Category Archives: Building a Business

The Ultimate October Blueprint

Our regular readers know that we often cogitate over the roles both skill and luck play in sports and business.  The eve of the baseball postseason feels like a good time to revisit the subject.

Especially since we’ve found new data, even in an admittedly a small sample size.

In The Ultimate October Blueprint, David Schoenfield studies the past 5 post seasons (“when the strike zone started increasing in size and offense began to decline“) and draws a few tentative conclusions:

  • Don’t strike out – even though you’re facing better pitching!
  • Contact trumps power, although “the weird thing is that home runs in the playoffs have matched the frequency of home runs in the regular season.”    But none of the teams who led all playoff teams in HRs – in either league – have won the World Series in the past 5 seasons.
  • Use your bullpen, early and often.  Starting pitchers don’t fare as well as they start going through the lineup a second and third time, so don’t let them lose a game in the middle innings.  “Go to the bullpen. Hope they do the job.”

However… all of the past 10 World Series teams had a starter step up in the postseason:

The thing is, sometimes that starter is a Bumgarner or Verlander or Lincecum, but sometimes it’s an untested rookie like Wacha, a veteran having a so-so season like Lester (he had a 3.75 ERA in 2013) or a mid-rotation starter like Vogelsong. And sometimes it’s Colby Lewis…

The last team to lead the majors in starters’ ERA during the regular season and win the World Series? The 1995 Braves. In other words, having a season’s worth of gems from your rotation guarantees nothing in October — maybe bad news for all five NL playoff teams, who rank 1-5 in the majors in rotation ERA.

But here’s an indicator that may help: In looking for which pitchers may come up big in October, it appears a strong finish is important.

The data also suggest that velocity is overrated.  “Maybe when the chips are down, it’s those crafty veterans throwing in the low 90s and situational relievers who win you World Series.”

Schoenfield summarizes advice for when the contest isn’t long enough for the Moneyball math to work:

[The playoffs are unpredictable but] there are a few strategies that seem to work: Battle pitchers with two strikes and put the ball in play; turn the game over to your bullpen in those middle innings; rely on one starting pitcher if you have to; get some big home runs from unlikely heroes along the way. And maybe hope you have a starting pitcher who can throw five innings of relief on two days’ rest in Game 7 of the World Series.  [As Madison Bumgarner did for last year’s Giants. – ed]

The fan inside us is fascinated by the prospect of advantages gained in the short term, but over the long term our conclusion remains the same:

While big data may help make accurate predictions or guide knotty optimization choices or help avoid common biases, it doesn’t control events and can be undone by cluster luckModels are useful in predicting things we cannot control, but for those in the midst of the game – players or entrepreneurs – the results have to be achieved, not just predicted.

 

Related stories:

Introverted traits are undervalued in the business world

In “Do I put off a human vibe to you?” (January 2014) we wrote that while the “extrovert ideal” may capture the public imagination, our experience with quiet and cerebral entrepreneurs has demonstrated that one doesn’t have to be an extroverted leader in order to run a successful high-growth company.

dilbert-introvertsWriting in yesterday’s Wall Street Journal, Elizabeth Bernstein makes the same argument, in longer form, citing unique skills that introverts offer:

– a propensity for balanced and critical thinking
– a knack for quietly empowering others and less interest in personal glory
– the ability to focus for long periods and to use solitude to think originally and create something out of nothing
– a more focused mindset to their leadership style
– better at dealing with setbacks (because they need less external validation)
– more realistic when listening to feedback or analyzing information (because they do less public promotion of themselves)

Bernstein then argues that the different styles work under different circumstances. From “Why Introverts Make Great Entrepreneurs“:

An introvert’s desire to put the spotlight on others and really listen—and to model this skill for others—will be a huge advantage to his or her company, in sales, management, partnering and just about any other aspect of the business, Ms. Buelow says. “The best businesspeople aren’t necessarily the best talkers, but the best listeners, the people who ask the right questions,” she says.

That was borne out in a study reported in the Harvard Business Review in December 2010. Adam Grant, a professor at the University of Pennsylvania’s Wharton School , and his colleagues found that when employees were proactive, introverted leaders generated better performance and higher profits than extroverted leaders did.

Why? Extroverts are better at leading passive employees who need a lot of direction, says Dr. Helgoe. “But if you have a very creative, self-motivated staff, introverts are better at channeling that talent and staying out of the way—listening, taking in ideas, helping employees shine.”

