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2013 Year-End Newsletter
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Table of Contents.............................................................................................................. p. 2 About ................................................................................................................................ p. 2 The Importance and Difficulty of Scouting Make-up...................................................... p. 3 Mike Moustakas and Why Royals Fans Shouldn’t Panic ................................................ p. 6 Player Report: Marcust Stroman, RHP (TOR, AA) ......................................................... p. 11 Oakland A’s and Winning Without Good Starting Pitching............................................ p. 12 What’s the Deal with Shutdown Innings? ....................................................................... p. 14 Who is the Best Catcher in the American League? ........................................................ p. 16 Comparing the 3 WAR Measures ................................................................................... p. 20 A Belated Farewell to the Toddfather .............................................................................. p. 28 Chris Denorfia and the Importance of Making Contact .................................................. p. 31 How Much is a First Round Pick Worth? ........................................................................ p. 33 Batting Leadoff Contact Info .......................................................................................... p. 39
About Batting Leadoff is a website dedicated to providing readers with premium baseball content. From analyzing Pitch F/X data, to providing extensive scouting reports on coveted prep players, to breaking down your favorite team’s strengths and weaknesses, Batting Leadoff covers it all. Our collection of blogs is comprehensively researched and skillfully written to give readers the most informative and entertaining insights on the web.
Further, Batting Leadoff serves as a showcase for talented people looking to start careers in baseball operations, statistical analysis, and scouting. Through the Batting Leadoff space, writers are able to show professional personnel how they could one day contribute to an MLB Front Office.
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The Importance and Difficulty of Scouting Make-up (9/18/13)
By Hudson Belinsky
Makeup is without a doubt the most
fascinating aspect of scouting. There are so
many different ideas about what constitutes
good makeup, and scouts rarely have the
opportunity to truly tap into a player’s
makeup. The learning curve of scouting
players’ tools, in my opinion, takes
approximately five years to master. After five
years, a scout should quickly be able to
develop a very strong understanding of a
player’s tools profile. I’ve been scouting for
about three years, and I
feel like I’m almost there.
But makeup presents a
unique challenge to
scouts, regardless of how
long they’ve been
scouting. Today, I’ll offer
up what I’ve gathered
about makeup so far, but
my conception of makeup
is likely to change
throughout my scouting
career. This will serve as a jumping off point
for a further discussion of evaluating the
most important aspect of baseball player
projection.
Makeup has several components. Among
these components are overall character,
coachability, the ability to successfully
manage and cope with failure, respect for
the game, baseball instinct, hustle, pre-game
preparation, desire to succeed,
understanding of work ethic, and willingness
to put in hard work. It’s very difficult to
evaluate all of these for a player, but you can
usually draw conclusions from a few of
them.
At minor league games, there is always a
group of pitchers charting the game.
Examining these pitchers, from a distance,
provides excellent insight. You can quickly
determine who is a baseball rat and who is
just a person who plays baseball. This
summer, I sat in on a series between the
Reading Fightin’ Phils and the Trenton
Thunder. I had heard good things about
Phillies pitching prospect Jesse Biddle for
quite some time, and my observations of
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Biddle during that series make me believe he
will have little trouble actualizing his
physical gifts.
During that series, I constantly overheard
Biddle discussing what exactly was
happening on the field, noting that Pitcher X
needed to establish this or that
pitch, was flying open, was creating
velocity separation between his
fastball and changeup correctly or
incorrectly, etc. Biddle has a desire
to understand the game as best as he
possibly can so that he can apply what he
sees to his own game. He understands what
he needs to do to make himself a better
baseball player, and has an intense desire to
develop himself. Biddle is a baseball rat.
Furthermore, I got to see how Biddle
interacted with fans in search of his
autograph. The 21-year-old would toy with
the annoying dudes who go around getting
prospects’ autographs before reluctantly
signing a ball or a card. At one point, Biddle
interacted with a young child and the child’s
parents. Not only did Biddle sign
autographs, he inquired about the family’s
experience at the game and thanked them
for their fandom.
Before I had gone to see Biddle, one scout
told me about how he saw Biddle show up to
the field extremely early one day to work on
his hitting. This is a player who thoroughly
understands what being a Major League
baseball player entails, and he passes every
single makeup test. People throw around the
term “80 makeup” rather liberally, but
Biddle is one of the few players whose
makeup would receive the highest mark on
my scouting report. I have little doubt about
Biddle’s ability to reach the majors and
succeed
there.
In that
same
series, I
saw a very enigmatic example of makeup
that still makes me uneasy. Slade Heathcott,
first of all, is a lunatic. He’s very passionate
about his faith, yet very easily pushed to
extreme anger. His desire to succeed is
undeniable, but he has issues dealing with
failure that will follow him throughout his
career, and may prevent him from
actualizing the physical gifts that made him
the Yankees’ first round pick in 2009.
At one point in the series, Slade took a called
third strike that he didn’t think should have
been called. He quickly erupted at the
umpire and got himself ejected from the
game, but his ejection did not stop his
screaming. Slade struggles to contextualize
each at-bat as just another at-bat, and hates
failure perhaps more than he loves success.
In that same series, Slade chased down every
fly ball as if his life depended on catching it,
and quickly reacted to the game as it came to
him.
“Biddle, has a desire to understand the game as best as he possibly can so that he can apply what he sees to his own game”
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Judging by the way Slade plays the game (as
well as his muscular composition), I don’t
think there’s any issue with work ethic or
desire to succeed. The issues are with
baseball instincts, failure management, and
perhaps coachability, given his well below-
average approach to hitting. These may be
issues that he can overcome; players are
kids, and kids grow up. But these issues
could also prevent Heathcott from being
anything more than a fourth outfielder.
There are people that would conclude that
Slade has poor makeup. I can see the
argument, but this is where evaluating
makeup gets really difficult. There’s also an
argument that his raw personality traits
could turn him into an All-Star some day.
It’s up to each scout to
place value in certain
traits over others, and
players like Biddle are
a very rare breed;
most of the time a
player has faults in
one way or another
that cloud the overall
judgment of his
makeup. I’m still in
the process of valuing
makeup traits, and I
believe that this will
be an ever-evolving process throughout my
career.
Before I close this article, I’m going to offer a
few thoughts on everyone’s favorite
prospect, Byron Buxton. As a scout, it’s
usually wise to search for a player’s flaws;
Buxton doesn’t have any. Buxton has varying
degrees of strength, but every one of his
tools will be above-average, and, in my
opinion, he has a chance for three 80 tools
(arm, speed, defense), a 70 hit tool, and a 60
power tool. I firmly believe that Buxton will,
in short order, be in the conversation as the
best player in baseball, but that has as much
to do with his makeup as it does his tools.
I had at least three chances to observe
Buxton up close in 2013. In Spring Training,
Buxton showed me every tool and spent
plenty of time discussing the game with his
coaches, probably becoming something of a
nuisance to seasoned baseball minds like
Paul Molitor and Doug Mientkiewicz. I saw
about 20 games in Cedar Rapids, and spent
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a decent amount of time close to the Twins’
Low-A affiliate there. Buxton was always
around, waiting for someone to throw to him
in the cages. When I spoke to him, he
expressed a burning desire to get out of
Low-A; not because he wanted to escape
the City of Five Smells, but because he
wanted to reach the Major Leagues as soon
as he possibly could. My final interaction
with Buxton was a brief encounter that took
place in the Team USA clubhouse at the
Futures Game. As I walked by him, sure that
he wouldn’t remember me, I asked him if he
was happy to be out of Cedar Rapids. He
smiled and said, “One step closer.”
