Category Archives: The Art & Science of Investing

The only thing he ever made fly was government money

On this day in 1906, the Wright Brothers were granted a patent for their “flying machine.” In honor of the anniversary, we reprint this – one of our most popular, most-read pieces.  

(Original publish date: April 17, 2013)

The process of productive capital allocation is a critical ingredient of innovation and job growth.   Entrepreneurs spending their own (and their partners’) money will create more jobs, more innovation, and a more vibrant economy than politicians picking winners and losers based on cronyism, campaign contributions, and constituent pork.

When government strays out from funding basic research into either applied research or the means of production, the results range from poor to scandalous.  Ideas are infinite, and in the absence of competent execution, they are worth nothing.  Even if the idea has merit, the true expertise is crowded out.  There are better ways policymakers can help encourage innovation.

The invention of the airplane provides an excellent example.  While we’re all aware it was the Wright Brothers, many interesting details about funding the innovation don’t make it into school textbooks.  In A Tale of ‘Government Investment’  Lee Habeeb & Mike Leven recount the race between the bicycle shop owner/operators and the government-backed head of the Smithsonian.

Who better to win the race [to powered flight] for us, thought our leaders, than the best and brightest minds the government could buy? They chose Samuel Langley. [The War Department gave Langley $50,000, an enormous sum at the time, which The Smithsonian augmented with taxpayer funds of its own.]  You don’t know him, but in his day, Langley was a big deal. He had a big brain and lots of credentials. A renowned scientist and a professor of astronomy, he wrote books about aviation and was the head of the Smithsonian.  It was the kind of decision that well-intentioned bureaucrats would make throughout the century — and still make today. Give taxpayer money to the smartest guys in the room, the ones with lots of degrees. They’ll innovate and do good for us.

For that Solyndra-type investment the country got the “Great Aerodrome,” which “fell like a ton of mortar’ into the Potomac River – twice.  Representative Gilbert Hitchcock of Nebraska remarked, “You tell Langley for me that the only thing he ever made fly was government money.”

Samuel Pierpont Langley’s Aerodrome and launching apparatus.

Nine days after that second failed test flight, a “sturdy, well-designed craft, costing about $1000, struggled into the air in Kitty Hawk.”

How did two Ohio brothers accomplish what the combined efforts of the War Department, The Smithsonian, and other people’s money could not?  The authors cite James Tobin’s To Conquer the Air: The Wright Brothers and The Great Race for Flight (2004) to provide a few answers:

  • Langley saw the problem as one of power:  how to go from zero to 60 in 70 feet, the stress of which was too great for the materials used.  The Wright Brothers, inspired by the practical skills and insights gained from tinkering in their bike shop, understood the problem was one of balance (on a bike, balance+practice = control).  They invented the three-axis control (pitch, yaw, roll) still standard on fixed-wing aircraft today.  Their entrepreneurial technical expertise was an advantage neither the government nor other private competitors (Alexander Graham Bell) could match.
  • Since they couldn’t afford repeated test flights the Wright Brothers were forced to develop a wind tunnel to test their aerodynamics.  This saved money and time, since they weren’t bogged down repairing the wrecks of a flawed design.
  • No government money also meant no government strings.  They were freer to experiment and innovate without worrying about non-essential requests and hidden agendas.  They also managed to do more with less since they couldn’t afford subsidy-induced waste.

Habeeb & Levin also offer this fascinating, if not unexpected, coda:

Though the Wrights beat Langley and the Smithsonian, the race didn’t end there. Powerful interests vied for the patent to this revolutionary invention and, more important, for the credit for it. With Smithsonian approval, a well-known aviation expert modified Langley’s Aerodrome and in 1914 made some short flights designed to bypass the Wright brothers’ patent application and to vindicate the Smithsonian and its fearless leader, Samuel Langley.

That’s right. The Smithsonian’s brain trust couldn’t beat the bicycle-shop owners fair and square, so they used their power to steal the credit. And then they used their bully pulpit to rewrite history. In 1914, America’s most esteemed historical museum cooked the books and displayed the Smithsonian-funded Langley Aerodrome in its museum as the first manned aircraft heavier than air and capable of flight.

Orville Wright, who outlived his brother Wilbur, accused the Smithsonian of falsifying the historical record. So upset was he that he sent the 1903 Kitty Hawk Flyer, the plane that made aviation history, to a science museum in . . . London.