This article from last month’s WSJ makes a similar point about “ambiverts,” those with both introverted and extroverted traits, neither dominant, who adapt their individual leadership styles to the situation:

[Ambiverts] have more balanced, or nuanced, personalities [and can] move between being social or being solitary, speaking up or listening carefully with greater ease than either extroverts or introverts. “It is like they’re bilingual,” saysDaniel Pink, a business book author and host of Crowd Control, a TV series on human behavior, who has studied ambiversion. “They have a wider range of skills and can connect with a wider range of people in the same way someone who speaks English and Spanish can.”

In practice we need each other.  The best teams typically will have some of both who play to each other’s strengths.  But it doesn’t have to be the extrovert in the entrepreneur- CEO’s chair.  Here’s how Bernstein closes her piece:

Of course, introverts do have some qualities that aren’t that well-suited for entrepreneurship: They can be too internally focused and sometimes shun networking. Extroverts are natural networkers and certainly know how to rally the troops.

But it’s time to recognize that introvert traits have long been undervalued in the business world—and it may be time for extroverts to try and be more like introverts.

The roles of skill and luck in sports and business.

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The 2015 baseball season is demonstrating that when it comes to untangling the roles skill and luck play in sports and business, luck may play a greater role than we’d like to think.

With technology and best practices so widely and easily articulated and disseminated, the difference between the best competitors and the worst is less than in the past.  So a hot stretch of cluster luck can make the difference.

Case in point:  so many teams currently hover close to .500, in contention for the 10 playoff births, that the trade market has been slow to develop.  Teams can think in terms of limping into the playoffs and then getting hot, and so take longer to choose whether they’re buying or selling assets.

The pre-season projected standings predicted such parity, with only 23 projected wins separating the leaders from the laggards entering this season and only 2 teams projected to finish with 90+ wins.  Welcome to MLB’s 2015 Projected Standings, Where Everyone (and No One) Is a Winner:

Projection systems tend to forecast more conservative winning percentages than we’re used to seeing in the final standings. That’s because projected win totals reflect the most common outcomes of thousands or even millions of simulations, whereas a single season, with its wild fluctuations in luck, offers ample opportunity for teams to significantly exceed or fall far short of their true talent levels…  As Phil Birnbaum and Neil Paine have noted, there’s an absolute limit to the accuracy of baseball projections. Even if we were omniscient when it came to team talent levels, we wouldn’t be able to predict luck. And luck has large effects: As Birnbaum wrote, “On average, nine teams per season will be lucky by six wins or more.”

It’s not only harder to separate yourself from the pack, there’s also less incentive to do so:

Last August, Birnbaum wrote that in a rational market, an expanded playoff field should make bad teams more willing than before to spend on free agents, and good teams more willing to tighten their belts. “With more teams qualifying for the post-season, there’s less point making yourself into a 98-win team when a 93-win team will probably be good enough,” Birnbaum wrote. “And, even an average team has a shot at a wild card, if they get lucky, so why not spend a few bucks to raise your talent from 79 games to (say) 83 games, like maybe the Blue Jays did last year?” That’s exactly what we’ve seen. … (However) as soon as it sinks in that not all “postseason” spots have equal value, teams might start prioritizing division titles over coin-flip wild-card games and aiming, once again, for greatness instead of good-enough-ness.

Somewhat ironically, the team suffering the most from bad luck so far this season is the very same team who invented “Moneyball.”

Billy Beane actually built a competitive team, but one that’s had an absolutely brutal run of luck. By BaseRuns, the A’s have played .596 baseball, good for a 51-34 record that would make them the second-best team in baseball. In reality, though, the A’s are a wildly frustrating 38-47 (.447), leaving them a whopping 13 games behind their expected record. No other team in baseball is more than five games below its BaseRuns-expected record. Oakland is 6-21 in one-run games; that .222 winning percentage would be the worst figure over a full season in 80 years.

The A’s will have more luck in one-run games. And they’ll play more like the .596 team than the .447 team over the rest of the season. If anybody in baseball has faith in trusting that longer view of performance, it’s Beane. The problem, of course, is that they may be buried too far in the standings to catch up.

 

Related stories:

Power Score – Your Formula for Leadership Success

powerscoreI don’t typically read “business books” on vacation, but I made an exception for “Power Score – Your Formula for Leadership Success”.

