Can Big Data Tackle Football with Machine Learning?

Mockup of telestrator diagram. Based on Image:UT Longhorn football – handoff to Melton in Big12 championship game.JPG by Johntex.

In the public mind, the movie Moneyball captures the fusion of sports and statistics for competitive advantage.

MLB and the NBA have embraced the video analysis of computer vision and the data crunching of machine learning for some time.

Now, the NFL could be next. Derrick Harris at Gigaom offers insights on this trend as reported on by Judy Battista for the New York Times.

Most intriguing is the idea that NFLers have not embraced these technologies as readily as their pro sports peers because the complexity of the game defies decoupling teamwork into discrete actions readily attributed to individual players.

Think of offensive linemen working together to protect quarterbacks and running backs. Or defensive linebackers who don’t get many tackles, but make it difficult for the offense to execute.

For this reason, many NFL coaches prefer to assess players based on how they look on film.

Over time, however, this resistance to analytics is likely to fade as machine learning based applications that use computer vision prove their value and become easier to use.

In fact, it could simply be a matter of identifying more subtle metrics to extract and analyze that previously evaded human detection.

For example, during the 2012 playoffs, the Wall Street Journal’s John Letzig reported on how MLB used motion-analysis software from Sportvision Inc. to quantify an outfielder’s ability to get a jump on fielding fly balls.

Given the rich data complexity of football, it’s hard to imagine coaches not eating up algorithm-powered, in-situ forecasts that take player stats, weather and game scenarios into account and identify those variables most likely to influence what happens next.

Or team management not angling for competitive advantage at the lowest possible cost by pinpointing those overlooked, game-deciding metrics that don’t correlate with salary levels like fourth down conversions (just like on-base percentage was the key focus in Moneyball).

In other instances, humans can request recalibration of the algorithms so video tracking models ignore what they consider to be noise and add additional factors they view as pivotal.

We reported on how Zest Finance continually improved the accuracy of its credit underwriting assessments in the payday lending market by taking this approach with respect to 70,000 variables.

Part of football’s very appeal is its complexity and the many inter-dependencies that make it tick. And so it’s a natural for the video scrutiny and data mining that computer vision and machine learning make possible.

Are you involved in an activity where many individuals come together to form a whole greater than the sum of its parts?

How could analysis of its finer points of interaction unlock hidden value in your business?


IBM: Computers that Think in 5 Senses within 5 Years

Smart phones getting smarter

Nancy Houser, writing for Digital Journal, surveys the landscape of cognitive computing and provides a lucid view of what to expect over the next 5 years.

Smart phones getting smarter
Smart phones are ramping up their intelligence (Courtesy of Digital Journal, Photo: Espen Irwing Swang)
A key theme is that of computers moving from glorified calculators to thinking machines that augment all 5 of your senses.

For example, imagine computers responding to your inquiry with richer, more valuable outputs like the texture of fabric or the aroma of fresh cut grass.

What’s making it possible to enhance and extend our senses with intelligence is the convergence of computer vision, machine learning, big data, speech recognition, natural language processing, smartphones and biometric sensors.

One hurdle to humanizing computers has been making the leap from performing predefined tasks based on programmed logic to handling new tasks autonomously without dedicated software programs already in place. (e.g., unsupervised machine learning.)

Towards bridging the gap, Houser discusses the promising work of Prof. Chris Eliasmith on computing efforts to reverse engineer the biology of the mammalian brain.

In the past, human brain structure and operation has been replicated in fine detail to accomplish tasks like handwriting recognition, adding via counting, and even interpreting patterns associated with higher-level intelligence.

Now, by adding interfaces that sense touch, pressure and heat, we’re beginning to see more human-like computers that can park cars and perform biometric security functions. IBM is already at work on cognitive computing applications for retailers.

In some respects, cognitive computing could be the near term forerunner of cyborg singularity speculated in the Google hire of Ray Kurzweil.

So, how do you see, hear, smell, touch, taste cognitive computing in the future of your business?

Why Humans Won’t Be Replaced by Machine Learning

Courtesy of VentureBeat

Laura Teller, guest-posting at VentureBeat makes the case that human domain expertise will never be replaced by smart machines.

For starters, smart machines need human experts to correct their mistakes and provide the closed loop feedback that makes machine learning possible.

But the bigger reasons why Teller believes humans will always remain preeminent have to do with the three levels of cognition developed by Prof. Terrence Deacon, Ph.D., Chair of the Department of Anthropology at University of California, Berkeley.

The three levels are iconic, indexic and symbolic.

Iconic cognition happens when a computer identifies something. For example, facial recognition software can distinguish human faces from everything else.

Indexic cognition takes place when we make associations. Pointing your thumb at your chest means you’re talking about yourself.

Iconic and indexic rely on patterns and rules and both readily scale.

Symbolic thought focuses on abstractions and the human tendency to “complete the picture.”

We think symbolically when we brainstorm, daydream, listen to our instincts, and question the status quo. In so doing, we rely on emotion, experience, visions, and logic… and create the conditions for innovation.

Fans of Star Trek will be familiar with this dichotomy of the iconic and indexic versus the symbolic in the characters of Captain Kirk and Mr. Spock.

(In fact, this analogy is a form of symbolic cognition :))

Mr. Spock thinks logically with the ability to mentally process vast storehouses of data quickly. Captain Kirk, while in need of Mr. Spock’s prodigious powers, always saves the day when forced to “go with his gut.”

Realizing this, imagine you had more of the iconic and indexic tasks of your business covered by machine learning. What would this free you up to do? Share your thoughts below…


Machine Learning Makes Finding Web Images Easier

Writing for the New York Times Science Desk, John Markoff reports on how computer vision and machine learning will create the next generation Internet where search engines find images and videos with the same degree of relevance as they do now with text.

And the need is crushing… in the next 60 seconds, YouTube will have uploaded 72 hours of video.

Today, unless images and videos are labeled, search engines have no way to match them against your query.  Even then, labels can be unreliable (e.g., “junk” versus the objects that comprise it).

To give search engines something akin to human sight, Stanford’s Dr. Fei Fei Li has teamed up with fellow computer scientists at Princeton to develop ImageNet, the world’s biggest image database.
Courtesy of

Given the enormity of the task and limited budget, Dr. Li connected with Mechanical Turk, the crowdsourcing system where, for a small payment per task, humans label photos. The database now has over 14 million images in over 21,000 categories thanks to the efforts of nearly 30,000 participants a year.

As the database of labeled images grows, machine learning algorithms enable software to recognize similar, unlabeled images. Over time, accuracy rates improve dramatically.

Surprisingly, when tested on a large collection of labeled images by Google computer scientists Andrew Ng and Jeff Dean, the system nearly doubled the accuracy of previous neural network algorithms designed to model human thought processes.

To further improve speed and accuracy, images are classified against WordNet, a hierarchical database of English words. With skillful programming to make educated choices about how to search the hierarchy, the database continues to rise to this growing challenge.

See the full article here.