Using Big Data in Football (Soccer)

The Lewis's book Moneyball revealed clever application of data-driven analytic in baseball and initiated the so called "Moneyball"-era in this sport. Currently, wide range of teams employ similar methods together with the traditional ones. I have been wondering if similar methods are applied in football and why not much is known about it if they are. I know many performance metrics are tracked by the clubs but I wondered if any data-driven analytics is employed.

What If Nazi Germany Had Excel?

Map of Operation Bagration

Yesterday I read an article about how data analysis solution would significantly reduce the cost of healthcare in US. After some illustration of the rising healthcare costs, a solution was proposed with introduction of fraud detection methods and tools. Fraud does impact the overall costs but I am sure it is just a part of the problem. It was obviously a paid article to promote a product line as the savior of our world. However, it provoked me thinking once again about the place of analytics in the decision-making and management processes. Well balanced views are rare as everyone promotes their own subject as crucial success factor - analysts are no different and the current hype with analytics and Big Data multiplies (by a factor greater than one) their advertising. To make things more fun, this line of thought could answer the geeky question of would Nazi Germany be more successful if they had a powerful analytic tool at their hands?

From Data Modeler to Data Scientist

I came across an interesting parallel between the data modeler and the data scientist. I like the comparison of both positions on some key aspects. However, I find such distinction between these two roles not that applicable for an analytical company or department  as most projects involve methods and techniques attributable to both positions. The exception is probably the IT organizations dealing with database design and not so much with data analysis and. It is a good read. Check it out here.

The Dark Side of Data Abundance

If there is something that defines our digital era it is the abundance of data. It has been created out of the cheap computing power and storage, Internet ubiquity and ease of sharing, open source and open data movements as well as the new requirements from the the governments. It is all great of course but many people and companies join the Dark side of this phenomenon. I hope it does not come as a surprise that "more data" phenomenon has a dark side - almost everything does. I can judge just a narrow aspect of it from the perspective of data analysis and decision making. What I see is sometimes scary and sometimes funny.

To Macro Or Not To Macro, That Is The Question!

A genius once wrote a famous monologue and 400 years later it still stirs up emotions and thoughts. Not surprisingly, Excel macros do not have such a dramatic appeal but using or not macros in a workbook is a question worth asking. Including one in the workbook is decided on the fly without much of a consideration. Quite often macro is the gin coming out of a programmer's hands that could magically solve any problem. As a favorite TV presenter would exclaim "How hard could it be?". Macros are brilliant helping hand but should be applied with consideration.

Book Review: Engineers of Victory by Paul Kennedy

A good book makes vacation time even better. My choice this year was particularly good as I took a copy of The Engineers of Victory: The Problem Solvers who Turned the Tide in the Second World War by Paul Kennedy with me. it combined two of my favorite topics of history and analytics in a very exciting way. It explains how the tide of WW2 has been turned in Allied Forces favor in the period 1942-1944.

Friday Laughs: LinkedIn

LinkedIn is a nice networking platform for professionals. However, some things are wrong with it. I like to share some thoughts on that.

The Neglected Basics

We often start developing models forgetting or neglecting the questions about the underlying assumptions for these models or take them for granted. Unfortunately it is not always the case and pay with time and efforts for that. 

Where Should We Focus The Analytical Efforts?

It is the least to say that analytics today is more popular than ever before. It is cheap, affordable and organizations push towards greater application of its methods. It is all good and pays the mortgage of quite a few of us but my mind lately has been occupied with the focus and scale of the analytical efforts. It seems on numerous occasions the mighty power of modern analytical tools and methods have been applied on wrong problem. People have been trying to solve the wrong problems for ages. Analyzing a problem deeper and deeper, applying tools and methods that make the buzz words today and being in the trend could lead us astray. We could focus on the tree and forget it is part of a forest.

Some Issues In Modeling Life-Cycle Component Of Markets

Some markets have strong component coming from products that need to be replaced because of burn-outs, breaks , wearing off or changes in properties that render them dangerous or useless. Automotive market is a good example for such market - tires wear off, the oil, spark-plugs and other components loos properties and so on. Another example for this type of market is the lighting market - the lamps burn out and need to be replaced. Modeling this type of market is eased because of the "mechanical" relations between entities. However the challenges the modeler faces are not negligible and could seriously damage the model quality.