Cricketr analyzes Ind-Aus faceoff in WTC 2023!!

“The unexamined life is not worth living.” – Socrates

“There is no easy way from the earth to the stars.” – Seneca

“If you want to go fast, go alone. If you want to go far, go together.” – African Proverb

1. Introduction

In this post, I put my R package cricketr to analyze the Indian and Australia World Test Championship (WTC) final squad ahead of the World Test Championship 2023.My R package cricketr had its birth on Jul 4, 2015. Cricketr uses data from Cricinfo.

You can download the latest PDF version of the book at ‘Cricket analytics with cricketr and cricpy: Analytics harmony with R and Python-6th edition‘

Indian squad

Rohit Sharma (Captain), Shubman Gill, Cheteshwar Pujara, Virat Kohli, Ajinkya Rahane, Ravindra Jadeja, Shardul Thakur, Mohd. Shami, Mohd. Siraj, Ishan Kishan (wk).

According to me, Ishan Kishan has more experience than KS Bharat, though Rishabh Pant would have been the ideal wicket keeper/left-handed batsman. I think Shardul Thakur would be handful in the English conditions. For a spinner it either Ashwin or Jadeja. Maybe the balance shifts in favor of Jadeja

Australian squad

Pat Cummins (capt), Alex Carey (wk), Cameron Green, Josh Hazlewood, Usman Khawaja, Marnus Labuschagne, Nathan Lyon, Todd Murphy, Steven Smith (vice-capt), Mitchell Starc, David Warner.

Not sure if Scott Boland would fill in, instead of Todd Murphy 1

Let me give you a lay-of-the-land (post) below

The post below is organized into the following parts

Analysis of Indian WTC batsmen from Jan 2016 – May 2023
Analysis of Indian WTC batsmen against Australia from Jan 2016 -May 2023
Analysis of Australian WTC batsmen from Jan 2016 – May 2023
Analysis of Australian WTC batsmen against India from Jan 2016 -May 2023
Analysis of Indian WTC bowlers from Jan 2016 – May 2023
Analysis of Indian WTC bowlers against Australia from Jan 2016 -May 2023
Analysis of Australian WTC bowlers from Jan 2016 – May 2023
Analysis of Australian WTC bowlers gainst India from Jan 2016 -May 2023
Team analysis of India and Australia

All the above analysis use data from ESPN Statsguru and use my R pakage cricketr

The data for the different players have been obtained using calls such as the ones below.

# Get Shubman Gill's batting data
#shubman <-getPlayerData(1070173,dir=".",file="shubman.csv",type="batting",homeOrAway=c(1,2), result=c(1,2,4))
#shubmansp <- getPlayerDataSp(1070173,tdir=".",tfile="shubmansp.csv",ttype="batting")

#Get Shubman Gill's data from Jan 2016 - May 2023
#df <-getPlayerDataHA(1070173,tfile="shubman1.csv",type="batting", matchType="Test")
#df1=getPlayerDataOppnHA(infile="shubman1.csv",outfile="shubmanTestAus.csv",startDate="2016-01-01",endDate="2023-05-01")

#Get Shubman Gills data from Jan 2016 - May 2023, against Australia
#df <-getPlayerDataHA(1070173,tfile="shubman1.csv",type="batting", matchType="Test")
#df1=getPlayerDataOppnHA(infile="shubman1.csv",outfile="shubmanTestAus.csv",opposition="Australia",startDate="2016-01-01",endDate="2023-05-01")

Note: To get data for bowlers we need to use the corresponding profile no and use type =‘bowling’. Details in my posts below

To do similar analysis please go through the following posts

Note 1: I will not be analysing each and every chart as the charts are quite self-explanatory

Note 2: I have had to tile charts together otherwise this will become a very, very long post. You are free to use my R package cricketr and check out for yourself ##3. Analysis of India WTC batsmen from Jan 2016 – May 2023

Findings

Kohli has the best average of 48+. India has won when Rohit and Rahane played well
Kohli’s tops the list in cumulative average runs, followed by Pujara and Rohit is 3rd. Gill is on the upswing.
Against Australia Pujara has the best cumulative average runs record followed by Rahane, with Gill in hot pursuit. In the strike rate department Gill tops followed by Rohit and Rahane
Since 2016 Smith, Labuschagne has an average of 53+ since 2016!! Warner & Khwaja are at ~46
Australia has won matches when Smith, Warner and Khwaja have played well.
Labuschagne, Smith and C Green have good records against India. Indian bowlers will need to contain them
Ashwin has the highest wickets followed by Jadeja against all teams. Ashwin’s performance has dropped over the years, while Siraj has been becoming better
Jadeja has the best economy rate followed by Ashwin
Against Australia specifically Jadeja has the best record followed by Ashwin. Jadeja has the best economy against Australia, followed by Siraj, then Ashwin
Cummins, Starc and Lyons are the best performers for Australia. Hazzlewood, Cummins have the best economy against all opposition
Against India Lyon, Cummins and Hazzlewood have performed well
Hazzlewood, Lyon have a good economy rate against India
Against Australia India has won 17 times, lost 60 and drawn 22 in Australia. At home India won 42, tied 2, lost 28 and drawn 24
At the Oval where the World Test Championship is going to be held India has won 4, lost 10 and drawn 10.

Note 3: You can also read this post at Rpubs at ind-aus-WTC !! The formatting will be nicer!

Note 4: You can download this post as PDF to read at your leisure ind-aus-WTC.pdf

2. Install the cricketr package

if (!require("cricketr")){
    install.packages("cricketr",lib = "c:/test")
}
library(cricketr)

3a. Basic analysis

The analyses below include – Runs frequency plot – Mean strike rate – Run Ranges

Kohli’s strike rate increases with increasing runs, while Gill’s seems to drop. So it is with Pujara & Rahane

par(mfrow=c(3,3))
par(mar=c(4,4,2,2))
batsmanRunsFreqPerf("kohliTest.csv","Kohli")
batsmanMeanStrikeRate("kohliTest.csv","Kohli")
batsmanRunsRanges("kohliTest.csv","Kohli")

batsmanRunsFreqPerf("rohitTest.csv","Rohit")
batsmanMeanStrikeRate("rohitTest.csv","Rohit")
batsmanRunsRanges("rohitTest.csv","Rohit")

batsmanRunsFreqPerf("shubmanTest.csv","S Gill")
batsmanMeanStrikeRate("shubmanTest.csv","S Gill")
batsmanRunsRanges("shubmanTest.csv","S Gill")

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))
batsmanRunsFreqPerf("rahaneTest.csv","Rahane")
batsmanMeanStrikeRate("rahaneTest.csv","Rahane")
batsmanRunsRanges("rahaneTest.csv","Rahane")

batsmanRunsFreqPerf("pujaraTest.csv","Pujara")
batsmanMeanStrikeRate("pujaraTest.csv","Pujara")
batsmanRunsRanges("pujaraTest.csv","Pujara")

3b. More analyses

Kohli hits roughly 5 4s in his 50 versus Gill,Pujara who is able to smash 6 4s.

par(mfrow=c(3,3))
par(mar=c(4,4,2,2))

batsman4s("kohliTest.csv","Kohli")
batsman6s("kohliTest.csv","Kohli")
batsmanMeanStrikeRate("kohliTest.csv","Kohli")

batsman4s("rohitTest.csv","Rohit")
batsman6s("rohitTest.csv","Rohit")
batsmanMeanStrikeRate("rohitTest.csv","Rohit")

batsman4s("shubmanTest.csv","S Gill")
batsman6s("shubmanTest.csv","S Gill")
batsmanMeanStrikeRate("shubmanTest.csv","S Gill")

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))

batsman4s("rahaneTest.csv","Rahane")
batsman6s("rahaneTest.csv","Rahane")
batsmanMeanStrikeRate("rahane.csv","Rahane")

batsman4s("pujaraTest.csv","Pujara")
batsman6s("pujaraTest.csv","Pujara")
batsmanMeanStrikeRate("pujaraTest.csv","Pujara")

3c.Boxplot histogram plot

This plot shows a combined boxplot of the Runs ranges and a histog2ram of the Runs Frequency Kohli’s average is 48, while Rohit,Pujara is 40 with Rahane and Gill around 33.

batsmanPerfBoxHist("kohliTest.csv","Kohli")

batsmanPerfBoxHist("rohitTest.csv","Rohit")

batsmanPerfBoxHist("shubmanTest.csv","S Gill")

batsmanPerfBoxHist("rahaneTest.csv","Rahane")

batsmanPerfBoxHist("pujaraTest.csv","Pujara")

3d. Contribution to won and lost matches

For the functions below you will have to use the getPlayerDataSp() function. When Rohit Sharma and Pujara have played well India have tended to win more often

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanContributionWonLost("kohlisp.csv","Kohli")
batsmanContributionWonLost("rohitsp.csv","Rohit")
batsmanContributionWonLost("rahanesp.csv","Rahane")
batsmanContributionWonLost("pujarasp.csv","Pujara")

3e. Performance at home and overseas

This function also requires the use of getPlayerDataSp() as shown above. This can only be used for Test matches

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanPerfHomeAway("kohlisp.csv","Kohli")
batsmanPerfHomeAway("rohitsp.csv","Rohit")
batsmanPerfHomeAway("rahanesp.csv","Rahane")
batsmanPerfHomeAway("pujarasp.csv","Pujara")

3f. Batsman average at different venues

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("kohliTest.csv","Kohli")
batsmanAvgRunsGround("rohitTest.csv","Rohit")
batsmanAvgRunsGround("rahaneTest.csv","Rahane")
batsmanAvgRunsGround("pujaraTest.csv","Pujara")

3g. Batsman average against different opposition

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsOpposition("kohliTest.csv","Kohli")
batsmanAvgRunsOpposition("rohitTest.csv","Rohit")
batsmanAvgRunsOpposition("rahaneTest.csv","Rahane")
batsmanAvgRunsOpposition("pujaraTest.csv","Pujara")

3h. Runs Likelihood of batsman

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanRunsLikelihood("kohli.csv","Kohli")

