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9 of 10 people found the following review helpful
What aspects of the future are predictable? This wide-ranging book, from an author who not only understands both data and theory but also can write engaging prose, is certainly the best non-technical discussion I have ever seen. The empirical fact, that in many contexts predictions have turned out to be much less accurate than their makers claim, has often been made (recently in The Black Swan, for instance), but deserves frequent repetition to counteract the media punditry to which we are all exposed. The 13 chapters tackle different subjects via a nice combination of details and overview. Though aimed at the general reader, even a professional statistician will find many interesting new details (for instance, I didn't know that local TV weather forecasters deliberately mis-state the probability of rain). The chapter titles are alas more cutesy than informative, so let me list their actual topics here, which are (predictability of): mortgage defaults, elections, baseball player performance, weather, earthquakes, economic indicators, epidemics, Bayes and sports betting, chess computers, professional poker, stock market, climate change, terrorism.

Because the point is that predictability differs in different contexts, the book wisely offers no grand theory. From the list above of topics, you will see the author focuses on contexts where there is a lot of past data, and the central issue (his signal/noise analogy) is determining which aspects of the data are useful in predicting the future. Using Tetlock's fox/hedgehog analogy, he advocates being a fox: "... pursue multiple approaches; incorporate ideas from different disciplines; see the universe as complicated, perhaps ... inherently unpredictable". And he advocates "thinking probabilistically", making probabilistic rather than deterministic predictions. Both are views I would heartily endorse.

Silver's own technical expertise is in three of the topics (baseball player performance, poker, election prediction from opinion polls). These are particularly amenable to the signal/noise paradigm -- with lots of data and only slowly changing ground rules -- and number-crunching can lead to prediction rules that are human-interpretable. But his accounts of the other topics are equally good. Indeed I may use several of his chapters as a basis for future lectures in my Berkeley "Probability in the real world" course and will assign my (Statistics major) students to read the book.

My main quibble concerns a glaring omission. There is an active field, inside and outside academia, called "machine learning", which seeks to develop prediction algorithms to be numerically calculated by computer without caring if they form human-interpretable rules. These are used in a huge range of contexts, from spotting trends in retail sales to genomic research. But Silver makes no mention of this parallel world. In particular, as this book notes, there is an intrinsic competitive aspect to prediction -- did my prediction work out more accurate than yours? -- which often has material consequences -- all financial speculation is in essence about predicting better than the market consensus. Machine learning has a culture of public competitions (best known being the Netflix Prize), from which we know quite a lot about how accurate its predictions can be. So Mr Silver, please add a chapter on machine learning in the second edition.
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13 of 15 people found the following review helpful
on November 7, 2012
I purchased this book prior to the election because I wanted to support the guy who kept me sane during this "noise heavy" election. Nate's reasoned approach to the polls is priceless. I look forward to Nate's influence in bringing sanity to the masses for the years to come.
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20 of 25 people found the following review helpful
on October 24, 2012
This book is similar to Steven Levitt's Freakonomics: A Rogue Economist Explores the Hidden Side of Everything (P.S.), Nassim Taleb's The Black Swan: Second Edition: The Impact of the Highly Improbable: With a new section: "On Robustness and Fragility", and James Surowiecki's The Wisdom of Crowds. All four books explore the intersection of data, human behavior, and outcomes. They explain how to quantify outcomes within the financial markets, professional sports or elections.

This book is especially interesting because Nate Silver has honed firsthand his statistical skills onto numerous domains including professional poker, baseball performance forecasting (he developed one of the best software program to do that), political elections (his "fivethirtyeight" blog). And, when he is not a firsthand practitioner he is a first class investigator.

The first seven chapters cover the errors and successes people have had in forecasting in various disciplines. Chapter eight is the most pedagogical, as the author explains the basics of Bayes Theorem that he considers as an overall solution to many of the errors we make in forecasting. The last five chapters focus on Bayesian thinking within various disciplines.

