This document outlines a project to analyze cricket statistics for four all-rounders (Ian Botham, Imran Khan, Kapil Dev, and Richard Hadlee) using R and PySpark. The goals are to gather test match data, analyze it to answer a specific question, and visualize the results. The question is which bowler was most effective over their career based on statistics like wickets, average, economy, and strike rate. The document describes collecting data from online sources using R, processing it with PySpark, calculating key statistics for each player, and creating visualizations to compare the players and rank their bowling ability.

1. EXTRACTING AND ANALYZING
CRICKET STATISTICS WITH R AND
PYSPARK
Parag Ahire
February 16, 2020

2. OUR GOALS TODAY
Gather data, analyze the data, visualize the data and draw some
conclusions to answer a specific question
 Data Domain – Test match data of the game of cricket
Utilize R to gather the data
Utilize PySpark/Python to summarize, visualize and analyze the
data

3. OUR PROGRESSION TODAY
 Get familiar with the domain and data
 Define the specific question
 Get familiar with
 The data source
 The subjects of focus
 Install the tools needed
 Write program to fetch the data
 Execute program to fetch the data
 Write program to summarize, visualize and analyze the data
 Answer the specific question

4. DATA DOMAIN
 The game of cricket
 Get familiar with the game
 Understand some terms, formats, player roles and rules
 Focus on four all-rounders
 Placed in the top 25 legends of the game
 Sir Ian Botham – England
 Imran Khan - Pakistan
 Kapil Dev - India
 Sir Richard Hadlee – New Zealand
 Use their bowling data

5. SPECIFIC QUESTION
Which of the four all-rounders was the most effective as a
bowler over their entire career? Consider statistics like wickets
taken, wickets per inning, average, economy and strike rate.
Rank the bowlers in descending order of their ability to
intimidate batsmen and deliver outstanding performances year-
after-year.

6. DATA SOURCE
ESPNCRICINFO – A sports news website exclusively for the
game of cricket
StatsGuru – A database of historical matches and players
from the 18th century to the present
Sample Test Scorecard

7. TOOLS USED
R – Version 3.1.2 or higher
R Studio - Optional
CRAN cricketr package
Java – Version 1.8
Spark – Version 3.0.0
Anaconda for Python 3
winutils.exe
WINRAR or 7-Zip

8. INSTALL R / R STUDIO / CRICKETR
WINDOWS
 R – https://cran.r-project.org/bin/windows/base/R-3.6.2-
win.exe
 R Studio - https://rstudio.com/products/rstudio/download/
 CRAN cricketr package
• install.packages(“cricketr”)
 Test the package
• library(cricketr)

9. INSTALL JDK / ANACONDA / SPARK
WINDOWS or MAC or LINUX
 Installing Apache Spark and Python
• https://sundog-education.com/spark-python/

10. SAMPLE R CODE
library(cricketr)
# Kapil Dev's player id : 30028
kapilDev = getPlayerData(30028, dir="C://Meetup", file="Kapil.csv",
type="bowling", homeOrAway=c(1,2),result=c(1,2,4))
write.csv(kapilDev, 'C:MeetupKapil.csv', row.names=FALSE,
quote=FALSE)
# Imran Khan's player id : 40560
# Ian Botham's player id : 9163
# Richard Hadlee's player id : 37224

11. SAMPLE PYSPARK CODE
def getBalls(overs):
balls = 0
dotLocation = overs.find('.')
balls = int(overs[0:dotLocation]) * 6
if dotLocation != -1:
balls += int(overs[(dotLocation + 1):])
return balls
def getBallsByColumn(overs, bpo):
balls = 0
dotLocation = overs.find('.')
balls = int(overs[0:dotLocation]) * bpo
if dotLocation != -1:
balls += int(overs[(dotLocation + 1):])
return balls

12. SAMPLE PYSPARK CODE
CareerBowlingStatistics = namedtuple("CareerBowlingStatistics", "Wickets, WicketsPerInning, Average, Economy, StrikeRate")
def calculateKeyStatistics(player, isBPOColumnPresent) :
print("nKey Statistics for " + player + " :")
file = "C:Meetup" + player + ".csv"
bowler = None
if isBPOColumnPresent == True:
bowler = sqlContext.read.format('com.databricks.spark.csv').options(header='true',
inferschema='false').schema(eightBallSchema).load(file)
else: bowler = sqlContext.read.format('com.databricks.spark.csv').options(header='true',
inferschema='false').schema(sixBallSchema).load(file)
bowlerValidRows = bowler.filter((bowler["Overs"] != "DNB") & (bowler["Overs"] != "TDNB"))
if isBPOColumnPresent == True:
bowlerValidRows = bowlerValidRows.withColumn('Balls', udf(getBallsByColumn)("Overs", "BPO"))
else:
bowlerValidRows = bowlerValidRows.withColumn('Balls', udf(getBalls)("Overs"))

