Are You Ready for Some Football? — Data Driven Digest

Here in Silicon Valley, Super Bowl 50 is not only coming up, it’s downright inescapable. The OpenText offices are positioned halfway between Santa Clara, where the game will actually be played on Feb. 7, and San Francisco, site of the official festivities (44 miles north of the stadium, but who’s counting?).  So in honor of the Big Game, this week we’re throwing on the shoulder pads and tackling some data visualizations related to American football.  Enjoy!

Bringing Analytics Into Play

In the area of statistics and data visualization, football has always taken a back seat to baseball, the game beloved by generations of bow-tied intellectuals.  But Big Data is changing the practice of everything from medicine to merchandising, so it’s no wonder that better analysis of the numbers is changing the play and appreciation of football.

Exhibit A:  Kevin Kelley, head football coach at Pulaski Academy in Little Rock, Ark., has a highly unusual style of play–no punts.  He’s seen the studies from academics such as UC Berkeley professor David Romer, concluding that teams shouldn’t punt when facing fourth downs with less than 4 yards gained, and he came to the conclusion that “field position didn’t matter nearly as much as everyone thought it did.”

Screenshot courtesy of
Screenshot courtesy of

As Kelley explains in an ESPN short film on the hub, if you try to punt when the ball is on your 5-yard line or less, the other team scores 92% of the time. Even 40 yards from your goal line, the other team still scores 77% of the time.

“Numbers have shown that what we’re doing is correct,” he says in the film. “There’s no question in my mind, or my coaches’ minds, that we wouldn’t have had the success we’ve had without bringing analytics into (play).”

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The coach’s data-driven approach has paid off, giving Pulaski multiple winning seasons over the past 12 years, including a 14-0 record in 2015. The highlight of their latest season: Beating Texas football powerhouse Highland Park 40-13 and snapping its 84-game home winning streak, which goes back to 1999.

Bigger, Faster, Stronger

No doubt most of Coach Kelley’s players dream of turning pro. But they’ll need to bulk up if they want to compete, especially as defensive linemen.  Two data scientists offer vivid demonstrations of how much bigger NFL players have gotten over the past few generations.

Software engineer and former astrophysicist Craig M. Booth crunched the data from 2013 NFL rosters to create charts of their heights and weights.  His chart makes it easy to see how various positions sort neatly into clusters:  light, nimble wide receivers and cornerbacks; tall defensive and tight ends; refrigerator-sized tackles and guards.

Screenshot courtesy of
Screenshot courtesy of

The way Booth mapped the height/weight correlation, with different colors and shapes indicating the various positions, isn’t rocket science.

It is, however, a great example of how automation is making data visualization an everyday tool. As he explains on his blog, he didn’t have to manually plot the data points for all 1,700-odd players in the NFL; he downloaded a database of the player measurements from the NFL’s Web site, then used an iPython script to display it.

For a historical perspective on how players have gotten bigger since 1950, Booth created a series of line charts showing how players’ weights have skyrocketed relative to their heights.

Backfield in Motion

Meanwhile, Noah Veltman, a member of the data-driven journalism team at New York City’s public radio station WNYC, has made the bulking-up trend even more vivid by adding a third dimension – time – to his visualization.   His animation draws on NFL player measurements going all the way back to 1920.

Screenshots courtesy of
Screenshots courtesy of


He observes that football players’ increasing size is partly due to the fact that Americans in general have gotten taller and heavier over time – though partly also due to increasing specialization of body type by position. You can see a wider range of height-and-weight combinations as the years go by.  And from the 1990s on, they begin falling into clusters.  (You could also factor in more weight training, rougher styles of play, and other trends, but we’ll leave that discussion to the true football geeks.)

Bars, Lines, and Bubbles

Now, what kind of play are we seeing from these bigger, better-trained players? Craig M. Booth recently unveiled an even more interesting football-related project, an interactive visualizer of the performance of every NFL team from 2000 on.  He uses the Google charts API to display data from on everything from points scored by team by quarter to total passing or penalty yards.

Screenshot courtesy of
Screenshot courtesy of

You can customize the visualizer by the teams tracked, which variables appear on the X and Y-axes, whether they’re on a linear or logarithmic scale, and whether to display the data as bubble plots, bar charts, or line graphs. It can serve up all kinds of interesting correlations.  (Even though OpenText offers powerful predictive capacities in our Big Data Analytics suite, we disavow any use of this information to predict the outcome of a certain football game on February 7…)

OpenText Named a Leader in the Internet of Things

Speaking of sharing data points, OpenText was honored recently in the area of Internet of Things by Dresner Advisory Services, a leading analyst firm in the field of business intelligence, with its first-ever Technical Innovation Awards.

You can view an infographic on Dresner’s Wisdom of Crowds research.

Recent Data Driven Digests:

January 19: Crowd-Sourcing the Internet of Things

January 15: Location Intelligence

January 5: Life and Expectations


Stannie Holt

Stannie Holt is a Marketing Content Writer at OpenText. She has over 20 years' experience as a journalist, market research analyst, and content marketing expert in the fields of enterprise business software, machine learning, e-discovery, and analytics.

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