Nate Silver's coverage of the credit rating agencies "Catastrophic failure of prediction" (first chapter title) is excellent. In a single sentence on page 13, he captures the cause of the financial crisis: "In advance of the financial crisis, the system was so highly leveraged that a single lax assumption in the credit rating agencies played a huge role in bringing down the whole global financial system." Silver states that the AAA rated CDOs were deemed to have a default rate of only 0.12%. The actual default rate was 28% or over 200 times greater! This was because the rating agencies missed out the correlation between mortgage default rates at different locations when a nationwide home price downturn hit (see figure 1.2 on page 28. Watch out that he mislabeled column 3 and 4 from the right). Silver assesses that overall leverage was too high during the housing bubble. Fannie Mae and Freddie Mac had a debt-to-equity leverage of 70-to-1. Lehman Brothers and other investment banks were leveraged over 30-to-1. Borrowers had often loan-to-value ratios of 100% on their homes. The volume of credit default swaps, MBS, CDOs represented 30 to 60 times the volume of home sales during the bubble years (fig. 1.5 page 35). Nate Silver summarizes the errors made. Investors trusted the rating agencies. The rating agencies assumed home prices would never decline on a nationwide basis because they never had since the Great Depression. Lenders and borrowers believed rising home prices would bail them out through refinancing. Policymakers believed the financial system had enough capital and was self-disciplined. And, economists completely missed the ensuing severe recession.

Nate Silver focuses next on political predictions. This field of experts was so bad at predicting it motivated him to enter it by starting his fivethirtyeight blog. He documents their failings extensively. Within this chapter he refers to the theory of Philip Tetlock, professor of psychology and political science at Berkeley. Tetlock had surveyed predictions of experts in various fields. And, he categorized them within two archetypes: the hedgehogs and the foxes. The hedgehogs are dogmatic, rarely change their minds, and are very confident of their forecast. The foxes are just the opposite. They update their forecasts as often as new information warrants it. As a result, they make better forecasts.

The chapter on baseball is one of the best because of Silver's extensive firsthand experience. He uncovers many concepts applicable to many sports such as the age-curve of baseball performance (pg. 81). All sports have a predetermined age-curve. Actually, every single aspects of life including life itself have predetermined age-curves. His description of what it takes to be a successful professional baseball player (pg. 97) has also surprisingly broad applications. The conclusion of the chapter is also fascinating. It describes baseball management as a competitive arms race of intelligence gathering to extract small competitive edges. And, that those competitive edges are short-lived. That's a very interesting application of the Efficient Market Hypothesis.

The chapter on economists documents how inaccurate their forecasts are. The majority can't forecast a recession that has already started as they missed out on the three most recent ones (1990, 2001, 2007). In November 2007, the average economic forecast was 2.4% real GDP growth in 2008. Instead, real GDP shrank by -3.3%. Economists assigned only a 1-in-2000 chance of the economy shrinking that much. Yet, home prices were already declining. Foreclosures had picked up. Bear Stearns had gone belly up six months ago. Those were powerful signals the housing and financial markets were on the edge of a cliff. Also, economists are way too confident. The few times you can extract confidence intervals from the economic profession they are invariably way too narrow because they underestimate the error level within their forecasts (pg. 182). Nate Silver states that: "this property of overconfident prediction has been observed also in medical research, political science, finance, and psychology" (pg. 183). Despite our having so much more data and computer power at our hands, economic forecasting has not improved since 1968. This is because our underlying understanding of cause and effects has not changed much since.

Chapter 8 introduces Bayes's Theorem. Here Nate Silver often refers to a very good book on the subject: The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy by Sharon Bertsch McGrayne.

Chapter 9 and 10 about chess and poker are excellent. Kasparov was ultimately beaten by a computer bug. IBM Big Blue made a move late in the last game that did not make any sense (the team who programmed it confirmed it was due to a small programming bug). Kasparov who was in a vulnerable position could not figure out that move and in despair resigned the game and lost the series. The Pareto principle of prediction on page 312 and 314 and the ensuing economics of poker are really interesting. Poker winning are heavily dependent on the one worst player at a table. If he leaves, the winnings are a lot harder to reap.

