Informed choices through Machine Learning – Analyzing Kohli, Tendulkar and Dravid

Having just completed the highly stimulating & inspiring Stanford’s Machine Learning course at Coursera, by the incomparable Professor Andrew Ng I wanted to give my newly acquired knowledge a try. As a start, I decided to try my hand at  analyzing one of India’s fastest growing stars, namely Virat Kohli . For the data on Virat Kohli I used the ‘Statistics database’ at ESPN Cricinfo. To make matters more interesting,  I also pulled data on the iconic  Sachin Tendulkar and the Mr. Dependable,  Rahul Dravid.

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(Also do check out my R package cricketr  Introducing cricketr! : An R package to analyze performances of cricketers and my interactive Shiny app implementation using my R package cricketr  – Sixer – R package cricketr’s new Shiny avatar )

Based on the data of these batsmen I perform some predictions with the help of machine learning algorithms. That I have a proclivity for prediction, is not surprising, considering the fact that my Dad was an astrologer who had reasonable success at this esoteric art. While he would be concerned with planetary positions, about Rahu in the 7th house being in the malefic etc., I on the other hand focus my  predictions on multivariate regression analysis and K-Means. The first part of my post gives the results of my analysis and some predictions for Kohli, Tendulkar and Dravid.

The second part of the post contains a brief outline of the implementation and not the actual details of implementation. This is ensure that I don’t violate Coursera’s Machine Learning’ Honor Code.

This code, data used and the output obtained  can be accessed at GitHub at ml-cricket-analysis

Analysis and prediction of Kohli, Tendulkar and Dravid with Machine Learning As mentioned above, I pulled the data for the 3 cricketers Virat Kohli, Sachin Tendulkar and Rahul Dravid. The data taken from Cricinfo database for the 3 batsman is based on  the following assumptions

  • Only ‘Minutes at Crease’ and ‘Balls Faced’ were taken as features against the output variable ‘Runs scored’
  • Only test matches were taken. This included both test ‘at home’ and ‘away tests’
  • The data was cleaned to remove any DNB (did not bat) values
  • No extra weightage was given to ‘not out’. So if Kohli made ‘28*’ 28 not out, this was taken to be 28 runs

 Regression Analysis for Virat Kohli There are 51 data points for Virat Kohli regarding Tests played. The data for Kohli is displayed as a 3D scatter plot where x-axis is ‘minutes’ and y-axis is ‘balls faced’. The vertical z-axis is the ‘runs scored’. Multivariate regression analysis was performed to find the best fitting plane for the runs scored based on the selected features of ‘minutes’ and ‘balls faced’.

This is based on minimizing the cost function and then performing gradient descent for 400 iterations to check for convergence. This plane is shown as the 3-D plane that provides the best fit for the data points for Kohli. The diagram below shows the prediction plane of  expected runs for a combination of ‘minutes at crease’ and ‘balls faced’. Here are 2 such plots for Virat Kohli. kohliAnother view of the prediction plane kohli-1 Prediction for Kohli I have also computed the predicted runs that will be scored by Kohli for different combinations of ‘minutes at crease’ and ‘balls faced’. As an example, from the table below, we can see that the predicted runs for Kohli   after being in the crease for 110 minutes  and facing 135 balls is 54 runs. kohli-score Regression analysis for Sachin Tendulkar There was a lot more data on Tendulkar and I was able to dump close to 329 data points. As before the ‘minutes at crease’, ‘balls faced’ vs ‘runs scored’ were plotted as a 3D scatter plot. The prediction plane is calculated using gradient descent and is shown as a plane in the diagram below srt Another view of this below srt-1 Predicted runs for Tendulkar The table below gives the predicted runs for Tendulkar for a combination of  time at crease and balls faced.  Hence,  Tendulkar will score 57 runs in 110 minutes after  facing 135 deliveries srt-score Regression Analysis for Rahul Dravid The same was done for ‘the Wall’ Dravid. The prediction plane is below dravid dravid-1 Predicted runs for Dravid The predicted runs for Dravid for combinations of batting time and balls faced is included below.  The predicted runs for Dravid after facing 135 deliveries in 110 minutes is 44. dravid-scoreFurther analysis While the ‘prediction plane’ was useful,  it somehow does not give a clear picture of how effective each batsman is. Clearly the 3D plots show at least 3 clusters for each batsman. For all batsmen, the clustering is densest near the origin, become less dense towards the middle and sparse on the other end. This is an indication during which session during their innings the batsman is most prone to get out. So I decided to perform K-Means clustering on the data for the 3 batsman. This gives the 3 general tendencies  for each batsman. The output is included below

