Using R for Cricket Analysis #rstats

ESPN Crincinfo is the best site for cricket data (you can see an earlier detailed post on the database  here  ), and using the XML package in R we can easily scrape and manipulate data

Here is the code.

#Note I can also break the url string and use paste command to modify this url with parameters
tables$"Overall figures"

#Now see this- since I only got 50 results in each page, I look at the url of next page

table1=tables$"Overall figures"
table2=tables$"Overall figures"

#Now I need to join these two tables vertically


Note-I can also automate the web scraping .
Now the data is within R, we can use something like Deducer to visualize.
Created by Pretty R at Analytics

The Analytics (or stats) dashboard at continues to disappoint, and is a major reason for people to move out of hosting (since they need better analytics like that by Google Analytics which cant be enabled on the default mode)

Its not really beautiful unlike the rest of WordPress Universe!

It can be made better if people try harder! Analytics matters

Here are some points

1) Bar charts and Histograms are not really the best way to visualize trends across time

2) Location Analytics is limited to just country level analysis and the heatmap (?) is aweful in terms of distinguishing gradients 

3) Referrers Tab needs to do a better job on distinguishing between mobile and non mobile traffic, social and non social traffic (and there are better ways to visualize than just a simple list)!

4)  I cant even export my traffic stats (and forget an api !) so I am stuck with the bad data viz here

Interview James G Kobielus IBM Big Data

Here is an interview with  James G Kobielus, who is the Senior Program Director, Product Marketing, Big Data Analytics Solutions at IBM. Special thanks to Payal Patel Cudia of IBM’s communication team,for helping with the logistics for this.

Ajay -What are the specific parts of the IBM Platform that deal with the three layers of Big Data -variety, velocity and volume

James-Well first of all, let’s talk about the IBM Information Management portfolio. Our big data platform addresses the three layers of big data to varying degrees either together in a product , or two out of the three or even one of the three aspects. We don’t have separate products for the variety, velocity and volume separately.

Let us define these three layers-Volume refers to the hundreds of terabytes and petabytes of stored data inside organizations today. Velocity refers to the whole continuum from batch to real time continuous and streaming data.

Variety refers to multi-structure data from structured to unstructured files, managed and stored in a common platform analyzed through common tooling.

For Volume-IBM has a highly scalable Big Data platform. This includes Netezza and Infosphere groups of products, and Watson-like technologies that can support petabytes volume of data for analytics. But really the support of volume ranges across IBM’s Information Management portfolio both on the database side and the advanced analytics side.

For real time Velocity, we have real time data acquisition. We have a product called IBM Infosphere, part of our Big Data platform, that is specifically built for streaming real time data acquisition and delivery through complex event processing. We have a very rich range of offerings that help clients build a Hadoop environment that can scale.

Our Hadoop platform is the most real time capable of all in the industry. We are differentiated by our sheer breadth, sophistication and functional depth and tooling integrated in our Hadoop platform. We are differentiated by our streaming offering integrated into the Hadoop platform. We also offer a great range of modeling and analysis tools, pretty much more than any other offering in the Big Data space.

Attached- Jim’s slides from Hadoop World

Ajay- Any plans for Mahout for Hadoop

Jim- I cant speak about product plans. We have plans but I cant tell you anything more. We do have a feature in Big Insights called System ML, a library for machine learning.

Ajay- How integral are acquisitions for IBM in the Big Data space (Netezza,Cognos,SPSS etc). Is it true that everything that you have in Big Data is acquired or is the famous IBM R and D contributing here . (see a partial list of IBM acquisitions at at )

Jim- We have developed a lot on our own. We have the deepest R and D of anybody in the industry in all things Big Data.

For example – Watson has Big Insights Hadoop at its core. Apache Hadoop is the heart and soul of Big Data (see ). A great deal that makes Big Insights so differentiated is that not everything that has been built has been built by the Hadoop community.

