Lessons from Shoe Dog

I first came across Shoe Dog, Nike founder Phil Knight’s memoir, from this post on Tren Griffin’s blog. Soon enough, I saw the book pop up multiple times on my GoodReads, and when I got the Kindle sample last week, I noticed that all my friends on GoodReads had given it a five star rating.

So while I normally don’t read autobiographies (the only other one that I remember liking is Andrea Pirlo’s), the recommendations made this one hard to resist. And it was a brilliant read. Finished the book in two days.

It’s a story very well told, written in an engaging style that makes you sometimes wonder if it is a work of fiction. Knight has eschewed the boring details and focussed on the interesting, and impactful, stuff, and the book is full of stories about the early days of the firm (it basically “ends” with Nike going public).

What struck me about Nike’s story is the number of times it nearly went under (which is what possibly makes it such a great story). In the light of those challenges (lawsuits, supply issues, constant working capital troubles, leverage), it is a wonder that the company survived long enough to thrive! In that sense, while it makes sense to draw business lessons from Nike’s story, I sometimes wonder if it’s simply a case of survivorship bias.

For starters, for nearly twenty years after founding, Nike remained a closely held private company, with little outside investment. While the company was mostly profitable (except on one occasion when it broke up with a supplier), it was forever cash poor. Well into its fifteenth year of operations, Knight talks about the company “not having money” for stuff like advertising (for example).

Instead, the company relied on heavy leverage, borrowing as much as it could from any bank that would deal with it (which was basically all banks in Oregon – in the 1960s and 70s, there was no inter-state banking in the US). Several times, the company came close to running out of money, when banks refused to extend its credit. But then it survived.

Griffin’s review of the book shows all this as “learnings” – innovative sources of financing, high growth, dealing with crises, but to me it looks like a lot of bad financial management. Too little equity for too long, an obsession for control (finally Nike went public only when Knight figured he could issue dual class stock), high leverage and all that.

The other thing that struck me about Nike is that even in the late 70s, when the company was 15 years old, it seemed like a bunch of buddies of Knight running it – there wasn’t that much of professional management around, and this could again be attributed to the business being continuously short on cash. I guess times are different now, and equity financing is more available, and firms can start hiring professional managers early, but a 15 year old company being seemingly run in a chaotic manner seemed odd.

Finally, back in business school, we were told that when applying to companies such as Nike or Adidas, we should highlight whatever sporting achievements we might have on our CVs. That struck me as odd – what impact could having played cricket for my hostel wing possibly have on how I could sell shoes?

Reading the book, though, it seems like a culture issue. In several places in the book Knight talks about the firm being driven by a “passion for sport”, with the early employees all being sportsmen. Culture permeates, and you hire more people like you. There is this vague sense of brotherhood, among people who have played competitive sport, and that’s hard to permeate for non sportspersons. And the culture goes on. Whether this lack of diversity is good for the company is another matter!

Duckworth Lewis Book

Yesterday at the local council library, I came across this book called “Duckworth Lewis” written by Frank Duckworth and Tony Lewis (who “invented” the eponymous rain rule). While I’d never heard about the book, given my general interest in sports analytics I picked it up, and duly finished reading it by this morning.

The good thing about the book is that though it’s in some way a collective autobiography of Duckworth and Lewis, they restrict their usual life details to a minimum, and mostly focus on what they are famous for. There are occasions when they go into too much detail describing a trip to either Australia or the West Indies, but it’s easy to filter out such stuff and read the book for the rain rule.

Then again, it isn’t a great book. If you’re not interested in cricket analytics there isn’t that much for you to know from the book. But given that it’s a quick read, it doesn’t hurt so much! Anyway, here are some pertinent observations:

  1. Duckworth and Lewis didn’t get paid much for their method. They managed to get the ICC to accept their method sometime in the mid 90s, but it wasn’t until the early 2000s, by when Lewis had become a business school professor, that they managed to strike a financial deal with ICC. Even when they did, they make it sound like they didn’t make much money off it.
  2. The method came about when Duckworth quickly put together something for a statistics conference he was organising, where another speaker who was supposed to speak about cricket pulled out at the last minute. Lewis later came across the paper, and then got one of his undergrad students to do a project about it. The two men subsequently collaborated
  3. It’s amazing (not in a positive way) the kind of data that went into the method. Until the early 2000s, the only dataset that was used to calibrate the method was what was put together by Lewis’s undergrad. And this was mostly English County games, played over 40, 55 and 60 overs. Even after that, the frequency of updation with new data (which reflects new playing styles and strategies) is rather low.
  4. The system doesn’t seem to have been particularly well software engineered – it was initially simply coded up by Duckworth, and until as late as 2007 it ran on the DOS operating system. It was only in 2008 or so, when Steven Stern joined the team (now the method is called DLS to include his name), that a windows version was introduced.
  5. There is very little discussion of alternate methods, and though there is a chapter about it, Duckworth and Lewis are rather dismissive about them. For example, another popular method is by this guy called V Jayadevan from Thrissur. Here is some excellent analysis by Srinivas Bhogle where he compares the two methods. Duckworth and Lewis spend a couple of pages listing a couple of scenarios where Jayadevan’s method doesn’t work, and then spends a paragraph disparaging Bhogle for his support of the VJD method.
  6. This was the biggest takeaway from the book for me – the Duckworth Lewis method doesn’t equalise probabilities of victory of the two teams before and after the rain interruption. Instead, the method equalises the margin of victory between the teams before and after the break. So let’s say a team was 10 runs behind the DL “par score” when it rains. When the game restarts, the target is set such that the team is still 10 runs behind the par score! They make an attempt to explain why this is superior to equalising probabilities of winning  but don’t go too far with it.
  7. The adoption of Duckworth Lewis seems like a fairly random event. Following the World Cup 1992 debacle (when South Africa’s target went from 22 off 13 to 22 off 1 ball after a rain break), there was a demand for new rain rules. Duckworth and Lewis somehow managed to explain their method to the ECB secretary. And since it was superior to everything that was there then, it simply got adopted. And then it became incumbent, and became hard to dislodge!
  8. There is no mention in the book about the inherent unfairness of the DL method (in that it can be unfair to some playing styles).

Ok this is already turning out to be a long post, but one final takeaway is that there’s a fair amount of randomness in sports analytics, and you shouldn’t get into it if your only potential customer is a national sporting body. In that sense, developments such as the IPL are good for sports analytics!

An unauthorised biography of an unauthorised biography

I just finished reading a book which was like a Telugu movie – the beginning promised much, as did the reviews. About a third into the book, I was sending excerpts from its chapters to friends. Two thirds in, I was rather engrossed. And then it all fell apart, going into polemic territory in the last third.

I’m talking about Felix Martin’s Money: The unauthorised biography. When I found the book on the shelves of Blossom Book House two weekends back, I immediately reached for my phone and checked for reviews. Largely positive reviews by The Guardian and The Economist meant that I was compelled to buy it. And the first two thirds of the book was pretty excellent.

There is one very strong idea in the book – that we should look at money not as a commodity but as a system of maintaining credit. Martin gives the example of the Fei in a Pacific Island called Yap to illustrate this, and makes a rather compelling case for not treating money as a commodity.

And he does this by giving examples from ancient and medieval history – the book is peppered with nice examples from Mesopotamia and Greece and the Warring States of China. In between he returns to modern times and talks about how Argentina in the 2000s and Ireland in the 1960s reacted to closure of banks – all of it lending further credence to his theory of money being a means of credit rather than a commodity.

He talks about the pyramidal structure of credit in medieval Italy and the fairs of Lyons. Considerable footage is given to the formation of the Bank of England and John Locke’s recommendations on debasement of the currency (these parts were easier for me to appreciate, having read Neal Stephenson’s The Baroque Cycle) and John Law’s exploits in France.

And then, with the book nicely set up two thirds in, he turns it into a polemic against investment banks and what prompted the Great Financial Crisis of 2008. Again, some of the stuff is impressive, like Walter Bagehot’s recommendations following a credit crisis in the 1860s, and Keynes’s recommendations after the First World War. But the last sixty pages or so are close to unreadable, especially for someone who’s fairly closely followed the 2008 crisis.

This is not the first time that a book on history falls away when it gets to modern history. Another example of this is Yuval Noah Harari’s Sapiens, which again begins extremely strongly in its description of prehistory and ancient history, but somehow falls away when it comes to the modern world (ending with a rather unreadable chapter on immortality and the Methuselah project). There are more examples that I can’t currently recall of books that do a great job of ancient history but fall apart when they come to modern times.