K-Means for Virat The K-Means for Virat Kohli indicate the follow

Centroids found 255.000000 104.478261 19.900000
Centroids found 194.000000 80.000000 15.650000
Centroids found 103.000000 38.739130 7.000000

Analysis of Virat Kohli’s batting tendency
Kohli has a 45.098 percent tendency to bat for 104 minutes,  face 80 balls and score 38 runs
Kohli has a 39.216 percent tendency to bat for 19 minutes, face 15 balls and score 7 runs
Kohli has a 15.686 percent tendency to bat for 255 minutes, face 194 balls and score 103 runs

The computation of this included in the diagram below


K-means for Sachin Tendulkar

The K-Means for Sachin Tendulkar indicate the following

Centroids found 166.132530 353.092593 43.748691
Centroids found 121.421687 250.666667 30.486911
Centroids found 65.180723 138.740741 15.748691

Analysis of Sachin Tendulkar’s performance

Tendulkar has a 58.232 percent tendency to bat for 43 minutes, face 30 balls and score 15 runs
Tendulkar has a 25.305 percent tendency to bat for 166 minutes, face 121 balls and score 65 runs
Tendulkar has a 16.463 percent tendency to bat for 353 minutes, face 250 balls and score 138 runs
srt-kmeans K-Means for Rahul Dravid

Centroids found 191.836364 409.000000 50.506024
Centroids found 137.381818 290.692308 34.493976
Centroids found 56.945455 131.500000 13.445783

Analysis of Rahul Dravid’s performance
Dravid has a 50.610 percent tendency to bat for 50 minutes,  face 34 balls and score 13 runs
Dravid has a 33.537 percent tendency to bat for 191 minutes,  face 137 balls and score 56 runs
Dravid has a 15.854 percent tendency to bat for 409 minutes, face 290 balls and score 131 runs
dravid-kmeans Some implementation details The entire analysis and coding was done with Octave 3.2.4. I have included the outline of the code for performing the multivariate regression. In essence the pseudo code for this

  1. Read the batsman data (Minutes, balls faced versus Runs scored)
  2. Calculate the cost
  3. Perform Gradient descent

The cost was plotted against the number of iterations to ensure convergence while performing gradient descent convergence-kohli Plot the 3-D plane that best fits the data
The outline of this code, data used and the output obtained  can be accessed at GitHub at ml-cricket-analysis

Conclusion: Comparing the results from the K-Means Tendulkar has around 48% to make a score greater than 60
Tendulkar has a 25.305 percent tendency to bat for 166 minutes, face 121 balls and score 65 runs
Tendulkar has a 16.463 percent tendency to bat for 353 minutes, face 250 balls and score 138 runs

And Dravid has a similar 48% tendency to score greater than 56 runs
Dravid has a 33.537 percent tendency to bat for 191 minutes,  face 137 balls and score 56 runs
Dravid has a 15.854 percent tendency to bat for 409 minutes, face 290 balls and score 131 runs

Kohli has around 45% to score greater than 38 runs
Kohli has a 45.098 percent tendency to bat for 104 minutes,  face 80 balls and score 38 runs

Also Kohli has a lesser percentage to score lower runs as against the other two
Kohli has a 39.216 percent tendency to bat for 19 minutes, face 15 balls and score 7 runs

The results must be looked in proper perspective as Kohli is just starting his career while the other 2 are veterans. Kohli has a long way to go and I am certain that he will blaze a trail of glory in the years to come!

Watch this space!

