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.
- Afghanistan
- Australia
- Bangladesh
- England
- India
- New Zealand
- Pakistan
- South Africa
- Sri Lanka
- West Indies
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)
# 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)
# 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")