Byron Buxton was born to play baseball. Not
football, not basketball. I wonder if scouts
realized this when he was an amateur, or if
the tools alone were what made Buxton the
no. 2 overall pick in 2012. Regardless,
Buxton has the makeup to actualize his
tools, and should serve as an example to
young players everywhere.
Makeup is incredibly difficult to get your
finger on, but it’s so important to really
understand a player’s makeup before
bringing him into your organization. If you
can truly evaluate makeup, you can make
yourself a true scout.
Mike Moustakas and Why Royals Fans Shouldn’t Panic (9/20/13)
By Alex Smith
After hitting 24 homers for Chatsworth High
School and winning Baseball America’s High
School Player of the Year Award, Mike
Moustakas became the 2nd overall selection
of the 2007 Major League Baseball First-
Year Player Draft.
The talented third base prospect then
breezed through the Royals farm system,
clubbing 80 combined home runs in 2010
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and 2011 between Double-A and Triple-A. Moustakas had become a staple of top prospect lists
floating around the web and Royals fans were already dreaming of watching him hit 40+ home
runs a year for their favorite team.
Two and half full seasons into his career, that dream has yet to become a reality. After what
seemed to be a promising 2012 campaign, in which Moustakas hit 20 home runs, he has regressed
in 2013- posting a slash line of .234/.288/.362 and only hitting 11 long-balls.
However, while watching Moustakas might be frustrating for the Kansas City faithful, I urge them
to be patient. Moustakas’ struggles are not that uncommon for power hitting prospects.
To prove this point, let’s take a look at Major League Baseball’s current home run leaderboard. At
the top is Chris Davis, a player who like Moustakas was once one of baseball’s most heralded
prospects. With raw power that grades close to 80 on the scout scale, Davis rose to the big
leagues with grand expectations. However, it has taken Davis six years to actually live up to his
potential. Only now, at age 27, has Davis finally been able to transfer his raw power potential into
monstrous in-game power at the big league level.
Davis is not the only player this delay of success applies to. If you keep going down the home run
leaderboard, only Miguel Cabrera, Mark Trumbo, Alfonso Soriano, and Evan Longoria reached
their power potential at an early age amongst the top ten.
Edwin Encarnacion, who has hit 36 home runs so far this year, never posted a slugging
percentage above .486 in his first seven seasons. Paul Goldschmidt only hit 20 home runs last
year in almost 600 at bats before embarking on his gargantuan 2013 campaign. Pedro Alvarez
slugged merely .289 in his second major league season before blowing up in 2012. And even the
burly Adam Dunn had home run totals in the 20s during his first couple of seasons before surging
to hit 46 home runs in 2004. The list of talented sluggers who took time to get used to major
league pitching goes on and on, and its very conceivable that Moustakas will one day make his
way onto that list.
As I was thinking about this phenomenon over the course of the week, I decided to discuss it with
one of my teammates, Ryan Karl. Ryan is a transfer from Louisville and will be one of the premier
sluggers on the Cornell baseball team this spring.
What he said to address the point makes sense. If you’re a power hitter, you’re approach at the
plate every single time is to hit the ball really, really hard. You don’t care if you strike out, because
that’s an acceptable side effect of having this approach.
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Because of this, it becomes more difficult to square balls up off the center of the bat, usually a
prerequisite for a home run,
especially if pitchers already
know your weaknesses and
have the stuff and ability to
pitch to them.
As hitters go up in level, the
quality of the pitching
improves, pitchers can locate
more precisely, have
increased movement, and
greater velocity. Additionally,
scouting reports on each
individual hitter become
more advanced. Pitchers not only know more about a hitter, but they also have the ability to use
that knowledge to get the hitter out.
While every level of play has its own learning curve, the learning curve in the majors is the largest
since it’s baseball in its pinnacle form, and major league pitchers are considerably better than
their minor league counterparts.
Let’s say for example, a team figures out a hitter often whiffs at breaking balls outside of the
zone. In the minors, a pitcher may be able to throw a breaking ball that starts in the zone and
breaks out of it, but it will likely have less sharp-breaking action and have looser spin than a
major league breaking ball. This gives a hitter more time to identify the pitch and choose not to
swing.
In the majors though, the pitcher is likely to throw a breaking ball with incredible sharp-breaking
action and much tighter spin, giving a hitter less time to identify the pitch, thus increasing his
likelihood to swing and miss or make weak contact. Even the slightest difference in sharpness and
tightness of spin can make it significantly harder for a batter to get his barrel to the ball.
Pitchers ability to read these scouting reports and pitch to them, makes it ever more important for
hitters to study their own scouting reports of the pitcher and make their own adjustments. And
sometimes, particularly if a hitter has dominated at every previous level, this is not a skill he has
developed and a skill that it will take time for him to develop.
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If we go back to the Chris Davis example, he has always had a problem with striking out far too
frequently. However, in the minors, this issue did not take away from him doing everything else
he does well. In the majors though it was a different story, and early in his career it caused him
grave issues. Despite having relatively successful 2008 and 2009 seasons, Davis’ swinging strike
percentages were 17.2% and 19.1% respectively. To put that in perspective, Pedro Alvarez
currently leads the majors with a swinging strike percentage of 16.8%.
In 2010, Davis attempted to make an adjustment to make contact more and his swinging strike
percentage dropped to 11%. This drastic decrease though caused Davis to sacrifice his in-game
power, and he posted a lowly isolated power of .100.
It has taken quite a few years, but Davis has seemingly finally found the right balance between
driving the ball and simply making contact. Learning how to make proper adjustments to your
game is extremely difficult, but Davis through persistence and dedication to his craft has reached
the point where he’s finally tapping into his massive power potential.
Moustakas’ problem, unlike Davis, is not
swinging and missing; it’s chasing bad
pitches out of the zone and hitting the ball
weakly. Brooks Baseball classifies pitches
into three categories, Fastballs (4-seam, 2-
seam, etc.), Breaking Pitches (curveball,
slider, etc.), and Off-Speed pitches
(changeups, splitters, etc.). They also
compute a statistic titled “strike-zone
discrimination” comparing a hitter’s swing
percentages against different pitch types
in and out of the zone. Against breaking
pitches and off-speed pitches, Moustakas’ strike-zone discrimination numbers are 0.54 and 0.82
respectively, well below the target of having a strike-zone discrimination around or greater than 1.
While this issue is plaguing Moustakas horribly right now, it is something he can certainly fix,
since his talent is still there. It’s just going to take a lot of time and hard work. Like Davis, don’t
expect Moustakas’ career to be a progression. His struggles could very well carry over into
2014. But if the Royals are willing to be patient with him and he’s willing to be patient with
himself, it wouldn’t be all to surprising to witness a Mike Moustakas power explosion in Kansas
City in the future. He does have four years of team control left after all.
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Links/Partners
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Player Report: Marcus Stroman, RHP (TOR, AA) (9/10/13) Each week Batting Leadoff posts two scouting reports on players Featured Blogger Mike Parnell has seen this year along with video of the player. The scouting report features a summary of the player and his grades for each tool or pitch. Occasionally, Mike adds additional comments on the player if he feels it is necessary. Here is Mike’s scouting report on Toronto Blue Jays top prospect Marcus Stroman from earlier this year. For a larger version of the report visit this link: http://battingleadoff.com/2013/09/10/player-report-marcus-stroman-rhp-tor-aa/
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The Oakland A’s and Winning Without Good Starting Pitching (9/23/13)
By Chris Moran
The Oakland Athletics starting pitchers have posted a 105 xFIP-, and accumulated 10.7 WAR, figures that are
21st and 16th in MLB, respectively. As the below table shows, pitching independent stats do not show much
love for the Athletics starting pitchers, with their walk rate being the only number not in the bottom half of
the league.
