Magnus Carlsen Performance Analysis from 2001 to 2022

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Abstract

This project presents a comprehensive analysis of Magnus Carlsen's performance in chess from 2001 to 2022, utilizing two distinct datasets (ds1 and ds2). The analysis covers various facets of Carlsen's gameplay across different dimensions:

• Performance Metrics: Examines win, loss, and draw rates over time represented in bar plots and pie charts.
• Rating Analysis: Investigates performance trends based on Carlsen's White and Black Elo ratings using line charts.
• Site and Opponent Analysis: Explores Carlsen's performance across different chess sites, average opponent Elo ratings, and specific chess events.
• Opening Strategies: Provides detailed analyses of Carlsen's success rates and strategic patterns in relation to chess openings, including ECO codes and variants.
• Opponent Analysis: Analyzes Carlsen's performance against specific opponents categorized by their Elo thresholds.
• Game Dynamics: Concludes with insights into game termination types, chess piece dynamics, and capture analyses influencing Carlsen's game outcomes.

This documentation aims to offer a nuanced understanding of Magnus Carlsen's strategic prowess and performance dynamics over the past two decades.

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Libraries Used in the project:

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pandas:

pandas is a powerful library for data manipulation and analysis. It provides data structures like DataFrames and Series, which are useful for handling structured data. seaborn (import seaborn as sns):

seaborn:

seaborn is a statistical data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. matplotlib.pyplot (import matplotlib.pyplot as plt):

matplotlib:

matplotlib.pyplot is a collection of command-style functions that make matplotlib work like MATLAB. It is a comprehensive library for creating static, animated, and interactive visualizations in Python. These libraries together provide a robust toolkit for data analysis, manipulation, and visualization in Python.

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Functions and Methods used in the project

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Seaborn Functions

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seaborn.scatterplot(): Creates a scatter plot, which is a type of plot that displays values for two variables as points.

Usage: sns.scatterplot(data=data, x='numeric', y='value')

seaborn.lineplot(): Creates a line plot, which shows data points connected by straight lines.

Usage: sns.lineplot(data=data, x='numeric', y='value')

seaborn.barplot(): Creates a bar plot, which displays data with rectangular bars representing the value of each category.

Usage: sns.barplot(data=data, x='category', y='value')

seaborn.boxplot(): Creates a box plot, which displays the distribution of data based on a five-number summary: minimum, first quartile, median, third quartile, and maximum.

Usage: sns.boxplot(data=data, x='category', y='value')

Matplotlib Functions

matplotlib.pyplot.pie(): Creates a pie chart, which shows proportions of a whole as slices of a pie.

Usage:category_counts = data['category'].value_counts()

plt.pie(category_counts, labels=category_counts.index, autopct='%1.1f%%')

plt.show()

Pandas Dataframe Methods

DataFrame.info(): Provides a concise summary of a DataFrame, including the number of non-null entries and data types for each column.

Usage: data.info()

DataFrame.describe(): Generates descriptive statistics that summarize the central tendency, dispersion, and shape of a dataset’s distribution, excluding NaN values.

Usage: data.describe()

Series.value_counts(): Returns a Series containing counts of unique values.

Usage: data['category'].value_counts()

DataFrame.groupby(): Groups DataFrame using a mapper or by a series of columns, allowing aggregation or transformation operations.

Usage: data.groupby('category')

DataFrame.agg(): Aggregates data based on one or more operations on grouped data.

Usage: data.groupby('category').agg({'value': 'sum'})

DataFrame.merge(): Merges DataFrame or named Series objects with a database-style join.

Usage: merged_data = pd.merge(data1, data2, on='key_column')

Step-by-Step Data Analytics Process

1. Data Collection:

• I collected ds1 and ds2 datasets using the Kaggle website and it contains 4314 games played by Magnus from 2001 to 2022 in ds1 dataset and in ds2 datase it contains 3 lakh game detailed info played by Magnus.

2. Data Cleaning and Preparation (Using Excel)

• Remove duplicates
• Handle missing values
• Correct errors
• Format data
• Save cleaned data

3. Data Loading (Using Python)

• Import libraries
• Load data into a Pandas DataFrame

4. Exploratory Data Analysis (EDA)

• Overview of data
• Value counts
• Visualizations
  • Scatterplot
  • Lineplot
  • Barplot
  • Boxplot
  • Pie chart

5. Data Transformation and Feature Engineering

• Group and aggregate data
• Merge datasets

6. Statistical Analysis

• Descriptive statistics
• Inferential statistics

7. Insights and Interpretation

• Actionable Recommendations:
  • Magnus Carlsen should focus on improving his troublesome openings to maintain his No. 1 position in chess.
  • Consider exploring alternative openings such as:
    • C41 Philidor's defence: 1. e4 e5 2. Nf3 d6
    • C45 Scotch game: 1. e4 e5 2. Nf3 Nc6 3. d4 exd4 4. Nxd4
    • E11 Bogo-Indian defence, Monticelli trap: 1. d4 Nf6 2. c4 e6 3. Nf3 Bb4+ 4. Bd2 Bxd2+ 5. Qxd2 b6 6. g3 Bb7 7. Bg2 O-O 8. Nc3 Ne4 9. Qc2 Nxc3 10. Ng5

9. Summary:

• Based on this analysis, when playing against Magnus Carlsen, it would be advantageous to prefer the white pieces and use openings like the Ruy Lopez, Berlin Defense, and Sicilian to provide a tough competition.

10. Presentation

• Prepare a comprehensive report or presentation that includes the visualizations and key insights from the analysis.
• Share the findings with stakeholders or use them for further detailed analysis.