Statistical Prediction or Human Intuition? The Story of Moneyball

It would not be unreasonable if one’s impression of Michael Lewis’ fantastic Moneyball , is of a drama between stat nerds and old baseball scouts. This impression is most conspicuous in passages where Billy Beane, the manager of the Oakland Athletics, debates his seasoned scouts about recruiting Jeremy Brown, a senior catcher from the University of Alabama. The scouts were less than enthused about Brown because his 5-foot 10 inch, and yet 226-pound, frame did not fit the ideal image of a baseball athlete. Indeed, one of the scouts says of Brown:

“ ‘This kid wears a large pair of underwear’,…..

‘Okay’, says Billy.

‘It’s soft body,’ says the most vocal old scout. ‘A fleshy kind of a body.’

‘Oh, you mean like Babe Ruth?’ Says Billy…..

‘I don’t know’, says the scout. ‘A body like that can be low energy.’

‘Sometimes low energy is just being cool,’ says Billy.

‘Yeah,’ says the scout. ‘Well, in this case low energy is because when he walks, his thighs stick together.’

‘I repeat: we’re not selling jeans here,’ says Billy.’ ”

The passage with Beane and scouts is humorous, but it also suggests something important about decision-making, whether it be for a baseball team or some other form of business. At the time of writing Moneyball, the author Lewis was not aware but a leader of behavioral economics Richard Thaler and law professor Cass Sunstein observed that the scouts were basing their judgments of a player’s performance on a simple rule of thumb or heuristic known as the availability bias. When scouts think of a successful baseball player the image of someone who is tall and lean comes readily to their minds. Thus, the availability of this image led the scouts to believe that players with athletic bodies are more successful than those with less athletic ones.

The rule-of-thumb thinking is understandable because people do not have the cognitive ability nor the time to assess every detail before making a decision, but Beane realized that this way of thinking is no longer viable. The Oakland A’s lost three of their best players and were competing against teams that have triple their budget such as the New York Yankees. To stand a chance, Beane relied heavily on the statistical mind of the Harvard economics graduate, Paul DePodesta. Despite his lack of experience in playing baseball, DePodesta’s strong background in statistics and knowledge from previous works of baseball analysts such as Bill James, proved to be a game-changer.

Building on James’ insights, DePodesta used regression analysis, a common method in econometrics but in data science is known as a supervised learning technique, to determine which statistics (batting averages, slugging percentages, etc.) predict offensive success. He found that on-base percentage, by far, is the strongest predictor. His analysis showed that Jeremy Brown was likely to have a high on-base percentage and indeed, Brown had an on-base percentage of 0.451 in the 2002 season which is considerably higher than the average in major league baseball. Using DePodesta’s analysis, Beane transformed how Oakland A’s recruited their players and the team won 103 games, matching the Yankees and beating their previous year’s record.

Although DePodesta’s statistical analysis revolutionized how major league baseball teams recruited players, it would be incorrect to conclude that baseball teams (or firms generally) should rely solely on statistical analysis or that information from scouts is always inferior. In The Signal and The Noise, Nate Silver argues that the real lesson of Moneyball is that optimal decision making occurs when baseball teams have an effective process where all information is collected and weighed appropriately. For one thing, Beane increased the scouting budget since the 2002 season. He realized that relentless information gathering is the key to a baseball team’s success and should not be limited by the type of information, whether it be quantitative or qualitative. Some information cannot be easily measured but is still relevant. As Silver notes,

“If Prospect A is hitting 0.300 with twenty home runs and works at soup kitchen during his off days, and Prospect B is hitting 0.300 with twenty home runs but hits up night clubs and snorts coke during his free time, there is probably no way to quantify this distinction. But you’d sure as hell want to take it into account”.

The other way that statistical analysis is limited is that in most cases, statistical predictions still require human judgment. The economists Ajay Agrawal, Joshua Grans and Avi Goldfarb argue in Prediction Machines that since no prediction is 100% accurate, decision makers in a firm (or baseball team) still have to weigh the benefits and costs of each action. Though statistics on offensive success are pretty reliable, Silver pointed out that defensive success is less so because it is difficult to quantify. Not accounting for the defensive capabilities of a player, however, is costly. Silver estimated that Oakland A’s defective defense cost them 8 to 10 wins per season in the mid-1990s. Thus, the general manager, analysts, and scouts have to gather all relevant information and deliberate on how much weight they should put on a player’s offensive and defensive capabilities.

It is not by accident nor luck that the most successful baseball teams are the ones who incorporate all information. Even Silver’s statistical predictions of individual player performance were outperformed by the predictions in Baseball America because the magazine incorporates information from scouts and analysts. Similar to baseball, it is likely the case that the most successful firms will be the ones who learn to integrate quantitative and qualitative information and to have processes where relevant stakeholders can objectively weigh the benefits and costs of each action. This, of course, is easier said than done but as Billy Beane, played by Brad Pitt in the film Moneyball, responds to the scout who did not like how the team is changing their recruitment strategy, “Adapt or die”.

