Copa Format

The ongoing copa america is probably the worst designed sporting event I’ve ever seen, in terms of tournament format. Yes, there have been tournaments that have come close in the past, like the Asia Cup 08, which had a funny format so as to ensure at least two India-Pakistan matches (but that ensured that the chances of an India-Pakistan FINAL were really low). Then there was Euro 2008, where teams qualifying for the knockout from the same group ended up in the same half of the draw. And then, in hindsight, there was the Cricket World Cup 2007, when two upsets threw out two of the favourites before the “real tournament” had begun.

But in the face of the current Copa America, all of those can be described as being extremely well-designed tournaments. The Copa format is so bad that I seriously doubt that this post is going to be exhaustive in listing out all its flaws. Since there are so many of them, and I don’t want to keep saying “moreover”, “next” or “furthermore”, I’ll do it in bullet points. The points are in random order

  • You have 12 countries in the first round which you want to reduce to 8 for the second round. What do you do? Four groups of three with top two from each qualifying right? Instead, they have 3 groups of 4, with the two best third placed teams also qualifying. So you spend 18 matches (2/3rd of the tournament) throwing out one-third of the teams! Ok but I understand (as Atul Mathew points out on twitter) this is the standard format of Copa so I guess I’ll let it be
  • The organizers seem to have clearly drawn from the experience of 2007 CWC, when India and Pakistan went out in the first round. And given how the first two rounds of matches played out, it wouldn’t have been hard to imagine one or both of Argentina and Brazil going out, which would have killed the competition. I guess that’s the reason the Copa adopts this tamasha of third placed teams and stuff.
  • The last matches in each group are not simultaneously played, and the “seeded teams” in each group (Argentina, Uruguay, Brazil) got to play the last games, and thus figure out what exactly they needed to do (fix it even, maybe?) so that they got a favourable draw in the quarters. Actually, as I’ll explain in a subsequent tweet, it was more like “favourable opponent” rather than “favourable draw”. Check out Jonathan Wilson’s piece on watching Brazil-Ecuador with a bunch of Chile fans
  • Now you have in the second round Brazil taking on Paraguay, whom they’ve faced once before in the group stages. Again, daft format that allows a team to play the third placed team in its own group in the second round itself. I remember FIFA 1994 handling third placed teams well, to make sure they didn’t meet teams they’d played before in the second round
  • Take a look at the quarter-finals fixtures, and do  a sensitivity analysis of what would have happened if either Brazil had done slightly worse or Argentina had done better. You will notice that as long as Argentina and Brazil finished their respective groups as either number 1 or number 2, they would end up in different halves of the tournament! Oh, the lengths the organizers have gone to ensure they maximize the chances of getting a Brazil-Argentina final. Another off-shoot is again teams from the same group having to meet in the semis. For example, if Venezuela beat Chile this weekend, then either Brazil or Paraguay could get to the final of the tournament by not ever facing a team that started anywhere outside of group B!!
As I mentioned this list is unlikely to be exhaustive. And I hope for the sake of giving the organizers a kick in the butt, Paraguay and Uruguay will do the needful and throw out Brazil and Argentina respectively. They’re fully capable of doing that, based on tournament form.

 

In search of uncertainty

Back when I was in school, I was a math stud. At least people around me thought so. I knew I wanted to pursue a career in science, and that in part led me to taking science in class XI, and subsequently writing JEE which led to the path I ultimately took. Around the same time (when I was in high school), I started playing chess competitively. I was quite good at it, and I knew that with more effort I could make it big in the game. But then, that never happened, and given that I would fall sick after every tournament, I retired.

It was in 2002, I think, that I was introduced to contract bridge, and I took an instant liking for it. All the strategising and brainwork of chess would be involved once again, and I knew I’d get pretty good at this game, too. But there was one fundamental difference which made bridge so much more exciting – the starting position was randomized (I’m not making a case for Fischer Chess here, mind you). The randomization of starting positions meant that you could play an innumerable number of “hands” with the same set of people without ever getting bored. I simply loved it.

It was around that time that I started losing interest in math and other hard sciences. They had gotten to the point where they were too obscure, and boring, I thought, and that to make an impact in them, I wanted to move towards something less precise, and hard. That was probably what led me to do an MBA. And during the course of my MBA I discovered my interest in economics and social sciences, but am yet to do anything significant on that front, though, apart from the odd blog here or there.

I think what drove me from what I had thought is my topic of interest to what I think now it is is the nature of open problems. In hard sciences, where a lot of things are “known” it’s getting really hard to do anything of substance unless you get really deep in, into the territories of obscurity. In “fuzzy sciences”, on the other hand, nothing too much is “known”, and there will always be scope for doing good interesting work without it getting too obscure.

