Nov/Dec 2012

The human brain simultaneously processes millions of images, movement, sound and other esoteric information from multiple sources. The brain is exceptionally efficient and effective in its capacity to prescribe and direct a course of action and eclipses any computing power available today. Smartphones now record and share images, audios and videos at an incredibly increasing rate, forcing our brains to process more.

• Fritz Venter
• Andrew Stein

Everyone needs it, few know how we get it, and many feel compelled to slow down efforts to finding and producing oil. One of the primary assets of successful, thriving societies is a low-cost energy source. What drives low cost? Supply greater than demand! What drives supply? Finding supplies in sufficient quantities so producing oil and gas is economically viable. Finding and producing hydrocarbons is technically challenging and economically risky. The process generates a large amount of data, and the industry needs new technologies and approaches to integrate and interpret this data to drive faster and more accurate decisions. Doing so will lead to safely finding new resources, increasing recovery rates and reducing environmental impacts.

• Adam Farris

Distribution Processing performs calculations with uncertainties just as if they were ordinary numbers. It can be done in Microsoft Excel without using macros, and it leverages the current generation of simulation tools. ProbabilityMananagement.org is a non-profit organization actively promoting this new arithmetic of uncertainty through educational tools, standards and best practices. This article introduces distribution processing in Excel and describes the ecosystem into which it fits. But first let’s take a detour through medieval Italy to review the adoption of the arithmetic we take for granted today.

• Sam L. Savage

Most advocates of analytics do not have problems performing analysis. Their challenge is effectively communicating with their colleagues and getting them on board and getting buy-in.

• Gary Cokins

Organizations are increasingly adopting predictive analytics, and they’re adopting these predictive analytics more broadly. Many are now using dozens or even hundreds of predictive analytic models. These models are now used in real-time decision-making and in operational, production systems. However many analytic teams rely on tools and approaches that will not scale to this level of adoption. These teams lack a repeatable, efficient process for creating and effectively deploying predictive analytic models into production. To succeed they need to operationalize analytics.

• James Taylor

What if basketball analytics could formulate an “end-all” value that could justly evaluate team and/or player performance? Regardless of the complexity of its formulation, those immersed in the world of basketball analytics are challenged with this mission: to translate a game of interdependent factors into simple measures of player and team performance. The “Four Factors of Basketball Success,” established by basketball statistician pioneer Dean Oliver, have long played a role in the understanding and the evaluation of team success (www.basketball-reference.com/about/factors.html). Simply put: If you’re better than your opponent at making field goals, creating turnovers, grabbing rebounds and getting to the foul line, then you’re going to win many more games than you will lose.

• William Cade