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!

Ratings revisited

Sometimes I get a bit narcissistic, and check how my book is doing. I log on to the seller portal to see how many copies have been sold. I go to the Amazon page and see what are the other books that people who have bought my book are buying (on the US store it’s Ray Dalio’s Principles, as of now. On the UK and India stores, Sidin’s Bombay Fever is the beer to my book’s diapers).

And then I check if there are new reviews of my book. When friends write them, they notify me, so it’s easy to track. What I discover when I visit my Amazon page are the reviews written by people I don’t know. And so far, most of them have been good.

So today was one of those narcissistic days, and I was initially a bit disappointed to see a new four-star review. I started wondering what this person found wrong with my book. And then I read through the review and found it to be wholly positive.

A quick conversation with the wife followed, and she pointed out that this reviewer perhaps reserves five stars for the exceptional. And then my mind went back to this topic that I’d blogged about way back in 2015 – about rating systems.

The “4.8” score that Amazon gives as an average of all the ratings on my book so far is a rather crude measure – since one reviewer’s 4* rating might differ significantly from another reviewer’s.

For example, my “default rating” for a book might be 5/5, with 4/5 reserved for books I don’t like and 3/5 for atrocious books. On the other hand, you might use the “full scale” and use 3/5 as your average rating, giving 4 for books you really like and very rarely giving a 5.

By simply taking an arithmetic average of ratings, it is possible to overstate the quality of a product that has for whatever reason been rated mostly by people with high default ratings (such a correlation is plausible). Similarly a low average rating for a product might mask the fact that it was rated by people who inherently give low ratings.

As I argue in the penultimate chapter of my book (or maybe the chapter before that – it’s been a while since I finished it), one way that platforms foster transactions is by increasing information flow between the buyer and the seller (this is one thing I’ve gotten good at – plugging my book’s name in random sentences), and one way to do this is by sharing reviews and ratings.

From this perspective, for a platform’s judgment on a product or seller (usually it’s the seller, but for products such as AirBnb, information about buyers also matters) to be credible, it is important that they be aggregated in the right manner.

One way to do this is to use some kind of a Z-score (relative to other ratings that the rater has given) and then come up with a normalised rating. But then this needs to be readjusted for the quality of the other items that this rater has rated. So you can think of some kind of a Singular Value Decomposition you can perform on ratings to find out the “true value” of a product (ok this is an achievement – using a linear algebra reference given how badly I suck in the topic).

I mean – it need not be THAT complicated, but the basic point is that it is important that platforms aggregate ratings in the right manner in order to convey accurate information about counterparties.

Book Release

So my book Between the buyer and the seller is now available on Amazon, in both print and kindle versions. You can go here to buy. Thanks to Amazon’s print on demand service, it’s available worldwide.

It’s been a long time coming. I completed the first draft way back in April 2016. Writing it was no easy task, but was definitely helped by the presence of one awesome coffee shop close to where I was staying in Barcelona.

Having written one draft, I went around finding publishers. It wasn’t a trivial process. In the process, I found out enough about the publishing industry to get a new prologue for the book (I guess that should be part of the Kindle sample).

And then in the course of the backs and forths with the publishers I found a lot of what I’d written to be absolute shit, and so revised the book two times. Then in December last year, the Takshashila Institution decided to publish it.

And then they sent it to some experts for expert opinion. Said opinion came back positive but with some suggestions. So I revised the book yet another time and implemented these suggestions. Then there was the copy editing process and yet another revision. Then the book design (if not anything, doesn’t it at least look good?) and typesetting and stuff. And formatting.

In the meantime, Shashi Tharoor and Bibek Debroy wrote some nice blurbs for the book – they’re printed at the back of the book now. And then some more hoops and procedures and printing and publishing and fighting with Amazon and the book is now out! For you to purchase.

I want to put out a special word of thanks to Anupam Manur, who has effectively “produced” the book, managing the entire process on Takshashila’s behalf. He’s been patient with my periodic abuses, and diligently got work done. The night before his wedding, he was up fixing some stuff on Amazon for the book.

Anyway, enough of my story. Now go buy the book and read it. Let me, and others, know what you think of the book. And spread the word!

Oh, and I want to thank all of you, my patient blog readers, for the encouragement through the last 13 years. It’s your collective effort and support that has made me a better writer, and resulted in this book coming out!


Football transfer markets

So the 2017 “summer transfer window” is going to close in three days’ time. It’s been an unusual market, with oddly inflated valuations – such as Neymar going for ~ €200 million from Barcelona to PSG, and Manchester City paying in excess of £50 million each for a pair of full backs (Kyle Walker and Benjamin Mendy).

Meanwhile, transfers are on in the NBA as well. Given that American sporting leagues have a rather socialist structure, there is no money exchanged. Instead, you have complicated structures such as this one between the Cleveland Cavaliers and Boston Celtics:

 by trading Kyrie Irving (pictured, left), their star point guard, to the Boston Celtics. In exchange, Mr Altman received a package of three players headlined by Isaiah Thomas (right), plus a pick in the 2018 entry draft

A week back, renowned blogger Amit Varma interviewed me for his The Seen and the unseen podcast. The topic was football transfers, something that I talk about in the first chapter of my soon-to-be-published book. In that, he asked me what the football transfer market might look like in the absence of price. And I mentioned that PSG might have had to give up their entire team in order to buy Neymar in that situation.

Anyway, listen to the entire podcast episode here.

Oh, and I don’t know if I mentioned it here before, but my book is ready now and will be released on the 8th of September. It’s being published by the Takshashila Institution.

You can pre-order the book on Amazon. For some reason, the Kindle India store doesn’t have a facility to pre-order, so if you live in India and want to read the book on Kindle, you’ll have to wait until the 8th of September. Kindle stores elsewhere already allow you to pre-order. Follow the link above.

