Data Mining in the National Basketball Association

fantasicgilamonsterData Management

Nov 20, 2013 (3 years and 11 months ago)

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Data Mining in the National Basketball Association


When the Orlando Magic were devastated in the first two games of the 1997 National Basketball

Association (NBA) Finals against the second
-
seed Miami Heat, the team's fans bega
n to hang their heads in
shame.

But fortunately, the Magic had another trick up their sleeve.

A data mining application developed
uncovered a secret buried beneath the layers of statistics collected at every gam
e.

The application, Advanced Scout, is specifically tailored for NBA coaches and statisticians.
Advanced Scout showed the Orlando Magic coaches something that none of them had previously
recognized. When Brian Shaw and Darrell Armstrong

were in the game
, something was sparked within
their teammate Penny Hardaway
,
the Magic's leading scorer at that time.

Armstrong received more

play
-
time and hence, Hardaway was far more effective.

The Magic went on to win the next two games

and nearly caused the upset of
the year.

Fans everywhere rallied around the team and naysayers quickly
replaced their doubts with season ticket purchases for the following year.

Coaches, like business executives, carefully study data to enhance their natural intuition when

making strate
gic decisions. But unlike business, the direct results of coaching decisions are played out
under the eyes of millions of fans, and wrong calls can turn a team's fans against it
,

leading to lower ticket
sales and possibly a vacancy in the head coaching pos
ition.

By helping
coaches

make better decisions,
data
mining applications are

playing a huge role in establishing incredible fan support and loyalt
y
. T
hat means
millions of dollars in gate traffic, t
elevision sales and licensing.

Before
these data mining applications
, some teams, such as the Orlando Magic, began developing
business

intelligence software to find patterns in the piles of game data that the coaching staff collected
during

play.

But with an average of 200 possessions a game an
d about 1,200 games a year, the sheer
volume of statistics was overwhelming, and the applications produced only basic results
--
the kind of
stat
istics

anyone could find in a local newspaper.

During the course of each game, members of the NBA's Game Stats program

ma
nually enter game
statistics into
laptops
.

This data is then uploaded to
a

server. Coaches can log on to the
data mining
application

before, during or

after a game to download this public data and merge it with the private data
that each team collects

independently on its laptops or PCs.

Using the
data mining

software, coaches can drill down into a vast array of statistics and dat
a

and unearth comprehensible patterns that were previously hidden among seemingly unrelated stat
istics
.

Coaches can ask
which players are most effective in correlation wi
th time and the opposing players.

Coach
es

are

able

to get, in real

time, statistical evaluations that allow
them

to put in the very best players
for specific

points in the game
.


Th
e

application really helps
coaches

understand the relationships

am
ong the combin
ations of players on the court
,

chang
es

the way
they
coach
their teams, and

help
s
them
make
more effective decisions."

Data collected by
the data mining application

is also stamped with a universal time code. This
means that when
coaches see
an i
ntriguing pattern
, they can get the exact

moment in the game where it
occurred and instantly cue this up on videotape.

Coaches

used to spend weeks scouring the tapes, but now
they

can instantly
pull up a key spot in the game that, before
using data mining, they

probably would

n
o
t
even have known to look for.

While coaches currently have a robust tool with them at courtside to optimize player line
-
up,

data mining
functionali
ty will soon be expanded to include analyzing the effectiveness of

specific plays that teams have designed.

Coaches are going to be able, right from

courtside
,

and in real

time
,

to ask
the application

which play will be the most effective rel
ative to the

time elapsed and the
specific combin
ations of players on the court
.