Tag Archives: statistics

Getting the data to model society like we model the natural world

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A recent session at the American Association for the Advancement of Science included a discussion of how to model the social world:

Dirk Helbing was speaking at a session entitled “Predictability: from physical to data sciences”. This was an opportunity for participating scientists to share ways in which they have applied statistical methodologies they usually use in the physical sciences to issues which are more ‘societal’ in nature. Examples stretched from use of Twitter data to accurately predict where a person is at any moment of each day, to use of social network data in identifying the tipping point at which opinions held by a minority of committed individuals influence the majority view (essentially looking at how new social movements develop) through to reducing travel time across an entire road system by analysing mobile phone and GIS (Geographical Information Systems) data…

With their eye on the big picture, Dr Helbing and multidisciplinary colleagues are collaborating on FuturICT, a 10-year, 1 billion EUR programme which, starting in 2013, is set to explore social and economic life on earth to create a huge computer simulation intended to simulate the interactions of all aspects of social and physical processes on the planet. This open resource will be available to us all and particularly targeted at policy and decision makers. The simulation will make clear the conditions and mechanisms underpinning systemic instabilities in areas as diverse as finance, security, health, the environment and crime. It is hoped that knowing why and being able to see how global crises and social breakdown happen, will mean that we will be able to prevent or mitigate them.

Modelling so many complex matters will take time but in the future, we should be able to use tools to predict collective social phenomena as confidently as we predict physical pheno[men]a such as the weather now.

This will require a tremendous amount of data. It may also require asking for a lot more data from individual members of society in a way that has not happened yet. To this point, individuals have been willing to volunteer information in places like Facebook and Twitter but we will need much more consistent information than that to truly develop models like are suggested here. Additionally, once that minute to minute information is collected, it needs to be put in a central dataset or location to see all the possible connections. Who is going to keep and police this information? People might be convinced to participate if they could see the payoff. A social model will be able to do what exactly – limit or stop crime or wars? Help reduce discrimination? Thus, getting the data from people might be as much of a problem as knowing what to do with it once it is obtained.

Using analytics and statistics in sports and society: a ways to go

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Truehoop has been doing a fine job covering the 2013 MIT Sloan Sports Analytics Conference. One post from last Saturday highlighted five quotes “On how far people have delved into the potential of analytics“:

“We are nowhere yet.”
— Morey

“There is a human element in sports that is not quantifiable. These players bleed for you, give you everything they have, and there’s a bond there.”
— Bill Polian, ESPN NFL analyst

“When visualizing data, it’s not about how much can I put in but how much can I take out.”
— Joe Ward, The New York Times sports graphics editor

“If you are not becoming a digital CMO (Chief Marketing Officer), you are becoming extinct.”
— Tim McDermott, Philadelphia Eagles CMO

“Even if God came down and said this model is correct … there is still randomness, and you can be wrong.”
— Phil Birnbaum, By The Numbers editor

In other words, there is a lot of potential in these statistics and models but we have a long way to go in deploying them correctly. I think this is a good reminder when thinking about big data as well: simply having the numbers and recognizing they might mean something is a long way from making sense of the numbers and improving lives because of our new knowledge.

Looking at the data behind the claim that more black men are in jail than college

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A scholar looks at his own usage of a statistic and where it came from:

About six years ago I wrote, “In 2000, the Justice Policy Institute (JPI) found evidence that more black men are in prison than in college,” in my first “Breaking Barriers” (pdf) report. At the time, I did not question the veracity of this statement. The statement fit well among other stats that I used to establish the need for more solution-focused research on black male achievement…

Today there are approximately 600,000 more black men in college than in jail, and the best research evidence suggests that the line was never true to begin with. In this two-part entry in Show Me the Numbers, the Journal of Negro Education’s monthly series for The Root, I examine the dubious origins, widespread use and harmful effects of what is arguably the most frequently quoted statistic about black men in the United States…

In September 2012, in response to the Congressional Black Caucus Foundation’s screening of the film Hoodwinked, directed by Janks Morton, JPI issued a press release titled, “JPI Stands by Data in 2002 on Education and Incarceration.” However, if one examines the IPEDS data from 2001 to 2011, it is clear that many colleges and universities were not reporting JPI’s data 10 years ago.

In 2011, 4,503 colleges and universities across the United States reported having at least one black male student. In 2001, only 2,734 colleges and universities reported having at least one black male student, with more than 1,000 not reporting any data at all. When perusing the IPEDS list of colleges with significant black male populations today but none reported in 2001, I noticed several historically black colleges and universities, including Bowie State University, and my own alma mater, Temple University. Ironically, I was enrolled at Temple as a doctoral candidate in 2001.