## Summary of  Kohli 's runs scoring likelihood
## **************************************************
## 
## There is a 52.91 % likelihood that Kohli  will make  12 Runs in  26 balls over 35  Minutes 
## There is a 30.81 % likelihood that Kohli  will make  52 Runs in  100 balls over  139  Minutes 
## There is a 16.28 % likelihood that Kohli  will make  142 Runs in  237 balls over 335  Minutes

batsmanRunsLikelihood("rohit.csv","Rohit")

## Summary of  Rohit 's runs scoring likelihood
## **************************************************
## 
## There is a 43.24 % likelihood that Rohit  will make  10 Runs in  21 balls over 32  Minutes 
## There is a 45.95 % likelihood that Rohit  will make  46 Runs in  85 balls over  124  Minutes 
## There is a 10.81 % likelihood that Rohit  will make  110 Runs in  199 balls over 282  Minutes

batsmanRunsLikelihood("rahane.csv","Rahane")

## Summary of  Rahane 's runs scoring likelihood
## **************************************************
## 
## There is a 7.75 % likelihood that Rahane  will make  124 Runs in  224 balls over 318  Minutes 
## There is a 62.02 % likelihood that Rahane  will make  12 Runs in  26 balls over  37  Minutes 
## There is a 30.23 % likelihood that Rahane  will make  55 Runs in  113 balls over 162  Minutes

batsmanRunsLikelihood("pujara.csv","Pujara")

## Summary of  Pujara 's runs scoring likelihood
## **************************************************
## 
## There is a 60.49 % likelihood that Pujara  will make  15 Runs in  38 balls over 55  Minutes 
## There is a 31.48 % likelihood that Pujara  will make  62 Runs in  142 balls over  204  Minutes 
## There is a 8.02 % likelihood that Pujara  will make  153 Runs in  319 balls over 445  Minutes

3h1. Moving average of batsman

Kohli’s moving average in tests seem to havw dropped after a peak in 2017, 2018. So it is with Rahane

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanMovingAverage("kohli.csv","Kohli")
batsmanMovingAverage("rohit.csv","Rohit")
batsmanMovingAverage("rahane.csv","Rahane")
batsmanMovingAverage("pujara.csv","Pujara")

3i. Cumulative Average runs of batsman in career

Kohli’s cumulative average averages to ~48. Shubman Gill’s cumulative average is on the rise.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanCumulativeAverageRuns("kohliTest.csv","Kohli")

batsmanCumulativeAverageRuns("rohitTest.csv","Rohit")

batsmanCumulativeAverageRuns("rahaneTest.csv","Rahane")

batsmanCumulativeAverageRuns("pujaraTest.csv","Pujara")

batsmanCumulativeAverageRuns("shubmanTest.csv","S Gill")

3j Cumulative Average strike rate of batsman in career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanCumulativeStrikeRate("kohliTest.csv","Kohli")

batsmanCumulativeStrikeRate("rohitTest.csv","Rohit")

batsmanCumulativeStrikeRate("rahaneTest.csv","Rahane")

batsmanCumulativeStrikeRate("pujaraTest.csv","Pujara")

batsmanCumulativeStrikeRate("shubmanTest.csv","S Gill")

3k. Future Runs forecast

Here are plots that forecast how the batsman will perform in future. In this case 90% of the career runs trend is uses as the training set. the remaining 10% is the test set.

A Holt-Winters forecating model is used to forecast future performance based on the 90% training set. The forecated runs trend is plotted. The test set is also plotted to see how close the forecast and the actual matches

Take a look at the runs forecasted for the batsman below.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanPerfForecast("kohli.csv","Kohli")
batsmanPerfForecast("rohit.csv","Rohit")
batsmanPerfForecast("rahane.csv","Rahane")
batsmanPerfForecast("pujara.csv","Pujara")

3l. Relative Mean Strike Rate plot

The plot below compares the Mean Strike Rate of the batsman for each of the runs ranges of 10 and plots them. The plot indicate the following

frames <- list("kohliTest.csv","rohitTest.csv","pujaraTest.csv","rahaneTest.csv","shubmanTest.csv")
names <- list("Kohli","Rohit","Pujara","Rahane","S Gill")
relativeBatsmanSR(frames,names)

3m. Relative Runs Frequency plot

The plot below gives the relative Runs Frequency Percetages for each 10 run bucket. The plot below show

frames <- list("kohliTest.csv","rohitTest.csv","pujaraTest.csv","rahaneTest.csv","shubmanTest.csv")
names <- list("Kohli","Rohit","Pujara","Rahane","S Gill")
relativeRunsFreqPerf(frames,names)

3n. Relative cumulative average runs in career

Kohli’s tops the list, followed by Pujara and Rohit is 3rd. Gill is on the upswing. Hope he performs well.

frames <- list("kohliTest.csv","rohitTest.csv","pujaraTest.csv","rahaneTest.csv","shubmanTest.csv")
names <- list("Kohli","Rohit","Pujara","Rahane","S Gill")
relativeBatsmanCumulativeAvgRuns(frames,names)

3o. Relative cumulative average strike rate in career

ROhit has the best strike rate followed by Kohli, with Shubman Gill ctaching up fast

frames <- list("kohliTest.csv","rohitTest.csv","pujaraTest.csv","rahaneTest.csv","shubmanTest.csv")
names <- list("Kohli","Rohit","Pujara","Rahane","S Gill")
relativeBatsmanCumulativeStrikeRate(frames,names)

3p. Check Batsman In-Form or Out-of-Form

The below computation uses Null Hypothesis testing and p-value to determine if the batsman is in-form or out-of-form. For this 90% of the career runs is chosen as the population and the mean computed. The last 10% is chosen to be the sample set and the sample Mean and the sample Standard Deviation are caculated.

The Null Hypothesis (H0) assumes that the batsman continues to stay in-form where the sample mean is within 95% confidence interval of population mean The Alternative (Ha) assumes that the batsman is out of form the sample mean is beyond the 95% confidence interval of the population mean.

A significance value of 0.05 is chosen and p-value us computed If p-value >= .05 – Batsman In-Form If p-value < 0.05 – Batsman Out-of-Form

Note Ideally the p-value should be done for a population that follows the Normal Distribution. But the runs population is usually left skewed. So some correction may be needed. I will revisit this later

This is done for the Top 4 batsman

checkBatsmanInForm("kohli.csv","Kohli")

## [1] "**************************** Form status of Kohli ****************************\n\n Population size: 154  Mean of population: 47.03 \n Sample size: 18  Mean of sample: 32.22 SD of sample: 42.45 \n\n Null hypothesis H0 : Kohli 's sample average is within 95% confidence interval of population average\n Alternative hypothesis Ha : Kohli 's sample average is below the 95% confidence interval of population average\n\n Kohli 's Form Status: In-Form because the p value: 0.078058  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBatsmanInForm("rohit.csv","Rohit")

## [1] "**************************** Form status of Rohit ****************************\n\n Population size: 66  Mean of population: 37.03 \n Sample size: 8  Mean of sample: 37.88 SD of sample: 35.38 \n\n Null hypothesis H0 : Rohit 's sample average is within 95% confidence interval of population average\n Alternative hypothesis Ha : Rohit 's sample average is below the 95% confidence interval of population average\n\n Rohit 's Form Status: In-Form because the p value: 0.526254  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBatsmanInForm("rahane.csv","Rahane")

## [1] "**************************** Form status of Rahane ****************************\n\n Population size: 116  Mean of population: 34.78 \n Sample size: 13  Mean of sample: 21.38 SD of sample: 21.96 \n\n Null hypothesis H0 : Rahane 's sample average is within 95% confidence interval of population average\n Alternative hypothesis Ha : Rahane 's sample average is below the 95% confidence interval of population average\n\n Rahane 's Form Status: Out-of-Form because the p value: 0.023244  is less than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBatsmanInForm("pujara.csv","Pujara")

## [1] "**************************** Form status of Pujara ****************************\n\n Population size: 145  Mean of population: 41.93 \n Sample size: 17  Mean of sample: 33.24 SD of sample: 31.74 \n\n Null hypothesis H0 : Pujara 's sample average is within 95% confidence interval of population average\n Alternative hypothesis Ha : Pujara 's sample average is below the 95% confidence interval of population average\n\n Pujara 's Form Status: In-Form because the p value: 0.137319  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBatsmanInForm("shubman.csv","S Gill")

## [1] "**************************** Form status of S Gill ****************************\n\n Population size: 23  Mean of population: 30.43 \n Sample size: 3  Mean of sample: 51.33 SD of sample: 66.88 \n\n Null hypothesis H0 : S Gill 's sample average is within 95% confidence interval of population average\n Alternative hypothesis Ha : S Gill 's sample average is below the 95% confidence interval of population average\n\n S Gill 's Form Status: In-Form because the p value: 0.687033  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

3q. Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

BF <- seq( 10, 400,length=15)
Mins <- seq(30,600,length=15)
newDF <- data.frame(BF,Mins)
kohli1 <- batsmanRunsPredict("kohli.csv","Kohli",newdataframe=newDF)
rohit1 <- batsmanRunsPredict("rohit.csv","Rohit",newdataframe=newDF)
pujara1 <- batsmanRunsPredict("pujara.csv","Pujara",newdataframe=newDF)
rahane1 <- batsmanRunsPredict("rahane.csv","Rahane",newdataframe=newDF)
sgill1 <- batsmanRunsPredict("shubman.csv","S Gill",newdataframe=newDF)

batsmen <-cbind(round(kohli1$Runs),round(rohit1$Runs),round(pujara1$Runs),round(rahane1$Runs),round(sgill1$Runs))
colnames(batsmen) <- c("Kohli","Rohit","Pujara","Rahane","S Gill")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns

##    BallsFaced MinsAtCrease Kohli Rohit Pujara Rahane S Gill
## 1          10           30     6     3      3      2      7
## 2          38           71    24    19     16     17     24
## 3          66          111    41    35     29     31     40
## 4          94          152    58    51     42     45     56
## 5         121          193    76    66     55     59     73
## 6         149          234    93    82     68     74     89
## 7         177          274   110    98     80     88    106
## 8         205          315   128   114     93    102    122
## 9         233          356   145   129    106    116    139
## 10        261          396   163   145    119    130    155
## 11        289          437   180   161    132    145    171
## 12        316          478   197   177    144    159    188
## 13        344          519   215   192    157    173    204
## 14        372          559   232   208    170    187    221
## 15        400          600   249   224    183    202    237

4. Analysis of India WTC batsmen from Jan 2016 – May 2023 against Australia

4a. Relative cumulative average

Against Australia specifically between 2016 – 2023, Pujara has the best record followed by Rahane, with Gill in hot pursuit. Kohli and Rohit trail behind

frames <- list("kohliTestAus.csv","rohitTestAus.csv","pujaraTestAus.csv","rahaneTestAus.csv","shubmanTestAus.csv")
names <- list("Kohli","Rohit","Pujara","Rahane","S Gill")
relativeBatsmanCumulativeAvgRuns(frames,names)

4b. Relative cumulative average strike rate in career

In the Strike Rate department Gill tops followed by Rohit and Rahane

frames <- list("kohliTestAus.csv","rohitTestAus.csv","pujaraTestAus.csv","rahaneTestAus.csv","shubmanTestAus.csv")
names <- list("Kohli","Rohit","Pujara","Rahane","S Gill")
relativeBatsmanCumulativeStrikeRate(frames,names)

5. Analysis of Australia WTC batsmen from Jan 2016 – May 2023

5a Basic analyses

par(mfrow=c(3,3))
par(mar=c(4,4,2,2))
batsmanRunsFreqPerf("stevesmithTest.csv","S Smith")
batsmanMeanStrikeRate("stevesmithTest.csv","S Smith")
batsmanRunsRanges("stevesmithTest.csv","S Smith")

batsmanRunsFreqPerf("warnerTest.csv","Warner")
batsmanMeanStrikeRate("warnerTest.csv","Warner")
batsmanRunsRanges("warnerTest.csv","Warner")

batsmanRunsFreqPerf("labuschagneTest.csv","M Labuschagne")
batsmanMeanStrikeRate("labuschagneTest.csv","M Labuschagne")
batsmanRunsRanges("labuschagneTest.csv","M Labuschagne")

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))
batsmanRunsFreqPerf("cgreenTest.csv","C Green")
batsmanMeanStrikeRate("cgreenTest.csv","C Green")
batsmanRunsRanges("cgreenTest.csv","C Green")

batsmanRunsFreqPerf("khwajaTest.csv","Khwaja")
batsmanMeanStrikeRate("khwajaTest.csv","Khwaja")
batsmanRunsRanges("khwajaTest.csv","Khwaja")

5b. More analyses

par(mfrow=c(3,3))
par(mar=c(4,4,2,2))
batsman4s("stevesmithTest.csv","S Smith")
batsman6s("stevesmithTest.csv","S Smith")
batsmanMeanStrikeRate("stevesmithTest.csv","S Smith")

batsman4s("warnerTest.csv","Warner")
batsman6s("warnerTest.csv","Warner")
batsmanMeanStrikeRate("warnerTest.csv","Warner")

batsman4s("labuschagneTest.csv","M Labuschagne")
batsman6s("labuschagneTest.csv","M Labuschagne")
batsmanMeanStrikeRate("labuschagneTest.csv","M Labuschagne")

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))
batsman4s("cgreenTest.csv","C Green")
batsman6s("cgreenTest.csv","C Green")
batsmanMeanStrikeRate("cgreenTest.csv","C Green")

batsman4s("khwajaTest.csv","Khwaja")
batsman6s("khwajaTest.csv","Khwaja")
batsmanMeanStrikeRate("khwajaTest.csv","Khwaja")

5c.Boxplot histogram plot

This plot shows a combined boxplot of the Runs ranges and a histog2ram of the Runs Frequency

Smith, Labuschagne has an average of 53+ since 2016!! Warner & Khwaja are at ~46

batsmanPerfBoxHist("stevesmithTest.csv","S Smith")

batsmanPerfBoxHist("warnerTest.csv","Warner")

batsmanPerfBoxHist("labuschagneTest.csv","M Labuschagne")

batsmanPerfBoxHist("cgreenTest.csv","C Green")

batsmanPerfBoxHist("khwajaTest.csv","Khwaja")

5d. Contribution to won and lost matches

For the 2 functions below you will have to use the getPlayerDataSp() function. Australia has won matches when Smith, Warner and Khwaja have played well.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanContributionWonLost("stevesmithsp.csv","S Smith")
batsmanContributionWonLost("warnersp.csv","Warner")
batsmanContributionWonLost("labuschagnesp.csv","M Labuschagne")
batsmanContributionWonLost("cgreensp.csv","C Green")

batsmanContributionWonLost("khwajasp.csv","Khwaja")

5e. Performance at home and overseas

This function also requires the use of getPlayerDataSp() as shown above. This can only be used for Test matches

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanPerfHomeAway("stevesmithsp.csv","S Smith")
batsmanPerfHomeAway("warnersp.csv","Warner")
batsmanPerfHomeAway("labuschagnesp.csv","M Labuschagne")
batsmanPerfHomeAway("cgreensp.csv","C Green")

batsmanPerfHomeAway("khwajasp.csv","Khwaja")

5f. Batsman average at different venues

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanAvgRunsGround("stevesmithTest.csv","S Smith")
batsmanAvgRunsGround("warnerTest.csv","Warner")
batsmanAvgRunsGround("labuschagneTest.csv","M Labuschagne")
batsmanAvgRunsGround("cgreenTest.csv","C Green")

batsmanAvgRunsGround("khwajaTest.csv","Khwaja")

5g. Batsman average against different opposition

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanAvgRunsOpposition("stevesmithTest.csv","S Smith")
batsmanAvgRunsOpposition("warnerTest.csv","Warner")
batsmanAvgRunsOpposition("labuschagneTest.csv","M Labuschagne")
batsmanAvgRunsOpposition("khwajaTest.csv","Khwaja")

5h. Runs Likelihood of batsman

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanRunsLikelihood("stevesmithTest.csv","S Smith")

## Summary of  S Smith 's runs scoring likelihood
## **************************************************
## 
## There is a 58.76 % likelihood that S Smith  will make  21 Runs in  38 balls over 56  Minutes 
## There is a 24.74 % likelihood that S Smith  will make  70 Runs in  148 balls over  210  Minutes 
## There is a 16.49 % likelihood that S Smith  will make  148 Runs in  268 balls over 398  Minutes

batsmanRunsLikelihood("warnerTest.csv","Warner")

## Summary of  Warner 's runs scoring likelihood
## **************************************************
## 
## There is a 7.22 % likelihood that Warner  will make  155 Runs in  253 balls over 372  Minutes 
## There is a 62.89 % likelihood that Warner  will make  14 Runs in  21 balls over  32  Minutes 
## There is a 29.9 % likelihood that Warner  will make  65 Runs in  94 balls over 135  Minutes

batsmanRunsLikelihood("labuschagneTest.csv","M Labuschagne")

## Summary of  M Labuschagne 's runs scoring likelihood
## **************************************************
## 
## There is a 32.76 % likelihood that M Labuschagne  will make  74 Runs in  144 balls over 206  Minutes 
## There is a 55.17 % likelihood that M Labuschagne  will make  22 Runs in  37 balls over  54  Minutes 
## There is a 12.07 % likelihood that M Labuschagne  will make  168 Runs in  297 balls over 420  Minutes

batsmanRunsLikelihood("khwajaTest.csv","Khwaja")

## Summary of  Khwaja 's runs scoring likelihood
## **************************************************
## 
## There is a 64.94 % likelihood that Khwaja  will make  14 Runs in  29 balls over 42  Minutes 
## There is a 27.27 % likelihood that Khwaja  will make  79 Runs in  148 balls over  210  Minutes 
## There is a 7.79 % likelihood that Khwaja  will make  165 Runs in  351 balls over 515  Minutes

5i. Moving average of batsman

Smith and Warner’s moving average has been on a downward trend lately. Khwaja is playing well

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanMovingAverage("stevesmith.csv","S Smith")
batsmanMovingAverage("warner.csv","Warner")
batsmanMovingAverage("labuschagne.csv","M Labuschagne")
batsmanMovingAverage("khwaja.csv","Khwaja")

5j. Cumulative Average runs of batsman in career

Labuschagne, SMith and Warner havwe very good cumulative average

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanCumulativeAverageRuns("stevesmithTest.csv","S Smith")

batsmanCumulativeAverageRuns("warnerTest.csv","Warner")

batsmanCumulativeAverageRuns("labuschagneTest.csv","M Labuschagne")

batsmanCumulativeAverageRuns("khwajaTest.csv","Khwaja")

5k. Cumulative Average strike rate of batsman in career

Warner towers over the others in the cumulative strike rate, followed by Labuschagne and Smith

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanCumulativeStrikeRate("stevesmithTest.csv","S Smith")

batsmanCumulativeStrikeRate("warnerTest.csv","Warner")

batsmanCumulativeStrikeRate("labuschagneTest.csv","M Labuschagne")

batsmanCumulativeStrikeRate("khwajaTest.csv","Khwaja")

5l. Future Runs forecast

Here are plots that forecast how the batsman will perform in future. In this case 90% of the career runs trend is uses as the training set. the remaining 10% is the test set.

