All
right,
good
afternoon,
and
today
I'm
going
to
be
talking
about
the
predictive
analysis
of
online
chess
outcomes
and
success.
My
name
is
Allison
Clift
and
I
had
the
opportunity
to
work
on
this
project
with
another
another
student
in
my
business
analytics
program,
Calbe
Abbas
Agaria,
however,
he
is
not
with
us
here
today.
To
begin,
we
analyzed
low
and
moderately-
rated
online
chess
players.
Since
the
COVID-19
pandemic,
there
was
an
increase
in
Internet
usage
as
well
as
with
the
advancement
of
technology,
people
have
switched
over
to
playing
online
chess
as
it
is
more
readily
available
to
users.
We
wanted
to
look
at
the
effectiveness
of
different
game
strategies,
specific
moves,
and
individual
techniques,
and
their
impact
on
potential
wins
or
potential
losses
in
the
game
of
chess.
Player
data
was
pulled
from
chess.com,
which
is
where
we
were
able
to
view
profile
of
the
player,
titled
players,
their
statistics,
and
the
online
gamer
status.
We
utilized
JMP
and
Python
to
be
able
to
complete
the
study.
We
noted
the
Portable Game Notation,
also
known
as
the
PGN.
This
was
used
to
determine
the
openings,
blunders,
and
mistakes
that
were
occurring
during
the
competition.
We
learnt
that
looking
at
individual
moves
on
their
own
was
not
as
predictive
as
looking
at
move
combinations
as
a
whole.
It
was
found
that
the
prediction
of
chess
was
much
more
accurate
when
we
looked
at
different
move
combinations.
We
were
able
to
identify
moves
from
moderately-
rated
players
to
employ
leading
up
to
game-
losing
moves
such
as
blunders
or
different
opening
moves
that
led
to
more
success.
The
analysis
aims
to
help
chest
trainers
and
coaches
in
finding
weak
points
and
beginner
to
moderately-
rated
players
to
help
them
increase
their
player
rating.
They
will
also
be
able
to
formulate
better
strategies
and
training
exercises
to
help
these
players
improve
their
skills.
Like
I
said,
the
increasing
popularity
of
virtual
chess
really
encouraged
us
to
complete
this
study.
We
wanted
to
investigate
and
understand
the
differing
game
strategies
employed
by
beginner
and
moderately-
rated
players.
We
wanted
to
determine
the
optimal
winning
strategy
for
these
players
to
help
them
increase
their
rating
on
the
online
platform.
We
wanted
to
learn
how
to
help
these
players
be
able
to
determine
a
specific
strategy
to
utilize
moving
forward.
To
begin
with
our
methods,
we
started
by
sampling
the
data
we
received
from
chess.com.
After
cleaning
and
mining
the
data,
we
were
able
to
collect
a
random
sample
of
players
from
the
United
States,
the
United
Kingdom,
Canada,
Australia,
India,
and
Bangladesh.
Looking
through
our
own
research,
we
found
that
this
is
where
chess
was
most
popular
in
the
past
few
years.
So we
really
wanted
to
look
at
that
data
in
specific.
Specifically,
we
looked
at
the
data
from
October
4th,
2020
to
March
4th,
2021.
We
did
this
in
order
to
avoid
potential
implications
from
looking
at
data
that
occurred
during
the
COVID-19
pandemic
when
internet
usage
was
at
its
highest.
We
also
were
able
to
do
some
feature
generation.
We
generated
two
features
which
allowed
for
the
users
to
determine
move
combinations
that
led
up
to
blunders
or
mistakes.
Here,
we
created
the
Blunder PGN
and
the
Mistake PGN.
The
Blunder PGN
was
just
the
record
of
moves
that
were
made
by
a
player
leading
up
to
a
blunder
and
chess.
The
Mistake PGN
was
just
a
collection
of
moves
that
a
player
made
leading
up
to
a
mistake.
This
is
what
allowed
us
to
complete
our
analysis.
Next,
we
utilized
a
Python
code
to
merge,
join,
and
compare
all
of
the
data
that
we
collected.
