Quicken Loans’ $1 billion bracket challenge set to find more mortgage customers

Your odds of winning $1 billion from Quicken Loans for having a perfect NCAA bracket are really low – and the company will get great free data on potential mortgage customers.

To register for the contest, you have to sign up for a Yahoo account—a boon in itself for Yahoo, on whose site the contest is run. Then you’re asked to enter your name, address, email, birthday, and the answers to several questions about your home mortgage situation. All of this information goes to Quicken Loans, the fourth-largest mortgage-lender in the U.S.

It’s no coincidence that this information—where do you live? Do you want to buy a home? What’s your current mortgage rate?—is exactly what you need if you want to sell someone a home loan…

It’s not uncommon for companies like Quicken to pay between $50 and $300 for a single high-quality mortgage lead, Lykken says.

Quicken says the info-gathering is not intended for lead generation. Instead, the company says it’s building a base of relationships with people who may want home loans in the future. “The people that are playing the Billion Dollar Bracket kind of fit our demographic,” says Jay Farner, Quicken’s president and marketing chief. “But for the most part, unless they’ve opted in and said ‘please call me,’ it’s not a mortgage lead for us.”

This is the magic of the Internet for companies: users are willing to trade their information for some good. On Facebook, it is a trade of ongoing personal information for social interaction. In this bracket challenge, it is the trade of personal information for the chance to win both (1) $1 billion and (2) the ultimate bragging rights of having a completely correct bracket when millions of others couldn’t do it. Instead of having to make broad appeals to all consumers, companies can instead target specific consumers.

The argument in this article is that the particular trade here is not good for the average player: with the odds at “a 1 in 8,500 chance that anyone wins,” it is not worth giving up personal information. But, this is the sort of calculation that all Internet users must make all the time with all sorts of sites. Do I want to give up information about my music tastes to Spotify if they can use that to sell me targeted ads? What happens when Amazon gets information about hundreds of products I like? What if Google can see all of my searches? These trade-offs are harder to calculate and to avoid making them, the average user won’t be able to do much online.

Reading between the lines of an ABC News story on the bad odds of winning the $500 million Powerball lottery

Check out this ABC News video about the odds of winning the $500 million Powerball lottery.

Several things are striking about the content of the video beyond the bad odds of winning: 1 in 175 million chance.

1. A journalist admits he doesn’t know much about math or statistics. It is not uncommon for reporters to go to experts like statisticians in times like these (appealing to the expert boosts the credentials of the story) but it is more unusual for journalists to admit they are doing so because they don’t know the information. I’ve argued before we need more journalists who understand statistics and science.

2. The reporter mentions some interesting odds that are more favorable than winning the Powerball. One of these is the idea that you are more likely to be possessed by the devil today than win the lottery. Who exactly keeps track of these figures and how accurate are they?

3. The story includes some talk about being more likely to win in particular states than others. Really? This sounds more like statistical noise or something related to the population of the states with multiple Powerball winners (like Illinois and New Jersey).

4. Interesting closing: the math expert himself hasn’t bought a lottery ticket before. So the moral of the story is that people shouldn’t buy any tickets?

More $1 million lottery winners each year than NBA players since 1990 that have career earnings over $1 million

I’ve written before about using the average vs. the median salary in the NBA lockout discussions and here is some more fuel to add to the fire: there are more $1 million lottery winners each year than NBA players who since 1990 have had career earnings of more than $1 million.

I want to call foul on the mainstream media. As I mentioned, a majority of the players in the league make less than $2 million, and yet people like Stephen A. Smith throw around that $5 million figure as gospel. We keep hearing the NBA lockout being described as “millionaires versus billionaires”. But most NBA players won’t become big earners like Kobe and LeBron. Here’s a fun breakdown:

Since the 1990-1991 season 1461 players have entered the NBA and of those:

  • 490 — or 33% — never earned $1 million in career earnings*
  • and that means… 971 have earned at least $1 million in career earnings*
  • 752 have averaged a salary of at least $1 million per year*
  • 643 have earned at least $5 million in career earnings*
  • 165 averaged a salary of at least $5 million per year*

As we can see, less than half of all NBA players in the last 20 years — the period of time where NBA salaries have been at their highest — have hit that $5 million mark over their entire careers. Just over one third — 33% — of all NBA players in the last 20 years have not even hit the $1 million mark in career earnings. And these numbers have been adjusted for inflation!

Here’s a fun comparison: on average, 1600 people win a lottery of at least $1 million every year! That’s right; the lottery has produced almost twice as many millionaires in the last year as the NBA has in the last twenty years!  The popular perception is that once a player enters the NBA they will earn millions and millions of dollars. The truth is that many players don’t hit that high mark.

Both events, winning the big lottery jackpot and becoming a NBA player, are statistically unlikely. However, I suspect that most Americans would say that winning the lottery is much more unlikely. But this blog post points out that even when players do make it to the NBA, a third don’t rake in the big career earnings associated with professional athletes (measured here as $1 million).

This would make for an interesting discussion starter for any professional athlete’s union: should the union be more concerned with allowing a smaller percentage of the athletes maximize their salaries or be more interested in guaranteeing a baseline for the majority of the league that are not stars?

The lottery figures themselves are interesting:

According to the TLC television show, “The Lottery Changed My Life,” more than 1600 new lottery millionaires are created each year. That doesn’t include people that have won jackpots of, say, $100,000 because than the number would be much higher. Still, 1600 is quite a high number.

If 1600 win at least a million in the lotto every year, it means that there are more than 130 each month, more than 30 each week, and more than 4 each day. That’s a lot of winners.

It would be interesting to see more documentation on this.

Discovering fake randomness

In the midst of a story involving fake data generated for DailyKos by the polling firm, Research 2000, TechDirt summarizes how exactly it was discovered that Research 2000 was faking the data. Several statisticians approached Kos after seeing some irregularities in cross-tab (table) data. The summary and the original analysis on DailyKos are fascinating: even truly random data follows certain parameters. One takeaway: faking random data is a lot harder than it looks. Another takeaway (for me at least): statistics can be both useful and enjoyable.

The three issues as summarized on DailyKos:

Issue one: astronomically low odds that both male and female figures would both be even or odd numbers.

In one respect, however, the numbers for M and F do not differ: if one is even, so is the other, and likewise for odd. Given that the M and F results usually differ, knowing that say 43% of M were favorable (Fav) to Obama gives essentially no clue as to whether say 59% or say 60% of F would be. Thus knowing whether M Fav is even or odd tells us essentially nothing about whether F Fav would be even or odd.

Issue two: the margin between favorability and unfavorability ratings did not display enough variance. If the polls were truly working with random samples, there would be broader range of values.

What little variation there was in the difference of those cross-tab margins seemed to happen slowly over many weeks, not like the week-to-week random jitter expected for real statistics.

Issue three: the changes in favorability ratings from week to week were too random. In most polls like this that track week to week, the most common result is no change. Research 2000 results had too many changes from week to week – often small changes, a percent either way.

For each individual issue, the odds are quite low that each would arise with truly random data. Put all three together happening with the same data and the odds are even lower.

Besides issues regarding integrity of data collection (and it becomes clearer why many people harbor a distrust toward polls and statistics), this is a great example of statistical detective work. Too often, many of us see numbers and quickly trust them (or distrust them). In reality, it takes just a little work to dig deeper into figures to discover what exactly is being measured and how it is being measured. The “what” and “how” matter tremendously as they can radically alter the interpretation of the data. Citizens and journalists need some of these abilities to decipher all the numbers we encounter on a daily basis.