Consumable Analytics

AuthorTim Chartier
Date01 May 2017
Published date01 May 2017
DOIhttp://doi.org/10.1002/jcaf.22264
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© 2017 Wiley Periodicals, Inc.
Published online in Wiley Online Library (wileyonlinelibrary.com).
DOI 10.1002/jcaf.22264
Consumable Analytics
Tim Chartier
Basketball entails running
up and down the court.
I remember practice in
middle school where we’d run
up and touch the baseline and
then back to touch the other
baseline. We’d run up and back
again and again. We could
have easily tracked, as possibly
my coach did, how far we’d
run. We’d also practice chang-
ing direction on the coach’s
whistle. Some players could
alter direction quickly, while
others seemed more concerned
about staying on their feet than
the rate of their acceleration.
Some players could run and
run. Others could dart from
spot to spot. Game strategies
were often designed to monop-
olize on such skills. How well
did we actualize those strate-
gies? Remember, it was middle
school basketball, so we also
had many other fundamental
objectives—like consistently
scoring on open shots.
Even at the professional
level of basketball, players’
abilities to stop and start with
the ball or how far they might
run in a game could, at best,
be estimated. Today, with the
availability of spatial data
tracked in every game, we can
examine parts of the game
with a high degree of accuracy.
In the fall of 2013, SportVU
cameras were installed in every
NBA arena. Every 25th of a
second throughout the game,
the cameras record the (x, y)
location of every player on the
court and the (x, y, z) loca-
tion of the ball. As such, every
NBA team has had the motion
of every player and the ball
captured for every game since
2013.
While interesting, SportVU
data in raw form, without
further processing, is limited.
Does it help to know Stephen
Curry was 15 feet behind the
three-point line at 4:24 in the
third quarter of a game? Pos-
sibly. If you additionally find
that he made a three-pointer
while covered closely, you have
more information. If you com-
pare this type of shot against
every game he played this sea-
son and find a trend, you have
more information. Note how
the data makes insight possible
but analysis of the data leads to
insight.
At the core of analytics is
data. Data, while the basis of
analytics, is generally not inher-
ently insightful. The analyst
must know who will view the
analysis, the decision or action
that will result from seeing the
analysis and the timeline of
the person receiving the work.
When factors such as these are
integrated into the research,
consumable analytics can result.
This isn’t a guaranteed result
of analysis. As an academic, I
often hear concerns that analyt-
ics produced from academia can
be esoteric, even if interesting.
Again, careful attention
must be paid as to the goal
of analysis. The businesses
that consult with my analytics
group at Davidson College take
action on our work. Sports
teams use our research as part
of their game preparation.
How do we keep from
producing narrowly defined
insights? First, we communicate
with whomever will make deci-
sions from our analysis. Even
in sports, a player has different
needs from a coach. A team has
different interests than a fan.
Knowing who will receive the
work impacts what we analyze
and create. Second, we learn the
types of decisions being made
from our analytics. Will a coach
use our analysis to help strat-
egize for a game? Will the work
be viewed in a few seconds
at halftime to devise correc-
tions to game time play? Will
a business use our analytics to
directly make a decision, or will
the insights be integrated into

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