We have built additions out of the necessity for security, modeling, monitoring, and governance capabilities into BigInsights to make it truly enterprise ready. That is one example of where we have leveraged open source and we have built our own tools and technologies and layered them on top of the open source code.

Yes of course we have done many strategic acquisitions over the last several years related to Big Data Management and we continue to do so. This quarter we have done 3 acquisitions with strong relevance to Big Data. One of them is Vivisimo ( ).

Vivisimo provides federated Big Data discovery, search and profiling capabilities to help you figure out what data is out there,what is relevance of that data to your data science project- to help you answer the question which data should you bring in your Hadoop Cluster.

 We also did Varicent , which is more performance management and we did TeaLeaf , which is a customer experience solution provider where customer experience management and optimization is one of the hot killer apps for Hadoop in the cloud. We have done great many acquisitions that have a clear relevance to Big Data.

Netezza already had a massively parallel analytics database product with an embedded library of models called Netezza Analytics, and in-database capabilties to massively parallelize Map Reduce and other analytics management functions inside the database. In many ways, Netezza provided capabilities similar to that IBM had provided for many years under the Smart Analytics Platform ( ) .

There is a differential between Netezza and ISAS.

ISAS was built predominantly in-house over several years . If you go back a decade ago IBM acquired Ascential Software , a product portfolio that was the heart and soul of IBM InfoSphere Information Manager that is core to our big Data platform. In addition to Netezza, IBM bought SPSS two years back. We already had data mining tools and predictive modeling in the InfoSphere portfolio, but we realized we needed to have the best of breed, SPSS provided that and so IBM acquired them.

 Cognos– We had some BI reporting capabilities in the InfoSphere portfolio that we had built ourselves and also acquired for various degrees from prior acquisitions. But clearly Cognos was one of the best BI vendors , and we were lacking such a rich tool set in our product in visualization and cubing and so for that reason we acquired Cognos.

There is also Unica – which is a marketing campaign optimization which in many ways is a killer app for Hadoop. Projects like that are driving many enterprises.

Ajay- How would you rank order these acquisitions in terms of strategic importance rather than data of acquisition or price paid.

Jim-Think of Big Data as an ecosystem that has components that are fitted to particular functions for data analytics and data management. Is the database the core, or the modeling tool the core, or the governance tools the core, or is the hardware platform the core. Everything is critically important. We would love to hear from you what you think have been most important. Each acquisition has helped play a critical role to build the deepest and broadest solution offering in Big Data. We offer the hardware, software, professional services, the hosting service. I don’t think there is any validity to a rank order system.

Ajay-What are the initiatives regarding open source that Big Data group have done or are planning?

Jim- What we are doing now- We are very much involved with the Apache Hadoop community. We continue to evolve the open source code that everyone leverages.. We have built BigInsights on Apache Hadoop. We have the closest, most up to date in terms of version number to Apache Hadoop ( Hbase,HDFS, Pig etc) of all commercial distributions with our BigInsights 1.4 .

We have an R library integrated with BigInsights . We have a R library integrated with Netezza Analytics. There is support for R Models within the SPSS portfolio. We already have a fair amount of support for R across the portfolio.

Ajay- What are some of the concerns (privacy,security,regulation) that you think can dampen the promise of Big Data.

Jim- There are no showstoppers, there is really a strong momentum. Some of the concerns within the Hadoop space are immaturity of the technology, the immaturity of some of the commercial offerings out there that implement Hadoop, the lack of standardization for formal sense for Hadoop.

There is no Open Standards Body that declares, ratifies the latest version of Mahout, Map Reduce, HDFS etc. There is no industry consensus reference framework for layering these different sub projects. There are no open APIs. There are no certifications or interoperability standards or organizations to certify different vendors interoperability around a common API or framework.