Power Score is the new book by Geoff Smart and Randy Street, the authors of “Who: The A Method for Hiring” (with an assist this time from their colleague Alan Foster).  “Who” has been required reading in our shop for several years and informs a lot of the questions we ask (and how we ask them) in our “people due diligence” when we are considering partnering with an entrepreneur or helping one of our portfolio companies hire a new senior executive.

So I was excited to read Power Score, which utilizes the data from 15,000 management interviews over twenty years that the authors and their team have done on behalf of corporations and private equity firms at their consulting company, ghSMART.  I love data, and I was impressed with how they mined their unique database to come up with a formula that facilitates successful leadership.

As it turns out, successful leaders get three things right:

1) Priorities – ensuring that they have priorities that are correct, clear and connected to their mission,

2) Who – making sure they have diagnosed their teams strengths and risks, deployed their people against the right priorities, and continually developed their people, and

3) Relationships – working to make sure that their culture and incentive structures support teams that are coordinated, committed and challenged and promote strong relationships with both employees and external constituencies.

The formula seems fairly simple (simple is good on vacation), but the execution is very hard, and very few leaders operate at consistently high levels in all three areas.

The authors offer a scoring system that challenges leaders and their teams to rate themselves on a 1-10 scale in each of the three areas and then multiply the scores (PxWxR) to see how they compare with the best proven leaders in the ghSMART database. (Hint:  500+ is pretty good but 9x9x9 = 729 is the Holy Grail!)  More importantly, they describe how to increase your Power Score by continuously improving in each area, and they also offer a lot of helpful real world examples of how great leaders do it.

The book is written in an easy to digest question and answer format and it won’t take long to finish, though I found myself rereading various sections throughout the book and applying them to companies I have been involved with over the years.  Much like they do in “Who” for identifying and recruiting outstanding talents, the authors offer a process that can’t help but enhance leadership success if executed faithfully.  And, again, unlike most business books it’s backed up by a lot of great data and research on what makes for a strong leader.

I highly recommend the book and plan to send copies to our entrepreneur partners at Ballast Point Ventures, all of whom are looking for that extra leadership edge in their quest to build great companies.  We’ve added it to “The Library in St. Pete” for books we highly recommend.  You don’t have to take Power Score on your next vacation, but then again haven’t you watched enough movies on your iPad during those long flights?

 

Just waves of confirmation bias?

We recently came across another excellent article on data, decision-making, and cognitive biases.  It’s 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.

The answer for Porzingis is obvious:  enroll him in an American D-I hoops powerhouse – we’d recommend a school in the Southeast or Texas – and hope that school enjoys a deep run next March.

Board dynamics that could suppress contrarian advice

Four years ago, on the sesquicentennial of the first shots fired in the Civil War, we cited a piece from The Wall Street Journal that described how the dynamics of General Lee’s staff undermined the accomplished leader, who, at Gettysburg, failed to sense his team’s growing doubts.

The lesson remains relevant for entrepreneurs so we revisit it this week – the sesquicentennial of Lee’s surrender at Appomattox Courthouse.

Lee’s generals knew that modern weapons and a determined enemy would turn the charge into a disaster.  But who among them would step forward to question the supremely confident general known as the “marble man”?  Lee would have been surprised to discover that his generals had doubts, because he considered himself open to their opinions. But his own stature and idea of himself created a barrier that only the trauma of failure could overcome.

It’s easy to imagine (Lee’s) staff struggling to effectively press their contrarian advice.  Any number of factors could cause one not to risk a career “Pickett’s Charge”:  the leader’s force of personality, the high stakes involved, the constrictions of time, the subtle team dynamics of consensus building, or even an over-reliance on formal procedures.  And what’s true for a strong general is true elsewhere – including CEOs and their boards of directors.

Owners of private companies get to pick both their investors and their board members, and therefore have an opportunity to create an environment of mutual accountability in which team members trust and challenge each other.

Entrepreneurs are almost always surprised how much value a good board can bring to their companies.  In our experience, boards work best when members’ informal modus operandi animate the formal framework of decision-making and the relationships strong enough to foster open dissent and compensate for the all-too-human tendency to learn only after it’s too late.

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.

Untangling skill and luck in sports and business, redux

We’ve come across another good piece on decision-making and the limits of decision models.  

