Author: Tinniam V Ganesh

Feed: R-bloggers. Author: Tinniam V Ganesh. The cricketr package has several functions that perform several different analyses on both batsman and bowlers. The package can also analyze performances of teams The package has function that plot percentage frequency runs or wickets, runs likelihood for a batsman, relative run/strike rates of batsman and relative performance/economy rate for bowlers are available. Other interesting functions include batting performance moving average, forecast and a function to check whether the batsmans in in-form or out-of-form.In addition performances of teams against different oppositions at different venues can be computed and plotted. The timeline of wins & ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. And she’s got brains enough for two, which is the exact quantity the girl who marries you will need. “I’m not absolutely certain of the facts, but I rather fancy it’s Shakespeare who says that it’s always just when a fellow is feeling particularly braced with things in general that Fate sneaks up behind him with the bit of lead piping.” “A melancholy-looking man, he had the appearance of one who has searched for the leak in life’s gas-pipe with a lighted candle.” “It isn’t often that Aunt Dahlia lets her angry passions rise, ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. “There’s something so paradoxical about pi. On the one hand, it represents order, as embodied by the shape of a circle, long held to be a symbol of perfection and eternity. On the other hand, pi is unruly, disheveled in appearance, its digits obeying no obvious rule, or at least none that we can perceive. Pi is elusive and mysterious, forever beyond reach. Its mix of order and disorder is what makes it so bewitching. ”  From  Infinite Powers by Steven Strogatz Anybody who wants to be “anybody” in Big Data must necessarily be ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. ‘Cricket analytics with cricketr and cricpy – Analytics harmony with R and Python’ is now available on Amazon in both paperback ($21.99) and kindle ($9.99/Rs 449) versions. The book includes analysis of cricketers using both my R package ‘cricketr’ and my python package ‘cricpy’ for all formats of the game namely Test, ODI and T20. Both packages use data from ESPN Cricinfo Statsguru. The paperback is available on Amazon for $21.99 and the kindle version is available for $9.99/Rs 449 Pick up your copy today! The book includes the following chapters CONTENTS Introduction 71 ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. Note: I had written a post about 3 years back on World Bank Data Analysis using World Development Indicators (WDI) & gVisMotionCharts. But the motion charts stopped working  some time ago. I have always been wanting to fix this and I now got to actually doing it. The issue was 2 of the WDI indicators had changed. After I fixed this I was able to host the generated motion chart using github.io pages. Please make sure that you enable flash player if you open the motion charts with Google Chrome. You may also have ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. Out[90]: [[‘Runs’, ‘Mins’, ‘BF’, ‘4s’, ‘6s’, ‘SR’, ‘Pos’, ‘Dismissal’, ‘Inns’, ‘Opposition’, ‘Ground’, ‘Start Date’], [’15’, ’28’, ’24’, ‘2’, ‘0’, ‘62.5’, ‘6’, ‘bowled’, ‘2’, ‘v Pakistan’, ‘Karachi’, ’15-Nov-89′], [‘DNB’, ‘-‘, ‘-‘, ‘-‘, ‘-‘, ‘-‘, ‘-‘, ‘-‘, ‘4’, ‘v Pakistan’, ‘Karachi’, ’15-Nov-89′], [’59’, ‘254’, ‘172’, ‘4’, ‘0’, ‘34.3’, ‘6’, ‘lbw’, ‘1’, ‘v Pakistan’, ‘Faisalabad’, ’23-Nov-89′], [‘8′, ’24’, ’16’, ‘1’, ‘0’, ’50’, ‘6’, ‘run out’, ‘3’, ‘v Pakistan’, ‘Faisalabad’, ’23-Nov-89′]] ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. The cricketr package has several functions that perform several different analyses on both batsman and bowlers. The package has function that plot percentage frequency runs or wickets, runs likelihood for a batsman, relative run/strike rates of batsman and relative performance/economy rate for bowlers are available.Other interesting functions include batting performance moving average, forecast and a function to check whether the batsmans in in-form or out-of-form.The data for a particular player can be obtained with the getPlayerData() function. To do you will need to go to ESPN CricInfo Player and type in the name of the player ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. Looking at the cumulative average runs we can see a gradual drop in the cumulative average for Tendulkar while Kohli and Gavaskar’s performance seems to be getting better 13. Cumulative average strike rate of batsmen Tendulkar’s strike rate is better than Kohli and Gavaskar par(mfrow=c(2,2)) par(mar=c(4,4,2,2)) batsmanCumulativeStrikeRate("./tendulkar.csv","Tendulkar") batsmanCumulativeStrikeRate("./kohli.csv","Kohli") batsmanCumulativeStrikeRate("./gavaskar.csv","Gavaskar") 14 Performance forecast of batsmen The forecasted performance for Kohli and Gavaskar is higher than that of Tendulkar par(mfrow=c(2,2)) par(mar=c(4,4,2,2)) batsmanPerfForecast("./tendulkar.csv","Sachin Tendulkar") batsmanPerfForecast("./kohli.csv","Kohli") batsmanPerfForecast("./gavaskar.csv","Gavaskar") 15. Relative strike rate of batsmen par(mar=c(4,4,2,2)) frames "./tendulkar.csv","./kohli.csv","gavaskar.csv") names "Tendulkar","Kohli","Gavaskar") relativeBatsmanSR(frames,names) 16. Relative Runs frequency of batsmen par(mar=c(4,4,2,2)) frames "./tendulkar.csv","./kohli.csv","gavaskar.csv") ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. “Lights, camera and … action – Take 4+!” This post includes  a rework of all presentation of ‘Elements of Neural Networks and Deep  Learning Parts 1-8 ‘ since my earlier presentations had some missing parts, omissions and some occasional errors. So I have re-recorded all the presentations.This series of presentation will do a deep-dive  into Deep Learning networks starting from the fundamentals. The equations required for performing learning in a L-layer Deep Learning network  are derived in detail, starting from the basics. Further, the presentations also discuss multi-class classification, regularization techniques, and gradient descent optimization methods in ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. This is the final set of presentations in my series ‘Elements of Neural Networks and Deep Learning’. This set follows the earlier 2 sets of presentations namely1. My presentations on ‘Elements of Neural Networks & Deep Learning’ -Part1,2,32. My presentations on ‘Elements of Neural Networks & Deep Learning’ -Parts 4,5 In this final set of presentations I discuss initialization methods, regularization techniques including dropout. Next I also discuss gradient descent optimization methods like momentum, rmsprop, adam etc. Lastly, I briefly also touch on hyper-parameter tuning approaches. The corresponding implementations are available in vectorized R, Python ... Read More