But truth is a stubborn thing. And in 1942, after much embarrassment, the Smithsonian recanted its false claims about the Aerodrome. The British museum returned the Wright brothers’ historic Flyer to America, and the Smithsonian put it on display in their Arts and Industries Building on December 17, 1948, 45 years to the day after the aircraft’s only flights. A grand government deception was at last foiled by facts and fate.

As for Samuel Langley, he died in obscurity a broken and disappointed man. Friends often noted that he could have beaten the Wright brothers if only he’d had more time — and more government funding.

Some things never change.

The Wright brothers’ airplane business never took off (groan) due to a combination of poor business decisions and sloppy patent work.  Wilbur sadly died young (in 1912 at age 45, of illness that some suspect was contracted due to exhaustion from the patent battles) and Orville sold the company in 1915.  So the industry grew under the leadership of other companies and other men.  (Although the Curtiss-Wright Corporation remains in business today producing high-tech components for the aerospace industry.)   One can’t help but wonder what the original inventors might have done had they been the beneficiary of a strong partnership with a VC fund…



Sunken treasure, poor governance, and the limits of decision models

What $12.7 million investment in 1988 yielded a vanishing $48 million in 1991, nothing again until this year, and yet may still have fabulous upside?  As ESPN films explains in In Deep Water, a real-world “National Treasure.”

When a hurricane sank the SS Central America in 1857, over 400 lives and at least 3 tons of California Gold Rush fortune were lost.  “At least” because the steamer was also rumored to carry in its hull an additional secret 15 tons of gold headed for NY banks.  The loss contributed to The Panic of 1857, as the public came to doubt the government’s ability to back its paper currency with specie.

Gold bars and coins from the wreck of S.S. Central America

131 years after the ship was lost, oceanic engineer Tommy Thompson  and a team of “data nerds” used Bayesian modeling to find the ship and new deep-water robot technologies to recover items from the ocean floor.  We caught the 30-for-30 movie this week and it captivated us on several levels:  (a) it’s the greatest lost treasure in American history, (b) it includes important lessons about corporate governance, (c) it demonstrates the importance of intuition, and (d) the tale ends with a local twist – fugitive entrepreneur/treasure hunter Thompson was just recently captured in our backyard (Boca Raton).

The story has parallels to another favorite of ours – The Greatest Comeback Ever and the Limits of Decision Models – in which intuition augmented or even trumped the computer model.  Following a hunch they discovered her on “the edge of the probability map,” ending one mystery but starting another.

Thompson – “a combination of Indiana Jones and Tesla” – used lack of transparency and poor corporate governance to keep his investors at bay for 16 (!) years.

The first seven years were consumed by a flurry of lawsuits from 19th century insurers and not directly his fault; his backers then patiently waited for the next nine years as Thompson told them the gold had to be marketed just so.  He sold 532 gold bars and thousands of coins for $48 million in 2000, purportedly to pay loans and legal bills.  In 2005 two investors sued, in 2006 some crew members sued, and Thompson became a recluse in a rented Vero Beach mansion which he paid for with “moldy smelling” $100 bills (they’d been buried underground).  He missed a 2012 court appearance and was officially on the lam up until being caught earlier this year in Boca Raton, FL.

A new company (Tampa-based Odyssey Marine Exploration) re-started salvage efforts in April 2014 – only 5% of the wreck was excavated by 1991, and it’s been left un-touched since then. Recovery efforts will continue indefinitely (is it 3 or 18 tons of gold?) and be used in part to reimburse the original investors.


Vintage Future VIII

42 years ago this past Friday, Martin Cooper of Motorola made the first ever cell phone call to Joel Engel of Bell Labs. Cooper was calling Engel to troll him about the fact that Motorola invented the thing first, although it was another 10 years before the company released the DynaTAC 8000X. So yeah…even the very first guy talking loudly on his cell was kind of a jerk about it.

That decade between trash talk and commercial introduction brought to mind our Vintage Future series in which we take a tongue-in-cheek look back at the failed predictions of past generations of investors and futurists, and the sometimes tortuous routes to success of unlikely ideas.

In our line of work it’s good to guard against the hubris inherent in projecting conventional wisdom too far out into the future, and to remind ourselves that today’s trend can be tomorrow’s punchline – and vice versa.