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On Why Struggling Cities and Towns Need High Tech Workers. The Case of Seattle.

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Seattle was not always considered a tech hub. In 1971, The Economist labeled Seattle as the “City of Despair” with a 13.1% unemployment rate and around 5% of workers with bachelor degrees. Similar to Detroit, Seattle’s economy relied heavily on declining industries such as manufacturing and lumber. Moreover, crime was increasing and workers were leaving in droves. The city currently, however, is thriving with a 3.6% unemployment rate and over 39% of the adult population with bachelor degrees.

How did Seattle change so much? The economist Enrico Moretti in his illuminating book The New Geography of Jobs argues that Bill Gates’ decision to move the Microsoft headquarters to Seattle drastically altered the city’s economic trajectory. For one thing, the city became much more attractive to other tech entrepreneurs. Jeff Bezos who used to work at a Wall-Street firm in New York, decided to base the headquarters of Amazon in Seattle due to the city’s abundance of tech talent and private financing, resulting in thousands of new jobs. Moreover, former Microsoft employees generated jobs in Seattle by creating new businesses. Moretti estimates that former Microsoft employees, alone, created four thousand new businesses. The travel website Expedia, for example, was started by a Microsoft alumnus.

Other workers in Seattle indirectly benefited from the spending habits of high-tech workers. Adam Smith, the father of modern capitalism, stated that individuals acting in their self-interest can indirectly benefit society. Similarly, high tech workers spend their high salaries on real-estate agents to close on a new house, lawyers for financial maneuvering, doctors to diagnosis their back pain and baristas to order and sip their macchiatos. Indeed, Moretti estimates that Microsoft is indirectly responsible for creating 120,000 less-skilled service jobs (e.g. taxi drivers, real-estate agents, cleaners) and 80,000 college or advanced degree required jobs (e.g. doctors, nurses, and architects) in Seattle.

The former struggling Seattle was able to grow a thriving tech industry but current struggling cities and towns do not have the luxury of a homesick tech billionaire. To put alternative proposals on the table, the economist Noah Smith suggests that the federal government should significantly increase research spending among smaller, lower-ranked colleges. He argues that the additional research funding would allow lower-ranked colleges “to create new labs, hire new researchers and create more private partnerships and investment” in local towns and cities. The result of the college and private partnerships is that knowledge would spill over between both parties. As Moretti notes, research at colleges create the basic science from which private companies build new tech products and services.

Another proposal from economists is that the government can offer wage subsidies to encourage employers to hire local workers. Timothy Taylor pointed that the basic idea of employment subsidy programs is that “it’s better to pay people to work than it is to support them while they aren’t working”. It is not clear whether the criterion for wage subsidies should be based on geography, individual worker or firm. Edward Glaeser, Lawrence Summers and Ben Austin argue that wage subsidies should be targeted based on the employment rate of geographic areas. In geographic areas where employment is low, workers should receive higher wage subsidies to encourage employment while areas which have high employment should receive lower or no wage subsidies.

Though there are policy levers that the government can use to mitigate the growing geographic inequality, the self-perpetuating forces of high-tech workers and businesses accumulating in a handful of geographic areas are likely to make those areas even more attractive and will lead to further growth while declining areas are likely to continue in their decline. Even if we know that these policy proposals will work, it will require a certain level of consensus among voters to actually pass these proposals. Despite these significant challenges, I feel that Edward Glaeser and Lawrence Summers are right to say that we have to do something to make sure more residents have more jobs.

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In Defense of Philosophy

It was really disappointing to hear that one of my heroes, astrophysicist and famous science educator Dr. Neil deGrasse Tyson bash philosophy. During an interview with Nerdist podcast (beginning at 20:19), one of the interviewers commented that there was too much questioning in philosophy. Dr. Tyson responded, “That can really mess you up”. He elaborates, “My concern there is that philosophers believe they are actually asking deep questions about nature. To the scientist… its what are you doing? Why are you concerning yourself with the meaning of meaning?”.

The interviewer responded that learning both science and philosophy could be useful, “I think a healthy balance of both is good”. Dr. Tyson disagreed, “Well, I am still worried about a healthy balance.” He continues, “If you are distracted by your questions so you cannot move forward you are not a productive contributor to our understanding of the natural world”.  Moreover, if someone feels inclined to think more philosophically, Dr. Tyson would respond, “I’m moving on, I’m leaving you behind, and you can’t even cross the street because you’re distracted by the deep questions you’ve asked of yourself. I don’t have time for that.”

