Two families, both making big investments of time and money to involve their kids in sports. But the investments they’re able to make are very different. In Part 5 of “Contested,” our series on sports, society and culture: Sports and the American Dream.
Composite Photo: Thomas Schmidt, left, video still by Ian McClerin, and Jalani (“JT”) Taylor, video still by Hannah Colton.
This is an awesome and eye-opening episode. The misconceptions about sports as a “way out” are apparently even worse than I thought they were. The statistics about becoming an elite physician being better than being a pro athlete are just stunning. The availability heuristic we’re given with relation to sports constantly on television and in the media is apparently heavily hampering a lot of people specifically and society at large.
Very few people really make any money through sports. Less than 5,000 men and women all-in make a living by doing it.
There are more black cardiologists in the US than there are black men in the NBA. The odds of getting an elite job by going to medical school are infinitely better than trying to get into professional sports.
According to our statistical model, based on 2010-2015 regular season inning-by-inning scoring data,3 you should leave after the sixth inning if the leading team is ahead by four or more runs. There is a less than 5 percent chance that the other team will deliver a miracle comeback. If the run differential exceeds two at the top of the ninth, it’s safe to head to the exits. What about blowouts in the first inning? If your time is that precious — and you’re willing to view the money spent on tickets as a sunk cost — our advice is to rev up your car’s engine if the leading team jumps ahead by six runs or more.
In developing the cheat sheet, we tolerate a 5 percent false positive error rate. Take the 2016 season as an example. The impatient fan who took our advice would have left early in 1,750 games, but in 61 of those games, the eventual winner came from behind to win, and so the fan missed out on some later-inning excitement. For that season, our model attained an accuracy rate of 97 percent.
We really need the other bound as attempting to see the exciting last minute come backs are some of the best parts of baseball!
I don’t think she’s used the specific words in the book yet, but O’Neil is fundamentally writing about social justice and transparency. To a great extent both governments and increasingly large corporations are using these Weapons of Math Destruction inappropriately. Often it may be the case that the algorithms are so opaque as to be incomprehensible by their creators/users, but, as I suspect in many cases, they’re being used to actively create social injustice by benefiting some classes and decimating others. The evolving case of Facebook’s involvement in potentially shifting the outcome of the 2016 Presidential election especially via “dark posts” is an interesting case in point with regard to these examples.
In some sense these algorithms are like viruses running rampant in a large population without the availability of antibiotics to tamp down or modify their effects. Without feedback mechanisms and the ability to see what is going on as it happens the scale issue she touches on can quickly cause even greater harm over short periods of time.
I like that one of the first examples she uses for modeling is that of preparing food for a family. It’s simple, accessible, and generic enough that the majority of people can relate directly to it. It has lots of transparency (even more than her sabermetrics example from baseball). Sadly, however, there is a large swath of the American population that is poor, uneducated, and living in horrific food deserts that they may not grasp the subtleties of even this simple model. As I was reading, it occurred to me that there is a reasonable political football that gets pushed around from time to time in many countries that relates to food and food subsidies. In the United States it’s known as the Supplemental Nutrition Assistance Program (aka SNAP) and it’s regularly changing, though fortunately for many it has some nutritionists who help to provide a feedback mechanism for it. I suspect it would make a great example of the type of Weapon of Mass Destruction she’s discussing in this book. Those who are interested in a quick overview of it and some of the consequences can find a short audio introduction to it via the Eat This Podcast episode How much does a nutritious diet cost? Depends what you mean by “nutritious” or Crime and nourishment Some costs and consequences of the Supplemental Nutrition Assistance Program which discusses an interesting crime related sub-consequence of something as simple as when SNAP benefits are distributed.
I suspect that O’Neil won’t go as far as to bring religion into her thesis, so I’ll do it for her, but I’ll do so from a more general moral philosophical standpoint which underpins much of the Judeo-Christian heritage so prevalent in our society. One of my pet peeves of moralizing (often Republican) conservatives (who often both wear their religion on their sleeves as well as beat others with it–here’s a good recent case in point) is that they never seem to follow the Golden Rule which is stated in multiple ways in the Bible including:
He will reply, ‘Truly I tell you, whatever you did not do for one of the least of these, you did not do for me.
In a country that (says it) values meritocracy, much of the establishment doesn’t seem to put much, if any value, into these basic principles as they would like to indicate that they do.
I’ve previously highlighted the application of mathematical game theory before briefly in relation to the Golden Rule, but from a meritocracy perspective, why can’t it operate at all levels? By this I’ll make tangential reference to Cesar Hidalgo‘s thesis in his book Why Information Grows in which he looks not at just individuals (person-bytes), but larger structures like firms/companies (firmbytes), governments, and even nations. Why can’t these larger structures have their own meritocracy? When America “competes” against other countries, why shouldn’t it be doing so in a meritocracy of nations? To do this requires that we as individuals (as well as corporations, city, state, and even national governments) need to help each other out to do what we can’t do alone. One often hears the aphorism that “a chain is only as strong as it’s weakest link”, why then would we actively go out of our way to create weak links within our own society, particularly as many in government decry the cultures and actions of other nations which we view as trying to defeat us? To me the statistical mechanics of the situation require that we help each other to advance the status quo of humanity. Evolution and the Red Queeen Hypothesis dictates that humanity won’t regress back to the mean, it may be regressing itself toward extinction otherwise.