Take a look at the runs forecasted for the batsman below.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

batsmanPerfForecast("stevesmithTest.csv","S Smith")
batsmanPerfForecast("warnerTest.csv","Warner")
batsmanPerfForecast("labuschagneTest.csv","M Labuschagne")
batsmanPerfForecast("khwajaTest.csv","Khwaja")

5m. Relative Mean Strike Rate plot

The plot below compares the Mean Strike Rate of the batsman for each of the runs ranges of 10 and plots them. The plot indicate the following

frames <- list("stevesmithTest.csv","warnerTest.csv","khwajaTest.csv","labuschagneTest.csv","cgreenTest.csv")
names <- list("S Smith","Warner","Khwaja","Labuschagne","C Green")
relativeBatsmanSR(frames,names)

5n. Relative Runs Frequency plot

The plot below gives the relative Runs Frequency Percetages for each 10 run bucket. The plot below show

frames <- list("stevesmithTest.csv","warnerTest.csv","khwajaTest.csv","labuschagneTest.csv","cgreenTest.csv")
names <- list("S Smith","Warner","Khwaja","Labuschagne","C Green")
relativeRunsFreqPerf(frames,names)

5o. Relative cumulative average runs in career

frames <- list("stevesmithTest.csv","warnerTest.csv","khwajaTest.csv","labuschagneTest.csv","cgreenTest.csv")
names <- list("S Smith","Warner","Khwaja","Labuschagne","C Green")
relativeBatsmanCumulativeAvgRuns(frames,names)

5p. Relative cumulative average strike rate in career

frames <- list("stevesmithTest.csv","warnerTest.csv","khwajaTest.csv","labuschagneTest.csv","cgreenTest.csv")
names <- list("S Smith","Warner","Khwaja","Labuschagne","C Green")
relativeBatsmanCumulativeStrikeRate(frames,names)

5q. Check Batsman In-Form or Out-of-Form

A significance value of 0.05 is chosen and p-value us computed If p-value >= .05 – Batsman In-Form If p-value < 0.05 – Batsman Out-of-Form

This is done for the Top 4 batsman

checkBatsmanInForm("stevesmith.csv","S Smith")

## [1] "**************************** Form status of S Smith ****************************\n\n Population size: 144  Mean of population: 53.76 \n Sample size: 17  Mean of sample: 45.65 SD of sample: 56.4 \n\n Null hypothesis H0 : S Smith 's sample average is within 95% confidence interval of population average\n Alternative hypothesis Ha : S Smith 's sample average is below the 95% confidence interval of population average\n\n S Smith 's Form Status: In-Form because the p value: 0.280533  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBatsmanInForm("warner.csv","Warner")

## [1] "**************************** Form status of Warner ****************************\n\n Population size: 164  Mean of population: 45.2 \n Sample size: 19  Mean of sample: 26.63 SD of sample: 44.62 \n\n Null hypothesis H0 : Warner 's sample average is within 95% confidence interval of population average\n Alternative hypothesis Ha : Warner 's sample average is below the 95% confidence interval of population average\n\n Warner 's Form Status: Out-of-Form because the p value: 0.042744  is less than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBatsmanInForm("labuschagne.csv","M Labuschagne")

## [1] "**************************** Form status of M Labuschagne ****************************\n\n Population size: 52  Mean of population: 59.56 \n Sample size: 6  Mean of sample: 29.67 SD of sample: 19.96 \n\n Null hypothesis H0 : M Labuschagne 's sample average is within 95% confidence interval of population average\n Alternative hypothesis Ha : M Labuschagne 's sample average is below the 95% confidence interval of population average\n\n M Labuschagne 's Form Status: Out-of-Form because the p value: 0.005239  is less than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBatsmanInForm("khwaja.csv","Khwaja")

## [1] "**************************** Form status of Khwaja ****************************\n\n Population size: 89  Mean of population: 41.62 \n Sample size: 10  Mean of sample: 53.1 SD of sample: 76.34 \n\n Null hypothesis H0 : Khwaja 's sample average is within 95% confidence interval of population average\n Alternative hypothesis Ha : Khwaja 's sample average is below the 95% confidence interval of population average\n\n Khwaja 's Form Status: In-Form because the p value: 0.677691  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

5r. Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

BF <- seq( 10, 400,length=15)
Mins <- seq(30,600,length=15)
newDF <- data.frame(BF,Mins)
ssmith1 <- batsmanRunsPredict("stevesmith.csv","S Smith",newdataframe=newDF)

warner1 <- batsmanRunsPredict("warner.csv","Warner",newdataframe=newDF)

khwaja1 <- batsmanRunsPredict("khwaja.csv","Khwaja",newdataframe=newDF)
labuschagne1 <- batsmanRunsPredict("labuschagne.csv","Labuschagne",newdataframe=newDF)

cgreen1 <- batsmanRunsPredict("cgreen.csv","C Green",newdataframe=newDF)

batsmen <-cbind(round(ssmith1$Runs),round(warner1$Runs),round(khwaja1$Runs),round(labuschagne1$Runs),round(cgreen1$Runs))
colnames(batsmen) <- c("S Smith","Warner","Khwaja","Labuschagne","C Green")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns

##    BallsFaced MinsAtCrease S Smith Warner Khwaja Labuschagne C Green
## 1          10           30       7     10     10           9      13
## 2          38           71      23     30     24          24      29
## 3          66          111      38     50     38          40      44
## 4          94          152      53     70     53          55      60
## 5         121          193      69     90     67          70      75
## 6         149          234      84    110     81          85      91
## 7         177          274     100    130     95         100     106
## 8         205          315     115    150    109         116     122
## 9         233          356     130    170    123         131     137
## 10        261          396     146    190    137         146     153
## 11        289          437     161    210    151         161     168
## 12        316          478     177    230    165         176     184
## 13        344          519     192    250    179         192     199
## 14        372          559     207    270    193         207     215
## 15        400          600     223    290    207         222     230

6. Analysis of Australia WTC batsmen from Jan 2016 – May 2023 against India

6a. Relative cumulative average runs in career

Labuschagne, Smith and C Green have good records against India

frames <- list("stevesmithTestInd.csv","warnerTestInd.csv","khwajaTestInd.csv","labuschagneTestInd.csv","cgreenTestInd.csv")
names <- list("S Smith","Warner","Khwaja","Labuschagne","C Green")
relativeBatsmanCumulativeAvgRuns(frames,names)

6b. Relative cumulative average strike rate in career

Warner, Labuschagne and Smith have a good strike rate against India

frames <- list("stevesmithTestInd.csv","warnerTestInd.csv","khwajaTestInd.csv","labuschagneTestInd.csv","cgreenTestInd.csv")
names <- list("S Smith","Warner","Khwaja","Labuschagne","C Green")
relativeBatsmanCumulativeStrikeRate(frames,names)

7. Analysis of India WTC bowlers from Jan 2016 – May 2023

7a Wickets frequency chart

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))
bowlerWktsFreqPercent("shamiTest.csv","Shami")
bowlerWktsFreqPercent("sirajTest.csv","Siraj")
bowlerWktsFreqPercent("ashwinTest.csv","Ashwin")
bowlerWktsFreqPercent("jadejaTest.csv","Jadeja")
bowlerWktsFreqPercent("shardulTest.csv","Shardul")

7b Wickets Runs chart

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))

bowlerWktsRunsPlot("shamiTest.csv","Shami")
bowlerWktsRunsPlot("sirajTest.csv","Siraj")
bowlerWktsRunsPlot("ashwinTest.csv","Ashwin")
bowlerWktsRunsPlot("jadejaTest.csv","Jadeja")
bowlerWktsRunsPlot("shardulTest.csv","Shardul")

7c. Average wickets at different venues

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))

bowlerAvgWktsGround("shamiTest.csv","Shami")
bowlerAvgWktsGround("sirajTest.csv","Siraj")
bowlerAvgWktsGround("ashwinTest.csv","Ashwin")
bowlerAvgWktsGround("jadejaTest.csv","Jadeja")
bowlerAvgWktsGround("shardulTest.csv","Shardul")

7d Average wickets against different opposition

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))

bowlerAvgWktsOpposition("shamiTest.csv","Shami")
bowlerAvgWktsOpposition("sirajTest.csv","Siraj")
bowlerAvgWktsOpposition("ashwinTest.csv","Ashwin")
bowlerAvgWktsOpposition("jadejaTest.csv","Jadeja")
bowlerAvgWktsOpposition("shardulTest.csv","Shardul")

7e Cumulative average wickets taken

Ashwin’s performance has dropped over the years, while Siraj has been becoming better

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

bowlerCumulativeAvgWickets("shamiTest.csv","Shami")

bowlerCumulativeAvgWickets("sirajTest.csv","Siraj")

bowlerCumulativeAvgWickets("ashwinTest.csv","Ashwin")

bowlerCumulativeAvgWickets("jadejaTest.csv","Jadeja")

bowlerCumulativeAvgWickets("shardulTest.csv","Shardul")

7g Cumulative average economy rate

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))

bowlerCumulativeAvgEconRate("shamiTest.csv","Shami")

bowlerCumulativeAvgEconRate("sirajTest.csv","Siraj")

bowlerCumulativeAvgEconRate("ashwinTest.csv","Ashwin")

bowlerCumulativeAvgEconRate("jadejaTest.csv","Jadeja")

bowlerCumulativeAvgEconRate("shardulTest.csv","Shardul")

7h Wicket forecast

Here are plots that forecast how the bowler will perform in future. In this case 90% of the career wickets trend is used as the training set. the remaining 10% is the test set.

A Holt-Winters forecasting model is used to forecast future performance based on the 90% training set. The forecasted wickets trend is plotted. The test set is also plotted to see how close the forecast and the actual matches

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))

bowlerPerfForecast("shamiTest.csv","Shami")
#bowlerPerfForecast("sirajTest.csv","Siraj")
bowlerPerfForecast("ashwinTest.csv","Ashwin")
bowlerPerfForecast("jadejaTest.csv","Jadeja")
bowlerPerfForecast("shardulTest.csv","Shardul")

7i Relative Wickets Frequency Percentage

frames <- list("shamiTest.csv","sirajTest.csv","ashwinTest.csv","jadejaTest.csv","shardulTest.csv")
names <- list("Shami","Siraj","Ashwin","Jadeja","Shardul")
relativeBowlingPerf(frames,names)

7j Relative Economy Rate against wickets taken

frames <- list("shamiTest.csv","sirajTest.csv","ashwinTest.csv","jadejaTest.csv","shardulTest.csv")
names <- list("Shami","Siraj","Ashwin","Jadeja","Shardul")
relativeBowlingER(frames,names)

7k Relative cumulative average wickets of bowlers in career

Ashwin has the highest wickets followed by Jadeja against all teams

frames <- list("shamiTest.csv","sirajTest.csv","ashwinTest.csv","jadejaTest.csv","shardulTest.csv")
names <- list("Shami","Siraj","Ashwin","Jadeja","Shardul")
relativeBowlerCumulativeAvgWickets(frames,names)

7l Relative cumulative average economy rate of bowlers

Jadeja has the best economy rate followed by Ashwin

frames <- list("shamiTest.csv","sirajTest.csv","ashwinTest.csv","jadejaTest.csv","shardulTest.csv")
names <- list("Shami","Siraj","Ashwin","Jadeja","Shardul")
relativeBowlerCumulativeAvgEconRate(frames,names)

7m Check for bowler in-form/out-of-form

The below computation uses Null Hypothesis testing and p-value to determine if the bowler is in-form or out-of-form. For this 90% of the career wickets is chosen as the population and the mean computed. The last 10% is chosen to be the sample set and the sample Mean and the sample Standard Deviation are caculated.