If I am understanding correctly, Dr. Tyson’s argument is that philosophy is a waste time because unlike science, philosophy spends too much time 1) asking deep questions about the world and 2) understanding the meaning of the words. To Dr. Tyson, what matters, is that, we actually answer questions about the world. Since science excels at this and philosophy does not; science is worth pursuing and philosophy is not.

To the query about philosophers asking deep questions, Mr. Damon Linker (a writer for The Week) does a fine job rebutting Dr. Tyson. The article is worth a full read.

But what Mr. Linker does not address is Dr. Tyson’s other criticism, that philosophy spends too much time on the meaning of words (also known as semantics). Dr. Tyson is right that philosophy spends a significant amount of time understanding the meaning of words. But that is not a bad thing. The point of that is to clarify our thoughts so we can better understand what we are talking about. This is not some trivial exercise. Being very clear about the words we use affects how we view and approach the world. For example, many economists equated economic growth with development. As a consequence, many governments and development institutions also prioritized growth as the end goal of development.

Appropriately enough though, it took an economist with serious philosophical training, Dr. Amartya Sen, to argue that definition is inadequate. To him, growth, although an important means to development, should not be its end goal because it does not necessarily meet other needs important for the well-being of the poor such as health and education.

One illustration that Dr. Sen uses to distinguish growth and development is comparing the life expectancy between African Americans and individuals in substantially poorer countries. He points out that “African Americans as a group have no higher-indeed have a lower-chance of reaching advance ages than do people born in the immensely poorer economies of China, the Indian state of Kerala, Sri Lanka, Jamaica, or Costa Rica”(Sen, 1999, p.21). Men in China and in Kerala decisively outlive African American men in terms of surviving old age groups. Even African American women end up having a survival pattern for higher ages similar to that of much poorer Chinese, and decidedly lower than the even poorer Indians in Kerala (Ibid, p.22). He then concludes that the causal influence go beyond income to include social arrangements and community relations such as medical coverage, public health care, school education, law and order, prevalence of violence, and so on (Ibid, p. 23)

Dr. Sen realized if development is about improving the lives of the poor, then we have to think hard about what kind of life are we trying to improve towards? In other words, what is the good life?  To him, the freedom to choose one’s own destiny or to live the life one values without harming other people’s freedoms is very important for a good life. Indeed, if one looks at history, it is not difficult to believe that a lot people strongly value their personal freedom.

This lead him to write Development As Freedom. In which he defines development as expanding people’s opportunities or capabilities to enjoy the life they value and poverty as the deprivation of those opportunities or capabilities. This definition is superior to the traditional definition, in the sense, that it incorporates other needs such as political and civil rights, health and education. Indeed, it is not difficult, to imagine that better political and civil rights, healthcare and educational opportunities would expand a person’s opportunities to live the life they value.

There were plenty of people who also criticized equating growth with development but what made Dr. Sen different from the rest, is that, he came up with a persuasive alternative. This is because he spent a considerable amount of time and energy trying to understand the meaning of development and poverty and his philosophical training, no doubt, helped significantly.

It is precisely because individuals asked deep questions and thought clearly about the words that they use, did our understanding of ourselves and the world improved substantially. It is sad to hear famous scientists and science educators dismiss the discipline that gave us so much, let alone, science.

 

References

Sen, A.K. (1999). Development as Freedom. Alfred A. Knopf: New York

 

 

 

 

 

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On Why The Anti-Gay Arizona Bill Is Not Based on Religious Freedom But Prejudice

Anderson Cooper interviewed Arizona state senator Al Melvin who voted for SB-1062 Monday night. The proposed legislation allows businesses to exercise their religious freedom to not service LGBT citizens. Senator Melvin states that the bill, ” is nothing more or less than protecting religious freedom in our state.” Cooper made the point that Arizona does not have any laws that protect LGBT citizens from discrimination, so why does Arizona need a law that specifically allows businesses to discriminate against LGBT citizens? Senator Melvin concedes the point but still argues that the bill is “not a discrimination bill but a religious freedom bill”.

Paraphrasing, Cooper responds that since Jesus was against divorce in the Bible, then shouldn’t Arizona propose a bill that allows businesses to discriminate against divorced people? Moreover, Cooper argues that Jesus never actually mentions gay people in the Bible. Senator Melvin does not accept Cooper’s point about divorced people and still repeats the same thing about how the bill is promoting religious freedom and is not based on discrimination.

The problem with proposing legislation based on “religious freedom” is that it is often motivated by people’s prejudices. Most people cherry pick parts of their religion that best fit their beliefs and biases. Senator Melvin and his supporters chose to pick parts in the Bible that rejects LGBT people because they are prejudiced against LGBT people. However, they conveniently ignored the parts in the Bible that explicitly said divorce is immoral. There is a fair argument to make about the state undermining religious freedom by not allowing church establishments or congregations but this is clearly not the case. This is discrimination based on prejudice and cloaked by religion, pure and simple.

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