Chapter 11 on the Efficient Market Hypothesis (EMH) is excellent. Nate Silver states that the stock market is efficient most of the time, although it is never perfectly efficient (that would preclude a market). But, it can be wildly inefficient on few occasions associated with bubbles and crashes. Nate Silver demonstrates how both technical analysis and fundamental analysis do not beat the market over the long run. Fig 11.3 on page 340 shows no correlation between the performance of mutual funds over the 2002 to 2006 period vs over the 2007 to 2011 period. Past performance is no guarantee of future returns. Next, Silver refers to Robert Shiller in showing the market is not as efficient as the EMH entails. Shiller looked at the P/E ratio of the S&P 500 over a trailing 10 year period and looked at prospective returns. And, the longer the period contemplated the greater the negative correlation between trailing P/E levels and future average yearly returns. This suggests that the market can get overvalued. But, the return correction is not apparent until looking at average return over a 10 to 20 year period. Next, Nate Silver refers to the works of Richard Thaler and Daniel Kahneman in behavioral economics to outline how market traders are not perfectly rational. They suffer from herd mentality, overconfidence, and being overly emotional rendering their trading pro-cyclical.

So, if the market is not so efficient, can you beat it? Probably not. On page 345, Nate Silver demonstrates how a hypothetical investor with perfect timing over a decade (1976-1986) would get killed by very small transaction costs. Even though this investor would handily beat the stock market before transaction costs, he would wipe out most of his capital after transaction costs. Silver next tests a prudent investment strategy over the 1970 to 2009 period. He assumes an investor is prudent and sells his position in the S&P 500 index whenever it had declined 25% from its peak and reinvests whenever it recovered 90% of its value. Such an investor would have earned only 2.6% per year vs close to 10% for a simple buy-and-hold strategy. Nate Silver does believe several hedge funds can beat the market. But, they have intellectual and technological resources that no retail investor and few mutual funds can match.

Chapter 12 on climate change is really interesting. He differentiates between where scientists agree and disagree. They all agree that the greenhouse effect exists and keeps the Earth warmer than it would otherwise be; that temperatures have risen over the past century; that greenhouse gases have contributed to that trend; and that water vapor is by far the most potent greenhouse gas (not CO2 as the Media conveys). The majority of scientists agree that rising CO2 concentration does contribute to rising temperature. But, there is a debate regarding how much. Where the scientific community is more divergent is regarding climate models and projections. They acknowledge that Al Gore's An Inconvenient Truth deterministic apocalyptic message was way off base.

Nate Silver explains why there is much uncertainty regarding climate models' projections. One uncertainty is figuring out CO2 levels 100 years down the road. Another uncertainty is getting the causal relationships right (there is a lot more than CO2 at play). Another uncertainty concerns whether those models are programmed correctly. Within the vast quantities of computer codes, are there a few bugs that contribute to generating erroneous forecasts? Nate Silver reviews the prediction of the IPCC's 1990 model and observes that temperatures have not risen as fast as the model predicted. Current temperatures are below the model's 95% confidence interval. This lead the IPCC to reduce their baseline temperature increase from 3 degree Celsius per century in 1990 to 1.8 degree in 1995. On page 407, Silver comes up with an interesting application of Bayes theorem applied to rising temperature predictions.

The last chapter on terrorism is intriguing. Terrorist attacks follow a similar Power Law as earthquakes. The frequency of events declines exponentially with increase in intensity. More violent events are much rarer than lesser ones. But, the few major events dominate the data in human casualties. For instance, 9/11 represented more than half of the total fatalities from terror attacks in NATO countries since 1979. Thus, it is worth exploring means of mitigating the impact of such events.
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22 of 28 people found the following review helpful
on December 20, 2012
Nate Silver's book is an important introduction to statistical analysis for the average person. Where it fails is when he delves into areas he doesn't know about: specifically finance.