The Null Hypothesis (H0) assumes that the bowler continues to stay in-form where the sample mean is within 95% confidence interval of population mean The Alternative (Ha) assumes that the bowler is out of form the sample mean is beyond the 95% confidence interval of the population mean.

A significance value of 0.05 is chosen and p-value us computed If p-value >= .05 – Batsman In-Form If p-value < 0.05 – Batsman Out-of-Form

Note: The check for the form status of the bowlers indicate

checkBowlerInForm("shami.csv","Shami")

## [1] "**************************** Form status of Shami ****************************\n\n Population size: 106  Mean of population: 1.93 \n Sample size: 12  Mean of sample: 1.33 SD of sample: 1.23 \n\n Null hypothesis H0 : Shami 's sample average is within 95% confidence interval \n        of population average\n Alternative hypothesis Ha : Shami 's sample average is below the 95% confidence\n        interval of population average\n\n Shami 's Form Status: In-Form because the p value: 0.058427  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBowlerInForm("siraj.csv","Siraj")

## [1] "**************************** Form status of Siraj ****************************\n\n Population size: 29  Mean of population: 1.59 \n Sample size: 4  Mean of sample: 0.25 SD of sample: 0.5 \n\n Null hypothesis H0 : Siraj 's sample average is within 95% confidence interval \n        of population average\n Alternative hypothesis Ha : Siraj 's sample average is below the 95% confidence\n        interval of population average\n\n Siraj 's Form Status: Out-of-Form because the p value: 0.002923  is less than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBowlerInForm("ashwin.csv","Ashwin")

## [1] "**************************** Form status of Ashwin ****************************\n\n Population size: 154  Mean of population: 2.77 \n Sample size: 18  Mean of sample: 2.44 SD of sample: 1.76 \n\n Null hypothesis H0 : Ashwin 's sample average is within 95% confidence interval \n        of population average\n Alternative hypothesis Ha : Ashwin 's sample average is below the 95% confidence\n        interval of population average\n\n Ashwin 's Form Status: In-Form because the p value: 0.218345  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBowlerInForm("jadeja.csv","Jadeja")

## [1] "**************************** Form status of Jadeja ****************************\n\n Population size: 108  Mean of population: 2.22 \n Sample size: 12  Mean of sample: 1.92 SD of sample: 2.35 \n\n Null hypothesis H0 : Jadeja 's sample average is within 95% confidence interval \n        of population average\n Alternative hypothesis Ha : Jadeja 's sample average is below the 95% confidence\n        interval of population average\n\n Jadeja 's Form Status: In-Form because the p value: 0.333095  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBowlerInForm("shardul.csv","Shardul")

## [1] "**************************** Form status of Shardul ****************************\n\n Population size: 13  Mean of population: 2 \n Sample size: 2  Mean of sample: 0.5 SD of sample: 0.71 \n\n Null hypothesis H0 : Shardul 's sample average is within 95% confidence interval \n        of population average\n Alternative hypothesis Ha : Shardul 's sample average is below the 95% confidence\n        interval of population average\n\n Shardul 's Form Status: Out-of-Form because the p value: 0.04807  is less than alpha=  0.05 \n *******************************************************************************************\n\n"

8. Analysis of India WTC bowlers from Jan 2016 – May 2023 against Australia

8a Relative cumulative average wickets of bowlers in career

Against Australia specifically Jadeja has the best record followed by Ashwin

frames <- list("shamiTestAus.csv","sirajTestAus.csv","ashwinTestAus.csv","jadejaTestAus.csv","shardulTestAus.csv")
names <- list("Shami","Siraj","Ashwin","Jadeja","Shardul")
relativeBowlerCumulativeAvgWickets(frames,names)

8b Relative cumulative average economy rate of bowlers

Jadeja has the best economy followed by Siraj, then Ashwin

frames <- list("shamiTestAus.csv","sirajTestAus.csv","ashwinTestAus.csv","jadejaTestAus.csv","shardulTestAus.csv")
names <- list("Shami","Siraj","Ashwin","Jadeja","Shardul")
relativeBowlerCumulativeAvgEconRate(frames,names)

8. Analysis of India WTC bowlers from Jan 2016 – May 2023

8a. Wickets frequency chart

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))
bowlerWktsFreqPercent("cumminsTest.csv","Cummins")
bowlerWktsFreqPercent("starcTest.csv","Starc")
bowlerWktsFreqPercent("hazzlewoodTest.csv","Hazzlewood")
bowlerWktsFreqPercent("todd.csv","Todd")
bowlerWktsFreqPercent("lyonTest.csv","N Lyon")

8b. Wickets frequency chart

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))

bowlerWktsRunsPlot("cumminsTest.csv","Cummins")
bowlerWktsRunsPlot("starcTest.csv","Starc")
bowlerWktsRunsPlot("hazzlewoodTest.csv","Hazzlewood")
bowlerWktsRunsPlot("todd.csv","Todd")
bowlerWktsRunsPlot("lyonTest.csv","N Lyon")

8c. Average wickets at different venues

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))

bowlerAvgWktsGround("cumminsTest.csv","Cummins")
bowlerAvgWktsGround("starcTest.csv","Starc")
bowlerAvgWktsGround("hazzlewoodTest.csv","Hazzlewood")
bowlerAvgWktsGround("todd.csv","Todd")
bowlerAvgWktsGround("lyonTest.csv","N Lyon")

8d Average wickets against different opposition

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))

bowlerAvgWktsOpposition("cumminsTest.csv","Cummins")
bowlerAvgWktsOpposition("starcTest.csv","Starc")
bowlerAvgWktsOpposition("hazzlewoodTest.csv","Hazzlewood")
bowlerAvgWktsOpposition("todd.csv","Todd")
bowlerAvgWktsOpposition("lyonTest.csv","N Lyon")

8e Cumulative average wickets taken

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))

bowlerCumulativeAvgWickets("cumminsTest.csv","Cummins")

bowlerCumulativeAvgWickets("starcTest.csv","Starc")

bowlerCumulativeAvgWickets("hazzlewoodTest.csv","Hazzlewood")

bowlerCumulativeAvgWickets("todd.csv","Todd")

bowlerCumulativeAvgWickets("lyonTest.csv","N Lyon")

8g Cumulative average economy rate

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))

bowlerCumulativeAvgEconRate("cumminsTest.csv","Cummins")

bowlerCumulativeAvgEconRate("starcTest.csv","Starc")

bowlerCumulativeAvgEconRate("hazzlewoodTest.csv","Hazzlewood")

bowlerCumulativeAvgEconRate("todd.csv","Todd")

bowlerCumulativeAvgEconRate("lyonTest.csv","N Lyon")

8f. Future Wickets forecast

Here are plots that forecast how the bowler will perform in future. In this case 90% of the career wickets trend is used as the training set. the remaining 10% is the test set.

A Holt-Winters forecasting model is used to forecast future performance based on the 90% training set. The forecated wickets trend is plotted. The test set is also plotted to see how close the forecast and the actual matches

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))

bowlerPerfForecast("cumminsTest.csv","Cummins")
bowlerPerfForecast("starcTest.csv","Starc")
bowlerPerfForecast("hazzlewoodTest.csv","Hazzlewood")
bowlerPerfForecast("lyonTest.csv","N Lyon")

8i. Relative Wickets Frequency Percentage

frames <- list("cumminsTest.csv","starcTest.csv","hazzlewoodTest.csv","todd.csv","lyonTest.csv")
names <- list("Cummins","Starc","Hazzlewood","Todd","N Lyon")
relativeBowlingPerf(frames,names)

8j Relative Economy Rate against wickets taken

frames <- list("cumminsTest.csv","starcTest.csv","hazzlewoodTest.csv","todd.csv","lyonTest.csv")
names <- list("Cummins","Starc","Hazzlewood","Todd","N Lyon")
relativeBowlingER(frames,names)

8k Relative cumulative average wickets of bowlers in career

Cummins, Starc and Lyons are the best performers

frames <- list("cumminsTest.csv","starcTest.csv","hazzlewoodTest.csv","todd.csv","lyonTest.csv")
names <- list("Cummins","Starc","Hazzlewood","Todd","N Lyon")
relativeBowlerCumulativeAvgWickets(frames,names)

8l Relative cumulative average economy rate of bowlers

Hazzlewood, Cummins have the best economy against all oppostion

frames <- list("cumminsTest.csv","starcTest.csv","hazzlewoodTest.csv","todd.csv","lyonTest.csv")
names <- list("Cummins","Starc","Hazzlewood","Todd","N Lyon")
relativeBowlerCumulativeAvgEconRate(frames,names)

8o Check for bowler in-form/out-of-form

A significance value of 0.05 is chosen and p-value us computed If p-value >= .05 – Batsman In-Form If p-value < 0.05 – Batsman Out-of-Form

Note: The check for the form status of the bowlers indicate

checkBowlerInForm("cummins.csv","Cummins")

