by Thomas Wailgum

Fantasy Football Guru Ignores Her Instincts, Trusts Analytics

Sep 08, 2010
Business IntelligenceCloud ComputingConsumer Electronics

IBM's Hetal Thaker bucks a couple of common stereotypes regarding football viewership and fantasy football leagues -- and uses predictive analytics to draft her way to success. Here's her advice on analyzing data on running backs and running your business.

Millions of NFL fans have completed their fantasy football drafts by now. The NFL schedule was set months ago and the season kicks off on Thursday night, with the Minnesota Vikings taking on the New Orleans Saints in prime time.

For many fantasy fans, the player-drafting strategies start with the best intentions: I’ll do my research, check out some pro football websites and make a list of the players I really want.

However, when draft day finally happens and you’ve neglected to do any research, that “strategy” quickly degrades into player selections based on “gut feelings” that may or may not correspond to actual statistical evidence, current news or historical player trends. (The five beers don’t help matters, either.)

In short: Most people wing it. Surveys have demonstrated an analogous situation inside businesses: Executives struggle to choose between relying on various spreadsheets (which might not be up to date or even correct) and gut feelings when trying to make strategic decisions. (See’s To Hell with Business Intelligence: 40 Percent of Execs Trust Gut.)

IBM Hetal Thaker Football

Hetal Thaker, a product manager at IBM and fantasy football enthusiast, is one woman who is definitely not winging it. Thaker uses widely available IBM predictive analytics software that taps into the large pool of qualitative and quantitative information available to fantasy team managers everywhere.

[ Read how the NFL uses off-the-shelf software to create its complex schedule ]

Her enviable record (three league wins in the last five years, since turning to the software) speaks for itself. Thaker recently spoke with Senior Editor Thomas Wailgum (who’s already been mathematically eliminated from winning his fantasy football league) about fantasy football strategies, trash talking, and her love of analytics and the woeful Detroit Lions. Of course you’ve got me thinking about how horribly I’ve drafted my team for this year. Thanks. Are your fellow league members aware of your predictive analytics secret?

Thaker: They’re quite aware that I’m using predictive analytics, not that they like it very much because it gives me an edge. “Predictive analytics” might be a term that scares some people off. How do you describe it to people in your leagues?

Thaker: It really gets into taking qualitative information—that textual, historical and current player information—and using that with the numeric and quantitative information. The combination is incredibly valuable. And you’re making your predictions that much stronger. So did you write your own application, or do you use IBM software? How does it work?

Thaker: I’m lucky because we have wonderful tools and apps at SPSS and IBM to use. One lets me get all the information I need and allows me to prepare and clean [the data] to make sure it’s correct. And then I use our statistics product, which is a Windows-based desktop application, and I just pull in the data. The nice thing is that it uses any format—whether it’s text or Excel.

Then I take that historical information and dump it into our modeling tool. It has a text-analytics piece, which allows me to take all of that qualitative information—the news stories, the injury reports, the analysts briefings—and rather than reading each individual piece, it lets me automatically categorize information: Basically put it into buckets of things as simple as negative or positive comments, such as “Are they hurt? On suspension?” So I have all these “positives” and “negatives,” and you can keep it as simple as that as if you want. But the tool allows me to dig into my details if I really want to get there.

So, I take that historical quantitative information, take my qualitative information, which I’ve now categorized, and put all that into my model. And when we go a step further—and I’m not a modeling guru whatsoever—it’s really this application that allows me to take all this information in and “auto-model” it. Meaning, I don’t know which model is best [for predicting the best fantasy football draft]. I simply say: Here’s my data, give me a bunch of models, and then it tells me which ones have the best fit or predictive value. From that, I pick my model to use.

Now I can predict a number of different elements: Let’s say I want to predict the number of touchdowns a receiver is going to get or number of total yards that a quarterback is going to throw. Or maybe I just want to know what my total fantasy football points are [going to be]. Because at the end of the day that’s what I’m going after.

It’s no different for a business, because at the end of the day the business is trying to increase the bottom line. It’s all about profits. But if you can focus on the right people, the right kinds of customers or right decisions to increase the bottom line—without doing a lot of work—then that’s exactly what you’re looking for. You buck a couple of common stereotypes regarding football viewership and fantasy leagues: 1. You are a woman. 2. You are a bit geeky—and I say that with all due respect. Do you feel like you’ve broken down some barriers for women but who might be reluctant to join a league?

