high batting average

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pages: 316 words: 105,384

Moneyball by Michael Lewis

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Cass Sunstein, high batting average, Norman Mailer, old-boy network, placebo effect, RAND corporation, Richard Thaler, systematic trading, the new new thing, the scientific method, upwardly mobile

It is startling, when you think about it, how much confusion there is about this. I find it remarkable that, in listing offenses, the league will list first—meaning best—not the team which scored the most runs, but the team with the highest batting average. It should be obvious that the purpose of an offense is not to compile a high batting average.” Because it was not obvious, at least to the people who ran baseball, James smelled a huge opportunity. How did runs score? “We can’t directly see how many runs each player creates,” he wrote, “but we can see how many runs each team creates.” He set out to build a model to predict how many runs a team would score, given its number of walks, hits, stolen bases, etc.

He still needed a baseball genius to divine his true worth but any moron could see the value of his older brother, at the plate. In all of baseball for the past few years there has been only one batter more useful to an offense: Barry Bonds. Giambi has all the crude offensive attributes—home runs, high batting average, a perennially high number of RBIs. He also has the subtler attributes. When he’s in the lineup, for instance, the opposing pitcher is forced to throw a lot more pitches than when he isn’t. The more pitches the opposing starting pitcher throws, the earlier he’ll be relieved. Relief pitchers aren’t starting pitchers for a reason: they aren’t as good.

pages: 561 words: 114,843

Startup CEO: A Field Guide to Scaling Up Your Business, + Website by Matt Blumberg

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activist fund / activist shareholder / activist investor, airport security, Albert Einstein, bank run, Broken windows theory, crowdsourcing, deskilling, fear of failure, high batting average, high net worth, hiring and firing, Inbox Zero, James Hargreaves, Jeff Bezos, job satisfaction, Kickstarter, knowledge economy, knowledge worker, Lean Startup, Mark Zuckerberg, minimum viable product, pattern recognition, performance metric, pets.com, rolodex, Rubik’s Cube, shareholder value, Silicon Valley, Skype

It was hard and we made mistakes aplenty but using a baseball metaphor, we managed to “hit with a high batting average.” We were able to successfully navigate them by following many of the themes that Matt outlines below and our efforts were rewarded when we sold Quickoffice to Google in 2012. The alignment of your business strategy with your resources is really tough and you will rarely know you have done it well or not so well, until after the fact. I believe your goal is to achieve that “high batting average” so your business will stay healthy enough to continue forward successfully. That’s the lesson I hope you take away from this section.

pages: 327 words: 103,336

Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts

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active measures, affirmative action, Albert Einstein, Amazon Mechanical Turk, Black Swan, butterfly effect, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, collapse of Lehman Brothers, complexity theory, correlation does not imply causation, crowdsourcing, death of newspapers, discovery of DNA, East Village, easy for humans, difficult for computers, edge city, en.wikipedia.org, Erik Brynjolfsson, framing effect, Geoffrey West, Santa Fe Institute, George Santayana, happiness index / gross national happiness, high batting average, hindsight bias, illegal immigration, industrial cluster, interest rate swap, invention of the printing press, invention of the telescope, invisible hand, Isaac Newton, Jane Jacobs, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, lake wobegon effect, Long Term Capital Management, loss aversion, medical malpractice, meta analysis, meta-analysis, Milgram experiment, natural language processing, Netflix Prize, Network effects, oil shock, packet switching, pattern recognition, performance metric, phenotype, Pierre-Simon Laplace, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, ultimatum game, urban planning, Vincenzo Peruggia: Mona Lisa, Watson beat the top human players on Jeopardy!, X Prize

Even for more ephemeral skills, like outstanding positional play in professional basketball, that are harder to measure directly but still help the team win, we have almost one hundred NBA games each season that we can watch to observe a player’s effect on his team and on the outcome.13 At first, it seems that an accomplishment like beating the S&P 500 for the year is a pretty good equivalent of a batting average for fund managers—and indeed fund managers with long streaks do tend to beat the S&P 500 more often than average, just like baseball players with high batting averages. By this measure, however, in a forty-year career a fund manager will get only forty “at-bats” total—simply not enough data to estimate the true value with any confidence.14 THE MATTHEW EFFECT And finance is in many respects an easy case—because the existence of indices like the S&P 500 at least provide agreed-upon benchmarks against which an individual investor’s performance can be measured.

pages: 314 words: 101,034

Every Patient Tells a Story by Lisa Sanders

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data acquisition, discovery of penicillin, high batting average, index card, medical residency, meta analysis, meta-analysis, natural language processing, pattern recognition, randomized controlled trial, Ronald Reagan

Indeed, the great majority of medical diagnoses—anywhere from 70 to 90 percent—are made on the basis of the patient’s story alone. Although this is well established, far too often neither the doctor nor the patient seems to appreciate the importance of what the patient has to say in the making of a diagnosis. And yet this is crucial information. None of our high-tech tests has such a high batting average. Neither does the physical exam. Nor is there any other way to obtain this information. Talking to the patient more often than not provides the essential clues to making a diagnosis. Moreover, what we learn from this simple interview frequently plays an important role in the patient’s health even after the diagnosis is made.