## [1] "**************************** Form status of Cummins ****************************\n\n Population size: 81  Mean of population: 2.46 \n Sample size: 9  Mean of sample: 2 SD of sample: 1.5 \n\n Null hypothesis H0 : Cummins 's sample average is within 95% confidence interval \n        of population average\n Alternative hypothesis Ha : Cummins 's sample average is below the 95% confidence\n        interval of population average\n\n Cummins 's Form Status: In-Form because the p value: 0.190785  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBowlerInForm("starc.csv","Starc")

## [1] "**************************** Form status of Starc ****************************\n\n Population size: 126  Mean of population: 2.18 \n Sample size: 15  Mean of sample: 1.67 SD of sample: 1.18 \n\n Null hypothesis H0 : Starc 's sample average is within 95% confidence interval \n        of population average\n Alternative hypothesis Ha : Starc 's sample average is below the 95% confidence\n        interval of population average\n\n Starc 's Form Status: In-Form because the p value: 0.057433  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBowlerInForm("hazzlewood.csv","Hazzlewood")

## [1] "**************************** Form status of Hazzlewood ****************************\n\n Population size: 99  Mean of population: 2.04 \n Sample size: 12  Mean of sample: 1.67 SD of sample: 1.5 \n\n Null hypothesis H0 : Hazzlewood 's sample average is within 95% confidence interval \n        of population average\n Alternative hypothesis Ha : Hazzlewood 's sample average is below the 95% confidence\n        interval of population average\n\n Hazzlewood 's Form Status: In-Form because the p value: 0.204787  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

checkBowlerInForm("lyon.csv","N Lyon")

## [1] "**************************** Form status of N Lyon ****************************\n\n Population size: 193  Mean of population: 2.08 \n Sample size: 22  Mean of sample: 2.95 SD of sample: 1.96 \n\n Null hypothesis H0 : N Lyon 's sample average is within 95% confidence interval \n        of population average\n Alternative hypothesis Ha : N Lyon 's sample average is below the 95% confidence\n        interval of population average\n\n N Lyon 's Form Status: In-Form because the p value: 0.975407  is greater than alpha=  0.05 \n *******************************************************************************************\n\n"

9. Analysis of Australia WTC bowlers from Jan 2016 – May 2023 against India

9a Relative cumulative average wickets of bowlers in career

Against India Lyon, Cummins and Hazzlewood have performed well

frames <- list("cumminsTestInd.csv","starcTestInd.csv","hazzlewoodTestInd.csv","lyonTestInd.csv")
names <- list("Cummins","Starc","Hazzlewood","N Lyon")
relativeBowlerCumulativeAvgWickets(frames,names)

9b Relative cumulative average economy rate of bowlers

Hazzlewood, Lyon have a good economy rate against India

frames <- list("cumminsTestInd.csv","starcTestInd.csv","hazzlewoodTestInd.csv","lyonTestInd.csv")
names <- list("Cummins","Starc","Hazzlewood","N Lyon")
relativeBowlerCumulativeAvgEconRate(frames,names)

10 Analysis of teams – India, Australia

#The data for India & Australia teams were obtained with the following calls

#indiaTest <-getTeamDataHomeAway(dir=".",teamView="bat",matchType="Test",file="indiaTest.csv",save=TRUE,teamName="India")
#australiaTest <- getTeamDataHomeAway(matchType="Test",file="australiaTest.csv",save=TRUE,teamName="Australia")

10a. Win-loss of India against all oppositions in Test cricket

Against Australia India has won 17 times, lost 60 and drawn 22 in Australia. At home India won 42, tied 2, lost 28 and drawn 24

teamWinLossStatusVsOpposition("indiaTest.csv",teamName="India",opposition=c("all"),homeOrAway=c("all"),matchType="Test",plot=TRUE)

10b. Win-loss of Australia against all oppositions in Test cricket

teamWinLossStatusVsOpposition("australiaTest.csv",teamName="Australia",opposition=c("all"),homeOrAway=c("all"),matchType="Test",plot=TRUE)

10c. Win-loss of India against Australia in Test cricket

Against Australia India has won 17 times, lost 60 and drawn 22 in Australia. At home India won 42, tied 2, lost 28 and drawn 24

teamWinLossStatusVsOpposition("indiaTest.csv",teamName="India",opposition=c("Australia"),homeOrAway=c("all"),matchType="Test",plot=TRUE)

10d. Win-loss of India at all away venues

At the Oval where WTC is going to be held India has won 4, lost 10 and drawn 10.

teamWinLossStatusAtGrounds("indiaTest.csv",teamName="India",opposition=c("all"),homeOrAway=c("away"),matchType="Test",plot=TRUE)

10d. Timeline of win-loss of India against Australia in Test cricket

plotTimelineofWinsLosses("indiaTest.csv",team="India",opposition=c("Australia"),
                         homeOrAway=c("away","neutral"), startDate="2016-01-01",endDate="2023-05-01")

11. Conclusion

The above analysis performs various analysis of India and Australia in home and away matches. While we know the performance of the player at India or Australia, we cannot judge how the match will progress in the neutral, swinging conditions of the Oval. Let us hope for a good match!

Feel free to try out your own analysis with cricketr. Have fun with cricketr!!

Also see

To see all posts click Index of posts

Big Data 7: yorkr waltzes with Apache NiFi

In this post, I construct an end-to-end Apache NiFi pipeline with my R package yorkr. This post is a mirror of my earlier post Big Data-5: kNiFing through cricket data with yorkpy based on my Python package yorkpy. The Apache NiFi Data Pipeilne flows all the way from the source, where the data is obtained, all the way to target analytics output. Apache NiFi was created to automate the flow of data between systems. NiFi dataflows enable the automated and managed flow of information between systems. This post automates the flow of data from Cricsheet, from where the zip file it is downloaded, unpacked, processed, transformed and finally T20 players are ranked.

This post uses the functions of my R package yorkr to rank IPL players. This is a example flow, of a typical Big Data pipeline where the data is ingested from many diverse source systems, transformed and then finally insights are generated. While I execute this NiFi example with my R package yorkr, in a typical Big Data pipeline where the data is huge, of the order of 100s of GB, we would be using the Hadoop ecosystem with Hive, HDFS Spark and so on. Since the data is taken from Cricsheet, which are few Megabytes, this approach would suffice. However if we hypothetically assume that there are several batches of cricket data that are being uploaded to the source, of different cricket matches happening all over the world, and the historical data exceeds several GBs, then we could use a similar Apache NiFi pattern to process the data and generate insights. If the data is was large and distributed across the Hadoop cluster , then we would need to use SparkR or SparklyR to process the data.

This is shown below pictorially

While this post displays the ranks of IPL batsmen, it is possible to create a cool dashboard using UI/UX technologies like AngularJS/ReactJS. Take a look at my post Big Data 6: The T20 Dance of Apache NiFi and yorkpy where I create a simple dashboard of multiple analytics

My R package yorkr can handle both men’s and women’s ODI, and all formats of T20 in Cricsheet namely Intl. T20 (men’s, women’s), IPL, BBL, Natwest T20, PSL, Women’s BBL etc. To know more details about yorkr see Revitalizing R package yorkr

The code can be forked from Github at yorkrWithApacheNiFi

You can take a look at the live demo of the NiFi pipeline at yorkr waltzes with Apache NiFi

Basic Flow

1. Overall flow

The overall NiFi flow contains 2 Process Groups a) DownloadAnd Unpack. b) Convert and Rank IPL batsmen. While it appears that the Process Groups are disconnected, they are not. The first process group downloads the T20 zip file, unpacks the. zip file and saves the YAML files in a specific folder. The second process group monitors this folder and starts processing as soon the YAML files are available. It processes the YAML converting it into dataframes before storing it as CSV file. The next processor then does the actual ranking of the batsmen before writing the output into IPLrank.txt

1.1 DownloadAndUnpack Process Group

This process group is shown below

1.1.1 GetT20Data

The GetT20Data Processor downloads the zip file given the URL

The ${T20data} variable points to the specific T20 format that needs to be downloaded. I have set this to https://cricsheet.org/downloads/ipl.zip. This could be set any other data set. In fact we could have parallel data flows for different T20/ Sports data sets and generate

1.1.2 SaveUnpackedData

This processor stores the YAML files in a predetermined folder, so that the data can be picked up by the 2nd Process Group for processing

1.2 ProcessAndRankT20Players Process Group

This is the second process group which converts the YAML files to pandas dataframes before storing them as. CSV files. The RankIPLPlayers will then read all the CSV files, stack them and then proceed to rank the IPL players. The Process Group is shown below

1.2.1 ListFile and FetchFile Processors

The left 2 Processors ListFile and FetchFile get all the YAML files from the folder and pass it to the next processor

1.2.2 convertYaml2DataFrame Processor

The convertYaml2DataFrame Processor uses the ExecuteStreamCommand which call Rscript. The Rscript invoked the yorkr function convertYaml2DataframeT20() as shown below

I also use a 16 concurrent tasks to convert 16 different flowfiles at once

library(yorkr)
args<-commandArgs(TRUE)
convertYaml2RDataframeT20(args[1], args[2], args[3])

1.2.3 MergeContent Processor

This processor’s only job is to trigger the rankIPLPlayers when all the FlowFiles have merged into 1 file.

1.2.4 RankT20Players

This processor is an ExecuteStreamCommand Processor that executes a Rscript which invokes a yorrkr function rankIPLT20Batsmen()

library(yorkr)
args<-commandArgs(TRUE)

rankIPLBatsmen(args[1],args[2],args[3])

1.2.5 OutputRankofT20Player Processor

This processor writes the generated rank to an output file.