Money would have been a significantly better book had it stopped at around the 220th page or so, following the recommendations of Walter Bagehot – but maybe with some final recommendations. Till then it’s a fantastic book, but then there seems to be a compulsion to provide recommendations, where it falls away (this is again a common bugbear, where books fall apart when they try to provide recommendations). I’d recommend you read it, but not beyond page 220 (totally ~280 pages).

Oh, and for a change I read the physical copy of the book (since I found a copy at Blossom Book House), so that copy is available to be lent out.

The Box: A review

So over the weekend I started and finished reading “The Box: How the shipping container made the world smaller and the world economy bigger” by Mark Levinson. It’s a fascinating book, and one that I had been intending to read for a very long time. Somehow it always kept slipping my mind whenever I wondered what book to buy next, and I’d pushed buying it for a long time now.

Finally, a few days back, when “unknown twitter celebrityKrish Ashok asked his followers to send him reading recommendations, and when he published the list, and I saw this book on the list, and I saw that the book was available on Kindle for Rs. 175, I just bought it. This is the first book in a very long time that I’ve bought “straight” off the Kindle Store, not bothering with a sample.

It’s a fascinating book, as it takes us through the 50-odd years of history of the shipping box. And on the way, it gives us insights into the development of the world economy through the 50s and 60s, and factors that led to the logistic revolution ushered in by the box.

We think of post world war America as this capitalist haven, where markets were free, and you could get jailed for communist leanings. We tend to think about this time as one of innovation and freedom of business, leading to high economic growth.

This wasn’t the case, though. While the US was nominally capitalist and markets were supposedly free, this was a time of heavy regulations, and the presence of cartels. International shipping rates, for example, till the mid-1970s, were set by “conferences” (basically cartels), after which the cartels broke down. It was not possible for a carrier to quote an integrated source-to-destination rate, and rates had to be quoted by leg. Someone who wanted to start a new train route had to prove to the regulators that it would not harm existing players!

And then there were the unions. Levinson devotes an entire chapter to how the unions were managed. Basically containerisation meant greater mechanisation and a reduction in demand for labour. And this was obviously not acceptable to the dockworker unions, and led to protracted battles which needed to be resolved before containerisation could take off. The most interesting story came from the UK, where unions in most established ports (primarily London and Liverpool) blocked containerisation, and went on strike in the specially developed container port at Tilbury. Felixstowe, which had hitherto been too obscure a port to attract unions’ attention, now unencumbered by unions, jumped on to the container business and is now by far the UK’s biggest port.

Levinson also pays much attention to how the container shaped economies in general. Prior to containerisation, the cost of changing mode of transport was very high, since individual items needed to be unloaded from one means of transport and loaded to another. Industries were usually located based on access to port, and ports came up to service nearby industries. Containerisation changed all that. Now that it was easy to transport using a series of different means of transport, the location advantage of being close to port was lost. And this had massive effects on the economy of regions.

Massive effects on economies also happened due to the scale factor that containerisation brought in. Small ports didn’t make any sense any more, since the transaction cost of berthing was too high. And so small ports started dying, with business being soncolidated into a few larger ports. The game changed into a winner take all mechanism.

In the 1950s and 60s, before the coming of the container, shipping was a low-capex high-opex (operational expenditure) business. Most ships were old and cheap, but costs in terms of labour and other things was high. With the coming of the containership, the cost structure inverted, with the capital expenditure now being extremely high, but opex being quite low. This led to “revenue management”, and a drop in prices, and ultimately the breaking of the cartels.

The book is full of insights, and chapters are organised by subject rather than in chronological order. It gets a little repetitive at times, but is mostly crisp (I read it in a weekend), and the insights mentioned above are only a sample. And it tells us not only the story of the box (which it does) but also the story of the world economy, and regulation, and competition, and unionisation and economies of scale. Highly recommended.

 

Review: The Theory That Would Not Die

I was introduced to Bayes’ Theorem of Conditional Probabilities in a rather innocuous manner back when I was in Standard 12. KVP Raghavan, our math teacher, talked about pulling black and white balls out of three different boxes. “If you select a box at random, draw two balls and find that both are black, what is the probability you selected box one?” , he asked and explained to us the concept of Bayes’ Theorem. It was intuitive, and I accepted it as truth.

I wouldn’t come across the theorem, however, for another four years or so, until in a course on Communication, I came across a concept called “Hidden Markov Models”. If you were to observe a signal, and it could have come out of four different transmitters, what are the odds that it was generated by transmitter one? Once again, it was rather intuitive. And once again, I wouldn’t come across or use this theorem for a few years.