Platform as a platform

This afternoon, as I was getting off the tube, I looked at the railway platform, and wondered how it compared to “platforms” as we now know in the context of “platform economics“. For those of you under a rock, platform economics talks about the economics of “platforms” that bring together two sides of a market to interact.

In that sense, Uber is a platform connecting drivers to passengers. Ebay is a platform connecting buyers and sellers of used goods. Paypal is a platform connecting people who want to pay and those who want to receive payment. And so forth (these are all textbook examples nowadays).

So is the railway platform a platform? And if not, is it correct that we refer to entities that run two-sided markets as platforms (arguably, the most intuitive meaning of the word “platform” in the last hundred or so years has been in the railway context)? These were some of the questions I grappled with as I walked along the length of the platform at Ealing Broadway.

For those of you who’re not in the know, I’ve written a book on market design. The Takshashila Institution is publishing it, and the book should be out fairly soon (manuscript is complete, but there’s still plenty to do). In that book, I have a chapter on taxi marketplaces such as Uber/Lyft/Ola, and how they’ve transformed the efficiency of the taxi market. Before I introduce these characters, though, I draw the history of the taxi market.

In that, I talk about taxi stands. Taxi stands work in the way of Thomas Schelling’s focal points. Passengers go there because they know empty taxis will go there. Taxi drivers looking for passengers go there because they know passengers looking for taxis will go there. This way, rather than waiting at a random place looking for either a passenger or a ride, going to the taxi stand is rational. And in that sense, taxi stands are a platforms.

In a way, railway platforms are platform in the same sense. Think of a train that wants to pick up passengers, and passengers who want to travel on a train. If there were no designated pick up points, trains would stop at random places, which passengers would have to guess. While engine drivers could see passengers waiting by the side, stopping at random places might have meant that the train would have had to go empty.

From this perspective, railway platforms act as platforms – they are focal points where trains and passengers come together. Passengers wait there because they know trains stop there, and vice versa. And helpfully, there is an actual physical platform that elevates passengers to the height of the train door so they can get on and off easily!

Isn’t this a wonderful way to have complicated a rather simple concept?


As I continue my progress towards publishing the book whose manuscript I’ve completed, I’ve started to think about the acknowledgements section, which I’m yet to write. Each time I read a new book now, I make sure I read the acknowledgements, and see who all people have thanked. I’ve not gone to the extent of formally collecting data from these acknowledgements, but I must say the effort is underway.

Based on a recent discovery, though, I think all this research is moot. Recently I was cleaning up an old cupboard (the kind that comes embedded in a cot) in my grandfather’s house, and happened to stumble upon my B.Tech. project. I’d brought it home and kept it aside, and happened to open it today.

Overall, in hindsight, I seem to have done a better job of my project than I’d imagined. I’ve always remembered that rather than solving the problem I’d taken up, I’d constructed a proof to show why it couldn’t be solved (something my mother always made fun of). But reading the report, it appears that I’ve gone beyond that, and constructed some approximate and randomised heuristics to tackle the problem – so I’m happy about that.

The more interesting bit is the acknowledgements section. It pretty much encapsulates my life at IITM. Again, I remember having done some research looking at other people’s acknowledgements to see who all they’d thanked, and I followed the same process – guides, professors, lab mates, etc. And then I’ve mentioned some friends.

The first part of the acknowledgements section is not particularly insightful so I’m not pasting it here. The second part makes for fun reading though, in hindsight. I like the way I’ve been fairly informal (in such a “formal” document as my B.Tech. project report), with puns and all.


The key thing to note is the last paragraph. I seriously mean it (even now) when I say that the best part of my life at IITM was the time spent at Patisserie, and all the discussions I had there. The discussions were diverse, with lots of different people, and we spoke about different things on different days.

It may not be a stretch to claim that I learnt more during my discussions there than during the time spent in classrooms. And if I today considered well-networked in my batch (and surrounding batches) at IITM, it’s again due to the time I spent there.

Now to think about how to adapt this acknowledgements section to something that makes sense for the manuscript I’ve written!

Help me name my book!

The more perceptive of you here would’ve known by now that I’ve finished the manuscript of a book on Liquidity. Having finished the draft, and one basic round of editing, I’m now sending it around to publishers, hoping to strike a deal.

One of these publishers wrote to me saying that while she loves the chapters I’ve sent her (a small sample), she doesn’t like the name of the book. “Liquidity”, she says, is too bland and doesn’t reflect the contents of the book, and has asked me to come up with a better name.

And I’m at a loss, in terms of coming up with a name. I don’t even know what kind of name I should pick for the book. So I need you to help out!

The book is about liquidity, in the context of different markets. Apart from the handful of obligatory chapters (my chapters are mostly tiny, and there are 21 of them) on financial markets, I have stories on markets in taxis, dating, footballers, real estate, agriculture, job hunting, food, etc.

Here is part of an introduction to the book I’ve written, which might help you help me!

Why do people with specialised skills find it hard to switch jobs? Why do transfer fees for footballers always seem either too high or too low? Why are real estate brokers still in business despite the large number of online portals that have sought to replace them?



… we analyse why the market for romantic relationships, both matrimonial and dating, is mostly broken, and none of the new platforms are doing anything to fix it. We take a look at how taxi regulation is inherently inefficient thanks to liquidity issues, and how Uber’s much- maligned surge pricing algorithm helps create liquidity by means of superior information exchange. We will also see how liquidity helped build up the credit derivatives market, and then ultimately led to the global financial crisis.

So if you have any cool ideas on what to name the book, or at least a framework I need to follow to name it, please do let me know in the comments here! It might help you to know that the “acknowledgements” part of the book hasn’t been written yet!