597
|
37
|
0.356
|
8
|
3
|
0.436
|
41
|
Chris Davis
|
584
|
138
|
0.37
|
0
|
7
|
0.618
|
42
|
Daniel Murphy
|
658
|
78
|
0.319
|
0
|
5
|
0.445
|
43
|
Shin-Soo Choo
|
569
|
54
|
0.423
|
3
|
2
|
0.527
|
44
|
Adam Jones
|
653
|
108
|
0.318
|
0
|
3
|
0.488
|
45
|
Michael Brantley
|
556
|
73
|
0.332
|
3
|
8
|
0.483
|
46
|
Carlos Gomez
|
536
|
73
|
0.338
|
1
|
6
|
0.487
|
47
|
Jason Kipnis
|
564
|
84
|
0.366
|
5
|
10
|
0.542
|
48
|
Alexei Ramirez
|
637
|
48
|
0.313
|
4
|
4
|
0.401
|
49
|
Chase Utley
|
476
|
69
|
0.348
|
0
|
5
|
0.503
|
50
|
Jose Altuve
|
626
|
52
|
0.316
|
4
|
8
|
0.418
|
·
Individual
Offensive Rating (IOR)- The Individual Offensive Rating measures a player’s
independent offensive production.
This statistic takes into account the offensive values that are produced
by only the player in consideration.
The components of IOR include Stolen Bases, Home Runs (in order to
obtain the amount of times that the player was able to score himself or Self
RBI), Hits, and Walks (including Intentional Walks and Hit By Pitches).
The Equation for IOR is as follows:
(SB + HR + H + BB) / (At-Bats)
The Proceeding data is based upon the
2013 MLB Regular Season statistics.
The ranks are based upon final batting average going in descending order. The data includes IOR in the last
column.
Rk
|
Name
|
AB
|
H
|
HR
|
SB
|
BB
|
HBP
|
IBB
|
IOR
|
1
|
Miguel Cabrera
|
555
|
193
|
44
|
3
|
90
|
5
|
19
|
0.638
|
2
|
Michael Cuddyer
|
489
|
162
|
20
|
10
|
46
|
2
|
5
|
0.501
|
3
|
Joe Mauer
|
445
|
144
|
11
|
0
|
61
|
0
|
7
|
0.501
|
4
|
Mike Trout
|
589
|
190
|
27
|
33
|
110
|
9
|
10
|
0.643
|
5
|
Chris Johnson
|
514
|
165
|
12
|
0
|
29
|
2
|
5
|
0.414
|
6
|
Freddie Freeman
|
551
|
176
|
23
|
1
|
66
|
7
|
10
|
0.514
|
7
|
Yadier Molina
|
505
|
161
|
12
|
3
|
30
|
3
|
4
|
0.422
|
8
|
Matt Carpenter
|
626
|
199
|
11
|
3
|
72
|
9
|
1
|
0.471
|
9
|
Jayson Werth
|
462
|
147
|
25
|
10
|
60
|
5
|
3
|
0.541
|
10
|
Andrew McCutchen
|
583
|
185
|
21
|
27
|
78
|
9
|
12
|
0.569
|
11
|
Adrian Beltre
|
631
|
199
|
30
|
1
|
50
|
7
|
12
|
0.474
|
12
|
Allen Craig
|
508
|
160
|
13
|
2
|
40
|
10
|
2
|
0.447
|
13
|
Robinson Cano
|
605
|
190
|
27
|
7
|
65
|
6
|
16
|
0.514
|
14
|
Troy Tulowitzki
|
446
|
139
|
25
|
1
|
57
|
4
|
5
|
0.518
|
15
|
David Ortiz
|
518
|
160
|
30
|
4
|
76
|
1
|
27
|
0.575
|
16
|
Joey Votto
|
581
|
177
|
24
|
6
|
135
|
4
|
19
