Tag: software

Interview Prof Benjamin Alamar , Sports Analytics

Here is an interview with Prof Benjamin Alamar, founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the Director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA.

Ajay – The movie Moneyball recently sparked out mainstream interest in analytics in sports.Describe the role of analytics in sports management

Benjamin- Analytics is impacting sports organizations on both the sport and business side.
On the Sport side, teams are using analytics, including advanced data management, predictive anlaytics, and information systems to gain a competitive edge. The use of analytics results in more accurate player valuations and projections, as well as determining effective strategies against specific opponents.
On the business side, teams are using the tools of analytics to increase revenue in a variety of ways including dynamic ticket pricing and optimizing of the placement of concession stands.
Ajay-  What are the ways analytics is used in specific sports that you have been part of?

Benjamin- A very typical first step for a team is to utilize the tools of predictive analytics to help inform their draft decisions.

Ajay- What are some of the tools, techniques and software that analytics in sports uses?
Benjamin- The tools of sports analytics do not differ much from the tools of business analytics. Regression analysis is fairly common as are other forms of data mining. In terms of software, R is a popular tool as is Excel and many of the other standard analysis tools.
Ajay- Describe your career journey and how you became involved in sports management. What are some of the tips you want to tell young students who wish to enter this field?

Benjamin- I got involved in sports through a company called Protrade Sports. Protrade initially was a fantasy sports company that was looking to develop a fantasy game based on advanced sports statistics and utilize a stock market concept instead of traditional drafting. I was hired due to my background in economics to develop the market aspect of the game.

There I met Roland Beech (who now works for the Mavericks) and Aaron Schatz (owner of footballoutsiders.com) and learned about the developing field of sports statistics. I then changed my research focus from economics to sports statistics and founded the Journal of Quantitative Analysis in Sports. Through the journal and my published research, I was able to establish a reputation of doing quality, useable work.

For students, I recommend developing very strong data management skills (sql and the like) and thinking carefully about what sort of questions a general manager or coach would care about. Being able to demonstrate analytic skills around actionable research will generally attract the attention of pro teams.

About-

Benjamin Alamar, Professor of Sport Management, Menlo College

Benjamin Alamar

Professor Benjamin Alamar is the founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the Director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA. He has published academic research in football, basketball and baseball, has presented at numerous conferences on sports analytics. He is also a co-creator of ESPN’s Total Quarterback Rating and a regular contributor to the Wall Street Journal. He has consulted for teams in the NBA and NFL, provided statistical analysis for author Michael Lewis for his recent book The Blind Side, and worked with numerous startup companies in the field of sports analytics. Professor Alamar is also an award winning economist who has worked academically and professionally in intellectual property valuation, public finance and public health. He received his PhD in economics from the University of California at Santa Barbara in 2001.

Prof Alamar is a speaker at Predictive Analytics World, San Fransisco and is doing a workshop there

http://www.predictiveanalyticsworld.com/sanfrancisco/2012/agenda.php#day2-17

2:55-3:15pm

All level tracks Track 1: Sports Analytics
Case Study: NFL, MLB, & NBA
Competing & Winning with Sports Analytics

The field of sports analytics ties together the tools of data management, predictive modeling and information systems to provide sports organization a competitive advantage. The field is rapidly developing based on new and expanded data sources, greater recognition of the value, and past success of a variety of sports organizations. Teams in the NFL, MLB, NBA, as well as other organizations have found a competitive edge with the application of sports analytics. The future of sports analytics can be seen through drawing on these past successes and the developments of new tools.

You can know more about Prof Alamar at his blog http://analyticfootball.blogspot.in/ or journal at http://www.degruyter.com/view/j/jqas. His detailed background can be seen at http://menlo.academia.edu/BenjaminAlamar/CurriculumVitae

Predictive Models Ain’t Easy to Deploy

 

This is a guest blog post by Carole Ann Matignon of Sparkling Logic. You can see more on Sparkling Logic at http://my.sparklinglogic.com/

Decision Management is about combining predictive models and business rules to automate decisions for your business. Insurance underwriting, loan origination or workout, claims processing are all very good use cases for that discipline… But there is a hiccup… It ain’t as easy you would expect…

What’s easy?

If you have a neat model, then most tools would allow you to export it as a PMML model – PMML stands for Predictive Model Markup Language and is a standard XML representation for predictive model formulas. Many model development tools let you export it without much effort. Many BRMS – Business rules Management Systems – let you import it. Tada… The model is ready for deployment.

What’s hard?

The problem that we keep seeing over and over in the industry is the issue around variables.

Those neat predictive models are formulas based on variables that may or may not exist as is in your object model. When the variable is itself a formula based on the object model, like the min, max or sum of Dollar amount spent in Groceries in the past 3 months, and the object model comes with transaction details, such that you can compute it by iterating through those transactions, then the problem is not “that” big. PMML 4 introduced some support for those variables.

