Business shot and their potential to affect another player

Business intelligence
is the collection of technologies that aid an organization with enabling their
workers with data, information, and knowledge to make better decisions. Organizations
are leveraging the business intelligence they collect to deploy and experiment
with different techniques to drive business decisions, functionality, and
profits. For example, in retail, business intelligence is used for customer
support to enable a user profile for customers and provide them with targeted
grocery coupons and advertisements. I chose this article because basketball is
my favorite sport to play and watch. Also, I’m currently taking IS 428 which is
data mining, and this article spoke about the combination of the NBA’s business
intelligence of using data mining to discover interesting patterns in
basketball game data for teams. As per my data mining class, data mining is a
business process for exploring large amounts of data to discover meaningful
patterns and rules. Data mining is a subject of business intelligence. It’s the
process of using raw data to infer important business relationships. Or in the
terms of the NBA, data mining is the process of using raw data to infer your
teams advantage over other teams. If you watch an NBA game today, you’ll hear terminologies
such as a small ball lineup, run or spread offense, or this particular player
is a key 3 and D player. Data and performance metrics have reinvented
basketball into a new game and helped provide metrics to these terms. The NBA
and its teams have embraced big data and analytics to maximize winning and player
potentials. With new metrics consistently being used, it currently shows that
NBA teams that run a small ball lineup, perform fast breaks and can spread the
floor have a likelihood of scoring more points, containing the other team and
winning basketball games. That’s why players that can shoot 3 pointers well and
play really good defense are coveted for rotational spots on NBA teams to
provide support for superstar players. The 3 and D player is important because
of their potential to make a higher percentage of 3 points shot and their
potential to affect another player from scoring on the defense end. Data
metrics collected from players can show which players excel at making jump
shots and playing defense. The way the game is played now has changed dramatically
from the way it was played 25 years ago. One is able to understand this new
wave by watching and listening to different NBA games because broadcasters
often talk about it. One main element I learned from this article was the NBA’s
use of advance scout, which was the first data mining and knowledge tool from
IBM that NBA teams utilized. Advanced scout was first introduced to the NBA in
the 1995-1996 season to most of the teams. Advanced scout first identified to
the Orlando Magic, during the playoffs against the Miami Heat, that using a
small ball line up would prove to be advantageous for them on the court. They
lost their previous two games and advanced scout provided information that with
certain players in the game, the Magic outscore the Heat by an average of 15
points. This tool helped the team make a better decision as they were able to
win the next two games and become an early example to prove that performance
analytics can provide useful decisional support for teams. The article talks
about the creation of advanced scout and the ripple effect it had. What I found
interesting was that before the introduction of advance scout, the best metric
collected for basketball success was the percentages of shots made by a player
or a team during the game. Now, there are so many high tech data metrics used
by an NBA team to increase their chances of winning more games.