|
0.628
|
17
|
Torii Hunter
|
606
|
184
|
17
|
3
|
26
|
7
|
0
|
0.391
|
18
|
Daniel Nava
|
458
|
139
|
12
|
0
|
51
|
15
|
2
|
0.478
|
19
|
Paul Goldschmidt
|
602
|
182
|
36
|
15
|
99
|
3
|
19
|
0.588
|
20
|
Eric Hosmer
|
623
|
188
|
17
|
11
|
51
|
1
|
4
|
0.437
|
21
|
Josh Donaldson
|
579
|
174
|
24
|
5
|
76
|
6
|
2
|
0.496
|
22
|
Victor Martinez
|
605
|
182
|
14
|
0
|
54
|
1
|
10
|
0.431
|
23
|
Dustin Pedroia
|
641
|
193
|
9
|
17
|
73
|
3
|
4
|
0.466
|
24
|
Matt Holliday
|
520
|
156
|
22
|
6
|
69
|
9
|
5
|
0.513
|
25
|
James Loney
|
549
|
164
|
13
|
3
|
44
|
0
|
6
|
0.419
|
26
|
Jacoby Ellsbury
|
577
|
172
|
9
|
52
|
47
|
5
|
3
|
0.499
|
27
|
Howie Kendrick
|
478
|
142
|
13
|
6
|
23
|
6
|
5
|
0.408
|
28
|
Marco Scutaro
|
488
|
145
|
2
|
2
|
45
|
2
|
0
|
0.402
|
29
|
Carlos Beltran
|
554
|
164
|
24
|
2
|
38
|
1
|
1
|
0.415
|
30
|
Buster Posey
|
520
|
153
|
15
|
2
|
60
|
8
|
8
|
0.473
|
31
|
Jean Segura
|
588
|
173
|
12
|
44
|
25
|
6
|
1
|
0.444
|
32
|
Shane Victorino
|
477
|
140
|
15
|
21
|
25
|
18
|
0
|
0.459
|
33
|
Adrian Gonzalez
|
583
|
171
|
22
|
1
|
47
|
1
|
6
|
0.425
|
34
|
Salvador Perez
|
496
|
145
|
13
|
0
|
21
|
4
|
2
|
0.373
|
35
|
Marlon Byrd
|
532
|
155
|
24
|
2
|
31
|
8
|
2
|
0.417
|
36
|
Jed Lowrie
|
603
|
175
|
15
|
1
|
50
|
2
|
3
|
0.408
|
37
|
Brandon Belt
|
509
|
147
|
17
|
5
|
52
|
6
|
4
|
0.454
|
38
|
Billy Butler
|
582
|
168
|
15
|
0
|
79
|
3
|
11
|
0.474
|
39
|
Adam Lind
|
465
|
134
|
23
|
1
|
51
|
1
|
5
|
0.462
|
40
|
Nori Aoki
|
597
|
171
|
8
|
20
|
55
|
11
|
1
|
0.446
|
41
|
Chris Davis
|
584
|
167
|
53
|
4
|
72
|
10
|
12
|
0.545
|
42
|
Daniel Murphy
|
658
|
188
|
13
|
23
|
32
|
2
|
2
|
0.395
|
43
|
Shin-Soo Choo
|
569
|
162
|
21
|
20
|
112
|
26
|
5
|
0.608
|
44
|
Adam Jones
|
653
|
186
|
33
|
14
|
25
|
8
|
4
|
0.413
|
45
|
Michael Brantley
|
556
|
158
|
10
|
17
|
40
|
4
|
1
|
0.414
|
46
|
Carlos Gomez
|
536
|
152
|
24
|
40
|
37
|
10
|
2
|
0.494
|
47
|
Jason Kipnis
|
564
|
160
|
17
|
30
|
76
|
3
|
3
|
0.512
|
48
|
Alexei Ramirez
|
637
|
181
|
6
|
30
|
26
|
3
|
2
|
0.389
|
49
|
Chase Utley
|
476
|
135
|
18
|
8
|
45
|
5
|
4
|
0.452
|
50
|
Jose Altuve
|
626
|
177
|
5
|
35
|
32
|
2
|
5
|
0.409
|
SHOWING THE VALUE
In this section, I will delve further
into my statistics to explicate their worth. I will also provide examples to show their relative value in
determining a player’s worth.