Stat xFIP- K% BB% GB% WAR
Number 105 18.1 6.8 38.8 10.7
Rank T-21st 19th T-7th 30th 16th
However, the A’s starting pitchers fare much better in terms of defense-dependent stats, and with the
exception of Brett Anderson, they have managed to stay healthy.
Stat ERA- BABIP LOB% HR/FB RA9 WAR Innings
Number 96 0.272 73.8 9.6 13.2 927.1
Rank T-6th 1st T-8th T-4th T-7th 6th
Finally, to give you an idea of how pedestrian their staff has been (at least in terms of sabermetric numbers,
more on that later), I prepared a table of the A’s starting pitchers this year.
Pitcher Innings xFIP- K% BB% BABIP LOB% HR/FB WAR
Bartolo Colon 178.1 102 14.0 3.7 0.297 78.9 5.5 3.5
Dan Straily 145.2 112 19.4 9.0 0.263 70.5 8.5 1.6
Jarrod Parker 186.2 110 16.6 8.0 0.262 73.6 9.5 1.6
A.J. Griffin 195 106 20.5 6.6 0.240 77.5 12.4 1.4
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Sonny Gray 50 74 24.8 6.9 0.267 71.7 7.7 1.3
Tommy Milone 148 108 17.8 6.0 0.286 72.3 10.7 1.1
Brett Anderson 23.2 91 21.3 11.7 0.366 55.6 17.6 0.1
The Coliseum is the 8th-most difficult park in
terms of hitting home runs, and the A’s fly ball
rate of 41.6% leads MLB (no other team gets a
higher percentage of fly balls than groundballs).
Gray has been excellent in the six starts he has
made, with a 52.2 GB%. Other than Anderson
and Gray, no A’s starting pitcher has a GB%
above 42.4%. Put a team full of fly ball pitchers in
a big ballpark with good outfield defense, and you
have a recipe for
overachieving
peripherals. This helps
explain how the A’s starting
pitchers have managed to
put together a 3.73 ERA
despite a 4.20 xFIP, easily
the biggest positive gap of
any team.
Except for newcomer Gray
(18th overall in 2011), the
A’s have not used high draft picks to get these
pitchers. In fact, since 2003, the A’s have only
selected four pitchers out of their nineteen first
round picks. Colon was an inexpensive free agent
signing. Parker and Anderson were acquired in
trades with the Diamondbacks where the A’s gave
up Haren and Trevor Cahill after getting some
solid years out of those arms. Milone, a former
10th-round pick, was acquired as part of the Gio
Gonzalez trade. Straily was a 24th-round pick in
2009. Griffin was a 13th-round pick in 2010. If
you click on the links, (or just keep reading) you
will find out that one other player from those two
rounds has reached the majors. (Keith Butler,
who managed a 5.44 xFIP in 20 innings with the
Cardinals this year). Most players drafted in
those rounds are no longer playing affiliated
baseball, not starting games for a playoff-bound
team.
As the A’s starting pitchers currently have a 105
xFIP- and they have clinched the AL West, I
thought it would be interesting to see how many
teams had made the playoffs with their starting
pitchers possessing a cumulative xFIP- of 105 or
worse. As xFIP- only goes back to 2002, the
search was restricted to 2002-2013.
The 2011 Diamondbacks finished 94-68, winning
the NL West. Diamondbacks starting pitchers
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posted a 107 xFIP-, good for 25th in MLB. Thanks
to some innings eaters, they tallied 12.0 WAR,
15th in MLB. Like the A’s, the Diamondbacks had
a staff of fly ball pitchers, as they posted the
lowest groundball percentage in the league.
Despite playing at cozy Chase Field, their HR/FB
ratio was only 9.8%, due in part to their rotation
getting the fourth-highest infield-fly rate. They
also had the third-lowest walk rate in MLB.
Featuring an outfield of Chris Young, Gerardo
Parra, and Justin Upton, the Diamondbacks led
MLB in UZR. The rotation featured excellent
seasons from Ian Kennedy and Daniel Hudson,
with a side of Josh Collmenter. Nobody else
reached +1 WAR. The Diamondbacks beat their
Pythagorean record by +6 wins. Their 28-16
record in 1-run games was the best in the league.
And that’s it. No other team has made the
playoffs since 2002 after having their starting
pitchers tally a 105 or worse xFIP-. The A’s
success this year isn’t quite unprecedented, but
it’s close. Unlike the Diamondbacks, the A’s have
played to their Pythagorean record. Rather than
emphasizing velocity (A’s starters are 28th in
fastball velocity) Billy Beane has sought
out young strike throwers who can stay
healthy (and Colon, an old strike thrower). By
putting them in a big ballpark with good
outfielders, the A’s have managed to make below-
average starting pitchers look solid. Billy Beane
and the A’s are finding a way to beat sabermetric
pitching stats such as xFIP and FIP. By drafting
pitchers later and making the most out of less
than electric arms, they have managed to insure
themselves against the risks associated with
young pitchers.
What’s the Deal With Shutdown Innings (11/13/13)
By Francesco Padulo
It is the bottom of the third inning in Game Four of the 2013 NLCS. The Cardinals are leading the Dodgers
two games to one and looking
to gain a decisive advantage
by taking Game Five in
LA. Lance Lynn takes the
mound for his third inning of
work after his team has given
him a three run cushion in
the top half of the frame. At
this point, most people
probably feel like the
Cardinals are completely in
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control. TBS announcer Ron Darling is not one of those people. He reminds millions of viewers that Lynn
has a National League-worst 6.67 ERA in all-important “shutdown innings.” But, is he right? Is the
shutdown inning really that important? Does the shutdown inning even exist?
In the age of sabermetrics, many people in baseball would probably argue that a shutdown inning is no more
important than any other inning at that given point in the game. You won’t find any win expectancy or
leverage index statistics to support the shutdown inning’s existence; believe me I tried. While
sabermetricians may be correct given the average regular season game, I argue that they are wrong when it
comes to the playoffs.
To begin to analyze
shutdown innings, we
first need to define
them. This can prove
to be easier said than
done, as every situation is slightly different, and
different people place a different amount of
importance on a given situation. However, for the
purposes of this article, I am defining a shutdown
inning as the half inning immediately after a
team scores to tie the game, take the lead, or cut a
deficit to three runs or fewer.
Once we establish this definition, we can begin to
quantify and evaluate shutdown innings. In the
2013 Postseason there were a total of 84
shutdown innings. Out of those innings, pitchers
were successful in shutting down the other team’s
offense 69 times, and they failed just 15 times. Of
the 15 times that a team failed in a shutdown
inning, 11 of them went on to lose the game. In 2
of the 4 games where a team failed in a shutdown
inning and went on to win the game, the
opposing team also failed in a shutdown inning.
Although it is not the largest sample size, these
statistics clearly show that while succeeding in a
shutdown inning does not guarantee a win, not
succeeding in those innings
certainly hurts a team’s
chances.
The reason behind this? I say
it’s mental. As a player myself, I know that guys
don’t think about shutdown innings when they
are out on the field or up at the plate, but in
baseball, it is easier to play when you are ahead.
