Full many a gem of purest ray serene,The dark unfathomed caves of ocean bear;Full many a flower is born to blush unseen,And waste its sweetness on the desert air.
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It is finally here! cricpy, the python avatar , of my R package cricketr is now ready to rock-n-roll! My R package cricketr had its genesis about 3 and some years ago and went through a couple of enhancements. During this time I have always thought about creating an equivalent python package like cricketr. Now I have finally done it.
So here it is. My python package ‘cricpy!!!’
This package uses the statistics info available in ESPN Cricinfo Statsguru. The current version of this package supports only Test cricket
You should be able to install the package using pip install cricpy and use the many functions available in the package. Please mindful of the ESPN Cricinfo Terms of Use
This post is also hosted on Rpubs at Introducing cricpy. You can also download the pdf version of this post at cricpy.pdf
Do check out my post on R package cricketr at Re-introducing cricketr! : An R package to analyze performances of cricketers
If you are passionate about cricket, and love analyzing cricket performances, then check out my 2 racy books on cricket! In my books, I perform detailed yet compact analysis of performances of both batsmen, bowlers besides evaluating team & match performances in Tests , ODIs, T20s & IPL. You can buy my books on cricket from Amazon at $12.99 for the paperback and $4.99/$6.99 respectively for the kindle versions. The books can be accessed at Cricket analytics with cricketr and Beaten by sheer pace-Cricket analytics with yorkr A must read for any cricket lover! Check it out!!
This package uses the statistics info available in ESPN Cricinfo Statsguru. T
9 . Highest Runs Likelihood
The plot below shows the Runs Likelihood for a batsman. For this the performance of Sachin is plotted as a 3D scatter plot with Runs versus Balls Faced + Minutes at crease. K-Means. The centroids of 3 clusters are computed and plotted. In this plot Dravid’s highest tendencies are computed and plotted using K-Means
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10. A look at the Top 4 batsman – Rahul Dravid, Alastair Cook, Brian Lara and Virat Kohli
The following batsmen have been very prolific in test cricket and will be used for teh analyses
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Rahul Dravid :Average:52.31,100’s – 36, 50’s – 63
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Alastair Cook : Average: 45.35, 100’s – 33, 50’s – 57
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Brian Lara : Average: 52.88, 100’s – 34 , 50’s – 48
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Virat Kohli: Average: 54.57 ,100’s – 24 , 50’s – 19
The following plots take a closer at their performances. The box plots show the median the 1st and 3rd quartile of the runs
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