When I first saw this, I first thought it might be an example of what sociologist Joel Best calls a “mutant statistic.” This is a statistic that might originally be based in fact but at some point undergoes a transformation and keeps getting repeated until it seems unchallengeable.

There might be some mutant statistic going here but it also appears to be an issue of methodology. As Toldson points out, it looks like this was a missing data issue: the 2001 survey did not include data from over 1,000 colleges. When more colleges were counted in 2011, the findings changed. If it is a methodological issue, then this issue should have been caught at the beginning.

As Best notes, it can take some time for bad statistics to be reversed. It will be interesting to see how long this particular “fact” continues to be repeated.

Proposing marriage through a statistical model

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Statistical models are supposed to help predict life, right? So why not use a statistical model to help make a marriage proposal?

I think this is clever. And the one-page paper that goes with it is not bad either.

h/t Instapundit

Argument: statistics can help us understand and enjoy baseball

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An editor and writer for Baseball Prospectus argues that we need science and statistics to understand baseball:

Fight it if you like, but baseball has become too complicated to solve without science. Every rotation of every pitch is measured now. Every inch that a baseball travels is measured now. Teams that used to get mocked for using spreadsheets now rely on databases packed with precise location and movement of every player on every play — and those teams are the norm, not the film-inspiring exceptions. This is exciting and it’s terrifying…

I’m not a mathematician and I’m not a scientist. I’m a guy who tries to understand baseball with common sense. In this era, that means embracing advanced metrics that I don’t really understand. That should make me a little uncomfortable, and it does. WAR is a crisscrossed mess of routes leading toward something that, basically, I have to take on faith…

Yet baseball’s front offices, the people in charge of $100 million payrolls and all your hope for the 2013 season, side overwhelmingly with data. For team executives, the basic framework of WAR — measuring players’ total performance against a consistent baseline — is commonplace, used by nearly every front office, according to insiders. The writers who helped guide the creation of WAR over the decades — including Bill James, Sean Smith and Keith Woolner — work for teams now. As James told me, the war over WAR has ceased where it matters. “There’s a practical necessity for measurements like that in a front office that make it irrelevant whether you like them or you don’t.”

Whether you do is up to you and ultimately matters only to you. In the larger perspective, the debate is over, and data won. So fight it if you’d like. But at a certain point, the question in any debate against science is: What are you really fighting and why?

As someone who likes data, I would statistics is just another tool that can help us understand baseball better. It doesn’t have to be an either/or argument, baseball with advanced statistics versus baseball without advanced statistics. Baseball with advanced statistics is a more complete and gets at some of the underlying mechanics of the game rather than the visual cues or the culturally accepted statistics.

While this story is specifically about baseball, I think it also mirrors larger conversations in American society about the use of statistics. Why interrupt people’s common sense understandings of the world with abstract data? Aren’t these new statistics difficult to understand and can’t they also be manipulated? Some of this is true: looking at data can involve seeing things in news ways and there are disagreements about how to define concepts as well as how to collect to interpret data. But, in the end, these statistics can help us better understand the world.

Sears hopes Moneyball addition to its board can help revive the company

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Here is an odd mixing of the data, sports, and business worlds: Sears recently named Paul Podesta to its board.

Paul DePodesta, one of the heroes of Michael Lewis’ “Moneyball: The Art of Winning an Unfair Game,” a great 2003 baseball book (and later a movie) about the 2002 A’s that’s more about business and epistemology than baseball, has been named to the board of Hoffman Estates-based Sears Holdings Corp.

To be sure, he’s an unconventional choice for the parent of Sears and Kmart. But Chairman Edward Lampert is thinking outside the box score, welcoming the New York Mets’ vice president of player development and amateur scouting into his clubhouse…

“What Paul DePodesta … did to bring analytics into the world of baseball is absolutely parallel to what needs to happen — and is happening — in retail,” said Greg Girard, program director of merchandising strategies and retail analytics for Framingham, Mass.-based IDC Retail Insights.

“It’s a big cultural change, but that’s something a board member can effect,” Girard said. “And he’s got street cred to take it down to the line of business guys who need to change, who need to bring analytics and analysis into retail decisions.”…

“Analytics has been something folks in retail have talked about for quite some time, but they’re redoubling their efforts now,” Girard said. “Drowning in data and not knowing what data’s relevant, which data to retain and for how long, is the No. 1 challenge retailers are having as they move into what we call Big Data.”

