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Made with Love in India

made-with-python

Cricket World Cup 2019

The ICC Cricket World Cup is the international championship of One Day International (ODI) cricket. The event is organised by the sport's governing body, the International Cricket Council (ICC), every four years, with preliminary qualification rounds leading up to a finals tournament. The tournament is one of the world's most viewed sporting events and is considered the "flagship event of the international cricket calendar" by the ICC.

The 2019 ICC Cricket World Cup is the 12th edition of the Cricket World Cup, scheduled to be hosted by England and Wales from 30 May to 14 July 2019.

Teams

  • Afghanistan
  • Australia
  • Bangladesh
  • England
  • India
  • New Zealand
  • Pakistan
  • South Africa
  • Sri Lanka
  • West Indies

Getting Started

Prerequisites - https://colab.research.google.com

# Dataset - ODI_DATASET.csv
# Starting from Jan-2013 to mid May-2019 - all the ODI results are included in this dataset

import warnings
warnings.filterwarnings('ignore')

# linear algebra
import numpy as np 

# data processing
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns


# ODI dataset from Jan-2013 to May-2019
# As I have used Google Colab to perform this notebook - hence added github raw URL to fetch the dataset

ODI_Data = pd.read_csv('https://raw.githubusercontent.com/nimitsolanki/Cricket-World-Cup-2019/master/data/ODI_DATASET.csv')


# Scores_ID

ODI_Data["Scores_ID"] = ODI_Data["Unnamed: 0"]
ODI_Data.drop(columns="Unnamed: 0",inplace=True)


# CWC-2019 pitches

WC_venue_pitches = ["The Oval, London","Trent Bridge, Nottingham","Sophia Gardens, Cardiff","County Ground, Bristol","Rose Bowl, Southampton","County Ground, Taunton","Old Trafford, Manchester","Edgbaston, Birmingham","Headingley, Leeds","Lord's, London","Riverside Ground, Chester-le-Street"]

#Total Grounds
WC_Ground_Stats = []
ODI_Grounds = ODI_Data.Ground
for i in ODI_Grounds:
    for j in WC_venue_pitches:
        if i in j:
          WC_Ground_Stats.append((i,j))   
          
          
          
# Listing ground names

Ground_names = dict(set(WC_Ground_Stats))
def Full_Ground_names(value):
    return Ground_names[value]
Ground_names


{'Birmingham': 'Edgbaston, Birmingham',
 'Bristol': 'County Ground, Bristol',
 'Cardiff': 'Sophia Gardens, Cardiff',
 'Chester-le-Street': 'Riverside Ground, Chester-le-Street',
 'Leeds': 'Headingley, Leeds',
 "Lord's": "Lord's, London",
 'Manchester': 'Old Trafford, Manchester',
 'Nottingham': 'Trent Bridge, Nottingham',
 'Southampton': 'Rose Bowl, Southampton',
 'The Oval': 'The Oval, London'}
 
 # Matching ODI's data with respect to the above listed grounds

WC_Grounds_History = ODI_Data[ODI_Data.Ground.isin([Ground[0] for Ground in WC_Ground_Stats])]
WC_Grounds_History["Ground"] = WC_Grounds_History.Ground.apply(Full_Ground_names)
WC_Grounds_History.head()


# Finding the World cup team's played on these grounds

Team_Matches = WC_Grounds_History.Country.value_counts().reset_index()
plt.figure(figsize=(15,8))
sns.barplot(x = "index", y = "Country", data = Team_Matches).set_title("Total Matches Played by each Country")
plt.xlabel("Country")
plt.ylabel("Matches Played")
plt.xticks(rotation = 60)

1

# Team wise Winning Percentage in England Pitches after removing the currupt data result

WC_Grounds_History = WC_Grounds_History[~WC_Grounds_History.Result.isin(["-"])]
WC_Grounds_History.Result.value_counts()

# Country results in percentage 

winnings = WC_Grounds_History[["Country","Result"]]
winnings["count"] = 1
Ground_Results_Per_Team = winnings.groupby(["Country","Result"]).aggregate(["sum"])
Ground_Results_Per_Team = Ground_Results_Per_Team.groupby(level=0).apply(lambda x:100 * x / float(x.sum())).reset_index()
Ground_Results_Per_Team.columns = ["Country","Result","Count"]
Ground_Results_Per_Team.head()

# Plotting Results in percentage

plt.figure(figsize=(15,8))
sns.barplot(x = "Country", y = "Count", hue = "Result", data = Ground_Results_Per_Team)
plt.ylabel("Percentage")
plt.title("Country - Results")
plt.xticks(rotation = 60)

2

# It's clear that India and England have highest Green bars - highest winning percentage.

# Let's see what happens when the Top Two Teams face?


India_vs_England = WC_Grounds_History[WC_Grounds_History.Country == "India"]\
[WC_Grounds_History.Opposition.str.contains("England")]
India_vs_England = India_vs_England.Result.value_counts().reset_index()
sns.barplot(x = "index", y = "Result", data = India_vs_England).set_title("India against England")
plt.xlabel("India")

3

License: MIT