The lack of standardization is troubling in this whole market. That creates risks for users because users are adopting multiple Hadoop products. There are lots of Hadoop deployments in the corporate world built around Apache Hadoop (purely open source). There may be no assurance that these multiple platforms will interoperate seamlessly. That’s a huge issue in terms of just magnifying the risk. And it increases the need for the end user to develop their own custom integrated code if they want to move data between platforms, or move map-reduce jobs between multiple distributions.

Also governance is a consideration. Right now Hadoop is used for high volume ETL on multi structured and unstructured data sources, or Hadoop is used for exploratory sand boxes for data scientists. These are important applications that are a majority of the Hadoop deployments . Some Hadoop deployments are stand alone unstructured data marts for specific applications like sentiment analysis like.

Hadoop is not yet ready for data warehousing. We don’t see a lot of Hadoop being used as an alternative to data warehouses for managing the single version of truth of system or record data. That day will come but there needs to be out there in the marketplace a broader range of data governance mechanisms , master data management, data profiling products that are mature that enterprises can use to make sure their data inside their Hadoop clusters is clean and is the single version of truth. That day has not arrived yet.

One of the great things about IBM’s acquisition of Vivisimo is that a piece of that overall governance picture is discovery and profiling for unstructured data , and that is done very well by Vivisimo for several years.

What we will see is vendors such as IBM will continue to evolve security features inside of our Hadoop platform. We will beef up our data governance capabilities for this new world of Hadoop as the core of Big Data, and we will continue to build up our ability to integrate multiple databases in our Hadoop platform so that customers can use data from a bit of Hadoop,some data from a bit of traditional relational data warehouse, maybe some noSQL technology for different roles within a very complex Big Data environment.

That latter hybrid deployment model is becoming standard across many enterprises for Big Data. A cause for concern is when your Big Data deployment has a bit of Hadoop, bit of noSQL, bit of EDW, bit of in-memory , there are no open standards or frameworks for putting it all together for a unified framework not just for interoperability but also for deployment.

There needs to be a virtualization or abstraction layer for unified access to all these different Big Data platforms by the users/developers writing the queries, by administrators so they can manage data and resources and jobs across all these disparate platforms in a seamless unified way with visual tooling. That grand scenario, the virtualization layer is not there yet in any standard way across the big data market. It will evolve, it may take 5-10 years to evolve but it will evolve.

So, that’s the concern that can dampen some of the enthusiasm for Big Data Analytics.


You can read more about Jim at or

follow him on Twitter at

You can read more about IBM Big Data at

Amazon gives away 750 hours /month of Windows based computing

and an additional 750 hours /month of Linux based computing. The windows instance is really quite easy for users to start getting the hang of cloud computing. and it is quite useful for people to tinker around, given Google’s retail cloud offerings are taking so long to hit the market

But it is only for new users.

WS Free Usage Tier now Includes Microsoft Windows on EC2

The AWS Free Usage Tier now allows you to run Microsoft Windows Server 2008 R2 on an EC2 t1.micro instance for up to 750 hours per month. This benefit is open to new AWS customers and to those who are already participating in the Free Usage Tier, and is available in all AWS Regions with the exception of GovCloud. This is an easy way for Windows users to start learning about and enjoying the benefits of cloud computing with AWS.

The micro instances provide a small amount of consistent processing power and the ability to burst to a higher level of usage from time to time. You can use this instance to learn about Amazon EC2, support a development and test environment, build an AWS application, or host a web site (or all of the above). We’ve fine-tuned the micro instances to make them even better at running Microsoft Windows Server.

You can launch your instance from the AWS Management Console:

We have lots of helpful resources to get you started:

Along with 750 instance hours of Windows Server 2008 R2 per month, the Free Usage Tier also provides another 750 instance hours to run Linux (also on a t1.micro), Elastic Load Balancer time and bandwidth, Elastic Block Storage, Amazon S3 Storage, and SimpleDB storage, a bunch of Simple Queue Service and Simple Notification Service requests, and some CloudWatch metrics and alarms (see the AWS Free Usage Tier page for details). We’ve also boosted the amount of EBS storage space offered in the Free Usage Tier to 30GB, and we’ve doubled the I/O requests in the Free Usage Tier, to 2 million.