Back in 2010 in “Entrepreneurial silver lining in today’s economic clouds” we mentioned that the patents for the Television, Jukebox, and Nylon were all granted during The Great Depression, and although we can’t confirm any patent information on the chocolate chip cookie, it too was invented at the same time (1930 to be precise).  In this, our VIIIth installment of Vintage Future, we share some of the less successful ideas from the Great Depression.


March Madness and the availability heurisitic

(Editor’s note:  This is a slightly modified re-print of a popular piece we published in April 2013.  Our readers enjoy the subject of how to improve their decision making skills, especially when sports can provide the context.)

Decision making and cognitive biases are common themes here at NVSE.  We’ve written about good board decisions, how the popularity of the Mona Lisa is based on circumstance rather than inherent artistic qualities, how the design of the decision-making process affects the decision, and how managers can undermine their decision making by over-relying on common sense, rationalizing instead of being rational, or making unconscious choices.

The availability heuristic refers to placing too much emphasis on data that is quick and easy to gather.  However in this particular instance – clutch performance during March Madness – it just might be more of a reliable bellwether than a problematic bias.

In Method to the Madness Peter Keating explains “why NBA GMs should go mad for the breakout stars of March.”

NBA teams scout hundreds of players across the country, tracking their every move for months on end, and put dozens of prospects through extensive workouts. Yet when it comes to draft night, clubs routinely rely on the same measure the rest of the country uses: NBA GMs, it turns out, favor players who had surprising success in the postseason. And the even bigger shocker? They’re right to do so.

Economists Casey Ichniowski of Columbia and Anne Preston of Haverford studied March Madness because they wanted to investigate whether employers often overweigh recent and vivid information when making decisions. Earlier research had shown that when we make judgments, we rely on data that’s accessible — the quickest and easiest stuff to gather — even when we know it’s important to be objective. Social scientists call this the “availability heuristic,” and it explains why Americans wrongly believe tornadoes kill more people than asthma: A spectacular catastrophe is easier to recall, so we overestimate its likelihood…

On average, a player who scores four points per game above expectations on a team that wins one more game than projected in the tournament will boost his draft position by 4.7 slots, according to Ichniowski and Preston. Now, here’s the thing: Players who get March Madness bumps deserve them. Ichniowski and Preston also examined what happened to players after their draft days… In every case, the group that got draft boosts from the NCAA tournament played better than those who didn’t. If anything, teams undervalue March Madness as a predictor of future success and stardom.

I usually repeat “sample size, sample size, sample size” about as often as and in the same tone that Jan Brady wailed “Marcia, Marcia, Marcia,” so I was shocked by these results. For most players, March Madness lasts only a game or two, yet it sends a signal powerful enough to last entire careers.

“I’m thinking of showing my sports class a clip of Michael Jordan beating the Cavaliers and asking if you could have ever predicted this, so that maybe you take MJ at No. 1 instead of No. 3,” Ichniowski says. “Then I’d like to show his NCAA shot [winning the national championship for North Carolina] and move to the question of how much to weight March Madness performance.” The answer: At least as much as NBA GMs do now. The NCAA tournament, with its pressure-packed contests featuring the best college players in the country in front of gigantic audiences, is truly a meaningful simulation of NBA conditions.

UPDATE (6/14/15):

Same sport, different draft; same struggle, different bias:  a story about Kristaps Porzingis, a 7’1″ 19-year-old playing in Liga ACB, perhaps the second-best basketball league in the world.  He’s “the type of prospect that has historically torn coaching staffs and front offices apart” as they try to assess his NBA bona fides before the draft.

All draft picks are crapshoots, but some feel like crappier shots than others. It’s uncouth to plainly say, “I have a bad feeling about this guy,” so we do our best to justify our vague inklings. The stronger our distaste, the stronger our effort. So of course it’s the foreigner with the spindly frame and the funny name who has people [grasping for answers]. … What is the draft if not complete pseudoscience?  …

He’s like a young Robin trying on Batman’s utility belt — the tools are there, and they’re incredible. They just don’t fit yet, and you can’t be too sure that they ever will. His issues on defense are the same most players his age experience. He bites on pump fakes, he gets caught ball-watching, and he can be a step slow recovering to his man. But there is a chance that, five years down the line, he’ll be doing things that only a handful of NBA big men can do at a high level.