You can take more risks and be more aggressive
when your team has the lead, and in doing so a
lot more pressure is placed on the opponent to
execute and play fundamentally sound baseball.
Additionally, in the playoffs especially, once a
team falls behind they begin to put extra pressure
on themselves. This combination of pressures
creates a mental environment for the trailing
team that is not conducive to success.
Yasiel Puig’s NLCS performance is a perfect
example of how a team’s position on the
scoreboard can affect a player’s performance. In
the first two games of the series, the Dodgers did
not lead a single inning. Puig was 0-10 with six
strikeouts in those two games after putting
together a very successful division series against
the Braves. It appeared as if he had a relapse of
“In 2 of the 4 games where a team failed in a shutdown inning and went on to win the game, the opposing team also failed in a shutdown inning.”
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the problems that plagued him mid-season:
taking unnecessarily long and hard swings and
chasing pitches way out of the zone. After Adrian
Gonzalez gave the Dodgers the lead in Game 3,
Puig proceeded to get hits in his next two at bats
including a two-strike opposite field triple. It
appeared that once they got the lead, Puig
relaxed and stopped trying to do too much.
Shutdown innings are what allow teams to
play from ahead. If a pitcher doesn’t have a
shutdown inning, then his team will never come
to bat with the lead, and will never benefit from
the mental advantage that that gives them. In the
playoffs, every inning, every at bat, and every
pitch is magnified, so it is even more important to
have every advantage you can get.
Who’s the Best Catcher in the American League (12/16/13)
By Bryan Robinson
The title of the best backstop in the National League is a discussion between the duo of Buster
Posey and Yadier Molina. They have established themselves as the cream of the crop, with others a clear
step behind. In the American League, this discussion has been easily resolved for the better part of the last
decade, as Joe Mauer has simply dominated. However, with the news that Mauer is moving from behind
the plate to first base, the question becomes surprisingly difficult to answer. That is, who is the best catcher
in the American League?
I’ve narrowed the list of potential candidates to the following handful of players: Jason Castro, Yan
Gomes, Brian McCann, Sal Perez, and Matt Wieters. Certainly, others could breakout and surprise
everyone, but these five are the most likely in my view to be the most productive AL backstops in 2014. Also
worth noting is that I’ve excluded Carlos Santana from the discussion, as there are reports of him
abandoning catching, in order to accommodate Gomes.
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Jason Castro
The 26-year-old Castro experienced a breakout campaign in 2013, to the tune of a 4.3 WAR. His year was
largely influenced by an uptick in power, as well as an inflated BABIP (.351). Castro is a major regression-
candidate going forward, especially when considering that his plate discipline took a turn for the worse last
year, resulting in a 26.5% strikeout rate. His defense grades out as roughly average, based on limited MLB
experience. Steamer projects his 2014 offensive numbers to replicate those of his career, coupled with the
improved defense he exhibited last year. Steamer also projects a 3.6 WAR in a full season.
Yan Gomes
Another 26 year-old breakout from 2013, Gomes produced 3.7 WAR with above average offense and defense
in just 322 plate appearances. As I mentioned earlier, his production has led the Indians to experiment
Carlos Santana at third base. As the most
inexperienced player on my list, he is the
hardest to project for 2014. His numbers across
the board were very similar to those of Castro,
except that he doesn’t walk nearly as often.
Steamer projects Gomes to regress from his
spectacular 2013 with a 3.1 WAR in two thirds
of a season. Gomes is definitely the wildcard of
the five players I’ve listed, as he could
conceivably regress further than Steamer
believes he will.
Brian McCann
McCann, fresh off of signing a five-year, $85 million contract with the Bronx Bombers, is the only catcher of
the five presented in this article that has changed teams for 2014. His new home should help his offense,
being a left-handed bat. Steamer agrees, by projecting McCann to match his career high in home runs next
year with 24. About to turn 30, his numbers shouldn’t begin to decline dramatically just yet. Also, his 2013
wRC+ of 122 was dragged down a bit by a suppressed .261 BABIP. Steamer believes that in roughly a full
season, McCann will produce 3.7 WAR, with a 112 wRC+. I find this offensive projection to be a tad
conservative, but it still places his 2014 WAR higher than that of both Castro and Gomes.
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Salvador Perez
Perez, a 90’s baby, has more than 250 games under his belt even though he is just 23. As a contact hitter, he
has shown consistency in his plate discipline through low walk rates and low strikeout rates. His on-base
percentage will continue to be average-ish, unless he starts to walk or sees a spike in his BABIP. His strength
is his defense, with a cannon for an arm. He has displayed some extra base power, with 27 homers, 49
doubles, and even 5 triples in his brief career (989 plate appearances). By nearly duplicating all of his career
statistics, Steamer projects Perez to produce 4.1 WAR in 2014. With youth on his side and a high-floor in the
event that he takes a step back, Perez looks to be a lock to be one of the American League’s best for years to
come. With a bit more patience and power, he could be the best as early as next year.
Matt Wieters
The final name on the list is the former top-prospect Wieters, who has had an inconsistent career thus far.
Surprisingly, his defense has been stronger than his bat. Wieters’ 2013 saw a dip in his BABIP, which is the
main cause for his sub-.300 OBP, which is .319 for his career. The 27 year-old will be entering his power-
prime, and it would be nice to see him improve on his high-.100’s ISO. Wieters’ numbers have been trending
in the wrong direction for the past two years, so it will be interesting to see if he can improve upon the just-
okay numbers Steamer projects; a slash line of .249/.319/.425 and a 3.2 WAR. He certainly has the ability,
especially when looking at the 4.6 WAR he delivered in 2011. I’m skeptical, but maybe a BABIP-bounce back
will create the spark he needs. He hasn’t had a BABIP greater than .290 since his rookie campaign of 2009.
Of the five names, none seem like slam-dunk picks to be the most productive American League catcher next
year. I would be tempted to bet on one of “the field” to out produce all of them; I’m thinking a surprise
breakout such as Mike Zunino or Hank Conger, or a resurgent Alex Avila, for example. Perhaps even
more likely is that Carlos Santana sticks around at catcher for a little bit longer. This will certainly be fun to
reflect on at the end of next year.
For the record, I’m taking Sal Perez as my pick. His defense is elite, as only Russell Martin and Yadier
Molina graded out better defensively by Fangraphs’
cohesive metric last year. The beautiful thing is that
he doesn’t have to produce any more offense than
he has thus far in his brief career to be a huge asset.
At the very least, he’ll be an average backstop for
the foreseeable future, and carries a tremendous
ceiling. And luckily for the Royals, he’s all theirs for
the next six years.
19
Links/Partners
20
Comparing the 3 WAR Measures (12/31/13)
By Morris Greenberg
Many now treat WAR as a definite measure of success of a player. However, it is important to realize, like
with any other statistic developed in the Sabermetric Revolution, it is not perfect. As Baseball-Reference
explains in its glossary entry on WAR:
There is no one way to determine WAR. There are hundreds of steps to make this
calculation, and dozens of places where reasonable people can disagree on the best way to
implement a particular part of the framework. We have taken the utmost care and study at
each step in the process, and believe all of our choices are well reasoned and defensible.
But WAR is necessarily an approximation and will never be as precise or accurate as one
would like.