Also see
1. My book ‘Practical Machine Learning with R and Python’ on Amazon
2.Introducing cricketr! : An R package to analyze performances of cricketers
3.Informed choices with Machine Learning 2 – Pitting together Kumble, Kapil and Chandra
4. Analyzing cricket’s batting legends – Through the mirage with R
5. What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
6. Bend it like Bluemix, MongoDB with autoscaling – Part 1


Simplifying Machine Learning – K- Means clusters – Part 6

Our brain is an extraordinary apparatus. It is amazing how we humans can instantaneously perceive shapes, objects, forms. For e.g. when see a scene with many objects we are immediately able to identify the different objects in the scene.  View this against the backdrop of a recent Google’s artificial brain experiment of a neural network with 16000 processors and a billion connections. This artificial brain was fed with 10 million thumbnails of you tube videos before it was able to recognize cat videos.

That’s an awful lot of work to recognize cat videos!

We can see that a lot of work involved getting a computer to do something as simple thing as this.

Consider how a baby learns to recognize objects for e.g. cat, dog, toy etc. The human brain does not try to measure the number of eyes, spacing between the eyes, the mouth shape of face etc. The brain immediately is able to distinguish the different animals. How does it do it? Amazing right?

In any case here is a machine learning algorithm that is capable of identifying structure in data. This is also known as K-Means and is a form of unsupervised learning algorithm.

The K-Means algorithm takes as input an unlabeled data set and identifies groups in the set. It tries to determine structure in the data set.

Take a look at the picture below


It is readily obvious that there are 2 clusters in the above diagram. However to the computer this is just a random set of points.

How does the K-Means cluster identify the clusters in the above diagram?

The algorithm is fairly simply and intuitive.

1)    Let us start by choosing 2 random points which we call as ‘cluster centroids

2)    We then associate each centroid with the points in the dataset that are closest to it.

3)    We then compute the average of each group of associated points in the centroid and move the centroid to that average.

4)    We then repeat steps 2 – 4 until there is no significant change in the centroid

This is shown below


The above algorithm can be implemented iteratively as follows

For training set (x1, x2, x3 …)

Randomly initialize K cluster centroids μ1, μ2, μ3 … μK


Repeat {

for 1 to m

c(i) = The cluster index from 1 to K that is closest to xi => (A)


for k = 1 to K

u(k) = average of all points assigned to K   => (B)



In step (A) the points xi closest to the centroid k is added to the centroid’s set. Hence if points 1,3,4,8 are in centroid 1 then

x1 = 1, x3 = 1, x4 = 1, x8 =1

In step (B) the mean of the points 1, 3, 4, 8 is taken

So the centroid

c1x = ¼ { x1 + x3 + x4 + x8} and c1y = ¼ { y1 + y3 + y4 + y8}

This becomes the new c1


However there can be occasions where the K-means cluster would get stuck in local optima. To choose optimum cluster centroid we have to determine the least cost. This can be done with the optimization objective.

The optimization objective of K-Means is as follows

K-Mean cluster determination is the problem of minimizing the distance of each point from its centroid. This is also known as the K-Means cost function or distortion function.

J(c1,c2…cm,, μ1,… μ2) = 1/m Σ|| xi – μc(i) ||2

I like to visualize the algorithm as follows.

In step 1 we can visualize that there is a force of attraction between the datapoints and the cluster centroid based on proximity of the centroid.

In step 2 we can visualize that each datapoint attracts the centroid towards it. The centroid moves to the point where the attraction among all the datapoints balances out. This is average mean squared difference.

As can be seen the objective is to determine the average of the mean squared error of each data point to its closest centroid.

Given a set of data points how we choose the random centroids? One way is to initially pick some random data points themselves as the cluster centroid. The algorithm is then iterated to identify the real cluster centroids.

As mentioned before the algorithm can sometimes get stuck in local optima.  One option is to choose another random set of data points and continue to iterate. We need to run this several times to determine the best clustering

There is also the problem of determining the number of cluster centroids. How we to determine how many clusters are would be there in a random data set? Visually we can easily identify the number of clusters. But a machine cannot.

One technique that can be used to determine the number of clusters is as follows. Start with 2, 3… 10 clusters and plot the cost function. Then pick the one with the least cost.

Note: This post, line previous posts on Machine Learning,  is based on the Coursera course on Machine Learning by Professor Andrew Ng

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