A couple of years back, I started following the blog of Columbia Statistics and Social Sciences Professor Andrew Gelman. Here, I came across the terms “Bayesian” and “non-Bayesian”. For a long time, the terms baffled me to no end. I just couldn’t get what the big deal about Bayes’ Theorem was – as far as I was concerned it was intuitive and “truth” and saw no reason to disbelieve it. However, Gelman frequently allured to this topic, and started using the term “frequentists” for non-Bayesians. It was puzzling as to why people refused to accept such an intuitive rule.

The Theory That Would Not Die is Shannon Bertsch McGrayne’s attempt to tell the history of the Bayes’ Theorem. The theorem, according to McGrayne,

survived five near-fatal blows: Bayes had shelved it; Price published it but was ignored; Laplace discovered his own version but later favored his frequency theory; frequentists virstually banned it; and the military kept it secret.

The book is about the development of the theorem and associated methods over the last two hundred and fifty years, ever since Rev. Thomas Bayes first came up with it. It talks about the controversies associated with the theorem, about people who supported, revived or opposed it; about key applications of the theorem, and about how it was frequently and for long periods virtually ostracized.

While the book is ostensibly about Bayes’s Theorem, it is also a story of how science develops, and comes to be. Bayes proposed his theorem but didn’t publish it. His friend Price put things together and published it but without any impact. Laplace independently discovered it, but later in his life moved away from it, using frequency-based methods instead. The French army revived it and used it to determine the most optimal way to fire artillery shells. But then academic statisticians shunned it and “Bayes” became a swearword in academic circles. Once again, it saw a revival at the Second World War, helping break codes and test weapons, but all this work was classified. And then it found supporters in unlikely places – biology departments, Harvard Business School and military labs, but statistics departments continued to oppose.

The above story is pretty representative of how a theory develops – initially it finds few takers. Then popularity grows, but the establishment doesn’t like it. It then finds support from unusual places. Soon, this support comes from enough places to build momentum. The establishment continues to oppose but is then bypassed. Soon everyone accepts it, but some doubters remain..

Coming back to Bayes’ Theorem – why is it controversial and why was it ostracized for long periods of time? Fundamentally it has to do with the definition of probability. According to “frequentists”, who should more correctly be called “objectivists”, probability is objective, and based on counting. Objectivists believe that probability is based on observation and data alone, and not from subjective beliefs. If you ask an objectivist, for example, the probability of rain in Bangalore tomorrow, he will be unable to give you an answer – “rain in Bangalore tomorrow” is not a repeatable event, and cannot be observed multiple times in order to build a model.

Bayesians, who should be more correctly be called “subjectivists”, on the other hand believe that probability can also come from subjective beliefs. So it is possible to infer the probability of rain in Bangalore tomorrow based on other factors – like the cloud cover in Bangalore today or today’s maximum temperature. According to subjectivists (which is the current prevailing thought), probability for one-time events is also defined, and can be inferred from other subjective factors.

Essentially, the the battle between Bayesians and frequentists is more to do with the definition of probability than with whether it makes sense to define inverse probabilities as in Bayes’ Theorem. The theorem is controversial only because the prevailing statistical establishment did not agree with the “subjectivist” definition of probability.

There are some books that I call as ‘blog-books’. These usually contain ideas that could be easily explained in a blog post, but is expanded into book length – possibly because it is easier to monetize a book-length manuscript than a blog-length one. When I first downloaded a sample of this book to my Kindle I was apprehensive that this book might also fall under that category – after all, how much can you talk about a theorem without getting too technical? However, McGrayne avoids falling into that trap. She peppers the book with interesting stories of the application of Bayes’ Theorem through the years, and also short biographical tidbits of some of the people who helped shape the theorem. Sometimes (especially towards the end) some of these examples (of applications) seem a bit laboured, but overall, the books sustains adequate interest from the reader through its length.

If I had one quibble with the book, it would be that even after the descriptions of the story of the theorem, the book talks about “Bayesian” and ‘non-Bayesian” camps, and talk about certain scientists “not doing enough to further the Bayesian cause”. For someone who is primarily interested in getting information out of data, and doesn’t care about the methods involved, it was a bit grating that scientists be graded on their “contribution to the Bayesian cause” rather than their “contribution to science”. Given the polarizing history of the theorem, however, it is perhaps not that surprising.

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
U
SD 12.27 (Kindle edition)
360 pages (including appendices and notes)