Maybe all of that hokey pseudoscience will prove prescient. Drafting isn’t an art, and it isn’t a science, but if you squint hard enough, it can look like a happy medium. It’s all just waves of confirmation bias on both ends of the spectrum posing as data points, right? It can tell you anything you want it to if you wait long enough. But it can’t, at the very moment, tell you the fate of Kristaps Porzingis. And so, like any other year, we’ll go on trying to find some illuminating detail that will solve the puzzle once and for all, blissfully ignorant to the fact that there’s only one person with the final pieces.

As with the NFL draft, pre-draft metrics have only some predictive power.  The data don’t predict a player’s ceiling, can’t account for what kind of system a player will enter, the talent he’ll have around him, the luck he’ll have with injuries, or the intangibles he possesses.

If you’re looking for a bellwether of NBA success, look to the NCAA tournament.  Its pressure-packed contests featuring the best college players in the country in front of gigantic audiences turns out to be a meaningful simulation of NBA conditions.  Even though it’s a very small sample size – for most players just a game or two – the data show that players who move up the draft board as a result of their performance in March Madness deserve it.

The crucial distinction to remember on this topic is that Big Data has limitsWhile it may help make accurate predictions or guide knotty optimization choices or help avoid common biases, it doesn’t control eventsModels can predict the rainfall and days of sunshine on a given farm in central Iowa but can’t change the weather.  A top draft pick may or may not develop based on the system, surrounding talent, &etc.

In our experience the best results often come from a combination of deliberation and intuition.  Too much data can lead to analysis paralysis, common sense can be a shockingly unreliable guide, and those who rely on intuition alone tend to overestimate its effectiveness.

Big Data in The Big D back in the Day

Before there was Moneyball, there was a little expansion football team in Dallas who invented Big Data (in sports) on their way to 20 consecutive winning seasons and 5 of the first XIII Super Bowls.  They managed to win just 2 of those 5, losing the others by 3, 4, and 4 points.

Yet even those losses serve as evidence in support of the idea of “Moneyball:”  Super Bowl V was so error-filled it’s an outlier known as “The Stupor Bowl,” while X & XIII were classics won by teams known for the type of big-game performances that can trump statistical strategies that need time for the math to work.

An immigrant statistician at IBM who knew nothing of football helped convert 260 scouting phrases (“he can run as fast as 2 cats“) into computer-friendly variables, input those into a giant box with less power than today’s laptops, and hired a psychologist to design a questionnaire sent to 10 scouts at 400 schools.  As a result, the Cowboys were able to uncover future Hall of Famers like Bob Hayes and Rayfield Wright at obscure colleges and fill roster spots with other athletes (e.g. basketball players & track stars) who’d never played one snap of college football..

Big data may help make accurate predictions or guide knotty optimization choices or help avoid common biases, but it doesn’t control events and can be undone by cluster luckModels are useful in predicting things we cannot control, but for players in the midst of a game the reality is different.  Players don’t predict performance; they have to achieve it.

For more on this subject, please see Untangling skill and luck in sports and business, The greatest comeback ever and the limits of decision models, and March Madness and the availability heuristic.

This short video from ESPN films tells the Cowboys’ story.  Inspired by what GM Tex Schramm saw at the 1960 Winter Olympics – IBM had placed chips inside skis to collect data – they created the blueprint for the modern NFL out of thin air.

In honor of the playoffs, and invoking The Rule of 3… here are two more recent examples of how data is changing the NFL:

PART IIThe Comeback of the Running Back argues that increased snap counts are favoring big-play running backs, especially as the game grinds on.

No running back was drafted in the first round of the last two NFL drafts. But general managers, coaches and scouts knew that the running game would return due to a mixture of economics and fitness. And now it has.

This season, running backs carved out a new role that looks like it will last: big-play specialists designed to exploit holes in today’s new-age defenses. Running backs may never be the focal point that they once were, but they are at least putting up a fight.

As the passing game gained prominence in recent years, two things happened: Linebackers and defensive backs shed weight to adjust to the new speed of the game, and up-tempo offenses resulted in more plays and more tired defenses. Those no-huddle offenses not only wore down defenses over the course of a game, but also the course of the season. This has led to exhausted defenses with lighter players who see hundreds more snaps than in the past

To be sure, winning teams tend to run the ball more, especially late in games as they protect leads, leading to some statistical inflation. But NFL strategy wonks say that this development is much more than that. Worn-out defenses, which are using more defensive backs to defend the pass, are having a tough time bringing down lumbering running backs

“People were focusing on the edges of the field, defenses were focusing on playing the pass and playing ‘basketball on grass,’ and what’s happening is the offenses are going back to oversize players, getting a power forward to go into the paint with those guys,” said former NFL general manager Phil Savage…

This is a classic phenomenon in sports: an undervalued commodity becoming valuable again because of a shift in how the game is played.