Similarly, finance, I thought, being a people-driven subject (the price of a stock is what a large set of people think its price is, there are no better models) will have lots of uncertainty, and scope to make assumptions, and thus scope to do good work without getting too obscure. But what I find is that given the influx of hard science grads in Wall Street over the last three decades, most of the large organizations are filled with people who simply choose to ignore the uncertainty and “interestingness” and instead try and solve deterministic problems based on models that they think completely represents the market.

And this has resulted in you having to do stuff that is really obscure and deep (like in the hard sciences) even in a non-deterministic field such as finance, simply because it’s populated by people from hard science background, and it takes way too much in order to go against the grain.

PS: Nice article by Tim Harford on why we can’t have any Da Vincis today. Broadly related to this, mostly on scientific research.

More on consulting partners

I’d written in an earlier post that consulting firms remain young nd dynamic by periodically promoting new people to partnership, and they in their quest to develop new markets and establish themselves, take on risks which can prove useful to underlings who now have a better chance to make a mark and establish themselves.

However, as I’d once experienced a long time back, there can be a major downside to this. The new partners, in their quest to establish themselves, can sometimes be too eager in the commitments they make to the clients. They are prone to promising way more than their team can logically deliver, and that contributes to putting additional and unnecessary pressure on the people working for them.

Nothing earthshattering, but thought I should mention this here for the sake of completeness, so that I don’t mislead you with my conclusions.

Methods of Negotiations

There are fundamentally two ways in which you can negotiate a price. You can either bargain or set a fixed price. Bargaining induces temporary transaction costs – you might end up fighting even, as you are trying to negotiate. But in the process you and the counterparty are giving each other complete information of what you are thinking, and at every step in the process, there is some new information that is going into the price. Finally, if you do manage to strike a deal, it will turn out to be one that both of you like (ok I guess that’s a tautology). Even when there is no deal, you know you at least tried.

In a fixed price environment, on the other hand, you need to take into consideration what the other person thinks the price should be. There’s a fair bit of game theory involved and you constantly need to be guessing, about what the other person might be thinking, and probably adjust your price accordingly. There is no information flow during the course of the deal, and that can severely affect the chances of a deal happening. The consequences in terms of mental strain could be enormous in case you are really keen that the deal goes through.

Some people find the fixed price environment romantic. They think it’s romantic that one can think exactly on behalf of the counterparty and offer them a fair deal. What they fail to discount is the amount of thought process and guessing that actually goes in to the process of determining the “fair deal”. What they discount is the disappointment that has occurred in the past when they’ve been offered an unfair deal, and can do nothing about it because the price is fixed. But I guess that’s the deal about romance – you remember all the nice parts and ignore that similar conditions could lead to not-so-nice outcomes.

Bargaining, on the other hand has none of this romance. It involves short-term costs, fights even. But that’s the best way to go about it if you are keen on striking a deal. Unfortunately the romantics think it’s too unromantic (guess it’s because it’s too practical) and think that if you want a high probability of a deal, you should be willing to offer a fixed price. And the fight continues.. Or maybe not – it could even be a “take it or leave it” thing.

Standard Error in Survey Statistics

Over the last week or more, one of the topics of discussion in the pink papers has been the employment statistics that were recently published by the NSSO. Mint, which first carried the story, has now started a whole series on it, titled “The Great Jobs Debate” where people from both sides of the fence have been using the paper to argue their case as to why the data makes or doesn’t make sense.

The story started when Mint Editor and Columnist Anil Padmanabhan (who, along with Aditya Sinha (now at DNA) and Aditi Phadnis (of Business Standard), ranks among my favourite political commentators in India) pointed out that the number of jobs created during the first UPA government (2004-09) was about 1 million, which is far less than the number of jobs created during the preceding NDA government (~ 60 million). And this has led to hue and cry from all sections. Arguments include leftists who say that jobless growth is because of too much reforms, rightists saying we aren’t creating jobs because we haven’t had enough reform, and some other people saying there’s something wrong in the data. Chief Statistician TCA Anant, in his column published in the paper, tried to use some obscurities in the sub-levels of the survey to point out why the data makes sense.

In today’s column, Niranjan Rajadhyaksha points out that the way employment is counted in India is very different from the way it is in developed countries. In the latter, employers give statistics of their payroll to the statistics collection agency periodically. However, due to the presence of the large unorganized sector, this is not possible in India so we resort to “surveys”, for which the NSSO is the primary organization.

In a survey, to estimate a quantity across a large sample, we simply take a much smaller sample, which is small enough for us to rigorously measure this quantity. Then, we try and extrapolate the results to the large sample. The key thing in survey is “standard error”, which is a measure of error that the “observed statistic” is different from the “true statistic”. What intrigues me is that there is absolutely no mention of the standard error in any of the communication about this NSSO survey (again I’m relying on the papers here, haven’t seen the primary data).