He makes the assumption that bad models caused the 2008 financial crisis and that fixing the models will fix the financial system. He makes a terrible assumption here. The wrong assumption is that the models were created in good faith. Bad models were used not out of ignorance, but because they gamed the system in the ways the banks and ratings agencies wanted them gamed. The bad models allowed the sale of sure-to-default mortgages as AAA securities. Anyone holding these securities when the crash came, or anyone who had insured these products (like AIG), would be in trouble when the defaults started. However, the mortgagors and bankers that sold these loans and securities made their money up front. And boy did they make it. Bonuses for mortgage securities brokers, and their senior managers, went through the roof during the pre-crisis years as more and more mortgages were sold and turned into securities for sale to dupes. The banksters made millions per year selling these dodgy instruments that only looked good because they had "models" that told us so. Thus the models were created not out of ignorance but out of criminally fraudulent inducement. These same banksters didn't really care if the banks they worked for went bust when the crisis hit because they had already made their tens and hundreds of millions in bonuses. The fact that they call got bailed out by the taxpayers was certainly a happy fact for them and did serve to increase their ill-gotten gains, but was not necessary for their crimes to succeed: the crimes had succeeded by the time the public became aware of the problem in '08 (as an aside, the Fed and other "regulators" had been suppressing the crisis since early 2007 with quiet emergency bailouts and other mechanisms hoping to outrun the '08 election before the public became aware. As we know, they were not able to do this, which in the end didn't really matter as our government bailed out the criminals just the same under the democrat as it would've under a republican).

Anyhow, Nate Silver ascribes simple bad math to a problem that is much greater and involves real criminality, not just ignorance of statistical modeling, and that is an egregious error. In fact, Silver should've stuck to the areas of his expertise: sports and political polling, and avoided the generalizations and over-confidence he criticizes in other statisticians by applying his knowledge in naive ways.
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8 of 9 people found the following review helpful
on January 12, 2013
Nate Silver became famous due to his innovative use of statistics in baseball followed, by correctly predicting the results of 49 out of 50 states in the 2008 election (50 out of 50 in 2012). In 2008 Silver launched his website FiveThirtyEight.com. In 2009 he was named one of The World's 100 Most Influential People by Time. In 2012 FiveThirtyEight.com won an award as the Best Political Blog from the International Academy of Digital Arts. Amazon.com named The Signal and the Noise the best non-fiction book of 2012.

The book is divided into four parts, each consisting of 3-4 chapters. The first part is about failures and successes of historic predictions (Silver presents his baseball predictions). The second part treats the problems of making predictions of dynamic systems like the weather. In the final parts Silver presents his statistical tool, the Bayes theorem, which he then uses to improve prediction skills (this theorem is also used by Daniel Kahneman in his book Thinking Fast and Slow, i.e. this is something we all should learn more about...).

In the third part of the book Silver applies this theorem to various examples, like sports betting and his own experience as a successful poker player. In the final part he uses Bayes on such difficult problems as terrorism, financial markets and global warming. When it comes to the latter Silver claims that there is no clear evidence of global warming. It's not possible to predict higher temperatures with certainty. Temperatures have been flat for a decade, quite the opposite of previous predictions. He is worried that neither side in the debate looks at facts and data, but instead selects noise that supports previous views. In the beautifully written introduction the dramatic increase in data, and therefore also noise, is presented as a problem just because it's easier for followers to find noise to support their views, which could be one reason why political partisanship has been increasing rapidly.

Mr Silver has written a very entertaining book. He takes on the numerous problems with statistics and shows how to find the truth (signal) among the data (noise). My main critique of the book is that the author has few solutions. The main conclusion is that we should be less certain about our views and predictions. In his view many experts who are seen in media tend to exaggerate, oversimplify and to be careless with facts. The reason being that in order to get airtime and attention you need to say something newsworthy that can make a headline, not necessarily what is relevant. One of the few exceptions being Hans Rosling who with his long term graphs has managed to explain more things than most experts - and get attention.