PART III The Worst QBs Over 40 (Passes) points to data that suggests winning % drops off substantially when a QB – even an elite one – exceeds 30-39 pass attempts in a game.

Tasking any quarterback with videogame style pass attempts however, may be a losing strategy. The Count looked at the career records of the quarterbacks expected to start in this season’s NFL playoffs (except Arizona’s Ryan Lindley, who only has six career starts) and found that only New England’s Tom Brady (39-24, .619) and Indianapolis’ Andrew Luck (11-11, .500) have records of at least .500 when attempting 40 or more throws.




End-of-year twitter digest, 2014

Thank you to all our readers for joining the conversation here in 2014.  We wish you all a happy and prosperous 2015, and look forward to seeing many of you at the 24th Annual Florida Venture Capital Conference, January 29 – 30, 2015 at the Diplomat Resort & Spa in Hollywood, Florida.

Offered for your reading pleasure, in case you missed any:  a compendium of our twitter highlights from 2014.

BPV twitter header


The innovator’s blind spot sock puppet spokesdog.  (PhotoTurns Out the Dot-Com Bust’s Worst Flops Were Actually Fantastic Ideas – or so argues Wired magazine.  There remain “many deliciously ideal symbols” of the epic failures during the bust, but “the irony is that nowadays, they’re all very good ideas.”

Now that the internet has become a much bigger part of our lives, now that we have mobile phones that make using the net so much easier, now that the Googles and the Amazons have built the digital infrastructure needed to support online services on a massive scale, now that a new breed of coding tools has made it easier for people to turn their business plans into reality, now that Amazon and others have streamlined the shipping infrastructure needed to inexpensively get stuff to your door, now that we’ve shed at least some of that irrational exuberance, the world is ready to cash in on the worst ideas of the ’90s…  (Emphasis added – ed)

The lesson here is that innovation is built on the shoulders of failure, and sometimes, the line between the world’s biggest success and the world’s biggest flop is a matter of timing or logistics or tools or infrastructure or luck, or—and here’s the lesson that today’s high flying startups should take to heart—scope of ambition.

Maybe if had kept its head down and worked harder on getting the dog food to our doors than assaulting U.S. airwaves with ads like the one below, they would have made it.

In Pitfalls of entrepreneurship, ecosystems of innovation, we discussed the book The Wide Lens and what author Ron Adner termed “the innovator’s blind spot: failing to see how success also depends on partners who themselves need to innovate and agree to adapt.”  Here’s Adner:

Companies understood how their success depends on meeting the needs of their end customers, delivering great innovation, and beating the competition…  To be sure, great customer insight and execution remain vital, [but] two distinct risks now take center stage:

  • Co-Innovation Risk: The extent to which the success of your innovation depends on the successful commercialization of other innovations.
  • Adoption Chain Risk: The extent to which partners will need to adopt your innovation before end consumers have a chance to assess the full value proposition.

…When you try to break out of the mold of incremental innovation, ecosystem challenges are likely to arise… a strategy that does not properly account for the external dependencies on which its success hinges does not make those dependencies disappear.  It just means that you will not see them until it is too late. … Dependence is not becoming more visible, but it is becoming more pervasive. What you don’t see can kill you.

Adner serves up an easy-to-grasp example, a 1998 precusor to iPods called “MPMan:”

It sold 50,000 players globally in its first year. But [it was very different than the Walkman] 20 years earlier.  You couldn’t purchase them in traditional retail settings.  Downloading an album – legally or not – could be a multi-hour affair. It didn’t matter that MPMan was first – it wouldn’t have mattered if they were 6th, 23rd, or 42nd. Without the widespread availability of mp3s and broadband, the value proposition could not come together.

For more examples, check out our Vintage Future series – a tongue-in-cheek-yet-barbed reminder that predicting technology trends is not for the weak at heart.  (And that’s before one tries to protect the IP and find a way to profit from it.  There are reasons we affectionately call the really early stage of investing adventure capital.)