1.3 Final Ranking of IPL T20 players

The Nodejs based web server picks up this file and displays on the web page the final ranks (the code is based on a good youtube for reading from file)

[1] "Chennai Super Kings"
[1] "Deccan Chargers"
[1] "Delhi Daredevils"
[1] "Kings XI Punjab"
[1] "Kochi Tuskers Kerala"
[1] "Kolkata Knight Riders"
[1] "Mumbai Indians"
[1] "Pune Warriors"
[1] "Rajasthan Royals"
[1] "Royal Challengers Bangalore"
[1] "Sunrisers Hyderabad"
[1] "Gujarat Lions"
[1] "Rising Pune Supergiants"
[1] "Chennai Super Kings-BattingDetails.RData"
[1] "Deccan Chargers-BattingDetails.RData"
[1] "Delhi Daredevils-BattingDetails.RData"
[1] "Kings XI Punjab-BattingDetails.RData"
[1] "Kochi Tuskers Kerala-BattingDetails.RData"
[1] "Kolkata Knight Riders-BattingDetails.RData"
[1] "Mumbai Indians-BattingDetails.RData"
[1] "Pune Warriors-BattingDetails.RData"
[1] "Rajasthan Royals-BattingDetails.RData"
[1] "Royal Challengers Bangalore-BattingDetails.RData"
[1] "Sunrisers Hyderabad-BattingDetails.RData"
[1] "Gujarat Lions-BattingDetails.RData"
[1] "Rising Pune Supergiants-BattingDetails.RData"
# A tibble: 429 x 4
   batsman     matches meanRuns meanSR
   <chr>         <int>    <dbl>  <dbl>
 1 DA Warner       130     37.9   128.
 2 LMP Simmons      29     37.2   106.
 3 CH Gayle        125     36.2   134.
 4 HM Amla          16     36.1   108.
 5 ML Hayden        30     35.9   129.
 6 SE Marsh         67     35.9   120.
 7 RR Pant          39     35.3   135.
 8 MEK Hussey       59     33.8   105.
 9 KL Rahul         59     33.5   128.
10 MN van Wyk        5     33.4   112.
# â€¦ with 419 more rows

Conclusion

This post demonstrated an end-to-end pipeline with Apache NiFi and R package yorkr. You can this pipeline and generated different analytics using the various functions of yorkr and display them on a dashboard.

Hope you enjoyed with post!

To see posts click Index of posts

Technology Trends – 2011 and beyond

There are lots of exciting things happening in the technological landscape. Innovation and development in every age is dependent on a set of key driving factors namely – the need for better, faster and cheaper, the need to handle disruptive technologies, the need to keep costs down and the need to absorb path breaking innovations. Given all these factors and the current trends in the industry the following technologies will enter mainstream in the years to come.

Long Term Evolution (LTE): LTE, also known as 4G technologies, has been born out of the disruptive entry of data hungry smart phones and tablet PCs. Besides, the need for better and faster applications has been the key driver of this technology. LTE is a data only technology that allows mobile users to access the internet on the move. LTE uses OFDM technology for sending and receiving data from user devices and also uses MIMO (multiple-in, multiple out). LTE is more economical, and spectrally efficient when compared to earlier 3.5G technologies like HSDPA, HSUPA and HSPA. LTE promises a better Quality of Experience (QoE) for end users.

IP Multimedia Systems IMS): IMS has been around for a while. However with the many advances in IP technology and the transport of media the time is now ripe for this technology to take wings and soar high. IMS uses the ubiquitous internet protocol for its core network both for media transport and for SIP signaling. Many innovative applications are possible with IMS including high definition video conferencing, multi-player interactive games, white boarding etc.

All senior management personnel of organizations are constantly faced with the need to keep costs down. The next two technologies hold a lot of promise in reducing costs for organizations and will surely play a key role in the years to come.

Cloud Computing: Cloud Computing obviates the need for upfront capital and infrastructure costs of organizations. Enterprises can deploy their applications on a public cloud which provides virtually infinite computing capacity in the hands of organizations. Organizations only pay as much as they use akin to utilities like electricity or water

Analytics: These days’ organizations are faced with a virtual deluge of data from their day to day operations. Whether the organizations belong to retail, health, finance, telecom, or transportation there is a lot of data that is generated. Data by itself is useless. This is where data analytics plays an important role. Predictive analytics help in classifying data, determining key trends and identifying correlations between data. This helps organizations in making strategic business decisions.

The following two technologies listed below are really path breaking and their applications are limitless.

Internet of Things: This technology envisages either passive or intelligent devices connected to the internet with a database at the back end for processing the data collected from these intelligent devices. This is also known as M2M (machine to machine) technology. The applications range from monitoring the structural integrity of bridges to implantable devices monitoring fatal heart diseases of patients.

Semantic Web (Web 3.0): This is the next stage in the evolution of the World Wide Web. The Web is now a vast repository of ideas, thoughts, blogs, observations etc. This technology envisages intelligent agents that can analyze the information in the web. These agents will determine the relations between information and make intelligent inferences. This technology will have to use artificial intelligence techniques, data mining and cloud computing to plumb the depths of the web

Conclusion: Creativity and innovation has been the hallmark of mankind from time immemorial. With the demand for smarter, cheaper and better the above technologies are bound to endure in the years to come.

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The Future of Telecom

Published in Voice & Data – Bright Future

Introduction: The close of the 20^th century will long be remembered for one thing. The dotcom bust followed by the downward spiral of many major telecom and technology companies. For those who believe in the theory of the 12 year economic cycle this downturn is right about to end and we should see good times soon. Even otherwise there is good news for those in the telecom domain. We could shortly be witness to golden years ahead. There are many signs that seem to indicate that the telecom industry is on the verge of many major breakthroughs. Technologies like LTE, IMS, smartphones, cloud computing point to interesting times ahead. In fact telecom is at a inflexion point when the fortunes seem to be pointed northward. This article looks at some of the promising technologies which are going to bring back the sunshine to telecom.

3G Technologies –Better Quality of Experience (QoE): The auction of the 3G spectrum ended after 131 days of hectic bidding for this cutting edge telecom technology. 3G promises a whole new customer experience backed by extremely high data speeds. 3G promises download speed of up to 2 Mbps for stationary subscribers and 384 Kbps for moving subscribers. It is very clear that such high data speeds will inspire a host of new and exciting applications. Applications that span location based services (LBS), m-Commerce and NFC communications will be simply be irresistible to the users. Moreover the ability to watch video clips or live action on mobile TV or on laptops enabled with 3G dongles will have a lot of takers for 3G technology. App stores for 3G are bound to do a roaring business as 3G takes off in India.

Smartphones – The game changers: In the last decade or so in the telecom industry no other invention has had such a disruptive effect in the telecom domain as smartphones. Smartphones like the IPhone, Droid or Nexus One have changed the rules of the game. The impact of smartphone has been so huge that it actually spawned an entire industry of developers who developed applications for smartphones, content developers and app stores. The irresistible appeal of smartphones is the ease of use and the ability to browse the net as though they were using a normal data connection. Users can watch youtube clips, play games or chat on the Smartphone.

IP Multimedia Systems (IMS) – Digital Convergence: IP Multimedia System (IMS) , based on 3GPP’s Release 5 Specification in 2005, has been in the wings for quite some time. The IMS envisions an access agnostic telecommunication architecture that will use an all-IP Core for the transport of medium be it voice, data or video. IMS uses SIP protocol for signaling between network elements and SDP for exchanging media between applications. The IMS architecture promises a whole slew of exciting application ranging from high quality video conference, high speed data access, white boarding or real time interactive gaining. IMS represents a true convergence of the telecom wireless concepts with the data communication protocols. The types of services that are possible with IMS will be only limited by imagination. With the entry of smartphones and tablet PCs, IMS is a technology that is waiting to happen and will soon become prime time

Long Term Evolution (LTE) – Blazing Speeds: Already there are upward of 5 billion mobile devices and a report from Cisco states that the total data navigating the net will exceed ½ a zettabyte (10²¹) by the year 2013. The exponential growth of data and the need to provide even higher Quality of Experience (QoE) led to the development of the LTE. LTE is considered 4G technology. LTE promises speeds anywhere between to 56 Mbps to 100 Mbps to users enabling unheard of speeds and applications. What makes LTE so attractive is that it promises better spectral efficiency and lower cost per bit than 3G networks. The competing technology for LTE is WIMAX which is also considered as 4G. But LTE has a better evolution path from 3G networks as opposed to WiMAX, While LTE is a packet only network there are sound strategies for handling voice traffic with LTE. The standards body 3GPP offers two options for handling voice. The first is the Circuit switched (CS) fallback to 2G/3G network. In this scenario data access will be through the packet network of LTE while voice calls will use legacy 2G/3G voice networks. The other alternative is the switch voice traffic to the IMS network with its all-IP Core. This method is supported by the One Voice initiative of many major telecom companies and accepted by GSMA. This strategy for handling voice through an IMS network is known as VoLTE (Voice over LTE)

Internet of Things- Towards a connected World: “The Internet of Things” visualizes a highly interconnected world made of tiny passive or intelligent devices that connect to large databases and to the internet. This technology promises to transform the network from a dumb-bit pipe to a truly “computing” network. The Internet of Things or M2M (machine-to-machine) envisages an anytime, anywhere, anyone, anything network. The devices in this M2M network will be made up of passive elements, sensors and intelligent devices that communicate with the network. The devices will be capable of sensing, identifying and responding to changes in the immediate environment. Radio Frequency Identification (RFIDs) is one of the early and key enabler of this technology. The uses for this technology range from warning when the structural integrity of bridges is compromised to implantable devices in heart patients warning doctors of possible heart attacks. The impact of the Internet of Things will be far-reaching. There are numerous applications for this technology. In fact, ubiquitous computing or the Internet of Things allows us to distribute processing power and intelligence throughout the network into a kind of ambient intelligence spread across the network. This technology promises to blur the lines between science fiction and reality.

