Last week, HP’s Vertica led a call with a trio of interesting statistics pros. The vendor dubbed it as a connecting point between “big data” and sports. That connection is what drew me into the call, as I’m eager to share any and all actual application of business use cases with unstructured data, advanced analytics and all of your big data V’s with our audience.

Instead, the sports side of the presentation was a range of very slick statistical models, with the occasional mention of the size of the data set. And on the Vertica side it was about, well, selling Vertica’s database capabilities. The pros at STATS and Sports Reference have some unquestionably cool stats tinkering going on, and HP’s Vertica has a recognized track record in the enterprise analytics field, but to me the call was kind of like inviting three guys to play a pick-up basketball game at a baseball diamond.

Neither party was guilty of any great injustice – this was a sponsored Web seminar, after all – though it was endemic of the conversation on analytics when it comes to sports. There’s a whole lot of promise from the boutique sports analytics space, which, when data comes slugged with the name of your favorite base-stealing outfielder, has little issue with attracting your attention. The problem I have is that the flash and dash leading the sports analytics conversation is regularly missing the broader business use distribution of analytics, BI and, I guess, big data capabilities. It’s not a semantic difference that would trip up your CIO, data modeler or application developer. But it’s a confusion (willful or not) of analytic terms that could dilute the real data discussions between department leaders and data diggers and, worse yet, isn’t transferable to the wider and much larger enterprise and SMB IT markets.

I, too, am pulled in by black-and-white sports applications of deep-dive analytics. What’s not to like about the idea that any small-market business could have as much insight and reach as the massive enterprise competition? (Although, truthfully, as a fan of the lowly Buffalo Bills, another recent analytic convert, I’d consider a pact with the netherworld at this point to get my team into a meaningful late-season game.)

But where’s the analytic use case by a plucky southern U.S. NHL team that overcomes the quality issues with its metadata to truly define its die-hard fan base and keep the team from pulling up stakes? Or the second-tier Premier League squad that finds the best analytic confluence between salesforce.com sales figures, supply chain data and their legacy ERP system? You know, the win over the dirty daily business problems, needs and investments.

Assuredly, there are solid examples tucked in the conference lineups at MIT Sloan’s celebrated sports conference and the growing body of similar events, or rolled out ahead of the “big game.” Huge sporting events open opportunities for social media connections and analytic ROI, like IBM’s annual U.S. Open initiative. A few years ago, the Dallas Cowboys gave us a peek into their innovative real-time merchandise sales metrics. One of our “25 Top Information Managers of 2012” was Anthony Perez at the NBA’s Orlando Magic, who was harnessing predictive models not to winnow his team’s poorest free-throw shooters, but for the business-minded perspective of who was likely to return as a season ticket holder, and how to market to those different ticket buyers and sellers on a cutthroat secondary market.

These are the ways toward clear conversion of the showy potential with professional sports’ application of analytics into the wider enterprise IT implementation and business-side discussion. While they may not bring forth the blockbuster Hollywood promise – which one of your favorite Forrester or Ventana analysts will be played by Brad Pitt?!? – I see these transferable uses as holding the most, big score business potential. Now, if we’d only hear more about these methods from the vendors, analysts and teams, who, by their competitive nature, hold efforts close to the jersey.

In professional baseball, one of the most dynamic team leaders outside of the bullpen is the five-tool player. It’s a rare player who can do it all extremely well, at the plate and in the field, with speed and power. Think Los Angeles Dodger star Matt Kemp, or before him all-around athlete (and nerve tonic addict on “The Simpsons”) Ken Griffey, Jr. Slick sports analytics are too often sold publicly to business as this all-encompassing player, the one thing their “team” is missing that can plug the competitive holes. While great for sports, these “Moneyball” models, stats and related play-by-play are much more akin to the journeyman designated hitter when it comes to business initiatives: a sometimes-used beefcake who strolls in to knock one out of the park but, more often than not, strikes out.

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