We present the WAR values with decimal places because this relates the WAR value back
to the runs contributed (as one win is about ten runs), but you should not take any full
season difference between two players of less than one to two wins to be definitive
(especially when the defensive metrics are included).1
With top Sabermetric websites like Baseball-Reference, Fangraphs, and Baseball Prospectus all having
different calculations of measuring the wins above a replacement player, we do not know the ordinal
rankings of players based on any one version of WAR. Additionally, even if we are 99% sure that one player
is better than another, we are much less sure of the difference in production in terms of WAR between the
two players from one measure alone.
In order to more accurately learn from rWAR (Baseball-Reference version), fWAR (Fangraphs version), and
WARP (Baseball Prospectus version), we need to see the differences in characteristics between the measures.
Note that I am not trying to find which measure is best of the three; to do this would probably be quite
subjective, especially considering that the actual WAR formulas are black boxes to me. I am merely looking
1 http://www.baseball-‐reference.com/about/war_explained.shtml
21
at trends in the data, specifically hitter data in this article. I plan to look at this further with pitchers in a
future article.
Are there relationships between the metrics?
One of the most basic questions to consider is if one measure of WAR can imply something about another
measure. To explore this question, I took all of the hitters who finished in the top 35 for the 2013 season in
any one of the three statistics, and compared their results:
The light blue line represents the 45˚ line, and the black line represents the linear line of best fit when
comparing rWAR and WARP. The y = mx + b form of the black line and the R^2 value for the equation
(indicator of how well the data points map to the line of best fit, where an R^2 of 1 means that every data
point lies on the line of best fit) are written at the top. If a player’s data point lies on the 45˚ line, this means
the rWAR and WARP of the specific player were equal this year. If it lies above the 45˚ line, then the player’s
WARP was greater than his rWAR, and if it lies below the 45˚ line, the player’s WARP was less than his
rWAR.
There are a few key insights to notice. First, rWAR and WARP are not the same, as there are observations
that do not lie on the 45˚ line. Second, we cannot derive either of the two statistics accurately from the other
y = 0.6588x + 1.5218 R² = 0.40611
0
2
4
6
8
10
12
0 2 4 6 8 10 12
WARP
rWAR
rWAR vs. WARP
22
(since the R^2 value is small). Third, neither measure is consistently greater or less than the other (since
there are some observations that lie above the 45˚ line and some that lie below the 45˚ line). This implies
that rWAR and WARP use similar replacement level thresholds, but their actual calculations of deriving wins
differs. I have included the other two graphs of this nature below, the same insights apply for them.
y = 0.7596x + 1.5647 R² = 0.70348
0
2
4
6
8
10
12
0 2 4 6 8 10 12
rWAR
fWAR
fWAR vs. rWAR
y = 0.6695x + 1.6525 R² = 0.51135
0
2
4
6
8
10
12
0 2 4 6 8 10 12
WARP
fWAR
fWAR vs. WARP
23
How differently distributed are the metrics?
Since WAR measures the value of a player over a theoretical replacement level player, it is important to see
how many players outperform the replacement level one. We gathered from the previous section that the
three statistics incorporate similar replacement level players. So, if this is true, we should expect the
measures to have roughly the same amount of people at each win level.
To test this, I looked at the distribution of the top 200, top 100, top 50, and top 25 players according to each
metric. To not clutter the story with graphs, I only included the histograms of the top 200 and top 25 in this
article:
fWAR top 200 rWAR top 200 WARP top 200
Bin Freq Rel % Cum % Bin Freq Rel % Cum % Bin Freq Rel % Cum %
1 0 0.0% 0.0% 1 0 0.0% 0.0% 1 0 0.0% 0.0%
2 70 35.0% 35.0% 2 68 34.0% 34.0% 2 75 37.5% 37.5%
3 49 24.5% 59.5% 3 50 25.0% 59.0% 3 51 25.5% 63.0%
4 38 19.0% 78.5% 4 29 14.5% 73.5% 4 30 15.0% 78.0%
5 18 9.0% 87.5% 5 24 12.0% 85.5% 5 22 11.0% 89.0%
6 14 7.0% 94.5% 6 13 6.5% 92.0% 6 9 4.5% 93.5%
7 6 3.0% 97.5% 7 9 4.5% 96.5% 7 8 4.0% 97.5%
8 3 1.5% 99.0% 8 4 2.0% 98.5% 8 4 2.0% 99.5%
9 1 0.5% 99.5% 9 2 1.0% 99.5% 9 0 0.0% 99.5%
10 0 0.0% 99.5% 10 1 0.5% 100.0% 10 0 0.0% 99.5%
11 1 0.5% 100.0% 11 0 0.0% 100.0% 11 1 0.5% 100.0%
More 0 0.0% 100.0% More 0 0.0% 100.0% More 0 0.0% 100.0%
For the top 200 players, there is little difference in the distribution between the three statistics. There are
slightly more players in the top 200 that have fWAR in the 4-5 bin than there are in the rWAR and WARP 4-
5 bins, but this is minimal given that we know these measures are not the same.
24
When we blow up the image of the top 200 players into the top 25 players, however, there is a clearer
difference in distribution between the 3 statistics.
fWAR top 25 rWAR top 25 WARP top 25
Bin Freq Rel % Cum % Bin Freq Rel % Cum % Bin Freq Rel % Cum %
4.5 0 0.0% 0.0% 4.5 0 0.0% 0.0% 4.5 0 0.0% 0.0%
5.09 0 0.0% 0.0% 5.09 0 0.0% 0.0% 5.09 5 20.0% 20.0%
5.68 11 44.0% 44.0% 5.68 5 20.0% 20.0% 5.68 6 24.0% 44.0%
6.27 5 20.0% 64.0% 6.27 6 24.0% 44.0% 6.27 7 28.0% 72.0%
6.86 3 12.0% 76.0% 6.86 7 28.0% 72.0% 6.86 2 8.0% 80.0%
7.45 1 4.0% 80.0% 7.45 2 8.0% 80.0% 7.45 2 8.0% 88.0%
8.05 3 12.0% 92.0% 8.05 2 8.0% 88.0% 8.05 2 8.0% 96.0%
8.64 1 4.0% 96.0% 8.64 2 8.0% 96.0% 8.64 0 0.0% 96.0%
9.23 0 0.0% 96.0% 9.23 1 4.0% 100.0% 9.23 0 0.0% 96.0%
9.82 0 0.0% 96.0% 9.82 0 0.0% 100.0% 9.82 0 0.0% 96.0%
11 1 4.0% 100.0% 11 0 0.0% 100.0% 11 1 4.0% 100.0%
More 0 0.0% 100.0% More 0 0.0% 100.0% More 0 0.0% 100.0%
WARP is the most bottom heavy of the bunch, having 5 players in the 5.09-5.68 bin while the other
measures have none in their equivalent bins, and consistently has a lesser portion of the data in higher bins.
This gives us some context into Mike Trout’s season; even though he performed similarly according to all
three metrics, WARP actually
gives him the most credit, since
the difference between his
WAR and any other top player’
WAR is greatest when
comparing WARPs.
Additionally, a more theoretical
idea we can take away from this
25
data is that all three WAR approximations approach similar limits (if not the same limit) for their
distribution functions when taking a large enough sample of the population. However, as we magnify the
data enough, we can see differences in the distributions of the three WAR approximations. This means that
fWAR, rWAR, and WARP all have similar processes for determining player value, but not the same.
What types of players seem to differ the most between the statistics? What kinds of players’
WAR values seem to be the most uniform?