·
Offensive
Team Contribution Rating (OTCR)- The OTCR is an important and unique statistic
because of the constituents that it incorporates in its calculation. OTCR includes a player’s ability to
take advantage of opportunities when his teammates get on base (RBI). Also, it considers the players ability
to move and/or score base runners via sacrifice (SAC). Lastly, the statistic of OBP is
included to show the player’s ability to get on base giving his teammates the
opportunity to score him.
·
Individual
Offensive Rating (IOR)- The IOR is an important and unique statistic because it
is designed to incorporate all of the valuable, offensive aspects of a player’s
game. There are four primary
criteria that are taken into consideration when determining a player’s
offensive value. These criteria
being mobility on the base paths, power, contact, and patience. To assess each of these aspects I
include Stolen Bases, Home Runs, Hits, and Walks, respectively. Also, all of the statistics used in the
determination of IOR are dependent only on the player being evaluated, hence
why this is an “Individual” Offensive Rating.
Through evaluating players with the
combination of both these stats, one can assess a player’s individual value and
team contributions. Of course,
with a first glance at the charts above, one can see the noticeable leaders of
each stat. Those leading players
being Miguel Cabrera, Mike Trout, Joey Votto, and many others. However, if you go further down the
charts to the 43rd ranked player Shin-Soo Choo, you will notice that
his IOR and OTCR are two of the best on both charts. His OAR is .527 and his IOR is an incredible .608. These numbers tell me not only that
Choo personally is a good player, but also if you incorporate him in a line-up
of other good players he will thrive immensely. Based on this production, if I was a General Manager I would
pay good money for a player of this caliber to be on my club. The Texas Rangers obviously saw
the same value in Choo because over the summer they inked the 32 year-old,
Korean to a well deserved 7-year $130 million deal (“Texas Rangers sign Shin-Soo Choo to
$130 million deal: Quick reactions”).
For another example, look at the 5th
ranked player, Chris Johnson. Chris
has a decent OTCR of .494, however
in comparison to the other elite players in the top 20, he ranks second to
last. Then, looking at IOR, Chris
ranks very low in the top 50 with .414.
This low IOR tells me that he individually is an average player, but
considering his OTCR, if you surround him with good players he will appear to
be an above average player. I
consider players with statistics similar to Johnsons to be complimentary
players.
However, lets move away from the consummate,
older veterans and evaluate some of the younger players. With evaluation of their OTCR and IOR,
I will determine who would be the best to build a team around. For this evaluation I will consider
players of 23 years of age or younger.
Also, to prevent any bias or unfair advantages, the batters in
consideration must have a minimum of 150 at-bats to qualify.
