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Ron Antinoja founded and runs a service called
Tendu at
www.tendu.net. The firm–so named for teams’ desire to
understand player and coaching tendencies to do
certain things in certain situations–expects
to track the velocity, location and result of about
90% of all major league pitches this season. Tendu tracks those pitches and their outcomes and stores that information in an Internet database that allows users to discover pitcher and hitter tendencies in any given situation. Antinoja recently chatted with BP about teams’ neverending
quest to gain the upper hand on the opponent.

Baseball Prospectus: What was the genesis of
Tendu?

Ron Antinoja: It came into being because I have
a background in software engineering. Prior to that, I
had a mediocre baseball career through high school. I
was never never good enough to be a star, but I felt
like I knew how to win. I became a software engineer,
but I was dreaming all along that one day I would do
something involving baseball.

One way I was driving along the freeway on my way to a
consulting assignment, and I just snapped. I turned
off to go to Stanford University, went straight to the
baseball field to talk to their coach, Dean Stotz. I
described what I wanted to do–it had to do with
collecting data, studying tendencies of players and
coaches. I believed that if people looked closely at
pitches, they could see tendencies. Human beings
repeat patterns, they go with what works. When they
find something that works, they repeat it; when
something doesn’t work, they avoid it–it’s an
underlying philosophy of human behavior. That’s the
underlying philosophy of my software. If a person had
information on what a player does in different
situations, he can make a good deduction as to what
they might do in the current situation.

BP: How did you apply your software background
toward actually getting the company started then?

RA: First we had to create tools that were
powerful and flexible for collecting all the data.
After we had the data collection tools in place, we
started building in the analysis aspects. The key was
to isolate game contexts. It all comes back to being
in a given context, a human being will think about
what’s worked in the past in this context. That
includes who’s on base, how many outs there are, the
size of the lead or deficit, how many pitches he’s
thrown, etc. I never believed I had so much knowledge
that I could figure out what would be the right
contextual factors to look at in any given moment. So
I built tools that could be used by baseball experts,
so that they could use their expertise to think of as
many scenarios as possible–they look at the
situations that they think are important.

BP: How do you collect the pitch data you need?
Who does it, and where do you find the people who do
it?

RA: It started as word of mouth, but we’ve also
done some advertising. When people come to us, there’s
an interview process they have to be go through, where
they sit in front of a TV with an expert. We’ll pick out
pitches, and ask them ‘what do you see?’ If they don’t know what
they’re looking at, they don’t work for us. If they do, we ask ‘why do
you
see that?’ Do they know about grips, catcher signals,
follow-throughs, catcher setups, all factors we need to track if we’re
to correctly identify every pitch that gets thrown. They don’t even get
to
try out if they don’t know these kinds of answers.

BP: So you’re tracking pitches watching game tapes instead of in
person. Why take that approach?

RA: Everyone does it off of TiVo, not live at games. It works better this way–it’s a very
difficult job, one that requires a lot of focus and patience. We’ll stop
and
rewind every pitch. The more people do it, the better they get, the
less they have to stop and rewind. But after they’re done we’ll still
compare our results to the game box score of at espn.com or mlb.com.

BP: It’s still got to be tough working off of TV broadcasts, where announcers can misindentify pitches, or they come back late from a commercial break and the inning’s already started…

RA: That’s what drives us really crazy, when they miss a pitch by coming
back late from a commercial or during replays. Hopefully one day we’ll be able to get more video straight from
stadiums–it’s already happening, just not enough yet. On average we might miss a couple
pitches a game. Of course our goal is to chart 100% of all pitches in a major league season. Last year we did 80%, this year we’re looking at something higher, around 90% hopefully.

BP: OK, so what do you
do with that kind of information to transform it into something a major
league baseball team will want and pay money to get?

RA: Once we’ve compiled the data by tracking pitch-by-pitch results, we use our analysis tools to make the data usable. Tendu has three analysis tools at the present time. There are more on the drawing board. We have the Pitching Chart tool, the
Hitting Spray tool and the Pitch Sequence Analyzer.