The issue that is not easy to fix, and yet quite frequent, is when the model development data model does not resemble the operational one. Your Data Warehouse very likely flattened the object model, and pre-computed some aggregations that make the mapping very hard to restore.

It is clearly not an impossible project as many organizations do that today. It comes with a significant overhead though that forces modelers to involve IT resources to extract the right data for the model to be operationalized. It is a heavy process that is well justified for heavy-duty models that were developed over a period of time, with a significant ROI.

This is a show-stopper though for other initiatives which do not have the same ROI, or would require too frequent model refresh to be viable. Here, I refer to “real” model refresh that involves a model reengineering, not just a re-weighting of the same variables.

For those initiatives where time is of the essence, the challenge will be to bring closer those two worlds, the modelers and the business rules experts, in order to streamline the development AND deployment of analytics beyond the model formula. The great opportunity I see is the potential for a better and coordinated tuning of the cut-off rules in the context of the model refinement. In other words: the opportunity to refine the strategy as a whole. Very ambitious? I don’t think so.

About Carole Ann Matignon

http://my.sparklinglogic.com/index.php/company/management-team

Carole-Ann Matignon Print E-mail

Carole-Ann MatignonCarole-Ann Matignon – Co-Founder, President & Chief Executive Officer

She is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry.  Her claim to fame is managing the strategy and direction of Blaze Advisor, the leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience.  She speaks often at Industry conferences and has conducted university classes in France and Washington DC.

She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication. At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs around change management.

While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM). She developed a growing interest in Optimization as well as Business Rules. At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart. She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).

Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business. Her technical background kept her very much in touch with technology as she advanced.

On Software

1) All software has bugs. Sometimes this is because people have been told to code in a hurry to meet shipping deadlines. Sometimes it is due to the way metal and other software interact with it. Mostly it is karma.

2) In the 21 st Century,It is okay to insult someone over his software , but not over most other things. Sometimes I think people are passionate not just for their own software but to just diss the other guys. It is a politically convenient release.

3) Bloggers writing about software are full of bull-by products. If they were any good in writing code, they would not have time to write a blog. Mostly bloggers on code are people whose coding enthusiasm is more than their coding competence.

4) Software is easier than it looks to people who know it. To those who dont know how to code, it will always be a bit of magic.

5) Despite immense progress, initiatives and encouragement- the number of females writing code is too low . Comparatively, figuratively and literally. If you are a male and want a social life- get into marketing while the hair is still black.

Man walks into Bar. Says to Women at Bar. ” Hey,What do you do, Me- I write code”

See!

6) People who write software end up making more money not just because they create useful stuff that helps get work done faster or helps reduce boredom for people. They make more money because they are mostly passionate, logical problem thinkers, focused, hard working and better read on a variety of subjects than others. That’s your cue to how to make money even if you cannot code.

7) I would rather write much more code rather than write poetry. But I sometimes think they are related. Just manipulating words in different languages to manipulate output in different machines or people.

8) Kids should be taught software at early age , as that is a skill that helps in their education and thinking. More education for the kids!

9) Laying off talented software people because you found a cheaper , younger alternative half across the globe is sometimes evil. It is also inevitable. Learn more software as you grow older.

10) The best software is the one in your head. It was written by a better programmer too.

 

Predictive analytics in the cloud : Angoss

I interviewed Angoss in depth here at http://www.decisionstats.com/interview-eberhard-miethke-and-dr-mamdouh-refaat-angoss-software/

Well they just announced a predictive analytics in the cloud.

 

http://www.angoss.com/predictive-analytics-solutions/cloud-solutions/

Solutions

Overview

KnowledgeCLOUD™ solutions deliver predictive analytics in the Cloud to help businesses gain competitive advantage in the areas of sales, marketing and risk management by unlocking the predictive power of their customer data.

KnowledgeCLOUD clients experience rapid time to value and reduced IT investment, and enjoy the benefits of Angoss’ industry leading predictive analytics – without the need for highly specialized human capital and technology.

KnowledgeCLOUD solutions serve clients in the asset management, insurance, banking, high tech, healthcare and retail industries. Industry solutions consist of a choice of analytical modules:

KnowledgeCLOUD for Sales/Marketing

KnowledgeCLOUD solutions are delivered via KnowledgeHUB™, a secure, scalable cloud-based analytical platform together with supporting deployment processes and professional services that deliver predictive analytics to clients in a hosted environment. Angoss industry leading predictive analytics technology is employed for the development of models and deployment of solutions.

Angoss’ deep analytics and domain expertise guarantees effectiveness – all solutions are back-tested for accuracy against historical data prior to deployment. Best practices are shared throughout the service to optimize your processes and success. Finely tuned client engagement and professional services ensure effective change management and program adoption throughout your organization.

