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.