Fascinating. People like Podesta are credited with starting a revolution in sports by developing new statistics and then using that information to outwit the market. For example, Podesta and a host of others before him (possibly with Bill James at the beginning), found that certain traits like on-base percentage were undervalued and teams, like the small-market Oakland Athletics, could build decent teams without overpaying for the biggest free agents. Of course, once other teams caught on to this idea, on-base percentage was no longer undervalued. The Boston Red Sox, one of the biggest spending baseball teams, picked up this idea and paid handsomely for such skills and went on to win two World Series championships. So teams now have to look at other undervalued areas. One recent area that Major League Baseball shut down was spending more on overseas talent and draft picks to build up a farm system quickly. These ideas are now spreading to other sports as some NBA teams are making use of such data and new precise data will soon be collected with soccer players while they are on the pitch.

The same thought process could apply to business. If so, the process might look like this: find new ways to measure retail activity or hone in on less understood data that is out there. Then maximize a response to these lesser-known concepts and move around competitors. When they start to catch on, keep innovating and stay ahead a step or two. Sears could use a lot of this moving forward as they have struggled in recent years. Even if Podesta is able to identify trends others have not, he would still have to convince a board and company to change course.

It will be interesting to see how Podesta comes out of this. If Sears continues to lose ground, how much of that will rub off on him? If there is a turnaround, how much credit would he get?

NCAA Scholarly Colloquium: ideology versus “In God we trust; everyone else should bring data”

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The Chronicle of Higher Education examines how much criticism of the NCAA will be allowed at its upcoming annual Scholarly Colloquium and includes a fascinating quote about how data should be used:

The colloquium was the brainchild of Myles Brand, a former NCAA president and philosopher who saw a need for more serious research on college sports. He and others believed that such an event could foster more open dialogue between the scholars who study sport issues and the people who work in the game.

Mr. Brand emphasized that the colloquium should be data-based and should avoid ideology. “Myles always used to joke: ‘In God we trust; everyone else should bring data,'” said Mr. Renfro, a former top adviser to Mr. Brand.

But as Mr. Renfro watched presentations at last year’s colloquium, which focused on changes the NCAA has made in its academic policies in recent years, he did not see a variety of perspectives.

“I was hearing virtually one voice being sung by a number of people … and it was relatively critical of the NCAA’s academic-reform effort,” he said. “I don’t care whether it was critical or not, but I care about whether there are different perspectives presented.”

This is a classic argument: data versus ideology, facts versus opinions. This short bit about Myles Brand makes it sound like Brand thought bringing more data to the table when discussing the NCAA would be a good thing. Data might blunt opinions and arguments and push people with an agenda to back up their arguments. It could lead to more constructive conversations. But, data is not completely divorced from ideology. Researchers choose what kind of topics to study. Data has to be collected in a good manner. Interpreting data is still an important skill; people can use data incorrectly. And it sounds like an issue here is that people might be able to use data to continue to criticize the NCAA – and this does not make the NCAA happy.

Generally, I’m in favor of bringing more data to the table when discussing issues. However, having data doesn’t necessarily solve problems. As I tell my statistics classes, I don’t want them to be people who blindly believe all data or statistics because it is data and I also don’t want them to be people who dismiss all data or statistics because they can be misused and twisted. It sounds like some of this still needs to be sorted out with the NCAA Scholarly Colloquium.

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

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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?

How statistics “change(s) the way you see the world”

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This article suggests looking at some well-known statistics problems will “change the way you see the world.” Enjoy the Monty Hall problem, the birthday paradox, gambler’s ruin, Abraham Wald’s memo, and Simpson’s paradox.

Here is what is missing from this article: explaining how statistics is helpful beyond these five particular cases. How would statistics help in a different situation? What is the day-to-day usefulness of statistics? I would suggest several things:

1. Statistics helps move us away from individualistic or anecdotal views of reality and toward a broader view.

2. Statistics can encourage us to ask questions about “reality” and look for data and hidden patterns.

3. Knowing about statistics can help people decipher the numbers that see every day in news stories and advertisements. What do survey or poll results mean? What numbers can I trust?

Tackling these sorts of issues would be much better for the public than looking at five fun and odd applications of statistics. Of course, these three points may not be as interesting as five statistical brain teasers but these five cases should be used to point us to the larger issues at hand.