Thaker: I hope so. When you hear “predictive analytics” or “fantasy football” it tends to be intimidating. When I first started at IBM [after IBM acquired SPSS], I had to give a presentation to all of these managers on any topic I chose. I ended up picking fantasy football [and the analytics application I created].

The room was split 50-50—half men, half women. The men were interested—actually, they were more interested to know who was on my team. And the women got it. I know at least two women who sat in that room listening to how fantasy football worked for 30 minutes, who ended up playing in a [fantasy] league the year after. Do you ever consider that you’re taking some of the fun out of the experience, since you’re being so scientific about it?

Thaker: No. I think this has just increased my fun, because I love sports and fantasy football. I don’t want to make this a boy-girl issue, but the boys always had the edge: They’ve always read the sports page; though, don’t get me wrong, I’ll read the sports page. But I’d rather read Vogue.

While building this model and my dictionaries for the text analytics, I’ve learned more about football than I ever knew, which got me excited about my draft. Because when it comes to draft time, I’m more informed. When I walk into my draft, I know who I’m targeting, I’m more intelligent about what I’m doing next, and I’m not winging it. So you’re competitive?

Thaker: Just a little bit. A year’s worth of bragging rights is worth all the effort in the world when it comes to my league. Would you consider selling your customized model to other fantasy football fans, like myself?

Thaker: It’s something I’ve been asked about. But I think it’s important to understand: It’s not building the model that is difficult, because the tools are so powerful anybody can really do it. You just need to know a little bit about football. Again, all of the out-of-the-box tools in both the statistics and modeling applications are really simple.

The real edge that I have is not so much in having tools to do this, but in getting the data. So I can give you the model, but you’re going to have to do the leg work to get the data. Like BYOD: Bring Your Own Data. And while this model is very good, in my opinion, the next step is just not about drafting but in helping figure out who to play each week. That’s what I’m working on next.

[ Check out why sports fans should thank tech vendors for making sports viewing so damn amazing ] Can you recall a draft selection that you’ve blown?

Thaker: Well, it wasn’t me personally, but it’s what happened with Tom Brady in 2008. [The New England Patriots’ quarterback suffered a season-ending knee injury in the first game.] Who knew this injury was going to happen? Those are things that are very difficult to predict and are considered anomalies.

But that information is going to be very valuable two years later, because it’s important to know what surgeries he had or that he sat out that full season.

Or look at what happened with [Minnesota Vikings running back] Adrian Peterson: A lot of people drafted him last year because he was the No. 1 draft pick from a fantasy standpoint in 2008. But with Brett Favre playing [as the Vikings new quarterback in 2009], Peterson wasn’t going to be running as much, since Favre was passing more. So Peterson didn’t get as many plays as he did the year before. It’s really important to take that kind of stuff into account. Your overall strategy is very different than many of us who show up at the draft with beer in hand and some vague notion of who’s draft-worthy and who’s not. Do you think that is analogous to some businesses today who still rely on various spreadsheets and gut feelings when trying to make strategic decisions?

Thaker: Absolutely. What I’ve seen at my time at SPSS and now IBM and from talking to a number of different customers is that too many organizations do things because they’ve always done it that way. Same thing with those at the draft who say: I always pick this player, I know he’s good, and that’s always how I draft.

Just like with the draft, the data is a critical piece. And today, most organizations have large amounts of data that they are not leveraging…or sharing among their departments. So what’s your record over the years?

Thaker: I’ve been playing for 11 years now. I had won once, in 2000 or 2001, and that was a total fluke. I started here [at SPSS] six years ago, and to learn about all of these applications, I built this [fantasy football] model. And I’ve won three times in the last five years since using it. You’ve done a lot better than your beloved Detroit Lions?

Thaker: Yeah. If I could use it to help them out, I would. So do you let your emotions toward Detroit bias your draft decisions or do you stick to your what your analytic models are telling you?

Thaker: I do. But it’s hard not to be biased. You mentioned “What’s the fun?”: Well, I don’t want to take away all the fun and be so rigid that I’m always going after the numbers. The last Lions players I drafted was Charlie Rogers, and he disappointed me. Do you have any Lions players on your draft this year?

Thaker: My first draft is tonight, and I’ve got two tomorrow. So I’ll have to get back to you on that. I’m going to guess probably not.

Thaker: [laughs] We’ll see how it goes. Thanks for your time and good luck, although you probably won’t need it.

Thomas Wailgum covers Enterprise Software, Data Management and Personal Productivity Apps for Follow Tom on Twitter @twailgum. Follow everything from on Twitter @CIOonline. Email Tom at