App Stores – The final verdict: The success of App Stores in the last couple of years has been nothing short of phenomenal. It is a complete ecosystem with App Store Developers, App Stores, and the Content Developers and Service Providers. Apps and App stores have changed the rules of the game so completely. No longer is a mobile phone’s snazzy looks enough for it to be a best seller. The mobile should be supported by cool downloadable apps for the user to use. App Stores and apps will play an increasingly important role with apps being developed for smartphones and tablet PCs. There are bound to be several interesting apps spanning technologies like Location Based Service (LBS), mobile Commerce, eTicketing, Near Field Communication

Cloud Computing – Utility computing: Cloud Computing has been around some but is slowly gaining more and more prominence. Cloud computing follows a utility model for computing where the cloud user only pays for the computing power and storage capacity used. Cloud computing not involve any upfront Capacity expenditure (Capex). Users of public clouds like EC2, App Engine or Azure can pay according to the usage of the resources provided by the cloud. Cloud technologies allow the CSPs to purchase processing power, platforms, and databases almost like a utility like electricity or water. The cloud exhibits an elastic behavior and expands to accommodate increasing demands and contracts when the demand drops. Cloud computing will be slowly be adopted by more and more organizations and enterprises in the years to come.

Analytics – Mining intelligence from data: Nowadays organizations all over are faced with a deluge of data. For raw data to be useful it has been analyzed, classified and important patterns determined from the data. This is where data mining and analytics come into play. Analytics uses statistical methods to classify data, determine correlations, identify patterns, and highlight and detect key trends among large data sets. Analytics enables industries to plumb the data sets through the process of selecting, exploring and modeling large amount of data to uncover previously unknown data patterns. The insights which analytics provides can be channelized to business advantage. Data mining and predictive analytics unlock the hidden secrets of data and help businesses make strategic decisions. Analytics is bound to become more common and will play a predominant role in all organizations in the years to come.

Internet TV – Hot off the net: If IMS represents the convergence of Telecom and the internet, Internet TV represents the marriage of TV and the internet. Internet TV is a technology whose time has come. Internet TV will bring a whole new user experience by allowing the viewer to be view rich content on his TV in an interactive manner. The technology titans like Apple, Microsoft and Google have their own version of this technology. Internet TV combines TV, the internet and apps for this new technology. Internet TV is bound to become popular with complementary technologies like IMS, LTE allowing for high speed data exchange and the popularity of websites like Youtube etc. Internet TV will receive a further boost from apps of smartphones and tablet PCs

IPv4 exhaustion – Damocles’ sword: While the future holds the promise of many new technologies it is also going throw a lot of attendant challenges. One serious problem that will need serious attention in the not too distant future is the IPv4 address space exhaustion. This problem may be even more serious than the Y2K problem. The issue is that IPv4 can address only 2 ³² or 4.3 billion devices. Already the pool has been exhausted because of new technologies like IMS which uses an all IP Core and the Internet of things with more devices, sensors connected to the internet – each identified by an IP address. The solution to this problem has been addressed long back and requires that the Internet adopt IPv6 addressing scheme. IPv6 uses 128-bit long address and allows 3.4 x 10³⁸ or 340 trillion, trillion, trillion unique addresses. However the conversion to IPv6 is not happening at the required pace and pretty soon will have to be adopted on war footing. It is clear that while the transition takes place, both IPv4 and IPv6 will co-exist so there will be an additional requirement of devices on the internet to be able to convert from one to another

Conclusion:

Technologies like IMS, LTE, and Internet TV have a lot of potential and hold a lot of promise. We as human beings have a constant need for better, faster and cheaper technologies. We can expect a lot of changes to happen in the next couple of years. We may once see rosy times ahead for telecom as a whole

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The rise of analytics

Published in The Hindu – The rise of analytics

We are slowly, but surely, heading towards the age of “information overload”. The Sloan Digital Sky Survey started in the year 2000 returned around 620 terabytes of data in 11 months — more data than had ever been amassed in the entire history of astronomy.

The Large Hadron Collider (LHC) at CERN, Europe’s particle physics laboratory, in Geneva will during its search for the origins of the universe and the elusive Higgs particle, early next year, spew out terabytes of data in its wake. Now there are upward of five billion devices connected to the Internet and the numbers are showing no signs of slowing down.

A recent report from Cisco, the data networking giant, states that the total data navigating the Net will cross 1/2 a zettabyte (10 {+2} {+1}) by the year 2013.

Such astronomical volumes of data are also handled daily by retail giants including Walmart and Target and telcos such as AT&T and Airtel. Also, advances in the Human Genome Project and technologies like the “Internet of Things” are bound to throw up large quantities of data.

The issue of storing data is now slowly becoming non-existent with the plummeting prices of semi-conductor memory and processors coupled with a doubling of their capacity every 18 months with the inevitability predicted by Moore’s law.

Plumbing the depths

Raw data is by itself quite useless. Data has to be classified, winnowed and analysed into useful information before if it can be utilised. This is where analytics and data mining come into play. Analytics, once the exclusive preserve of research labs and academia, has now entered the mainstream. Data mining and analytics are now used across a broad swath of industries — retail, insurance, manufacturing, healthcare and telecommunication. Analytics enables the extraction of intelligence, identification of trends and the ability to highlight the non-obvious from raw, amorphous data. Using the intelligence that is gleaned from predictive analytics, businesses can make strategic game-changing decisions.

Analytics uses statistical methods to classify data, determine correlations, identify patterns, and highlight and detect deviations among large data sets. Analytics includes in its realms complex software algorithms such as decision trees and neural nets to make predictions from existing data sets. For e.g. a retail store would be interested in knowing the buying patterns of its consumers. If the store could determine that product Y is almost always purchased when product X is purchased then the store could come up with clever schemes like an additional discount on product Z when both products X & Y are purchased. Similarly, telcos could use analytics to identify predominant trends that promote customer loyalty.

Studying behaviour

Telcos could come with voice and data plans that attract customers based on consumer behaviour, after analysing data from its point of sale and retail stores. They could use analytics to determine causes for customer churn and come with strategies to prevent it.

Analytics has also been used in the health industry in predicting and preventing fatal infections in infants based on patterns in real-time data like blood pressure, heart rate and respiration.

Analytics requires at its disposal large processing power. Advances in this field have been largely fuelled by similar advances in a companion technology, namely cloud computing. The latter allows computing power to be purchased on demand almost like a utility and has been a key enabler for analytics.

Data mining and analytics allows industries to plumb the data sets that are held in the organisations through the process of selecting, exploring and modelling large amount of data to uncover previously unknown data patterns which can be channelised to business advantage.

Analytics help in unlocking the secrets hidden in data and provide real insights to businesses; and enable businesses and industries to make intelligent and informed choices.

In this age of information deluge, data mining and analytics are bound to play an increasingly important role and will become indispensable to the future of businesses.

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Cloud, analytics key tools for today’s telcos

Published in Telecom Asia Aug 20, 2010 – http://bit.ly/dxKbsR

Operators facing dwindling revenue from wireline subscribers, fierce tariff wars and exploding mobile data traffic are continually being pressured to do more for less. Spending on infrastructure is increasing as they look to provide better service within slender budgets.

In these tough times telcos have to devise new and innovative strategies and make judicious technology choices. Two promising technologies, cloud computing and analytics, are shaping up as among the best choices to make.

Cloud architecture does away with the worry of planning the computing resources needed, the real estate, the costs of the acquiring them and thoughts of its obsolescence. It allows the CSPs to purchase processing power, platforms and databases almost as a utility like electricity or water.

Cloud consumers only pay for what they use. The magic of this promising technology is the elasticity that the cloud provides – it expands to accommodate increasing demands and contracts when the demand drops.

The cloud architectures of Amazon, Google and Microsoft – currently the three biggest cloud providers – vary widely in their capabilities and features. These strengths and weaknesses should be taken into account while planning a cloud system. Each is best suited for only a certain class of applications unique to each individual cloud provider.

On one end of the spectrum Amazon’s EC2 (Elastic Compute Cloud) provides a virtual machine and a wealth of associated tools for storage and notifications. But the trade-off for increased flexibility is that users must take responsibility for designing resiliency into their systems.

On the other end is Google’s App Engine, a highly scalable cloud architecture that handles failures but is a lot more restrictive. Microsoft’s Azure is based on the .NET architecture and in terms of flexibility and features lies between these two.

When implementing such architecture, an organization should take a long hard look its computing software inventory to decide which applications are worthy of migrating to the cloud. The best candidates are processing intensive in-house applications that deliver standardized functionality and interface, and whose software architecture is made up of loosely coupled communicating systems.

Applications that deal with sensitive data should be retained within the organization’s internal computing infrastructure, because security is currently the most glaring issue with the cloud. Cloud providers do provide various levels of security to users, but this is an area in keen need of standardization.

But if the CSP decides to build components of an OSS system – rather than buying a pre-packaged system – it makes good business sense to develop for the cloud.

A cloud-based application must have a few essential properties. First, it is preferable if the application was designed on SOA principles. Second, it should be loosely coupled. And lastly, it needs to be an application that can be scaled rapidly up or down based on the varying demands.

The other question is which legacy systems can be migrated. If the OSS/BSS systems are based on commercial off-the-shelf systems these can be excluded, but an offline bill processing system, for example, is typically a good candidate for migration.

Mining wisdom from data

The cloud can serve as the perfect companion for another increasingly vital operational practice – data analytics. The cloud is capable of modeling large amounts of data, and running models to process and analyze this data. It is possible to run thousands of simultaneous instances on the cloud and mine for business intelligence in the oceans of telecom data operators generate.

Today’s CSP maintains software systems generating all kinds of customer data, covering areas ranging from billing and order management to POS, VAS and provisioning. But perhaps the largest and richest vein of subscriber information is the call detail records database.

All this data is worthless, though, if it cannot be mined and analyzed. Formal data mining and data analytics tools can be used to identify patterns and trends that will allow operators to make strategic, knowledge-driven decisions.

Analytics involves many complex areas like predictive analytics, neural nets, decision trees and classification. Some of the approaches used in data analytics include prediction, deviation detection, degree of influence and classification.

With the intelligence that comes through analytics it is possible to determine customer buying patterns, identify causes for churn and develop strategies to promote loyalty. Call patterns based on demography or time of day will enable the CSPs to create innovative tariff schemes.

Determining the relations and buying patterns of users will provide opportunities for up-selling and cross-selling. The ability to identify marked deviation in customer behavior patterns help the CSP in deciding ahead of time whether this trend is a warning bell or an opportunity waiting to be tapped.

Tinniam V Ganesh

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