Typically, when we measure something by means of a survey, the “true value” is usually expressed in terms of the “95% confidence range”. What we say is “with 95% probability, the true value of XXXX lies between Xmin and Xmax”. An alternate way of representation is “we think the value of XXXX is centred at Xmid with a standard error of Xse”. So in order to communicate numbers computed from a survey, it is necessary to give out two numbers. So what is the NSSO doing by reporting just one number (most likely the mid)?

Samples used by NSSO are usually very small. At least, they are very small compared to the overall population, which makes the standard error to be very large. Could it be that the standard error is not reported because it’s so large that the mean doesn’t make sense? And if the standard error is so large, why should we even use this data as a basis to formulate policy?

So here’s my verdict: the “estimated mean” of the employment as of 2009 is not very different from the “estimated mean” of the employment as of 2004. However, given that the sample sizes are small, the standard error will be large. So it is very possible that the true mean of employment as of 2009 is actually much higher than the true mean of 2004 (by the same argument, it could be the other way round, which points at something more grave). So I conclude that given the data we have here (assuming standard errors aren’t available), we have insufficient data to conclude anything about the job creation during the UPA1 government, and its policy implications.

On Running a Consulting Firm

So most of the consulting firms are run as partnerships (as you might have already figured out). There was an experiment in the late 90s where a then leading firm was bought over by an IT company, and that saw stagnation for the next few years until the consultants did a “management buy out” in order to rid themselves of the IT company’s controls. By then, though, valuable time was lost, and last I heard this company was severely lagging its peers in terms of reputation, among other things.

As I had mentioned in the earlier post, the rut sets in once partners reach “steady state”, where they have an established set of relationships that they milk to get more business. And as I mentioned, it’s hard to get out of this rut, until employees start leaving protesting the poor quality of work, and lack of opportunities to make it big. And that starts sending the firm into a downward spiral. So what is it that the firms must do, in order to keep themselves dynamic, and not get into this kind of a rut?

The answer is something that is practiced by most leading consulting firms. Every few months or a year, these firms add to the partnership pool, mostly by promoting from within their ranks. Once thus promoted, it is the new partner’s responsibility to expand and generate new business for the firm, and he is not able to piggyback on the relationships established by the established partners. And thus, in his process to expand and get himself established, he has an incentive to take more risks. And take on projects with long-out-of-the-money option kind of payoffs.

Regular promotions to the partnership level means that there is always a bunch of partners who are thus taking risks, and that keeps the firm dynamic. I don’t know how well this works in practice, but in theory at least, this helps firms from getting into stagnation. That this is the model followed by most leading management consulting firms indicates that this is probably an appropriate approach.

So, if you think your consulting partnership is stagnating, get in more partners. Promote. Or make way. And keep the group dynamic and a great place to work.

The Wife’s Methods

During a particularly acrimonious fight last night, I found that I was losing myself, and had no clue what was happening. Tempers were frayed, voices were raised and a huge towel had become wet from our collective tears and nose-goo. And I was fighting a losing battle, against myself. It seemed like I was consuming myself, and there was no way out.

I walked up to the kitchen and pulled out two New York shot glasses from the shelf. I reached for the top shelf, where we store the stuff, and pulled out the Talisker bottle. And I filled the shot glasses, up to the brim, and we downed it, one glass each. Soon, it seemed like all was going to be fine with the world.

At once we calmed down. We started thinking more rationally now. The fight continued, but the voices got lowered, the collective discharge into the towel ebbed. We weren’t consumed by ourselves any more. Instead, we were now calmly talking to each other, trying to find a way out of the problem we had at hand. Note that we didn’t kiss-and-make-up-and-bury-the-fight like we used to earlier. We didn’t sleep until we’d finished our business and reached an agreement. But life had become so much better.

I must admit that over the last year or more, I’ve consistently underestimated the wife (earlier the girlfriend) and her methods. Sometimes I’ve never understood why she does things in a certain way (and expects me to do things the same way), at other times I’ve been too arrogant in my own thoughts, to give her methods a fair hearing. This was yet another such example.

It was I who had made an irrational decision that Talisker was meant for slow sipping and savouring. It was I who had thought it was “too expensive to be shot down”. And it was I who had made the wife promise she won’t gulp it down before buying the current bottle of Talsker. I admit it, I was wrong. Wrong. The wife, it turns out, had always been right.