In The Great Analytics Rankings, ESPN “unleashed (its) experts and an army of researchers” to look across the four major sports and assess each of the 122 professional teams on how much of their approach is predicated on analytics.

They ranked teams both within each sport and across the entire field of 122, and then took a look at the sport with the most developed methodologies – baseball – to ask, “do clubs that prioritize analytics win more?

Their conclusion:  there’s just a slight correlation between more analytics and more success.  It remains tough to eliminate the usefulness of having more money than other clubs, and with technology and best practices so widely disseminated and articulated (in baseball, at least) the early Moneyball advantages may have been arbitraged away.

All this isn’t to say analytics are a nonfactor. Of course they’re important. For one thing, you have to keep up with the rest of the sport, even if the advantages to be gained are small. A lot of small advantages can add up. Look at the Astros’ signing of Collin McHugh, a nondescript pitcher waived by the pitching-poor Rockies. The Astros studied the PITCHF/x data on McHugh and saw a curveball with a good spin rate and took a chance on him. As Business Week reported:

The Astros’ analysts noticed that McHugh had a world-class curveball. Most curves spin at about 1,500 times per minute; McHugh’s spins 2,000 times. The more spin, the more the ball moves during the pitch — and the more likely batters are to miss it. Houston snapped him up. “We identified him as someone whose surface statistics might not indicate his true value,” says David Stearns, the team’s 29-year-old assistant general manager.

It gets a little more interesting with sports earlier in the process than baseball; e.g., the NHL, in the midst of its own “analytical awakening,” with concepts such as “Corsi” and “Fenwick” bringing together schools old and new.  Only one NHL team cracked the Top 10 of ESPN’s rankings:  the Chicago Blackhawks.

While Joel Quenneville is an old-school coach, the Blackhawks use analytics to find players who might be undervalued elsewhere but fit exactly what Quenneville and the Blackhawks try to do on the ice systematically. It’s been a great combination, with Bowman and Quenneville teaming up to win two Stanley Cups.

“I don’t claim to have the answers — we have a formula that works for us,” Bowman said. “We’re always trying to expand and add a new component each year that we do a little more with.”

When it comes to untangling skill and luck in sports and businessbig 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 luck.  Models are useful in predicting things we cannot control, but for those in the midst of the game – players or entrepreneurs – the results have to be achieved, not just predicted.

The Luck Factor

the-luck-factorIn a bit of randomness courtesy of the interwebs this week, we stumbled upon a 12-year old article (and book) by Richard Wiseman, psychologist at the University of Hertfordshire, in which he argues that being lucky is an easy skill to learn and is based on four principles:  (1) creating or spotting chance opportunities, (2) listening to intuition, (3) creating self-fulfilling prophesies by expecting to be lucky, and (4) adopting a resilient attitude to bad luck.

As luck would have it, we’ve touched upon each of those four ideas here in our ongoing discussion of the role played by luck in sports and business.

(1) In Is there a process to introduce chocolate to peanut butter? we discussed the difference between luck and serendipity and how the right environment or attitude can foster the latter:

The term serendipity was coined in the 18th-century by novelist Horace Walpole, inspired by the Persian fairy tale about three princes traveling through the land of Serendip.  They “were always making discoveries, by accidents and sagacity, of things they were not in quest of.”  What distinguished their “abilities” from simple luck was that they could see meaningful combinations where others did not.

(2) We once wrote that good old-fashioned intuition has its place but it’s unwise to rely only on one’s instincts to decide when to rely on one’s instincts.

As we argued in Thinking consciously, unconsciously, and semi-consciously:  the best results often come from a combination of deliberation and intuition.  Too much deliberation can become analysis paralysis; and studies show that those who rely on intuition alone tend to overestimate its effectiveness.  (They recall the times it served them well and forget the times it didn’t.

(3) In A new envisagement of the world we quoted Samuel Eliot Morison’s 1943 Pulitzer Prize biography of Columbus to illustrate how his expectations of good fortune were not only self-fulfilling but became contagious:

At the end of 1492 most men in Western Europe felt exceedingly gloomy about the future. Christian civilization appeared to be shrinking in area and dividing into hostile units as its sphere contracted. For over a century there had been no important advance in natural science and registration in the universities dwindled as the instruction they offered became increasingly jejune and lifeless. Institutions were decaying, well-meaning people were growing cynical or desperate, and many intelligent men, for want of something better to do, were endeavoring to escape the present through studying the pagan past. . . .

