Thursday, March 10, 2016
Monday, April 06, 2015
Friday, December 26, 2014
I try to show that through three maps here.
The first one is a map of which party won which constituency (click on the individual constituencies to find out the winner/ runner up /and their respective voteshares). Obviously this is a constituency map prepared from Election Commission Data. (Shoutout to Datameet for helping source this from eci.nic.in)
The second map is simply a map that represents the proportion of people adhering to the dominant religions in the state (saffron represents Hinduism, green represents Islam and mild blue - mostly Buddhism among others), across various tehsils in the state. Data for this map is sourced from Census 2001. (To my knowledge, religion wise breakup of the census 2011 for the state is as yet unavailable online. I assume that the proportions haven't really changed much since 2001 even if the actual numbers have risen as is to be expected).
A eyeball comparison of the two maps shows how polarised the election was. The BJP simply won heavily in all the constituencies that were Hindu majority by a large margin (in the Jammu region), whereas the parties based in the valley won most of the seats with a Muslim majority. The Congress party did quite well in Ladakh and Kargil, where the chunk of "other religions"- Buddhism in particular- were concentrated.
There is of course Kishtwar, Doda (and to some extent Bhaderwah) with most of its Tehsils having a higher proportion of Muslims among the population, which has been won by the BJP.
A more detailed map that shows how each party performed in each constituency (intensity map showing vote percentages of the four main parties across all constituencies) will elaborate how there is a clear regional divide in the political choices in the state. (Use dropdown at the bottom of the map to choose the respective parties)
Map 3: Vote Percentages of Respective Parties across Constituencies
Sunday, August 24, 2014
India's abysmal performance in the recent series against England in that country has been universally panned. While India did manage to win one test and its bowlers performed creditably well (relatively) - running out of luck with dropped catches galore - it was the all round failure of the Indian batsmen that has caught the eye.
But there hasn't much too surprise with the Indian record in England recently. Indian batsmen have traditionally struggled outside the sub-continent as they have to encounter either faster pitches, better seaming and swing conditions or tracks that aren't flat enough. Indian pitches, on the other hand, are relatively more conducive to turn, include a number of flat tracks, and are more difficult for faster bowlers than is the case elsewhere. That is the commonly understood story.
Is there to empirically verify this using nifty data visualisation tools? There is!
We set out to find if Indian batsmen are relatively worse off than the average batsmen elsewhere on overseas tracks.
What we do here is to not just use simple averages to compare batsmen, but to use a measure which is called, "Runs over average batsmen" for our purposes.
It is not enough to simply compare averages of batsmen on overseas pitches as this measure will not compare a player from one era to another. That is because a batsman in a particular era could face better bowlers (or worse) as compared to another. There are also various rule changes/ cricketing conditions (one bouncer per over since the 1990s or no helmets prior to the mid-1970s for examples). It is therefore simply not accurate to term that X with an average of 50 in the 1990s and who has played just 25 innings overseas is better than Y with an average of 40 in the 1970s and who has played 60 innings.
Therefore, what we ought to do is find the average number of runs scored in a particular set of years in which a player played, and then calculate the difference between the total number of runs scored by the batsman and this average. This will be the "overall_value" of the batsman. It is an intuitive idea that is similar to what Australian economist Nicholas Rohde used in his controversial paper to study batting records across times.
To illustrate, take VVS Laxman. He has an overall average of 45.97. His overseas average is 42.64. He has played 225 innings (34 not outs) in his career. How does he compare to someone like Gundappa Viswanath, a similar stylist from the past? During VVS Laxman's career between 1997 and 2012, the average number of runs scored by batsmen was 33.1. In overseas tests, VVS Laxman added a difference of 8.92 (42.02 - 33.1) and therefore contributed 8.92 * 109 (such innings played) = 1040.1 runs as his overall value added as compared to the average batsman of his era. Similarly, Viswanath's overall value added was 312.39 runs over the average player of his era.
We do this exercise for all batsmen who have played test cricket from 1877 to the present. And present the results in a nifty graph as below. (Hover on each cell to see the data. Lighter colours depict a better value and darker a lower value for the batsmen. Click on the countries to view country specific data).
What we notice here is that there is not too much of a difference in the overall overseas records of Indian batsmen as compared to the best of the cricketing world. India does have a sizeable number of batsmen having above average "value added" runs in overseas tests as compared to the top team, Australia. Among Indian batsmen though (click on India to view more details for Indian batsmen alone), it is evident that the previous generation of batters- Sachin Tendulkar, Rahul Dravid, VVS Laxman and to a lesser extent, V. Sehwag and S. Ganguly, constituted the best ever core India has had since it entered test cricket. Barring Sunil Gavaskar and Mohinder Amarnath in the earlier generation, no other batsman of any other era has a better overseas record than the aforementioned.
The current generation, meanwhile, has a long way to go to live upto the record of the previous one. Barring A. Rahane to some extent, most other Indian batsmen of the current team has been poor on overseas tours relative to the average batsman of this era.
Saturday, August 16, 2014
The event prompted me to check out whether this diminutive, unheralded, unsung and hardworking bowler stood among his tribe of spin bowlers.
I did a simple data comparison. Extracted the Top 20 spin bowlers (by wickets taken) from Cricinfo's Statsguru, and then calculated a metric - "Bowl Index" (copied from this source). "Bowl Index" basically takes into account both bowling average and strike rate.
Here's the formula: (runs conceded)^2/(balls bowled * wickets taken)
And then I normalised the formula to account for total innings bowled (Bowl Index * 1000/Total Innings Bowled).
The resulting data is as below:
A Graphical representation of the above data list is below:
Rangana Herath, thus far, ranks just below Bishen Singh Bedi and Clarrie Grimmet in the all-time list. Not bad at all for the Lankan spin lynch-pin whom no one expected to take over the giant shoes of Muthiah Muralitharan.
Tuesday, July 29, 2014
Friday, June 07, 2013