Chrome Extension- MafiaaFire

The chrome extension MafiaaWire basically gives you an updated list of redirected websites. So the next time , your evil highness shuts down your favorite website- the list promises to give you an update.  While obviously entertainment intellectual property is a very obvious site category for such redirects, in some cases these extensions can be used for simple things like hosting dissents or protesters against govt corruption in non US countries .

Basically under the new SOPA act (an oline version of pepper spray even browsers like Firefox and Chrome would be liable for any such extension that can be used to download American Intellectual property illegally.

In the meantime – this is an interesting and creative use case of technology and sociology merging in the brave new world.

You can read about it here-

MAFIAAFire works by downloading a list which contains the names of the “blocked” sites as well as the sites to redirect to. This list is downloaded every time Firefox starts up or every two days on the Chrome version (although the user has the choice to force an update on the Chrome version instead of waiting for two days).

When a user types in a domain name from the list of blocked domains, the add-on recognizes this and automatically redirects the user to the secondary site. Since this happens before the browser connects to the DNS server, this renders any DNS blocks useless.

Although the add-on checks for which sites are entered into the address bar every time (as it needs to check if that site is on its block list), it does not log these requests nor send these requests to any central server. In other words: it does not track the user.


Download it from

Interesting times indeed!




Research on Social Games

Social Gaming is slightly different from arcade gaming, and the heavy duty PSP3, XBox, Wii world of gaming.  Some observations on my research ( 😉 ) on social gaming across internet is as follows-

There are mostly 3 types of social games-

1) Quest- Build a town/area/farm to earn in game money or points

2) Fight- fight other people /players /pigs earn in game money or points

3) Puzzle- Stack up, make three of a kind, etc

Most successful social games are a crossover between the above three kinds of social games (so build and fight, or fight and puzzle etc)

In addition most social games have some in game incentives that are peculiar to social networks only. In game incentives are mostly in game cash to build, energy to fight others, or shortcuts in puzzle games. These social gaming incentives are-

1) Some incentive to log in daily/regularly/visit game site more often

2) Some incentive to invite other players on the social network

A characteristic of this domain is blatant me-too, copying and ripping creative ideas (but not the creative itself)  from other social games. In general the successful game which is the early leader gets most of the players but other game studios can and do build up substantial long tail network of players by copying games. Thus there are a huge variety of games.

However there are massive hits like Farmville and Angry Birds, that prove that a single social game well executed can be very valuable and profitable to both itself as well as the primary social network hosting it.

Accordingly the leading game studios are Zynga, Electronic Arts and (yes) Microsoft while Google has been mostly a investor in these.

A good website for studying data about social games is while a sister website for studying developments is

As you can see below Appdata is a formidable data gatherer here (though I find the top App – Static HTML as both puzzling and a sign of un corrected automated data gathering),

but I expect more competition in this very lucrative segment.



Interview Beth Schultz Editor

Here is an interview with Beth Scultz Editor in Chief, . is the new online community on Predictive Analytics, and its a bit different in emphasizing quality more than just quantity. Beth is veteran in tech journalism and communities.

Ajay-Describe your journey in technology journalism and communication. What are the other online communities that you have been involved with?

Beth- I’m a longtime IT journalist, having begun my career covering the telecommunications industry at the brink of AT&T’s divestiture — many eons ago. Over the years, I’ve covered the rise of internal corporate networking; the advent of the Internet and creation of the Web for business purposes; the evolution of Web technology for use in building intranets, extranets, and e-commerce sites; the move toward a highly dynamic next-generation IT infrastructure that we now call cloud computing; and development of myriad enterprise applications, including business intelligence and the analytics surrounding them. I have been involved in developing online B2B communities primarily around next-generation enterprise IT infrastructure and applications. In addition, Shawn Hessinger, our community editor, has been involved in myriad Web sites aimed at creating community for small business owners.