pages: 452 words: 110,488

The Cheating Culture: Why More Americans Are Doing Wrong to Get Ahead by David Callahan

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1960s counterculture, affirmative action, corporate governance, corporate raider, creative destruction, David Brooks, deindustrialization, East Village, fixed income, forensic accounting, full employment, game design, greed is good, high batting average, housing crisis, illegal immigration, income inequality, job satisfaction, mandatory minimum, market fundamentalism, McMansion, microcredit, moral hazard, new economy, New Urbanism, offshore financial centre, oil shock, old-boy network, Plutocrats, plutocrats, postindustrial economy, profit maximization, profit motive, RAND corporation, Ray Oldenburg, Robert Bork, rolodex, Ronald Reagan, shareholder value, Shoshana Zuboff, Silicon Valley, Steve Jobs, The Bell Curve by Richard Herrnstein and Charles Murray, The Chicago School, Thorstein Veblen, War on Poverty, winner-take-all economy, World Values Survey, young professional, zero-sum game

Economic inequality has led to striking changes in our society. ♦ In America's new winner-take-all society there is infinitely more to gain, and to lose, when it comes to getting into the right college, getting the right job, becoming a "hot" reporter, showing good earnings on Wall Street, having a high batting average, or otherwise becoming a star achiever. ♦ Higher inequality has led to more divisions between Americans and weakened the social fabric—undermining the notion that we're all "in it together" and bound by the same rules. ♦ Inequality is also reshaping our politics as wealthier Americans get more adept at turning money into influence—twisting rules to their benefit and escaping punishment when they break the rules

pages: 385 words: 128,358

Inside the House of Money: Top Hedge Fund Traders on Profiting in a Global Market by Steven Drobny

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Albert Einstein, asset allocation, Berlin Wall, Bonfire of the Vanities, Bretton Woods, buy low sell high, capital controls, central bank independence, Chance favours the prepared mind, commoditize, commodity trading advisor, corporate governance, correlation coefficient, Credit Default Swap, diversification, diversified portfolio, family office, fixed income, glass ceiling, high batting average, implied volatility, index fund, inflation targeting, interest rate derivative, inventory management, John Meriwether, Long Term Capital Management, margin call, market bubble, Maui Hawaii, Mexican peso crisis / tequila crisis, moral hazard, Myron Scholes, new economy, Nick Leeson, oil shale / tar sands, oil shock, out of africa, paper trading, Paul Samuelson, Peter Thiel, price anchoring, purchasing power parity, reserve currency, risk tolerance, risk-adjusted returns, risk/return, rolodex, Sharpe ratio, short selling, Silicon Valley, The Wisdom of Crowds, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond, zero-sum game

Aside from their trading acumen, Stan and Nick are the class acts of the hedge fund business. Their word is their bond, they eschew publicity, and they are dedicated family men. George Soros, on the other hand, is much more volatile than Stan and Nick. He is the opposite of Warren Buffett. Buffett has a high batting average. George has a terrible batting average—it’s below 50 percent and possibly even below 30 percent—but when he wins it’s a grand slam. He’s like Babe Ruth in that respect. George used to say, “If you’re right in a position, you can never be big enough.” Having worked with both Soros and Jim Rogers, what differences did you note between former partners?

pages: 829 words: 186,976

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't by Nate Silver

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airport security, availability heuristic, Bayesian statistics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, big-box store, Black Swan, Broken windows theory, Carmen Reinhart, Claude Shannon: information theory, Climategate, Climatic Research Unit, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, computer age, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, Daniel Kahneman / Amos Tversky, diversification, Donald Trump, Edmond Halley, Edward Lorenz: Chaos theory, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, everywhere but in the productivity statistics, fear of failure, Fellow of the Royal Society, Freestyle chess, fudge factor, George Akerlof, haute cuisine, Henri Poincaré, high batting average, housing crisis, income per capita, index fund, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, invention of the printing press, invisible hand, Isaac Newton, James Watt: steam engine, John Nash: game theory, John von Neumann, Kenneth Rogoff, knowledge economy, locking in a profit, Loma Prieta earthquake, market bubble, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, Monroe Doctrine, mortgage debt, Nate Silver, negative equity, new economy, Norbert Wiener, PageRank, pattern recognition, pets.com, Pierre-Simon Laplace, prediction markets, Productivity paradox, random walk, Richard Thaler, Robert Shiller, Robert Shiller, Rodney Brooks, Ronald Reagan, Saturday Night Live, savings glut, security theater, short selling, Skype, statistical model, Steven Pinker, The Great Moderation, The Market for Lemons, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, transfer pricing, University of East Anglia, Watson beat the top human players on Jeopardy!, wikimedia commons

A good baseball projection system must accomplish three basic tasks: Account for the context of a player’s statistics Separate out skill from luck Understand how a player’s performance evolves as he ages—what is known as the aging curve The first task is relatively easy. Baseball, uniquely among the major American sports, has always been played on fields with nonstandard dimensions. It’s much easier to put up a high batting average in snug and boxy Fenway Park, whose contours are shaped by compact New England street grids, than in the cavernous environs of Dodger Stadium, which is surrounded by a moat of parking lot. By observing how players perform both at home and on the road, we can develop “park factors” to account for the degree of difficulty that a player faces.