The Pitching Chart tool is the one that has the
red-yellow-green zones
that are sometimes called hot and cold zones by other
people. We
designed this tool as a pitch effectiveness measure.
To measure
effectiveness, we use the at-bat outcome, which
indicates whether the hitter got on
base and how many bases did he get. The measure of
effectiveness can
use any of four well-known baseball algorithms:

  • Batting Average

  • On-Base Percentage

  • Slugging Percentage

  • On-Base Percentage plus Slugging Percentage (OPS)

The Pitching Chart tool can be used from either the
pitcher’s
perspective (pitches thrown by a pitcher) or the
hitter’s perspective (pitches
thrown to a hitter).



BP: What about the Hitting Spray tool? Aren’t there already other services out there that track where a hitter hits the ball against certain pitchers or in certain situations?

RA: Another way of looking at your question is to
compare the style
of baseball game preparation in the past, to what
Tendu’s tools offer.
In the past, analysis was based on a set of canned
reports. For
example, a hitting spray showing the location of the
batted ball for each
time that a hitter put the ball into play. There might
be more than one
hitting spray for a hitter, with one or more control
parameters changed.

One of my competitors delivers four hitting sprays for example. Those are:

  • versus RHPs with less than two strikes

  • versus RHPs with two strikes

  • versus LHPs with less than two strikes

  • versus LHPs with two strikes

They claim that these four situations are
the most valuable
and have been specified by years of interactions with major league teams. I
don’t question the value of those reports. But I
believe that there are
more than four hitting sprays that could have value. What I have seen is that
for any given player
that is being studied, if I use Tendu’s Hitting Spray
tool, I can view
five, 10, 20 hitting sprays in a few minutes,
changing control values for each one, and I may find an unusual pattern of batted
ball locations.

We call these unusual patterns red flags. A red flag
is a tendency that
may be exploited. For example, if a hitter hits a lot of non-fastballs low and away to the opposite field, the
coaching staff can
position its fielders in such a way as to give better
coverage, when they
know that the hitter is going to get mostly
non-fastballs low and away.

Tendu’s tools have been designed explicitly for
exploration. If a user
only wants to look at a specific set of canned
reports, then he
will only be able to find red flags that are clearly identified right on
those reports. Our
philosophy is that by exploring the data, by using a
baseball person’s natural
instincts to change the control parameters to produce
spray charts with
different sets of data, a smart user can find more red
flags that can
be useful during the upcoming game.

BP: So it sounds like the Pitching Chart and Hitting Spray tools are supposed to improve on what’s already out there. What then is a Pitch Sequence Analyzer and what does it do?

RA: The purpose of the Pitch Sequence Analyzer is to
enable a user to discover tendencies in the form of patterns and
frequencies hidden in a
pitcher’s sequence of pitches. You want to be able to do several things: determine how frequently a pattern is
used by a pitcher; search the database for each occurrence of a
pattern; predict the next pitch in a sequence from the
frequency of pitches that follow the pattern; discover a pitcher’s “out” pitch and learn how
the pitcher sets
up his “out” pitch; and find red flags in the sequences of pitches.

Users can browse the database of pitches, which are
organized as groups
of pitches from an at-bat. You can look at the
frequencies of seven
characteristics of a pitch for all pitches that a
pitcher has thrown or only
those pitches that match a pattern. Those seven
characteristics are:

  • pitch outcome

  • at-bat outcome

  • pitch type

  • vertical location of the pitch

  • horizontal location of the pitch

  • pitch break

  • and pitch speed

You can define patterns to search for. You can apply the Pitch Sequence Analyzer to all of
the pitches that a
pitcher has thrown during a given year, or all the
years that Tendu has
collected data (2002 and 2003) to find tendencies that
are there for
all game situations. Or, a user can load only subsets
of the data for
analysis, comparing the results to determine if a
pitcher’s tendencies are
different for the different situations.
You can compare early innings to late
innings to see if
a pitcher shows different tendencies. You can
look at the lead
size, the number of outs, or the runners on base, to
see if those factors change a pitcher’s tendencies.