This
data
was
compiled
of
five
games
per
player
from
about
a
1,000
to
1,400-
player
rating.
We
selected
1000
games
randomly
from
this
selection
of
data.
While
we
were
looking
at
this
data,
we
wanted
to
do...
We
measured
it
and
using
a
stockfish
depth
of
20.
To
describe
these
measures
a
little
bit
more,
it
was
measured
in
what
we
call
a
centipawn
in
chess.
A
plus
100
centipawn
signifies
that
there
is
an
advantage
of
one
pawn
of
the
white
player
over
the
black
player.
During
a
blunder,
this
means
that
a
move
made
by
one
player
has
cost
them
a
negative
500
centipawn
disadvantage.
A
mistake
is
equivalent
to
a
negative
300
centipawn
disadvantage.
A
blunder
is
normally
what
occurs
in
a
game
losing
mistake.
Down
to
the
bottom
you
can
see
some
analysis
that
we
conducted
via
JMP.
In
this
graph
right
here,
it
is
the
top
ten
most
used
openings
in
blunders.
As
you
can
see,
the
number
one
used
opening
that
leads
to
blunders
is
the
Queen's Pawn
Opening
London
system.
Secondly,
we
look
at
the
Scandinavian
Defence
that
is
oftenly
used
and
this
can
be
led
to
blunders
as
well.
I
will
mention
these
again
later
in
the
results
and
the
conclusions
of
our
presentation.
At
the
bottom
you
can
just
see
two
graphs.
These
graphs
just
show
the
number
of
wins
that
are
occurring
per
level
of
player.
We
can
look
at
the
lowest-
rated
players
up
to
the
highest-
rated
players.
These
show
just
the
average
number
of
losses
in
comparison.
Over
to
the
right
you
can
see
the
blunder
flag
which
this
is
just
the
white
player
versus
the
black
player.
At
the
bottom
is
the
list
of
frequencies
that
occur
during
these
moves
that
are
made
to
the
left.
For
example,
you
can
see
when
we
look
at
the
London
System
Opening,
it
is
about
half
and
half
for
white
players
and
black
players
in
the
wins
and
loss
ratio.
However,
when
we
look
at
the
Scandinavian
Defence,
we
can
see
that
the
white
players
often
make
blunders
more
often
compared
to
the
black
players.
When
we
look
at
our
results
using
the
Blunder PGN
and
the
Mistake PGN
features
that
we
developed,
we
were
able
to
identify
a
series
of
moves
that
most
moderately
players
employ
leading
up
to
a
losing
move.
We
identified
three
blunders
and
four
Mistake PGNs,
which
players
struggle
with
the
most
among
all
combinations.
For
one,
black
players
should
refrain
from
the
Blackmar Gambit
and
the
Scandinavian Defence.
The
Blackmar Gambit
only
results
in
about
29.3%
of
wins
for
black
chess
players.
Secondly,
the
Scandinavian Defence
only
equivalents
in
about
27.7%
for
players
that
are
using
the
black pawn
.
White pawn
players
generally
have
an
advantage
here.
They
do
struggle
with
center
openings
though.
When
we
look
at
what
moves
and
openings
the
w
hite pawn
players
utilize
when
they
move
strictly
forward
in
the
center,
they
tend
to
lose
games
more
often.
Lastly,
weak
openings
and
blundering
players.
There
were
a
few
openings
that
we
were
able
to
identify
that
consistently
led
to
blunders
in
both
players.
These
were
pawn
sacrifices
without
compensation,
queen
safety,
and
the
development
of
pieces.
While
we
look
at
all
of
this
data
together
and
all
of
our
results,
we
were
able
to
come
up
with
a
conclusion.
Moderately-
rated
players
are
most
accurate
and
successful
when
they
employ
standard
openings.
They
should
be
trained
on
the
fundamentals
of
chess
before
learning
how
to
move
on
to
complicated
openings.
Some
of
the
openings
that
we
suggest
that
beginner
players
start
off
with
are
the
London
System,
and
the
Giuoco Piano
game.
At
this
time,
I
would
just
like
to
thank
you
guys
and
I
will
be
accepting
any
questions
that
you
have
over
the
report.