Name
|
AB
|
OTCR
|
IOR
|
Bryce Harper
|
424
|
0.521
|
0.512
|
Manny Machado
|
667
|
0.438
|
0.360
|
Jurickson Profar
|
286
|
0.423
|
0.371
|
Mike Trout
|
589
|
0.610
|
0.643
|
Christian Yelich
|
240
|
0.441
|
0.483
|
Oswaldo Arcia
|
351
|
0.427
|
0.370
|
Nolan Arenado
|
486
|
0.416
|
0.344
|
Nick Franklin
|
369
|
0.428
|
0.390
|
Avisail Garcia
|
244
|
0.444
|
0.365
|
Wil Myers
|
335
|
0.524
|
0.466
|
Marcell Ozuna
|
275
|
0.423
|
0.349
|
Yasiel Puig
|
382
|
0.509
|
0.537
|
Jonathan Villar
|
210
|
0.392
|
0.452
|
Mike Zunino
|
173
|
0.377
|
0.358
|
Jose Altuve
|
626
|
0.418
|
0.409
|
Cody Asche
|
162
|
0.444
|
0.389
|
Rob Brantly
|
223
|
0.357
|
0.296
|
Starlin Castro
|
666
|
0.353
|
0.329
|
Derek Dietrich
|
215
|
0.382
|
0.349
|
Matt Dominguez
|
543
|
0.444
|
0.350
|
Freddie Freeman
|
551
|
0.603
|
0.514
|
Freddy Galvis
|
205
|
0.390
|
0.346
|
Scooter Gennett
|
213
|
0.483
|
0.413
|
Didi Gregorius
|
357
|
0.419
|
0.406
|
Robbie Grossman
|
257
|
0.437
|
0.405
|
Jason Heyward
|
382
|
0.451
|
0.445
|
Aaron Hicks
|
281
|
0.376
|
0.345
|
L.J. Hoes
|
170
|
0.397
|
0.406
|
Eric Hosmer
|
623
|
0.488
|
0.437
|
Jose Iglesias
|
350
|
0.449
|
0.400
|
Brett Lawrie
|
401
|
0.440
|
0.399
|
Brad Miller
|
306
|
0.449
|
0.389
|
Salvador Perez
|
496
|
0.492
|
0.373
|
Anthony Rendon
|
351
|
0.449
|
0.399
|
Anthony Rizzo
|
606
|
0.458
|
0.427
|
Jean Segura
|
588
|
0.419
|
0.444
|
Andrelton Simmons
|
606
|
0.408
|
0.358
|
Giancarlo Stanton
|
425
|
0.513
|
0.504
|
Ruben Tejada
|
208
|
0.322
|
0.288
|
Looking
at the data above, you have your prominent names that really transcend from the
rest of the list with great OTCR and IOR values. These players being Mike Trout, Bryce Harper, Yasiel Puig,
Freddie Freeman, and several others.
However, these are already well known players that organizations have
invested significant money in. I desire
to point some other less recognized players, who I believe are valuable.
First,
look at the 5th name on the list, Christian Yelich. Christian plays outfield for a
struggling, Miami Marlins baseball team.
The 2013 Marlins line-up was very sub-par, so it would be very difficult
for Yelich to obtain an OTCR on the same level as Mike Trout, who plays on an
offensively gifted, Angels line-up.
If you look at his OTCR its .441, which is pretty good considering the
team he plays for. However, what
makes me believe that he will develop into an offensive presence is that his
IOR is .483 and higher than his OTCR.
This tells me that Yelich, himself, performs very well and that if he
were to be placed in a better line-up, he would flourish.
The
OTCR, as insinuated in its title, is more of a product of the player working
with his teammates because of the statistics that are considered in its calculation. On the other hand, IOR is more of a
direct measure of a players individual ability. Players who have higher IORs than OTCRs are more likely to
be undervalued because individually the player is producing well, despite not
receiving much assistance from the surrounding players in his line-up. Typically, a well-rounded offensive
player will amass a relatively similar OTCR and IOR, with his OTCR actually
being higher than his IOR. The
greater OTCR is a result of being in a better line-up. This difference in the two statistics
is usually within .050 points. Any
difference greater than .050 indicates that the player might only be doing well
because of the assistance he receives from the other player in the line-up. Using the data from all the tables
above, a very offensively dominant player will have an IOR and OTCR above
.500. An above average offensive
player will have an IOR and OTCR above .400.
Another
couple of players in the same situation as Yelich, are Jean Segura and Jonathon
Villar. Segura is the shortstop
for the Milwaukee Brewers and Villar is the shortstop for the Houston
Astros. Both the Brewers and
Astros struggled offensively during the 2013 season. However, despite the adversity, both Segura and Villar
recorded good OTCRs. Also, both the
players recorded IORs greater than their OTCRs, telling me the same information
that I discerned from the Yelich situation.
I
am going to turn the tables now and look for players who I believe are
overrated, based on the data above.