For businesses looking to gain a competitive edge and put their data to work, Angoss is the ideal partner.

—-

Hmm. Analytics in the cloud . Reduce hardware costs. Reduce software costs . Increase profitability margins.

Hmmmmm

My favorite professor in North Carolina who calls cloud as a time sharing, are you listening Professor?

Self Driving Cars , Geo Coded Ads, End of Privacy

Imagine a world in which your car tracks everywhere you go. Over a period of time, it builds up a database of your driving habits, how long you stay at particular kinds of dining places, entertainment places (ahem!) , and the days, and times you do it.  You can no longer go to massage parlours without your data being checked by your car software admin (read – your home admin)

And that data is mined using machine learning algols to give you better ads for pizzas, or a reminder for food after every 3 hours , or an ad for beer every Thursday after 8 pm .

Welcome Brave New World!

How to learn to be a hacker easily

1) Are you sure. It is tough to be a hacker. And football players get all the attention.

2) Really? Read on

3) Read Hacker’s Code

http://muq.org/~cynbe/hackers-code.html

The Hacker’s Code

“A hacker of the Old Code.”

  • Hackers come and go, but a great hack is forever.
  • Public goods belong to the public.*
  • Software hoarding is evil.
    Software does the greatest good given to the greatest number.
  • Don’t be evil.
  • Sourceless software sucks.
  • People have rights.
    Organizations live on sufferance.
  • Governments are organizations.
  • If it is wrong when citizens do it,
    it is wrong when governments do it.
  • Information wants to be free.
    Information deserves to be free.
  • Being legal doesn’t make it right.
  • Being illegal doesn’t make it wrong.
  • Subverting tyranny is the highest duty.
  • Trust your technolust!

4) Read How to be a hacker by

Eric Steven Raymond

http://www.catb.org/~esr/faqs/hacker-howto.html

or just get the Hacker Attitude

The Hacker Attitude

1. The world is full of fascinating problems waiting to be solved.
2. No problem should ever have to be solved twice.
3. Boredom and drudgery are evil.
4. Freedom is good.
5. Attitude is no substitute for competence.
5) If you are tired of reading English, maybe I should move on to technical stuff
6) Create your hacking space, a virtual disk on your machine.
You will need to learn a bit of Linux. If you are a Windows user, I recommend creating a VMWare partition with Ubuntu
If you like Mac, I recommend the more aesthetic Linux Mint.
How to create your virtual disk-
read here-
Download VM Player here
http://www.vmware.com/support/product-support/player/
Down iso image of operating system here
http://ubuntu.com
Downloading is the longest thing in this exercise
Now just do what is written here
http://www.vmware.com/pdf/vmware_player40.pdf
or if you want to try and experiment with other ways to use Windows and Linux just read this
http://www.decisionstats.com/ways-to-use-both-windows-and-linux-together/
Moving data back and forth between your new virtual disk and your old real disk
http://www.decisionstats.com/moving-data-between-windows-and-ubuntu-vmware-partition/
7) Get Tor to hide your IP address when on internet
https://www.torproject.org/docs/tor-doc-windows.html.en
8a ) Block Ads using Ad-block plugin when surfing the internet (like 14.95 million other users)
https://addons.mozilla.org/en-US/firefox/addon/adblock-plus/
 8b) and use Mafiafire to get elusive websites
https://addons.mozilla.org/en-US/firefox/addon/mafiaafire-redirector/
9) Get a  Bit Torrent Client at http://www.utorrent.com/
This will help you download stuff
10) Hacker Culture Alert-
This instruction is purely for sharing the culture but not the techie work of being a hacker
The website Pirate bay acts like a search engine for Bit torrents 
http://thepiratebay.se/
Visiting it is considered bad since you can get lots of music, videos, movies etc for free, without paying copyright fees.
The website 4chan is considered a meeting place to meet other hackers. The site can be visually shocking
http://boards.4chan.org/b/
You need to do atleast set up these systems, read the websites and come back in N month time for second part in this series on how to learn to be a hacker. That will be the coding part.
END OF PART  1
Updated – sorry been a bit delayed on next part. Will post soon.

Top 5 XKCD on Data Visualization

By request, an analysis of Top 5  XKCDs on data visualization. Statisticians and Data Scientists to note-

1) DOT PLOT

 

2)  LINE PLOTS

3) FLOW CHARTS

4) PIE CHARTS and 5) BAR GRAPHS

I am not going into the big big graphs of course like the Star Wars Plot data visualization at

http://xkcd.com/657/ or the Money Chart at http://xkcd.com/980/ because I dont believe in data visualization to show off, but to keep it simple simply 🙂

Now I gotta find me a software that can write my blog for me 🙂