Now that we see that we cannot derive the value of one statistic from another very accurately, and all three
distribution functions approach similar limits, where does the breakdown occur between the three? Can we
characterize the differences in results to valuing specific kinds of skill sets differently? To explore this
question, I once again used players who finished in the top 35 in 2013 in any one of the 3 WAR measures in
my sample, but I also used players who finished in the middle 30 for any of the statistics, and players who
finished in the bottom 35, to characterize the entire population of MLB players better. I only used the middle
30 because there was much less overlap in the middle between the three approximations than there was in
the top and bottom tiers, so there was already plenty of a characterization of the middle portion of the
population.
After gathering all of this data, I calculated the standard deviations between the three metrics for every
player. The players who have the lowest standard deviations are the ones that have the most uniform WAR
measures, while the players who have the highest standard deviations are the ones who have WAR measures
that fluctuate the most. The table below shows the players whose standard deviations are more than 1
deviation away from the average deviation for a player in the sample, where the blue group contains the least
variant players in the sample, and the red group contains the most variant.
Name Team fWAR rWAR WARP AVG WAR STDEVA
Laynce Nix Phillies -0.7 -0.7 -0.63 -0.677 0.040
Chris Nelson Angels -0.7 -0.7 -0.78 -0.727 0.046
Alex Gonzalez Brewers -1.1 -1 -1.06 -1.053 0.050
Jeff Francoeur Giants -1.3 -1.4 -1.41 -1.370 0.061
Ryan Ludwick Reds -0.8 -0.9 -0.77 -0.823 0.068
26
Jamey Carroll Royals -0.9 -0.8 -0.95 -0.883 0.076
Billy Butler Royals 1.4 1.5 1.58 1.493 0.090
Jose Lobaton Rays 1.4 1.4 1.57 1.457 0.098
David Wright Mets 6 5.8 5.87 5.890 0.101
J.D. Martinez Astros -1.1 -1.3 -1.16 -1.187 0.103
Jose Tabata Pirates 1.1 1.2 0.99 1.097 0.105
Brent Lillibridge Yankees -1 -0.8 -0.8 -0.867 0.115
Carlos Triunfel Mariners -0.7 -0.9 -0.66 -0.753 0.129
Jayson Werth Nationals 4.6 4.8 4.84 4.747 0.129
Jhonatan Solano Nationals -0.4 -0.4 -0.65 -0.483 0.144
Kelly Johnson Rays 1.2 1.3 1.5 1.333 0.153
Angel Pagan Giants 1.3 1 1.23 1.177 0.157
John Jaso Athletics 1.2 1.1 1.42 1.240 0.164
Scott Hairston Nationals -0.7 -0.9 -0.56 -0.720 0.171
Christian Yelich Marlins 1.4 1.4 1.7 1.500 0.173
Casper Wells Phillies -1 -0.8 -1.15 -0.983 0.176
Adeiny Hechavarria Marlins -1.9 -2.1 -2.26 -2.087 0.180
Carlos Ruiz Phillies 1.4 1.7 1.74 1.613 0.186
Tyler Moore Nationals -1.2 -0.9 -0.85 -0.983 0.189
AVERAGE 0.3 0.3 0.36 0.331 0.1211
Anthony Rendon Nationals 1.5 0 1.47 0.990 0.857
Chris Parmelee Twins -0.2 0.6 -1.12 -0.240 0.861
Jeff Keppinger White Sox -1.5 -2 -0.27 -1.257 0.890
Daniel Descalso Cardinals -0.3 0.1 1.42 0.407 0.900
Lorenzo Cain Royals 2.6 3.2 1.41 2.403 0.911
Robinson Cano Yankees 6 7.6 6.04 6.547 0.912
Alcides Escobar Royals 1.1 0.3 -0.74 0.220 0.923
Marlon Byrd Mets/Pirates 4.1 5 3.12 4.073 0.940
27
Dan Uggla Braves 0.5 -1.3 -0.88 -0.560 0.942
Josh Donaldson Athletics 7.7 8 6.19 7.297 0.970
Dustin Pedroia Red Sox 5.4 6.5 4.53 5.477 0.987
Darwin Barney Cubs 0.4 -0.5 -1.72 -0.607 1.064
Andrelton Simmons Braves 4.7 6.8 5.41 5.637 1.068
Jean Segura Brewers 3.4 3.9 5.55 4.283 1.125
Shin-Soo Choo Reds 5.2 4.2 6.45 5.283 1.127
Ben Zobrist Rays 5.4 5.1 3.31 4.603 1.130
Gerardo Parra Diamondbacks 4.6 6.1 3.73 4.810 1.199
Carlos Gomez Brewers 7.6 8.4 6.04 7.347 1.200
A.J. Pollock Diamondbacks 3.6 3.5 1.47 2.857 1.202
J.J Hardy Orioles 3.4 3.7 1.47 2.857 1.210
Andrew McCutchen Pirates 8.2 8.2 6.03 7.477 1.253
Starling Marte Pirates 4.6 5.4 2.85 4.283 1.304
Ian Kinsler Rangers 2.5 4.9 5.27 4.223 1.504
AVERAGE 3.5 3.8 2.91 3.409 1.064
There are some trends from this subset that would likely carry over to the entire population. The first is that
increased playing time causes an increase in the variation between WAR measurements. Intuitively, this may
seem obvious, since any difference in two WAR formulas should only be accentuated by larger amounts of
data for a player. However, this means something important: for an individual player, the three different
WAR measures do not approach the same limit as his playing time increases, since the results become more
varied.
The second, and probably more interesting trend in these groupings, is the positional similarities in each
group. In the red group, most of the players are middle infielders and very strong defensive outfielders, while
in the blue group, most of the players are catchers, corner infielders, and bat-first outfielders. Note that this
does not necessarily mean that the three different WAR approximations drastically differ in valuing specific
positions. More likely, since different positions are expected to have different skill sets, positional groupings
generally show which areas seem to vary the most across the three formulas for WAR. Middle infielders tend
28
to be faster players that have good gloves, and not as much power. Perhaps the three WAR measures differ
most in these typical strengths of middle infielders. This would explain why a middle infielder like Kelly
Johnson appears in the blue group, since Kelly Johnson is atypical as a middle infielder (above average
power, and below average speed and glove).
This is just a starting point for comparing these three WAR measures. As WAR is used to analyze players so
often now, this is a deep topic that can definitely (and should be!) expanded. The results from this article are
important and a good starting point to answer this large question, and hopefully this inspires you to not just
take a WAR measure at face value, but consider the deeper meaning behind a WAR value of a player.
A Belated Farewell to
the Toddfather
(12/29/13)
By Caleb Pykkonen
I wanted my first piece for Batting Leadoff to
serve as a bit of an introduction to myself as a baseball fan, and as a discuss on a topic relevant to this year's
offseason. Since I grew up in Colorado, the Rockies have played as big a role in my baseball fandom as any
Major League franchise. Therefore, seeing as he retired at the end of this season, and because he was one of
my heroes as a young baseball player in the early 2000s, I decided it would be a good time to think
about Todd Helton’s Hall of Fame credentials.
As a Rockies fan, it has been painful to watch Helton the past four seasons. Injuries have hampered his play
since the 2009 season. Todd hasn’t played more than 124 games in any season since 2010, and he was all the
way down to 69 games in 2012. The Rockies’ front office has been hesitant to bring in a young first baseman
that might be able to replace Todd in the long run, although they did sign Michael Cuddyer two years ago.
All of this adds up to a disappointing finish to a 17-year career that could have ended much differently, and
probably much better, for both Helton and the Rockies.
29
Nevertheless, we shouldn’t let the drawn-out and
disappointing end to Todd Helton’s career make
us forget the player he was in the early 2000s.