Immediately, one player and his low ratings stick out the most. This player is Starlin Castro, the
shortstop for the Chicago Cubs. I
understand that the Chicago Cubs are trying to rebuild and that is part of the
reason for their mediocre-at-best line-up. However, Castros OTCR and IOR values are abysmal, amassing
to .353 and .329, respectively.
Upon entering the league in 2010, Castro appeared to be a developing
superstar, recording all-star caliber statistics. However, since 2011, Castros
numbers have depreciated significantly.
He may be able to recover, but at this point in time I have to mark
Castro as being overrated.
Next,
I will compare the IORs and OTCRs of players. I will be doing this through dividing the players IOR by their
OTCR, to receive an IOR/OTCR ratio.
This ratio should enable me to recognize strong individual offensive and
possibly underrated players.
The
data in the following graph is ranked upon the top IOR/OTCR ratios from the
2013 season, going in descending order.
Ranking
|
Name
|
AB ▾
|
IOR
|
OTCR
|
IOR/OTCR
|
1
|
Jarrod Dyson
|
213
|
0.535
|
0.425
|
1.261
|
2
|
Rajai Davis
|
331
|
0.492
|
0.394
|
1.251
|
3
|
Shin-Soo Choo
|
569
|
0.608
|
0.527
|
1.155
|
4
|
Jonathan Villar
|
210
|
0.452
|
0.392
|
1.153
|
5
|
Juan Pierre
|
308
|
0.377
|
0.329
|
1.143
|
6
|
Starling Marte
|
510
|
0.484
|
0.425
|
1.139
|
7
|
Craig Gentry
|
246
|
0.545
|
0.479
|
1.138
|
8
|
Jacoby Ellsbury
|
577
|
0.499
|
0.452
|
1.104
|
9
|
Jordan Schafer
|
231
|
0.489
|
0.444
|
1.103
|
10
|
Joey Votto
|
581
|
0.628
|
0.571
|
1.100
|
11
|
Eric Young
|
539
|
0.429
|
0.390
|
1.100
|
12
|
Nate McLouth
|
531
|
0.446
|
0.406
|
1.099
|
13
|
Christian Yelich
|
240
|
0.483
|
0.441
|
1.096
|
14
|
Curtis Granderson
|
214
|
0.435
|
0.401
|
1.083
|
15
|
Everth Cabrera
|
381
|
0.504
|
0.465
|
1.083
|
16
|
Rickie Weeks
|
350
|
0.397
|
0.375
|
1.060
|
17
|
Jean Segura
|
588
|
0.444
|
0.419
|
1.059
|
18
|
Elliot Johnson
|
254
|
0.370
|
0.349
|
1.059
|
19
|
Yasiel Puig
|
382
|
0.537
|
0.509
|
1.055
|
20
|
Mike Trout
|
589
|
0.643
|
0.610
|
1.054
|
21
|
Dexter Fowler
|
415
|
0.511
|
0.485
|
1.054
|
22
|
Ben Revere
|
315
|
0.429
|
0.408
|
1.051
|
23
|
Lucas Duda
|
318
|
0.484
|
0.462
|
1.048
|
24
|
Jimmy Rollins
|
600
|
0.408
|
0.393
|
1.039
|
25
|
B.J. Upton
|
391
|
0.363
|
0.352
|
1.031
|
26
|
Josh Rutledge
|
285
|
0.389
|
0.378
|
1.030
|
27
|
Carlos Pena
|
280
|
0.425
|
0.414
|
1.027
|
28
|
Andrew McCutchen
|
583
|
0.569
|
0.555
|
1.026
|
29
|
Nori Aoki
|
597
|
0.446
|
0.436
|
1.021
|
30
|
Jason Bay
|
206
|
0.413
|
0.405
|
1.019
|
The
preceding chart includes the top IOR/OTCR ratio ratings for the 2013 season. It is apparent that since the IOR/OTCR ratio ratings for
these players are greater than one, that their individual offensive production
is better than the offensive production they have with the assistance of their
line-up. However, looking upon the data, one can notice that some of the
players might have high IOR/OTCR ratio ratings, but both of their IOR and OTCR
are low. To discredit these
underachieving players, I will only consider the players with an IOR above
.400. Those who meet this
criterion have their names outlined in blue. Of course, in the list there are some prominent players like
Mike Trout, Yasiel Puig, Joey Votto, Andrew McCutchen, and several others. Looking at the other names that are
outlined in blue, you will also see Jonathan Villar, Christian Yelich, and Jean
Segura. These are the players that
I singled out as top performers in the previous chart.
