Since Wolfram|Alpha launched in 2009, we’ve often said that its knowledge base covers what you’d find in a pretty good reference library—and many of the new features we’ve highlighted over the past two and a half years have indeed been very reference-y: global agriculture data, public school statistics, species information, and tons of other socioeconomic, scientific, and mathematical content. Of course, Wolfram|Alpha has always been much more than a mere repository of reference data: we’ve made it possible for people to explore, compare, compute, and interact with all that data in unprecedented ways.
We’re not about to stop our work in those domains. But now that we have a kind of critical mass of essential information about the world, we’ve begun to reach “outside the library” and experiment with more everyday kinds of topics. Within the past couple of months, we’ve shown you how Wolfram|Alpha can tell you about planes flying overhead or help you to shop for appliances and consumer electronics. And now that the NFL playoffs are in full swing, we’re proud to announce that you can now use Wolfram|Alpha to explore statistics for every NFL team, game, and player from the past 25 years.
Which means you can now get immediate, accurate results to all kinds of natural-language queries. You could ask Wolfram|Alpha to compare passing yards for Aaron Rodgers, Drew Brees, and Tom Brady. Or ask about Steelers games with a combined score over 80 or Packers games with more than 400 passing yards. You can delve into player versus team matchups, too—how about Ben Roethlisberger games versus the Broncos or Drew Brees games versus the 49ers with more than 300 passing yards?
Plug those queries into a search engine and you’ll get a few million links to wade through. But Wolfram|Alpha returns specific, accurate results (powered by data from global sports statistics company STATS LLC) and automatically generates visualizations of team and player performance over time.
Curious about this weekend’s second-round NFL playoffs? Ask Wolfram|Alpha to compare the Broncos and Patriots or compare Tim Tebow and Tom Brady in 2011 and you’ll get a head-to-head comparison of team and player statistics for the current season. But what about past matchups? Try Giants versus Packers games—or if you want to see how the Packers previously fared with a home-field advantage, you could even ask about Packers home games versus the Giants. Wolfram|Alpha generates some summary statistics over all the games that meet those conditions, but you can also click on individual games to go directly to a more detailed view of each contest.
From analyzing our logs, we know that most people try fairly simple sports queries on Wolfram|Alpha: usually just team or player names, maybe paired with a specific season. And for any year from 1985 to the present, Wolfram|Alpha now has those queries covered: try asking about the Pittsburgh Steelers or Drew Brees in 2002 (or even Da Bears in 1985) to
get a concise statistical summary. But to be honest, there are plenty of online resources available if all you want is a big, static table of team or player statistics. Our goal is to do for sports what we’ve done for hundreds of other areas of human endeavor: give you direct answers to specific questions through an intuitive natural-language interface.
So we encourage you to dig a little deeper: ask about postseason sack yards for the Cowboys in 1995, the most points scored by a safety, or the Saints running back with the most yards per reception. Looking for team rankings? Try asking for the NFC East team with the most rushing touchdowns. Or even—sorry, Rams fans—the NFL team with the worst 3rd down conversion percentage.
So what are the limitations? As I said, right now we only have data from 1985 on, so queries about the “all-time best” team or player by any given metric or even about career-level stats for players aren’t supported; the results would be incomplete and misleading in most cases. We’ve also limited ourselves to the most common, top-level statistics; you can’t yet ask for most detailed splits (e.g. “sacks in the last two minutes of the half,” “pass completions when ahead by eight points,” and so on). And we aren’t yet supporting direct queries about play-by-play data. But all of those things are in the works, along with a lot more analysis and visualization of player and team performance.
You should also see Wolfram|Alpha’s ability to understand many more questions about our existing NFL knowledge base improve over the course of the next few weeks. And you’ll be a large part of that. Just like when Wolfram|Alpha first launched, we’re not quite certain how people are going to explore this data once they realize they can do much more than just type in their favorite team or player. So we’ll be paying close attention to your inputs, seeing what works and what doesn’t, and beefing up Wolfram|Alpha’s ability to understand and compute the most popular types of queries that roll in.
In addition to professional football, we’ve also got data on basketball, baseball, and more coming soon. We know we’ve only scratched the surface here, and we’ll continue to develop and enhance our pro football coverage throughout the year—so even after this season ends, we invite you to keep sending your comments and suggestions and sharing your favorite Wolfram|Alpha football queries.
For more examples and a complete list of NFL team and player stats you can explore, visit the Wolfram|Alpha Guide to Pro Football Statistics.