It’s a long and difficult journey from idea to successful business, involving many inter-related factors.  The best products don’t always win.  Compelling innovations can and do fail after launch – as did this 1997 precursor to Facebook.

Vintage Future VII

Our Vintage Future series takes a tongue-in-cheek look back at the failed predictions of past generations of investors and futurists, and the sometimes tortuous routes to success of unlikely ideas.

In our line of work it’s good to guard against the hubris inherent in projecting conventional wisdom too far out into the future, and to remind ourselves that today’s trend can be tomorrow’s punchline – and vice versa.

Our VIIth installment takes a look at “the greatest thing” ever invented and a simple innovation that dramatically altered how we see the world.

Even sliced bread took 18 years to succeed.  Otto Frederick Rohwedder, a jeweler from Missouri, built his prototype “Machine for slicing an entire loaf of bread at a single location” in 1912 but saw it destroyed in a fire.  15 years later he filed his patent, but the end product languished due to its untidy appearance and concerns about freshness.  One year later a St. Louis baker named Gustav Papendick put it in cardboard trays and wrapped it in wax paper, yet even then it didn’t take off until it helped a little company called Wonder Bread go national in 1930.

Grok this:  Betty White is older than sliced bread!

Grok this:  Betty White is older than sliced bread!

Except for a brief ban during WWII (the steel used to build the slicers had more pressing uses), sliced bread grew quickly and became a platform on which others could dream and build – in this case new types of spreads and jams.

Sometimes a simple idea – like digging ditches – can change the world.  Before most cables ran underground, all electrical, telephone and telegraph wires were suspended from high poles, creating strange and crowded streetscapes.


A phone tower with 5000 lines in Stockholm, Sweden in use 1887-1913

A startup must have good answers to seven questions

All happy companies are different because they found something unique that gives them a vision and a monopoly of sorts;  all unhappy companies are alike because they’ve failed to escape the essential sameness of competition.”

So says Peter Thiel – business subversive, founder of PayPal, first outside investor in Facebook, one of Silicon Valley’s leading investors, thinkers, and, since finding himself portrayed in the movie The Social Network, celebrities.

In the interview clip below, Mr. Thiel also says that we have “a very powerful but very narrow cone of progress around the world of bits, not so much in the world of atoms.”

The entire Uncommon Knowledge interview – which discusses competition in business, the value of monopolies, and the battle between humans and computers – can be found here.

Explosions of Creativity, a review of Peter Thiel’s Zero to One – Notes on Startups, or How to Build the Future a book based on “careful” notes taken by a student during a course on innovation Thiel taught at Stanford in 2012.

One suspects that the course was more a seminar bull session than a rigorous academic analysis (not that there’s anything wrong with that!) and it does not escape the genre, set forth in the subtitle, of “Notes.” The result is a loose collection of aphorisms and bits of wisdom, not a sustained inquiry.  Nor does the book probe deeply into Thiel’s own experience. There are occasional references to PayPal, but the bloody details of entrepreneuring in one of the most cutthroat eras of business history are omitted…

To Thiel, the only valuable ideas are those that most other people disagree with, and the initial point for successful entrepreneurs must be: “What valuable company is nobody building?” He thinks the dot-com crash taught the wrong lessons: It convinced Silicon Valley to eschew grand visions, avoid plans in favor of opportunistic flexibility, focus on improving on existing products already offered by competitors, and avoid products that need intensive sales efforts.

All of these ideas are wrong. A great startup must have a vision and a plan, it must avoid competition, and it should recognize that if a better mousetrap falls in a forest and no one knows about it, it might as well not exist.


To have a shot at success, a startup must have good answers to seven questions: Engineering — can you create a breakthrough, not just incremental improvements? He uses the figure that technical improvements must be ten times as good as incumbents to succeed. Timing — is now right? Monopoly — are you starting with a big share of a small market? People — do you have the right team? Distribution — can you deliver the product? Durability — is your market position defensible over time? The secret — have you identified a unique opportunity that others do not see?

The goal is market power, usually based on combinations of technical superiority, network effects, scale economies, and branding.

These are not earth-shaking insights, but it is useful to be reminded of them, because they are regularly ignored. Thiel notes the problem with the wave of green tech that swept over Silicon Valley in the Aughts: The companies lacked good answers not just to one or two of these questions; they had bad answers for all seven.

