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Exploring Player Synergy Using Association Rules
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Description
Identifier
Thesis
2716
Author
Keller, Patrick Daniel, 1985-
Title
Exploring
Player
Synergy
Using
Association
Rules
Publisher
Central Connecticut State University
Date of Publication
2018
Resource Type
Master's Thesis
Abstract
Extracting
the
maximum
performance
of a
given
roster
of
basketball
players
could
benefit
from
understanding
the
presence
or
absence
of
synergy
between
groups
of
players
.
Players
may
play
superior
basketball
when
another
player
, or
group
of
players
,
is
also
present
on the
court
.
Treating
basketball
game
data
as a
transactional
data
set
and
producing
association
rules
is
presented
as a
means
of
quantifying
player
synergy
.
Groups
of
players
ranging
from the
individual
to a
complete
set
of
five
players
on the
court
are
considered
.
Using
the
interest
measure
of
conviction
, these
association
rules
are
compared
to the
typical
performance
of the
team
. The
additional
interest
measure
of
improvement
provides
a
means
of
comparing
each
rule
to
all
rules
that
exist
as
smaller
subsets
of the
antecedent
. In
examining
the
rules
produced
using
the
Central
Connecticut
State
University
men's
basketball
team
conference
game
data
for the
2017-2018
season
,
multiple
sets
of
players
of
varying
size
were
found
to
perform
better
as a
group
than their
individual
performances
.
Several
groupings
were
found
to
perform
better
than the
team's
overall
performance
against
the
points
per
possession
,
points
per
possession
allowed
, and
plus-minus
metrics
used
in
rule
generation
.
Applying
association
rules
to
play
by
play
data
in this
manner
could
yield
insight
into
improved
player
substitution
decisions
in the
arena
of
college
basketball
.
Notes
"
Submitted
in
Partial
Fulfillment
of the
Requirements
for the
Degree
of
Master
of
Science
in
Data
Mining.
;
Thesis
advisor
:
Roger
Bilisoly.
;
M.S.,Central
Connecticut
State
University,,2018.
;
Includes
bibliographical
references
(leaves
67-68)
.
Subject
Basketball players.
Basketball teams.
Basketball--Statistics.
Department
Department of Mathematical Sciences
Advisor
Bilisoly, Roger
Type
Text
Software
System requirements: PC and World Wide Web browser.
Language
eng
OCLC number
1082260642
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