Back in his heyday, Helton was one of the most
fearsome first basemen in the game. From 2000-
2004 Helton racked up 34.7 WAR, per
Fangraphs. Also during that five-year span, Todd
managed 186 home runs, had a walk rate higher
than his strikeout rate in all but one of those
years, had two years of .300-plus Isolated Power
(.326 in 2000, and .349 in 2001), and won three
Gold Glove awards and four Silver Sluggers. In
fact, even if Helton’s last four years are included,
he still finished his career with a 14.1 walk
percentage and a 12.4 Strikeout percentage. Todd
also managed nine seasons with an On-Base
Percentage greater than .400, and he finished
with a .414 career OBP.
According the Baseball-Reference.com, Helton
ranks 26th in career OBP, 36th in career Slugging
Percentage, 20th in career OPS, 16th in career
Doubles, 35th all-time in walks, and 28th in career
Win-Probability-Added. If you’re inclined to use
clutch-performance as a measure of player-value,
Helton also finished his career ranked 26th in
Situational Wins Added (WPA/LI), also per
Baseball-Reference.
From the defensive side of things, advanced stats
don’t love Helton’s performance at first base.
However, I believe that we have yet to reach the
point where these advanced statistics tell the
entire defensive story (these all agree). Again
using Baseball-Reference to look at Todd's
defensive work, we can see that he was no scrub.
Todd finished 15th all-time in putouts (13th all-
time among first basemen), 2nd all-time among
first basemen in assists, and 6th all-time in
fielding percentage as a first baseman. Add the
three Gold Gloves he won in the early 2000s, and
you can argue that his defensive work would help
his case for the HOF.
In a vacuum, Todd Helton's resume seems
worthy of a bid to baseball's shrine. But, there are
some important marks against Helton that must
also be taken into consideration. For starters, he
played every single home game of his Major
League career in the hitter-friendly mile-high air
of Coors Field. I would argue that his offensive
statistics would still stand up to the Hall Of Fame
standard even taking into account his home park,
but I do not speak for many of the voters. Of
equal importance is the fact that Helton had the
best years of his career during the peak of
baseball's steroid era. Voters have already shown
that they are hesitant to vote steroid users into
the Hall, and although Helton has never been
accused of or caught using steroids, he might still
be viewed as being guilty by association.
Furthermore, just as I used Helton's defensive
numbers to build his HOF case, critics could
point to some of his advanced defensive statistics
as reasons that he shouldn't be in the Hall.
Finally, despite the fact that the blame should not
be placed on Helton for only making two
postseasons in his life, he was not able to help his
bid by building bountiful playoff success.
30
As is the case for most players with the potential to make it to Cooperstown, valid arguments can be made
both for and against Todd Helton. Although we had to endure a prolonged twilight to his career, I believe
Helton's numbers present a strong case. Todd never won an MVP award, but from 2000 until his lone World
Series run in 2007 he was one of the best players in all of baseball. Although WAR does not tell the entire
story, Fangraphs says Helton was the fifth best position player over that eight year stretch, and the only first
baseman better than him during
that time was Albert Pujols.
Helton might not have
maintained the same sustained
brilliance that was the signature
of many Hall of Famers, but he
was at the top of the sport for
nearly a decade. I believe that
alone is enough to punch him a
ticket.
Thus far I have merely presented
reasons why I think Todd Helton
SHOULD be inducted into
Cooperstown, and I could write
an entirely new post regarding
whether or not he WILL be
inducted. In the post-steroid era,
it's extremely difficult to gauge
the opinions of those in the
BBWAA. Helton's chances of
being inducted will likely take a
hit because he will be grouped
with Bonds, Clemens, Sosa and all the other known-users. Given time, however, we might see him become
the first Rockie voted into the Hall of Fame. We will just have to wait.
In the meantime, fans of the Rockies and fans of baseball can be happy that they got to witness #17 light up
Coors Field for nearly two decades. I hope that someday the members of the BBWAA can find it in their
hearts to give him a place among the other all-time greats. Who knows? They might surprise us.
31
Chris Denorfia and the Importance of Making
Contact (1/7/14)
By Mike Minio
During the 2013-14 offseason, Nick Cafardo of
the Boston Globe reported that the Red Sox have
approached the San Diego Padres and asked
about outfielder Chris Denorfia’s availability.
Assuming that the organization follows through
on their intent to begin the 2014 campaign with
rookie Jackie Bradley in centerfield, Denorfia
would be used primarily as a utility outfielder.
This article is not about the merits or
shortcomings of this potential trade, but rather
an analysis of the type of player Chris Denorfia is
and why
organizations
should look toward
him as well as the
tool he embodies
when assembling
the cohesive self-
contained unit we
call a team. What’s
so great about him?
Your grandfather –
or great-
grandfather –
would be the first to
point out that he doesn’t strike out nearly as
much as other players while still doing other
things on the field consistently.
We currently live in the Age of the Strikeout.
Both hitters’ and pitchers’ strikeout rates have
increased dramatically over the past decade.
Pitchers today – especially relievers – generally
have more explosive stuff than their counterparts
in previous generations. Furthermore, front
offices in the post-Moneyball period have
(rightly, I might add) placed a premium on
patience and power. More and more hitters
appear to follow Matt Stairs’ manual of how to
swing a bat: take the pitch if it’s outside the zone,
but swing as if your life depended on it if you
think you can it. As a result, players like Dunn,
Mark Reynolds, and Ryan Howard feast on
32
fastballs and post high wOBA, home run, and
WAR rates. However, they also have trouble with
breaking and off-speed pitches, thereby leading
to record-setting strikeout rates and abysmally
low batting averages. It is also important to note
that these players represent the top of the pile;
players of the same style are found all over the
major and minor leagues. Consequently, pitchers
have benefited immensely by increasing their
strikeout rates and lowering their Earned Run
Averages considerably. Luck cannot be on a
batter’s side if he is unable to put the ball in play.
Moreover, pitchers are facing the kind of hitters
they do best against: free swingers who have
trouble with pitches that aren’t fastballs.
Some of you might argue that these players in
question were far more productive than league
average during their primes. You’re right, but I’m
not contesting these players’ respective values.
Instead, I’m looking at their style of play – which
has obvious flaws – and viewing it as a generally
accepted tendency in baseball. Wherever there’s
a common trend, there will be players who do not
fit into that particular mold. Those are the
players who represent untapped undervalued
advantages. Chris Denorfia exemplifies the
contact-oriented approach that emphasizes
working the count, putting the bat on the ball,
and avoiding strikeouts. He does not hit for much
power, but he is not incapable of hitting doubles
and home runs either (21 2B and 10 HR in 2013).
His approach at the plate is relatively simple.
Denorfia almost always takes the first pitch
regardless of location (75% of PA) and proceeds
to adjust accordingly. His goal is to put himself in
a position where he can get a good ball to hit.
Below is a list of his career rates of contact,
courtesy of Fangraphs:
Outside-Swing% 25.0%
Zone-Swing% 57.7%
Swing% 40.9%
Outside-Contact% 62.8%
Zone-Contact% 89.3%
Contact% 81.0%
Swing-Strike% 7.6%
This table demonstrates the patient, but
consistent approach that Chris Denorfia has
taken throughout his career (there is very little
fluctuation from season-to-season). He does not
swing particularly often; he is a patient hitter who
would rather take a pitch both inside and outside
the zone if he feels he cannot hit it. His contact
rates stand out the most. Of all the pitches he
swings at, he makes some sort of contact 81.0% of
the time. When he chases something in the zone,
that figure increases to a whopping 89.3%. He
swings and misses in only 7.6% of all his swings.
