Test Cricket Batting Analysis

Overview

This project performs a detailed analysis of Test cricket batting records using data cleaning, feature engineering, and exploratory data analysis (EDA). The goal is to uncover insights into players' performances and career trends.

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Steps and Insights

1. Data Loading and Exploration

Loading the Dataset

import pandas as pd

df = pd.read_csv('TestMatch_Data - Test matches _ Batting records _ Highest career batting average _ ESPNcricinfo.csv')

• Initial Inspection:
  • Displayed the dataset using df.head() and verified dimensions with df.shape.
  • Checked for missing and duplicate values using df.isnull() and df.duplicated().

Key Observations:

• Columns contained mixed data formats (e.g., Span, Matches).
• Player names included country affiliations.

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2. Data Cleaning

Renaming Columns

df.rename(columns={
    'Mat': 'Matches',
    'NO': 'Not_Out',
    'HS': 'Highest_Inns_Score',
    'BF': 'Ball_Faced',
    'SR': 'Batting_Strike_Rate',
    '0': 'Ducks'
}, inplace=True)

Handling Missing and Duplicate Values

df.dropna(inplace=True)
df.drop_duplicates(inplace=True)

Formatting Columns

• Split Span into Debut_Year and Last_Year.

df['Debut_Year'] = df['Span'].str.split('-').str[0].astype(int)
df['Last_Year'] = df['Span'].str.split('-').str[1].astype(int)
df.drop(['Span'], axis=1, inplace=True)

• Extract Player Name and Country:

df['Player_Name'] = df['Player'].str.split('(').str[0]
df['Country'] = df['Player'].str.extract('\((.*?)\)')[0]
df.drop(['Player'], axis=1, inplace=True)

• Convert Columns to Numeric:

df['Highest_Inns_Score'] = df['Highest_Inns_Score'].str.replace('*', '').astype(int)
df['Matches'] = df['Matches'].astype(int)
df['4s'] = df['4s'].astype(int)
df['6s'] = df['6s'].astype(int)
df['Ball_Faced'] = df['Ball_Faced'].str.split('+|-').str[0].astype(int)

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3. Feature Engineering

Career Length

df['Career_Length'] = df['Last_Year'] - df['Debut_Year']

New Metrics

• Average Career Length:

df['Career_Length'].mean()

• Average Strike Rate for Players with Long Careers:

df[df['Career_Length'] > 10]['Batting_Strike_Rate'].mean()

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4. Exploratory Data Analysis (EDA)

Key Questions and Insights

1. Players Who Debuted Before 1960:

df[df['Debut_Year'] < 1960]['Player_Name'].count()

2. Highest Innings Scores by Country:

df.groupby('Country')['Highest_Inns_Score'].max()

3. Centuries Scored by Country:

df.groupby('Country')['100'].max()

4. Averages of Key Metrics by Country:

df.groupby('Country')[['100', '50', 'Ducks']].mean()

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5. Visualizations

Relationships Among Metrics

import seaborn as sns
sns.set_theme(style='darkgrid')
sns.scatterplot(df.groupby('Country')[['100', '50', 'Ducks']].mean())

Conclusion

This analysis provides a comprehensive view of Test cricket batting performances. It highlights:

• Career trends, including average career lengths and strike rates for long-tenured players.
• Country-wise performance metrics like highest innings scores and averages of centuries.
• The importance of data cleaning and feature engineering in deriving meaningful insights from raw data.