Highlights, Quotes, & Marginalia
Chapter One – Bomb Parts: What is a Model
You can often see troubles when grandparents visit a grandchild they haven’t seen for a while.
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Upon meeting her a year later, they can suffer a few awkward hours because their models are out of date.
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Racism, at the individual level, can be seen as a predictive model whirring away in billions of human minds around the world. It is built from faulty, incomplete, or generalized data. Whether it comes from experience or hearsay, the data indicates that certain types of people have behaved badly. That generates a binary prediction that all people of that race will behave that same way.
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Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models.
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the workings of a recidivism model are tucked away in algorithms, intelligible only to a tiny elite.
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A 2013 study by the New York Civil Liberties Union found that while black and Latino males between the ages of fourteen and twenty-four made up only 4.7 percent of the city’s population, they accounted for 40.6 percent of the stop-and-frisk checks by police.
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So if early “involvement” with the police signals recidivism, poor people and racial minorities look far riskier.
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The questionnaire does avoid asking about race, which is illegal. But with the wealth of detail each prisoner provides, that single illegal question is almost superfluous.
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judge would sustain it. This is the basis of our legal system. We are judged by what we do, not by who we are.
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(And they’ll be free to create them when they start buying their own food.) I should add that my model is highly unlikely to scale. I don’t see Walmart or the US Agriculture Department or any other titan embracing my app and imposing it on hundreds of millions of people, like some of the WMDs we’ll be discussing.
You have to love the obligatory parental aphorism about making your own rules when you have your own house. Yet the US SNAP program does just this. It could be an interesting example of this type of WMD. Highlight (yellow) page 28 | Location 497-499 Added on Friday, October 13, 2017 6:06:04 PM
three kinds of models.
namely: baseball, food, recidivism Highlight (yellow) page 27 | Location 489-489 Added on Friday, October 13, 2017 6:08:26 PM
The first question: Even if the participant is aware of being modeled, or what the model is used for, is the model opaque, or even invisible?
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many companies go out of their way to hide the results of their models or even their existence. One common justification is that the algorithm constitutes a “secret sauce” crucial to their business. It’s intellectual property, and it must be defended,
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the second question: Does the model work against the subject’s interest? In short, is it unfair? Does it damage or destroy lives?
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While many may benefit from it, it leads to suffering for others.
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The third question is whether a model has the capacity to grow exponentially. As a statistician would put it, can it scale?
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scale is what turns WMDs from local nuisances into tsunami forces, ones that define and delimit our lives.
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So to sum up, these are the three elements of a WMD: Opacity, Scale, and Damage. All of them will be present, to one degree or another, in the examples we’ll be covering
Think about this for a bit. Are there other potential characteristics? Highlight (yellow) page 31 | Location 540-542 Added on Friday, October 13, 2017 6:18:52 PM
You could argue, for example, that the recidivism scores are not totally opaque, since they spit out scores that prisoners, in some cases, can see. Yet they’re brimming with mystery, since the prisoners cannot see how their answers produce their score. The scoring algorithm is hidden.
This is similar to anti-class action laws and arbitration clauses that prevent classes from realizing they’re being discriminated against in the workplace or within healthcare. On behalf of insurance companies primarily, many lawmakers work to cap awards from litigation as well as to prevent class action suits which show much larger inequities that corporations would prefer to keep quiet. Some of the recent incidences like the cases of Ellen Pao, Susan J. Fowler, or even Harvey Weinstein are helping to remedy these types of things despite individuals being pressured to stay quiet so as not to bring others to the forefront and show a broader pattern of bad actions on the part of companies or individuals. (This topic could be an extended article or even book of its own.) Highlight (yellow) page 31 | Location 542-544 Added on Friday, October 13, 2017 6:20:59 PM
the point is not whether some people benefit. It’s that so many suffer.
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And here’s one more thing about algorithms: they can leap from one field to the next, and they often do. Research in epidemiology can hold insights for box office predictions; spam filters are being retooled to identify the AIDS virus. This is true of WMDs as well. So if mathematical models in prisons appear to succeed at their job—which really boils down to efficient management of people—they could spread into the rest of the economy along with the other WMDs, leaving us as collateral damage.
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Guide to highlight colors
Yellow–general highlights and highlights which don’t fit under another category below Orange–Vocabulary word; interesting and/or rare word Green–Reference to read Blue–Interesting Quote Gray–Typography Problem Red–Example to work through