Yet, even as the chroniclers of Nuremberg were correcting their proofs from Koberger’s press, a Spanish caravel named Nina scudded before a winter gale into Lisbon with news of a discovery that was to give old Europe another chance. In a few years we find the mental picture completely changed. Strong monarchs are stamping out privy conspiracy and rebellion; the Church, purged and chastened by the Protestant Reformation, puts her house in order; new ideas flare up throughout Italy, France, Germany and the northern nations; faith in God revives and the human spirit is renewed. The change is complete and startling: “A new envisagement of the world has begun, and men are no longer sighing after the imaginary golden age that lay in the distant past, but speculating as to the golden age that might possibly lie in the oncoming future.”

(4) In Deadlines, decisions, and cluster luck we lamented that although our hometown club was bouncing back from some bad luck, they were running out of time:

This article in Grantland assessed the playoff chances of all 30 MLB teams.  Our Rays were playing well but digging out from the huge hole they’d surprisingly dug for themselves before the All-Star break.  The conclusion?  If the season lasted 262 games they’d have time for the bad “cluster luck” to turn around:

(T)he Rays can trace much of their heartache to cluster luck.  I’ve written about hit clustering a couple of times this year, but here’s a quick recap:  Over the course of a week, month, or even an entire season, certain teams’ hitters will bunch their hits together better than others, while certain teams’ pitchers will scatter their hits apart better than others. The Rays have been, by far, the least lucky team in baseball when it comes to hit clustering.  As of Friday’s FiveThirtyEight piece, the Rays had lost a staggering 54 runs simply through poor hit-clustering luck, a full 20 runs worse than the next-unluckiest team, the Astros.  As Peta noted on Twitter, the Rays have turned double plays on less than 5 percent of the baserunners they’ve allowed, an abnormally low number that’s also the worst in baseball.  Some of that is due to defensive slippage, as Ben Zobrist and especially Yunel Escobar are seeing their range start to tail off as they age.  But most of it is likely a giant fluke.

Cluster luck also very likely explains their torrid streak since that article was published.  One of the things baseball fans like to point out about their sport is that the length of the season tends to be a great leveler of performance.  In this case, there is just too little time for the reversion to mean to accomplish a miracle (for Ray’s fans, including some of us.)

Moneyball moment for restaurants

Anthony Lye – President and CEO of portfolio company Red Book Connect – writes in Restaurant Hospitality magazine that widely available mobile and cloud technology have created a “Moneyball moment” for the restaurant industry.

The restaurant industry is having its Moneyball moment. Now that mobile and cloud technology are cheap and widely available, any restaurant can collect, analyze and act on huge swaths of data. Whether you have an Oakland A’s or New York Yankees budget, your restaurant has the ability to cut costs and increase profits by using big data that was invisible until recently.

When you collect in-store, near-store and above-store data, and then connect it all together in the cloud (i.e. on powerful computers located outside your restaurant), you see problems and opportunities that have gone unnoticed. You can begin to change conventional processes that have been killing your profit margins and losing you customers.

In-store data is all the information that comes from your restaurant. Your POS devices, fryers, refrigerators, temperature sensors and labor scheduling system can all communicate useful data. Combine them in the cloud, and you can detect patterns.

For example, one global restaurant brand learned that its fryers were a huge source of inefficiency. Five times per year, when they added new menu items, all 100,000 fryers had to be reconfigured and each required 30 minutes of labor. So, the company developed a way to push new instructions to fryers via the cloud. Now, an IT guy clicks one button, and all 100,000 fryers know how to cook the new menu item. It’s similar to the way Keanu Reeves learns Kung Fu in The Matrix.

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By comparison, near-store and above-store data is all the information that originates outside the restaurant and above the restaurants in the company hierarchies. Near-store data includes weather, Yelp reviews, sports calendars, school holidays, special events and any other external data that could affect revenue and therefore the inventory and labor.

Normally, a good manager keeps an eye on the near-store data and adjusts inventory and labor based on experience or gut feel. When you instead connect all this data in the cloud and compare it against sales, both real time and historical, you can build much more efficient labor and inventory models for your restaurant. Instead of guessing, your managers forecast inventory and labor needs with fewer errors, all on their smartphones.

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