Now
lets invert the IOR/OTCR ratio from the example above to find out the players
who might be considered as offensively overrated. The players with the higher OTCR/IOR ratings may have
significantly higher OTCRs than IORs due to the assistance of the players
surrounding them in the line-ups.
The
following data is based upon the final statistics from the 2013 season. The data is ranked based upon the
OTCR/IOR ratio, going in descending order.
Rank
|
Name
|
IOR
|
OTCR
|
OTCR/IOR
|
1
|
Josh Phegley
|
0.260
|
0.350
|
1.349
|
2
|
Salvador Perez
|
0.373
|
0.492
|
1.320
|
3
|
Mike Aviles
|
0.349
|
0.451
|
1.292
|
4
|
J.D. Martinez
|
0.314
|
0.404
|
1.285
|
5
|
A.J. Pierzynski
|
0.352
|
0.448
|
1.273
|
6
|
Matt Dominguez
|
0.350
|
0.444
|
1.270
|
7
|
Mike Carp
|
0.454
|
0.575
|
1.267
|
8
|
Torii Hunter
|
0.391
|
0.494
|
1.263
|
9
|
Jeff Keppinger
|
0.312
|
0.394
|
1.263
|
10
|
Brayan Pena
|
0.349
|
0.437
|
1.252
|
11
|
Matt Wieters
|
0.373
|
0.463
|
1.242
|
12
|
Ryan Raburn
|
0.473
|
0.587
|
1.241
|
13
|
Mark DeRosa
|
0.417
|
0.517
|
1.241
|
14
|
Dayan Viciedo
|
0.358
|
0.442
|
1.235
|
15
|
Justin Morneau
|
0.386
|
0.475
|
1.230
|
16
|
J.P. Arencibia
|
0.283
|
0.347
|
1.228
|
17
|
Lyle Overbay
|
0.357
|
0.437
|
1.222
|
18
|
David Lough
|
0.359
|
0.438
|
1.221
|
19
|
Manny Machado
|
0.360
|
0.438
|
1.218
|
20
|
Brian Roberts
|
0.392
|
0.478
|
1.218
|
21
|
Avisail Garcia
|
0.365
|
0.444
|
1.218
|
22
|
Lonnie Chisenhall
|
0.329
|
0.398
|
1.211
|
23
|
Erick Aybar
|
0.353
|
0.423
|
1.199
|
24
|
Asdrubal Cabrera
|
0.374
|
0.447
|
1.194
|
25
|
Luke Scott
|
0.419
|
0.500
|
1.193
|
26
|
Maicer Izturis
|
0.329
|
0.392
|
1.193
|
27
|
Alberto Callaspo
|
0.404
|
0.481
|
1.190
|
28
|
Ramon Santiago
|
0.337
|
0.400
|
1.190
|
29
|
Will Middlebrooks
|
0.356
|
0.423
|
1.188
|
30
|
Alex Avila
|
0.397
|
0.472
|
1.188
|
31
|
J.B. Shuck
|
0.380
|
0.450
|
1.185
|
32
|
Josh Hamilton
|
0.389
|
0.460
|
1.182
|
33
|
Adam Jones
|
0.413
|
0.488
|
1.180
|
34
|
James Loney
|
0.419
|
0.494
|
1.178
|
35
|
Jed Lowrie
|
0.408
|
0.480
|
1.177
|
36
|
Jose Lobaton
|
0.383
|
0.450
|
1.176
|
37
|
Melky Cabrera
|
0.360
|
0.424
|
1.176
|
38
|
Nelson Cruz
|
0.443
|
0.521
|
1.175
|
39
|
Colby Rasmus
|
0.424
|
0.499
|
1.175
|
40
|
Mark Trumbo
|
0.394
|
0.462
|
1.173
|
41
|
Vernon Wells
|
0.349
|
0.409
|
1.173
|
42
|
J.J. Hardy
|
0.376
|
0.441
|
1.172
|
43
|
Victor Martinez
|
0.431
|
0.505
|
1.172
|
44
|
Jarrod Saltalamacchia
|
0.424
|
0.496
|
1.170
|
45
|
Omar Infante
|
0.397
|
0.464
|
1.168
|
46
|
Michael Brantley
|
0.414
|
0.483
|
1.168
|
47
|
Munenori Kawasaki
|
0.413
|
0.480
|
1.164
|
48
|
Alfonso Soriano
|
0.479
|
0.558
|
1.164
|
49
|
Alcides Escobar
|
0.315
|
0.366
|
1.163
|
50
|
Albert Pujols
|
0.440