Although he does not hit for significant power
33
and struggles against righties, he was still a
consistently solid bat in the Padres’ order for the
past four seasons, batting
.280/.338/.414 and generating 8.2 Wins
Above Replacement. He only made $2
million last season and will make $2.25
million in 2014.
As these rates suggest, Denorfia exemplifies the
contact-oriented approach that has been so
glaringly absent from baseball in the past few
seasons. A player of this mold gives the manager
options when facing different types of pitchers. It
might be more beneficial to pinch hit a player like
Denorfia against some power pitchers with good
breaking stuff if other bench players are
particularly strikeout prone. He might not hit a
home run, but he could start a rally by getting on
base and make the opposing pitcher work in the
process whereas a player like Matt Stairs would
probably walk back to the dugout after three
swings. This
argument does
not suggest that a
general manager
should fill his
bench with five Enrique Wilsons; however, he
should have at least one position player on the
bench who can make contact consistently. Doing
so gives the field manager a potential rally starter
in the late innings against power pitching. So,
should the Red Sox consider trading for Chris
Denorfia? Absolutely. Assuming the price is right
and he will not regress significantly, he could be a
cost-effective addition to their club who adds
diversity to their bench.
How Much is a
First Round Pick
Worth? (11/14/13)
By Matthew
Provenzano
Recently, a couple of friends and I were just throwing the idea around: how often are first-round draft picks
successful? There is so much emphasis on them for either the draft, scouting purposes, or the possibility of
losing or gaining a first-round pick in a qualifying offer or trade situation. So, we decided to flip through the
“A player of this mold gives the manager options when facing different types of pitchers.”
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records of first-round picks and found that some of them were not surprising and were successful, some
were decent, and some were just total flops. But there was no visible trend that we could tell other than that
the first overall pick was generally the most successful. However, I wanted to go a bit further and created a
model in calculating first-round pick value. This isn’t meant to be a hard-and-fast rule in how the
draft should go, but it merely gives a general idea of how it usually goes on the aggregate since 1965. And
with that information, a team could use this expected payoff of pick value to either a) determine what type of
value they can get out of their slot or b) determine whether it is worth giving up their pick or, on the
converse, taking a supplemental first-round pick.
Building the Model
To start building this model, I started with a simple statistic that would help me in figuring out the expected
payoff for the first 32 picks in the draft. I first wanted to know what percentage of players in each slot
actually played in Major League Baseball. This can be represented here:
This one’s an important figure because the linear relationship shows that while the first overall pick is nearly
a sure bet that they will play in the Majors, the last pick in the first round is nearly a coin toss.
Next, I created a list of each slot’s total bWAR. This is good at getting a total metric, but it would definitely
get skewed by some outliers (Hall of Famers) and should therefore be controlled for. This need is most
evident in the 31st pick, where the total bWAR is 142.3, but Greg Maddux accounts for nearly 100 of that.
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Instead of having these large, successful careers skew the data, I would just create the average value in one
season, such that we can get a snapshot of what the average season looks like from an average player in each
slot. The previous chart has to be considered in respect to this, because it’s quite important the percentage of
players that do play. Thus, the expected payoff for a single season is as follows: bWAR/162 * Pr(playing)
such that “Pr(playing)” is the probability that a player in a particular slot plays in the Majors. This expected
payoff can be shown as:
What’s so fascinating about this chart is that the exact point where the average season eclipses starter-level
quality is just around the 15th pick. However, the deviation between the first and last pick in the round on
average is not very large; it’s the difference between slightly above starter-level and slightly-below starter-
level. This is telling for one season, but the length of careers also play a huge factor.
The fact that nearly half of the picks in the first round are, on average in an average season, starter-level
quality, shows that in the short-term, the difference between these slots aren’t telling of true value. So, I’ve
put in one, simple addition: the average number of seasons, which is merely the total number of games for
each slot divided by 162; it is illustrated here:
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While the difference in one season may not be great between picks, the longevity is what truly separates
them. Because once I multiplied the number of seasons and the average season, it now looks more like this:
This completes the model, which is best expressed by the Power relationship shown in the formula above.
We now have a basic model on calculating the expected payoff from each slot in the draft.
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What Does This All Mean?
First, it confirms the anecdotal observation that the first overall pick is very successful (ie: A-Rod, Ken
Griffey Jr., Bryce Harper, Daryl Strawberry, Joe Mauer, Adrian Gonzalez, Josh Hamilton, David
Price, etc. and etc. and etc.). Second, it shows that in general, while it definitely is more of a chance, the first
five overall picks are pretty successful. After that, it’s more or less in fate’s hands. Scouting has gotten better
at picking out talent and it’s even clear to see that since 1965, organizations have done an excellent job at
reflecting this Power relationship through their picks. But where they do fail is the extent to which teams
safeguard their draft picks when signing other players or in trades. Some organizations, hoping not to
jeopardize their future, hold on to their draft picks even when a starter-level player (or better) is ripe for the
taking. In this circumstance, it is the responsibility to weigh this expected value of the possible draft pick
against the expected value of a possible signed player. Let’s try a concrete example to put this into practice.
Practical Application & Conclusion
There have been rumors swirling around that the New York Yankees want to acquire Carlos Beltran, and
some have said that a secret offer has already been made. Since Beltran was offered a qualifying offer by the
Cardinals, the Yankees would have to sacrifice the 18th pick in the first round to acquire him. Using our final
model and 2014 Steamer predictions, we can find the better of two scenarios. If we plug 18 into the formula
in the last chart, we get an expected career WAR of 2.45. Beltran, on the other hand, is expected to get a
WAR of 1.8 in the 2014 season. And if he signs to a two or three year deal, it is nearly guaranteed (barring
injury), that he would eclipse the expected payoff from this draft pick. If the Yankees had to make a choice
between the two, the opportunity cost would be lower when acquiring Beltran. Yes, he’d probably be more
expensive, but the key with our model is the expected value. And while this is simplistic and is not the sole
engine behind these types of decisions, it is another tool in the decision making process.
Yes, there is the possibility of giving up an All-Star or
Hall of Famer, but there are many, many, many other
times where that did not happen. The hope of growing
a Hall of Famer is incredibly alluring, but an
organization should never be so blinded as to ignore
the expected payoff and should choose between the
better of two possible worlds.
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Links/Partners
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Batting Leadoff Contact Information Alex Smith – Founder E-Mail: [email protected] Twitter: @RealAlexSmith19 Chris Moran – Featured Blogger E-mail: [email protected] Twitter: @hangingslurves Mike Parnell – Featured Blogger E-mail: [email protected] Matthew Provenzano – Featured Blogger E-mail: [email protected] Twitter: @mpro6294 Bryan Robinson – Featured Blogger E-mail: [email protected] Twitter: @ProProjections Caleb Pykkonen – Featured Blogger E-mail: [email protected] Twitter: @cpikeonen Morris Greenberg – Featured Blogger E-mail: [email protected] Twitter: @Morris_Gberg Mike Minio – Featured Blogger E-mail: [email protected] Hudson Belinsky – Contributor E-mail: [email protected] Twitter: @hudsonbelinsky Francesco Padulo – Contributor
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E-mail: [email protected] Jesse Sherman – Editor E-mail: [email protected] Adam Kirsch – Editor E-mail: [email protected] James Goldstein – Editor/Advisor E-mail: [email protected]
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