“Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom." -- Clifford Stoll

Over the past two days at the MIT Sloan Sports Analytics Conference, I’ve sat through explanations of mathematical models that would make your head spin; they certainly knocked me off my axis. But talk of complicated mathematical models has been a brief interlude in the larger discussion driving this year’s conference: explanation and communication.

Whether it’s with a compelling visualization or simple awareness of the consumer, data and information are only as powerful as the ability to express the implications to others in a time frame that preserves their usefulness.

In all the mathematical machinations of the past few days, mention of one enormous variable has been absent -- time. What’s the power of all this information if it’s reflecting on things that happened two seasons ago?

Communication and application through careful focus and visualization are essential to results, but time is ultimately the limiting factor. Paper after paper, presentation after presentation, misconceptions and mysteries have been slowly chipped away this weekend. But each advancement is the product of months of careful work and refinement. The realizations are powerful, but the cost in hours and effort is steep, costs that slow the pace of new ideas and the more effective implementation of old ones.

Even if you have been able to morph data into wisdom and found an effective way to spread that knowledge, usefulness can be rendered moot by the incredible amount of time it may take to complete that process. Question, hypothesize, design, test, analyze -- to answer questions of the gravity being addressed at the Sloan Conference the scientific method is a laborious process, but nothing is more work than repeating this process in endless cycles as you step over roadblocks, false starts and wrong turns, refocusing and reshaping until an answer is found.

Today, Rajiv Maheswaran tackled the chronological component of statistical work head on in his presentation, "Interactive Data Visualization: The Next Step in Deconstructing the Rebound." Maheswaran, a research assistant professor at the University of Southern California’s computer science department, is a Sloan veteran and last year presented a paper called "Deconstructing the Offensive Rebound with Optical Tracking Data" that ultimately won the conference’s grand prize. He was back this year, sharing the results of a project born in the challenges of assembling his work from last year. The impetus was the simple fact that it took a year of refinement to shape and reshape the data that went into last year’s award-winning paper.

Technology allows us to gather a staggering amount of data from previously inaccessible sources. Maheswaran’s work, both this year and last, is built on information culled from the SportVU optical tracking system. The raw numbers provided by this system are reportedly vast and decidedly raw in their formatting. For meaning to be identified, they need to be massaged into the structure that suits your purpose. Thankfully, the technological revolution that allows for the collection of that data also allows the opportunity to supercharge the cycle of scientific process.

Maheswaran and his partners have built a software system that allows for incredible filtering and the creation of uniquely informative visualizations using data gleaned from the SportVU camera system. In a manner of seconds, he filtered data to show that Tyson Chandler is among the league’s most efficient scorers under the conditions of being close to the rim, with a defender less than one foot away and having taken a shot off the dribble. From there he quickly transitioned through filters related to movement of shooters, shot selection for teams, proximity of defenders, charting of screens set during a game and the movement of all 10 players on the floor.

As impressive as those statistical insights were, the "Earth is round" development was the speed with which they were accessed. Stealing Maheswaran’s analogy, what he was after is the Millennium Falcon, a vehicle that brings you to your desired location at hyperspeed. He also noted that we are at a unique point in time because of the convergence of computation and analytics. People, grossly inexperienced in both computer science and analytics, have the opportunity to vastly improve their professional outcomes by interacting with data in expedient and accessible ways.

Mastering the variables of speed and time, in the way this presentation demonstrated, raises the ceiling on the effect of basketball analytics. All of the sci-fi fantasies -- in-game adjustments based on statistics tabulated and formatted from the bench, rapid player evaluation for a trade proposal that comes from nowhere, making practice adjustments on the fly based on biometric player feedback -- become realistic if time can be conquered.

As Maheswaran noted, “Finding an answer to a question and putting a value to that answer are two different things.” When that process can be implemented in real time, the true value of all the analytic work we’ve seen this weekend is realized.