A disquieting combination of disproportionate importance and exceptional unpredictability

That title may sound a little like early stage investing, but it comes from a description of October baseball.  The Super-Rotation Rivalry explores the data behind the decision-making of two teams with very recent playoff history:  the Detroit Tigers and Oakland A’s.  (In both 2012 and 2013, Detroit eliminated the A’s because their ace – Justin Verlander – dominated in the final deciding game of the series.)

Both teams acquired aces at the trade deadline based on the theory that Moneyball may deliver results over a 162-game season, when the math has time to work, but in a short playoff series a team can be undone by a dominant pitcher or by cluster luck.

At the time it appeared as though those teams were likely to meet in the playoffs for a 3rd year in a row, so there was an element of game theory layered on top of the data analysis.  However the A’s swooned and landed in a single-elimination Wild Card playoff tonight in Kansas City.  Up 3 games on August 7, they went 18-30 to lose the division to the Angels by 10 games.

So we’ll have to wait to maybe see the game theory play out between the Tigers and A’s, but we will gather one more datum on the Moneyball-in-a-short-series argument.  (In this case, a very short series…)

Can stockpiling aces reduce playoff unpredictability?  It turns out the theory is hard to prove:

It’s possible there’s something to the “pitching wins pennants” hypothesis, but if so, it’s hard to see it in the stats. In 2012, Colin Wyers — then the director of research at Baseball Prospectus, now a “mathematical modeler” for the Astros — and I looked for evidence that teams with strong no. 1 starters outperformed expectations in the playoffs. We identified the ace of each playoff team from 1995 to 2011, rated each one using a normalized measure of ace-hood, and then checked for any correlation between the strength of each ace and the difference between his team’s regular-season and postseason winning percentages. There wasn’t one, which suggests that once you know a team’s regular-season record, knowing how good its best pitcher is doesn’t add any predictive power. Nor could Colin find any evidence of an effect after rerunning the analysis using the entirety of a team’s playoff rotation instead of its ace alone…

So why doesn’t the quality of a team’s top three starters or its ace register as significant? For one thing, the differences between teams are compressed in the playoffs, relative to the regular season: Teams with terrible staffs don’t make it to October, so the gulf between the best- and worst-pitching playoff teams isn’t as stark as we’re used to seeing during the season’s first six months. Perhaps more importantly, there’s more than one way to win baseball games, and even under an expanded playoff format, teams don’t get to October without doing something well. A team with an inferior pitching staff often makes up for its weakness on the mound by being better on offense.

If there’s no clear evidence that pitching acquires extra significance in the postseason, why is the belief that it does so persistent? It might be because it’s so hard not to notice the extent to which scoring is suppressed in the playoffs. There’s no question that playoff games tend to produce fewer crooked numbers: Last season, teams scored an average of 4.17 runs per game during the regular season, but in the postseason, their output declined to 3.78 runs per game, a 9.4 percent reduction. That figure fluctuates from year to year — in 2012, teams scored 19.2 percent fewer runs per game in the playoffs — but the direction of the difference is usually the same: down. During the 1995-2013 wild-card era, the gap has been exactly one run per game (half a run per team), or 10.6 percent.

Weather explains some of that effect; playoff games can be cold, and the lower the temperature, the less far the ball flies. Defense also plays a part, since playoff teams tend to be better than average at converting balls into outs. The bulk of the decline in scoring, however, stems from the difference in the postseason pitcher pool. … The pitchers on a given team’s postseason pitching staff are generally about half a run better than the same team’s full regular-season staff, and teams generally score about half a run less per game in the playoffs. The postseason scoring mystery is solved: It’s not that hitters lose their mojo once the calendar flips to October, it’s that they face superior opponents.

So in a sense, pitching is better during the playoffs, in that a team’s worst arms generally aren’t invited.

As it turns out, there are a few other hard-to-prove baseball theories that may be false:

Because October baseball subjects fans to a disquieting combination of disproportionate importance and exceptional unpredictability, it’s a fertile breeding ground for suspect narratives that attempt to explain small-sample postseason success or failure. Over the next few months, you might hear, for instance, that teams that “back into the playoffs” after a September slump are at a disadvantage against teams that end the regular season on a high note. Not so. You might be told that teams that rely on the home run can’t score in the playoffs, when small ball rules. In fact, the opposite is the case. Surely momentum matters? Uhuh. And we all know that there’s no substitute for postseason experience — except for a lack of postseason experience, which works just as well.


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