Regarding investing in stocks, Silver really doesn't have anything new to add, but he doesn't view the market as efficient. Rather his tentative conclusion is that the market is efficient 90% of the time. The main problem is that it's difficult to identify the interesting 10% (surprise...). Unfortunately Silver leaves it at that. The book however is easy to read and if you are interested in baseball, poker and the climate this is a book for you. If you have less of an interest in those subjects and more into investing, then there are better books. I highly recommend books written by people from the financial industry such as Maboussin, Taleb, and Montier.

This is probably not the last we have heard from the very creative Nate Silver. Since he is born 1978 he has plenty of time to find more signals in the noise that we all can learn from. I will definitely read his next book.

This is a review by investingbythebooks.com
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8 of 9 people found the following review helpful
on November 11, 2012
We are, by now, accustomed to books promising unorthodox or hyper-elegant explanations for worldly phenomenons. The Freakonomics guys and Malcolm Gladwell have sold millions of books supposedly unlocking the mysteries behind everything from abortion rates to the success of The Beatles. Now comes Nate Silver's contribution to the genre, an exhaustive analysis of the science of predictions. There are two distinctions that I think are a credit to Silver. He integrates his own story, his biography, with his analysis, to interesting effect. Nate Silver has become the world's unlikeliest celebrity, after having predicted 99 out of 100 state results in the last two Predidential elections. Everyone loves a success story. (Gladwell wrote a whole book on this subject). The journey from anonymous geek to cult figure to celebrity seer of all things political is interesting. That same story from the point of view of a lightningrod for firece debate and high profile dissention is irresistible. It's not the Nate is right (he is, seemingly always), but that so many other celebrities from the political arena proclaim him so very wrong, dismiss his methods as nonsense, and ridicule the precision of his mathematics -- all the way up until the moment he is proved right. The other distinction of course is that his methods are not controversial or even so much open to debate. They are mathematical and scientific - they can be proven to a degree of probabilistic certainty. The book doesn't seek to profit from surprise, unscientific conclusions: i.e, more abortions equal less crime! The Beatles are great because they played endless gigs in gritty Hamburg clubs! Silver's book is based on a world where 2 + 2 will always equal 4, and the chances of a 80% free throw shooter at the height of his career missing 5 free throws in a row can be quantified to a hundred thousandth of a percent. What Silver's book lacks in drama is made up for in logical sense. It helps us respect the world as less random and less inexplicable than many would have us (or want us to) believe. It's no wonder he's a target for pundits everywhere.
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12 of 14 people found the following review helpful
on November 4, 2012
One of the best books I've read on statistical theory. That's both good and bad. If you're not into this then you won't like the book. I did my doctoral thesis on epistemology so I loved it. It's not a handbook so if you want that you'll be disappointed.
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5 of 5 people found the following review helpful
on November 27, 2012
This is not a textbook but rather a narrative with some simple math and statistics illustrations.

Nate gives a clear description of bad vs. good analysis. It is clear that political pundits who know how to use good analysis have an advantage.

This is essentially how Nate predicted the 2012 national election with such high accuracy. He has been tracking the opinions, based on polls, and determining which are the most predictive. He uses a broad range of polls and weights them based on their past history of accuracy in past elections.

The book is intended for the lay person to understand that there is wide variation in the performance in the work of analysts. Part of the key differentiation between analysts that rely on discredited frequentist statistical methods, which are still being taught at our universities, and those that embrace Bayesian statistical methods, which are being used by actuaries, gamblers and anyone who has an an informed opinion potential future events.

The frequentists are discredited because their methods do not predict events well, and are also discredited because much research that draws incorrect conclusions are based on these methods. The bayesian approach is becoming the preferred approach for predictive model building because it uses all informed opinions about the future and tests them. The models built in this manner improve over time as opinions that are proven right take on more weight in predictions, and opinions that prove incorrect are diminished and removed from the analysis.
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10 of 12 people found the following review helpful
on December 8, 2012
This book was ok. It wasn't terrible and it was on a topic that I am quite interested in. However, it just didn't seem to really do the topic justice. It was a rather mild and sporadic introduction to some of the problems and situations that are encountered when predictions are necessary.