13. SAMPLE PYSPARK CODE
def calculateKeyStatistics(player, isBPOColumnPresent) :
….
totalBalls = bowlerValidRows.agg(func.sum("Balls")).collect()[0][0]
totalOvers = str(int(int(totalBalls) / 6)) + "." + str(int(totalBalls) % 6)
totalInnings = bowlerValidRows.count()
totalWickets = bowlerValidRows.agg(func.sum("Wkts")).collect()[0][0]
wicketPerInning = round((float(totalWickets) / totalInnings), 3)
totalRuns = bowlerValidRows.agg(func.sum("Runs")).collect()[0][0]
average = round((float(totalRuns) / totalWickets), 3)
economy = round((float((totalRuns) * 6) / totalBalls), 3)
bowlerValidRows = bowlerValidRows.withColumn("Balls", bowlerValidRows["Balls"].cast(IntegerType()))
strikeRate = round((float(totalBalls) / totalWickets), 3)
careerBowlingStatistics = CareerBowlingStatistics(int(totalWickets), float(wicketPerInning),
float(average), float(economy), float(strikeRate))
return careerBowlingStatistics

14. SAMPLE PYSPARK CODE
def plotStatistic(bowlers, values, xlabel, ylabel, title, fileName):
plt.style.use('ggplot')
width = 0.35
fig, ax = plt.subplots()
x = np.arange(len(bowlers))
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_title(title)
ax.set_xticks(x)
ax.set_xticklabels(bowlers)
# Internationl All-Rounder Colors
rects = ax.bar(x - width/2, values, width, label=xlabel,
color=['xkcd:bright blue', 'xkcd:lime green', 'xkcd:light blue', 'xkcd:grey'])
for rect in rects:
height = rect.get_height()
ax.annotate('{}'.format(height), xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points", ha='center', va='bottom')
fig.tight_layout()
plt.savefig('C:Meetup' + fileName + '.png', dpi=300, bbox_inches='tight')

15. CAREER - WICKETS

16. CAREER - WICKETS PER INNING

17. CAREER – STRIKE RATE

18. CAREER - AVERAGE

19. CAREER - ECONOMY

20. RANK – STATISTICAL CRITERIA
CRITERIA 1 2 3 4
STRIKE RATE Hadlee Imran Botham Kapil
WICKETS PER INNING Hadlee Imran Botham Kapil
AVERAGE Hadlee Imran Botham Kapil
ECONOMY Imran Hadlee Kapil Botham
WICKETS Kapil Hadlee Botham Imran

21. OVERAL RANKING – TOP STATISTICAL CRITERIA
CRITERIA 1 2 3 4
STRIKE RATE Hadlee Imran Botham Kapil
WICKETS PER INNING Hadlee Imran Botham Kapil
AVERAGE Hadlee Imran Botham Kapil

22. MOST DESTRUCTIVE BOWLER
UNFAIR TO COMPARE AND RANK THESE GREATS
THEY ARE ALL HIGH ACHIEVERS

23. REFERENCES
What is Cricket? Get to know the sport
Introduction to Cricket – 1
Introduction to Cricket – 2
Cricket: The Basic Introduction
Cricket for Americans
ESPN's Legends of Cricket - The Top 25 Cricketers Of All Time
ESPN Legends of Cricket : Sir Ian Botham
ESPN Legends of Cricket : Imran Khan
ESPN Legends of Cricket : Kapil Dev
ESPN Legends of Cricket : Sir Richard Hadlee

24. REFERENCES
ESPNCRICINFO
StatsGuru
Sample Test Match Scorecard
The R Project for Statistical Computing
Rstudio (Optional)
CRAN – Package cricketr
Installing Apache Spark and Python – Sundog Education
Sundog Education – Installing Apache Spark and Python
cricketr – getPlayerData
Code