|
0.512
|
1.163
|
Notice
that within this chart of the top 50 OTCR/IOR ratios there are several star
players who really had struggling seasons in 2013. These slumping sluggers include Albert Pujols, Josh
Hamilton, Asdrubal Cabrera, Vernon Wells, Matt Wieters, and several others. Everyone knows that those players had
rough seasons and probably will be resilient during the impending 2014 season.
However, the main piece of analysis I extract from this chart pertains to the
players who had statistically good years, but still appear on the chart. These players include Luke Scott, Will
Middlebrooks, Adam Jones, Victor Martinez, and several others. This shows me that these players might
have just done well during the past season due to the assistance from the
surrounding players in the line-up.
As
I stated previously, I believe that Josh Hamilton is an upper echelon player
and has a solid chance of being resilient during the 2014 season. However, to convey a point about
receiving offensive assistance from a line-up, I am going to compare Hamiltons
statistics from 2012 with those from 2013. During 2012, Hamilton was part of a Texas Rangers line-up
that batted .273 and scored 808 runs.
In 2013, Hamilton started his career as member of the Los Angeles Angels. During the 2013 campaign the Angels
batted .264 and scored 733 runs.
From just viewing these offensive numbers anyone can assess that the
2012 Rangers line-up was better offensively than the 2013 Angels line-up.
Year
|
Name
|
IOR
|
OTCR
|
OTCR/IOR
|
2012
|
Josh Hamilton
|
0.512
|
0.598
|
1.166
|
2013
|
Josh Hamilton
|
0.389
|
0.460
|
1.182
|
Both
the IOR and OTCR values for Josh Hamilton, going from the 2012 to 2013 season,
decreased significantly. His
OTCR/IOR ratio value even went up, meaning that Hamilton had a better
individual year in 2012. However,
the drastic increase in OTCR indicates that in a better offensive line-up,
Hamilton will capitalize on the assistance from the others in the line-up. Despite there being an increase in
OTCR/IOR ratio, the increase was nominal leaving the value still pretty
high. This shows that Hamilton is
not an offensive instigator, he will play well when the players around him are
playing well. The best way to
phrase it is that Hamilton is a good complimentary player, but not a spark plug
in the line-up.
Lastly,
to show the opposite scenario of Josh Hamilton, I will consider the statistics
of Shin-Soo Choo. Shin-Soo Choo,
as seen in the chart on pages 11-12, has a significantly higher IOR than OTCR. In 2012, Choo played for a Cleveland
Indians team that batted .251 and plated 667 runs. Then in 2013, Choo played for a Cincinnati Reds club that
batted .249 and amassed 698 runs.