He specifically cautions against hubris and generally his writing reflected that, but then he also sometimes says stuff like "...it is hard to improve on a good method for aggregating polls, like the one I use at [the author's prediction web site]."

Indeed, his real nugget of first hand wisdom seems to be that taking a poll of polls is better than direct polls. Ok, not terribly shocking. Of course it starts to sound a bit like a pyramid scheme since someone has to do some real work somewhere.

The author is a bit religious about "Bayesian" thinking. I would not argue that Bayes Theorem is useless or even generally ineffective, but as a solution to all of life's uncertainties, I feel it comes up short. For example, in the US people like to imagine themselves as "innocent until proven guilty" yet if we assign a prior probability of zero to being guilty, then Bayes Theorem will always produce innocence. I'm not saying Bayes Theorem is correctly applied here, but I am saying that there are cases of uncertainty where a firm adherence to it will not be ideal. The author made no such allowance as far as I can tell.

Despite having strong feelings about the futility of "frequentist" thinking, the author does not really explain exactly what it is and why it's bad. He seems to go into almost ad hominem criticism of R. A. Fisher. To me the idea of arbitrary confidence intervals is about as weird as the idea of sketchy prior probabilities.

His notion of "foxes" and "hedgehogs" might as well be translated as "winners" and "losers" for simplicity. As he points in other parts of the book, it's easy to see who the foxes must have been *in retrospect*.

Before reading this book, my explanation for why some predictions succeeded while most don't was one of two things: luck or cheating. Cheating covers things like insider trading or point shaving. This book did add a couple of possibilities for a mild type of cheating that I hadn't really considered so fully before. First there is the aforementioned aggregation trick. Noted. And second, fishiness.

The author seems to have done a lot of his prediction training at the poker table. (Hmm, until it got too hard.) He talks about "fish" who are the participants in a poker game who are simply low on the skills necessary to consistently win. If you put yourself in a situation where it's your predictions against theirs, you stand a good chance.

In addition to poker, the book wanders all over baseball stat geekery. I'm not really a baseball fan so those parts were a little noisy for me.

The author's folksy introductions to the genuinely impressive list of people he interviewed for the book were a little contrived. Sometimes I felt like his interviews had little signal but were supposed to just impress us with the name dropping. Donald Rumsfeld was someone who kicked off a chapter on counter-terrorism that really didn't shed much light on anything in particular for me.

With so much random baggage as contained in this book, it was inevitable to stumble across an area of personal interest. Unfortunately I was not impressed at all. The topic is earthquake prediction and the author isn't just entertaining a healthy skepticism about the ability to make predictions, rather he ridicules certain approaches, specifically animal reactions as precursor signals. I'm not saying that animals predict earthquakes, but the USGS web site specifically says "However, much research still needs to be done on this subject." And far from there being no conceivable explanations, there definitely exist compatible hypotheses to go with the very large body of historical evidence, for example in the work of Thomas Gold.

This book didn't really address the technical nuances of the mathematical tools we use to refine predictions. It didn't investigate the important philosophical implications of probability. It didn't thoroughly talk about the history or important people who have investigated the topic (especially minor but interesting people like Frank Ramsey, Bruno Di Fenetti, etc). It didn't offer any compelling insights that will make my decision making better.

At 454 pages, that's too much noise for not enough signal.
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11 of 13 people found the following review helpful
on November 7, 2012
His writing is plain, but the book makes up for it. Nate weaves back and forth between sports, political polling, gambling, science, and statistics pointing out the common themes of stats while giving stories of success and failures. He was 50/50 for predicting the 2012 election on the electoral college. He got 49/50 right in 2008, so he is an authority on stats.
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