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 inanalytics in sports.Describe the role of analytics in sportsmanagement
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.
Benjamin Alamar, Professor of Sport Management, Menlo College
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
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.
REVOLUTION ANALYTICS LAUNCHES “APPLICATIONS OF R IN BUSINESS” CONTEST
$20,000 in Prizes for Users Solving Business Problems with R
PALO ALTO, Calif. – September 1, 2011 – Revolution Analytics, the leading commercial provider of R software, services and support, today announced the launch of its “Applications of R in Business” contest to demonstrate real-world uses of applying R to business problems. The competition is open to all R users worldwide and submissions will be accepted through October 31. The Grand Prize winner for the best application using R or Revolution R will receive $10,000.
The bonus-prize winner for the best application using features unique to Revolution R Enterprise – such as itsbig-data analytics capabilities or its Web Services API for R – will receive $5,000. A panel of independent judges drawn from the R and business community will select the grand and bonus prize winners. Revolution Analytics will present five honorable mention prize winners each with $1,000.
“We’ve designed this contest to highlight the most interesting use cases of applying R and Revolution R to solving key business problems, such as Big Data,” said Jeff Erhardt, COO of Revolution Analytics. “The ability to process higher-volume datasets will continue to be a critical need and we encourage the submission of applications using large datasets. Our goal is to grow the collection of online materials describing how to use R for business applications so our customers can better leverage Big Analytics to meet their analytical and organizational needs.”
VideoLectures.net is a free and open access multimedia repository of video lectures, mainly of research and educational character. The lectures are given by distinguished scholars and scientists at the most important and prominent events like conferences, summer schools, workshops and science promotional events from many fields of Science. The portal is aimed at promoting science, exchanging ideas and fostering knowledge sharing by providing high quality didactic contents not only to the scientific community but also to the general public. All lectures, accompanying documents, information and links are systematically selected and classified through the editorial process taking into account also users’ comments.
The ECML-PKDD 2011 Discovery Challenge is organized in order to improve the website’s current recommender system. The challenge consists of two main tasks and a “side-by” contest. The provided data is for both of the tasks, and it is up to the contestants how it will be used for learning (building up) a recommender.
Due to the nature of the problem, each of the tasks has its own merit: task 1 simulates new-user and new- item recommendation (cold-start mode), task 2 simulates clickstream based recommendation (normal mode). Continue reading “Machine Learning Contest”
If you do a Google search for Data Mining Blog- for the past several years one Blog will come on top. data mining blog – Google Search http://bit.ly/kEdPlE
To honor 5 years of Sandro Saitta’s blog (yes thats 5 years!) , we cover an exclusive interview with him where he reveals his unique sauce for cool techie blogging.
Ajay- Describe your journey as a scientist and data miner, from early experiences, to schooling to your work/research/blogging.
Sandro- My first experience with data mining was my master project. I used decision tree to predict pollen concentration for the following week using input data such as wind, temperature and rain. The fact that an algorithm can make a computer learn from experience was really amazing to me. I found it so interesting that I started a PhD in data mining. This time, the field of application was civil engineering. Civil engineers put a lot of sensors on their structure in order to understand how they behave. With all these sensors they generate a lot of data. To interpret these data, I used data mining techniques such as feature selection and clustering. I started my blog, Data Mining Research, during my PhD, to share with other researchers.
I then started applying data mining in the stock market as my first job in industry. I realized the difference between image recognition, where 99% correct classification rate is state of the art, and stock market, where you’re happy with 55%. However, the company ambiance was not as good as I thought, so I moved to consulting. There, I applied data mining in behavioral targeting to increase click-through rates. When you compare the number of customers who click with the ones who don’t, then you really understand what class imbalance mean. A few months ago, I accepted a very good opportunity at SICPA. I’m looking forward to resolving new challenges there.
Ajay- Your blog is the top ranked blog for “data mining blog”. Could you share some tips on better blogging for analytics and technical people
Sandro- It’s always difficult to start a blog, since at the beginning you have no reader. Writing for nobody may seem stupid, but it is not. By writing my first posts during my PhD I was reorganizing my ideas. I was expressing concepts which were not always clear to me. I thus learned a lot and also improved my English level. Of course, it’s still not perfect, but I hope most people can understand me.
Next come the readers. A few dozen each week first. To increase this number, I then started to learn SEO (Search Engine Optimization) by reading books and blogs. I tested many techniques that increased Data Mining Research visibility in the blogosphere. I think SEO is interesting when you already have some content published (which means not at the very beginning of your blog). After a while, once your blog is nicely ranked, the main task is to work on the content of the blog. To be of interest, your content must be particular: original, informative or provocative for example. I also had the chance to have a good visibility thanks to well-known people in the field like Kevin Hillstrom, Gregory Piatetsky-Shapiro, Will Dwinnell / Dean Abbott, Vincent Granville, Matthew Hurst and many others.
Ajay- Whats your favorite statistical software and what are the various softwares that you have worked with. Could you compare and contrast these software as well.
Sandro- My favorite software at this point is SAS. I worked with it for two years. Once you know the language, you can perform ETL and data mining so easily. It’s also very fast compared to others. There are a lot of tools for data mining, but I cannot think of a tool that is as powerful as SAS and, in the same time, has a high-level programming language behind it.
I also worked with R and Matlab. R is very nice since you have all the up-to-date data mining algorithms implemented. However, working in the memory is not always a good choice, especially for ETL. Matlab is an excellent tool for prototyping. It’s not so fast and certainly not done for ETL, but the price is low regarding all the possibilities for data mining. According to me, SAS is the best choice for ETL and a good choice for data mining. Of course, there is the price.
Ajay- What are your favorite techniques and training resources for learning basics of data mining to say statisticians or business management graduates.
Sandro- I’m the kind of guy who likes to read books. I read data mining books one after the other. The fact that the same concepts are explained differently (and by different people) helps a lot in learning a topic like data mining. Of course, nothing replaces experience in the field. You can read hundreds of books, you will still not be a good practitioner until you really apply data mining in specific fields. My second choice after books is blogs. By reading data mining blogs, you will really see the issues and challenges in the field. It’s still not experience, but we are closer. Finally, web resources and networks such as KDnuggets of course, but also AnalyticBridge and LinkedIn.
Ajay- Describe your hobbies and how they help you ,if at all in your professional life.
Sandro- One of my hobbies is reading. I read a lot of books about data mining, SEO, Google as well as Sci-Fi and Fantasy. I’m a big fan of Asimov by the way. My other hobby is playing tennis. I think I simply use my hobbies as a way to find equilibrium in my life. I always try to find the best balance between work, family, friends and sport.
Ajay- What are your plans for your website for 2011-2012.
Sandro- I will continue to publish guest posts and interviews. I think it is important to let other people express themselves about data mining topics. I will not write about my current applications due to the policies of my current employer. But don’t worry, I still have a lot to write, whether it is technical or not. I will also emphasis more on my experience with data mining, advices for data miners, tips and tricks, and of course book reviews!
Standard Disclosure of Blogging- Sandro awarded me the Peoples Choice award for his blog for 2010 and carried out my interview. There is a lot of love between our respective wordpress blogs, but to reassure our puritan American readers- it is platonic and intellectual.
About Sandro S-
Sandro Saitta is a Data Mining Research Engineer at SICPA Security Solutions. He is also a blogger at Data Mining Research (www.dataminingblog.com). His interests include data mining, machine learning, search engine optimization and website marketing.