From first glance of these team statistics, one can notice that the 2013
Reds were a slightly more offensively talented club.
Year
|
Name
|
IOR
|
OTCR
|
OTCR/IOR
|
2012
|
Shin-Soo
Choo (CLE)
|
0.490
|
0.487
|
0.993
|
2013
|
Shin-Soo
Choo (CIN)
|
0.608
|
0.527
|
0.866
|
In
both years Choo marked an OTCR/IOR ratio lower than one, meaning that his
individual offensive production was greater than his offensive production with
the help of his line-up, in both years.
His offensive production increased, despite the fact that the two
line-ups had relatively similar production. These statistics indicate to me that Choo is the type of
player that instigates a lot of offense and supports the complimentary players
such as Josh Hamilton.
CONCLUSION AND FUTURE DIRECTIONS
Since the statistical innovations set
forth by Bill James and the creation of SABR, the realm of baseball statistics
has taken flight. The vast amount
of statistics that flowed in during this era makes it appear that there might
not be much room for modern innovation for baseball statistics. However, I believe that some of these
statistics merely graze upon the surface and can be combined with others or
manipulated in such a way that their meaning will become more significant in
determining player value. This
belief was the basis for my creation of the IOR and the OTCR. I utilized many of the current
statistics to convey individual production and individual offensive team
contribution ratings.
Now
it may seem that the OTCR can appear to have a negative presence to it. This negative aura might come from the
fact that when comparing the it with IOR, I emphasized that a player with an
IOR greater than his OTCR individually is a very talented baseball player. Then I said, on the contrary, if the
player hones an OTCR rating greater than his IOR, that he may be overrated or
just a strong complimentary player.
I understand there are a large amount of players that fall into the
second category of their OTCRs being greater than their IORs. I am not saying that all these players
are overrated. The player could
have both a terrific OTCR and IOR, but his OTCR overshadows his IOR. This just shows me that the player is a
very good complimentary player and would thrive immensely in a line-up with a
couple of these high IOR, offensively instigating players. I just believe that if the player has a
large gap between the IOR and OTCR that the player might be overrated.
As
far as future directions for baseball statistics, there is a variety of ways in
which one could innovate. One idea
would be trying to concoct a stat that would incorporate the amount at which a
player pads his stats. This stat
could possibly work for both pitchers and hitters. One could figure out a hitters batting average against teams
with ERAs less than 3.50, or the opposite, a pitchers ERA against teams that
have BAs above .250. I still
firmly believe that “clutch” has some influence upon the game of baseball, quantifying
this would be significant in the realm of baseball statistics. One could possibly even determine the
value of draft pick positions.
However, to find out what breakthrough will come next, we will just have
to wait and see.
WORKS CITED
-"Baseball Reference." Baseball-Reference.com.
N.p., n.d. Web. 04 May 2014. <http://www.baseball-reference.com/>.
-Gleeman, Aaron. "RISP-ect!
Cardinals Shatter the All-time Clutch Hitting Record." NBC Sports.
NBC, 30 Sept. 2013. Web. 4 May 2014.
<http%3A%2F%2Fhardballtalk.nbcsports.com%2F2013%2F09%2F30%2Frisp-cardinals-shattered-the-all-time-clutch-hitting-record%2F>.
-Grant, Evan. "Texas Rangers Sign
Shin-Soo Choo to $130 Million Deal: Quick Reactions." Texas Rangers
Blog. The Dallas Morning News, 27 Dec. 2013. Web. 04 May 2014.
<http://rangersblog.dallasnews.com/2013/12/texas-rangers-sign-shin-soo-choo-to-130-million-deal-quick-reaction.html/>.
-"Introducing the New At
Bat." MLB.com: The Official Site of Major League Baseball. N.p.,
n.d. Web. 04 May 2014. <http://mlb.mlb.com/home>.
-"SABR." Society for American Baseball Research.
N.p., n.d.
Web. 04 May 2014. <http://sabr.org/>.
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