 Ajay- Technology geeks get all the money while journalists get a story. Comments please

Beth- Great technology geeks — those being the ones with technology smarts as well as business savvy — do stand to make a lot of money. And some pursue that to all ends (with many entrepreneurs gunning for the acquisition) while others more or less fall into it. Few journalists, at least few tech journalists, have big dollars in mind. The gratification for journalists comes in being able to meet these folks, hear and deliver their stories — as appropriate — and help explain what makes this particular technology geek developing this certain type of product or service worth paying attention to.

 Ajay- Describe what you are trying to achieve with the All Analytics community and how it seeks to differentiate itself with other players in this space.

 Beth- With, we’re concentrating on creating the go-to site for CXOs, IT professionals, line-of-business managers, and other professionals to share best practices, concrete experiences, and research about data analytics, business intelligence, information optimization, and risk management, among many other topics. We differentiate ourself by featuring excellent editorial content from a top-notch group of bloggers, access to industry experts through weekly chats, ongoing lively and engaging message board discussions, and biweekly debates.

We’re a new property, and clearly in rapid building mode. However, we’ve already secured some of the industry’s most respected BI/analytics experts to participate as bloggers. For example, a small sampling of our current lineup includes the always-intrigueing John Barnes, a science fiction novelist and statistics guru; Sandra Gittlen, a longtime IT journalist with an affinity for BI coverage; Olivia Parr-Rud, an internationally recognized expert in BI and organizational alignment; Tom Redman, a well-known data-quality expert; and Steve Williams, a leading BI strategy consultant. I blog daily as well, and in particular love to share firsthand experiences of how organizations are benefiting from the use of BI, analytics, data warehousing, etc. We’ve featured inside looks at analytics initiatives at companies such as, Oberweis Dairy, the Cincinnati Zoo & Botanical Garden, and Thomson Reuters, for example.

In addition, we’ve hosted instant e-chats with Web and social media experts Joe Stanganelli and Pierre DeBois, and this Friday, Aug. 26, at 3 p.m. ET we’ll be hosting an e-chat with Marshall Sponder, Web metrics guru and author of the newly published book, Social Media Analytics: Effective Tools for Building, Interpreting, and Using Metrics. (Readers interested in participating in the chat do need to fill out a quick registration form, available here . The chat is available here .

Experts participating in our biweekly debate series, called Point/Counterpoint, have broached topics such as BI in the cloud, mobile BI and whether an analytics culture is truly possible to build.

Ajay-  What are some tips you would like to share about writing tech stories to aspiring bloggers.

Beth- I suppose my best advice is this: Don’t write about technology for technology’s sake. Always strive to tell the audience why they should care about a particular technology, product, or service. How might a reader use it to his or her company’s advantage, and what are the potential benefits? Improved productivity, increased revenue, better customer service? Providing anecdotal evidence goes a long way toward delivering that message, as well.

Ajay- What are the other IT world websites that have made a mark on the internet.

Beth- I’d be remiss if I didn’t give a shout out to UBM TechWeb sites, including InformationWeek, which has long charted the use of IT within the enterprise; Dark Reading, a great source for folks interested in securing an enterprise’s information assets; and Light Reading, which takes the pulse of the telecom industry.


Beth Schultz has more than two decades of experience as an IT writer and editor. Most recently, she brought her expertise to bear writing thought-provoking editorial and marketing materials on a variety of technology topics for leading IT publications and industry players. Previously, she oversaw multimedia content development, writing and editing for special feature packages at Network World. Beth has a keen ability to identify business and technology trends, developing expertise through in-depth analysis and early-adopter case studies. Over the years, she has earned more than a dozen national and regional editorial excellence awards for special issues from American Business Media, American Society of Business Press Editors,, and others.