Google Plus – Initial thoughts

Hareesh sent me an invite for Google Plus early on Wednesday morning. Thinking it’s another stupid thing like Wave, I ignored it. But feedback from twitter revealed that the product did show some promise, so later that evening I joined it. I’ve got some 150 friends already (god knows how long it took me to get to so many with either Orkut or Facebook), though I haven’t started using it yet. Some initial thoughts:

  • I like the concept of circles, and that it’s so easy to segregate your friends. This has become a huge problem in social networking, especially after all uncles and aunties got on to facebook. So far I’ve made an attempt to classify all my contacts into disjoint circles of “friends” “family” and “acquaintances”. I also like it that circles need not be disjoint, so I can make an exception to my rule and put the wife in both “friends” and “family”
  • I like that it’s a directed graph. That you can follow the public posts of someone without them having to follow you back. I don’t know why but I simply like this. I hate putting friendship requests and waiting endlessly for responses and stuff. So this directed stuff makes a lot of sense for me.
  • I need to find out how to import my blog there. Then I can close my blog feed on facebook which is infested with uncles and aunties. On Plus, they’ll be safely tucked away in the “family” circle which won’t be able to see much.
  • I don’t like being the “cut-vertex”. I don’t like being the one guy who links two subgroups of a larger group. On a similar note, I don’t like to go out simultaneously with disjoint sets of friends (i.e. two groups that didn’t know each other previously). I feel too tense trying to make sure everyone’s comfortable and clued in on what’s happening. Similar with conversations on facebook. So yeah, I’ll probably segregate my circles further and have more cliquey groups.
  • Again, directed graph means I can peacefully put ignore to people, without appearing rude. On FB, if some uncle comments and I don’t respond, he might take offense, and I’ll be cognizant of the fact that he takes offence. And I force myself to reply. On G+, if i”m not following him, I can peacefully put well left. Like I sometimes do to @Replies to me on twitter from people who I don’t follow

So seems promising. Too early to say if it’ll make me give up both twitter and facebook. I’m sure I won’t give up twitter for sure. Let’s wait and see.

Partners and Associates

Last week I’d written this post about managing studs, and while discussing that with some colleagues the other day, I realized that I could reformulate it without touching upon the studs and fighters theory. So let us consider a consulting firm. There is a partner, whose sole job is to solicit business for the firm, and to get the lion’s share of the benefits. And there are associates, trying hard to get noticed and promoted, and working for this partner. It’s the associates who do most of the work. Let’s assume that the firm is in “steady state”, where as long as they don’t mess up, there is a steady stream of business assured.

Under this assumption, all that the partner needs to do is to ensure he and his team don’t “mess up”. He knows that he has the relationships to keep the work flowing, and given that he doesn’t really do any work himself, he doesn’t care about the nature of work, or whether his associates find the work challenging, or interesting, and stuff. As long as the tap is open, and he makes his “partner’s cut”, he’s happy.

Given this, his incentives are towards work that is hard to go wrong. “Steady” work, where expectations are likely to be high, but the downside risk is quite low suits him absolutely fine, and he seeks to find more and more of that kind of stuff. There is little chance that his relationships with his steady clients can go wrong in this kind of a situation, right? So he goes about trying to find work with a “short deep-out-of-money option” payoff.

What about the associates? There will be some of them that are already established, and known to these steady clients. They know that it’s only a matter of time before they get promoted and hit the partnership pot of gold. They’ve made their mark, at a time when they had the opportunity to do so, and now they only need to hold fort till the end of the rainbow. And they hold on, perfectly happy to do work in which things can’t go wrong.

As for the other associates, who are still looking to establish themselves? What they’d ideally like would be the opportunity for “big wins”, which will make them be seen, and noticed, and enable them to make the move up the ladder when the time is right. Given their current standing, they don’t mind taking the risk – they have little to lose in terms of lost reputation. On the other hand they have everything to gain from pulling off improbable big wins. Basically they ideally like the “long deep-out-of-money option” payoff.  But the stream of projects the partners and other associates prefer doesn’t give them the opportunity to go for this kind of payoff! So they are stuck.

So, if you are working in a consulting firm, which is in reasonably steady state, where the partners don’t take part in day-to-day work, and where you are not yet established, you need to think if you’re in the right place.

Models

This is my first ever handwritten post. Wrote this using a Natraj 621 pencil in a notebook while involved in an otherwise painful activity for which I thankfully didn’t have to pay much attention to. I’m now typing it out verbatim from what I’d written. There might be inaccuracies because I have a lousy handwriting. I begin

People like models. People like models because it gives them a feeling of being in control. When you observe a completely random phenomenon, financial or otherwise, it causes a feeling of unease. You feel uncomfortable that there is something that is beyond the realm of your understanding, which is inherently uncontrollable. And so, in order to get a better handle of what is happening, you resort to a model.

The basic feature of models is that they need not be exact. They need not be precise. They are basically a broad representation of what is actually happening, in a form that is easily understood. As I explained above, the objective is to describe and understand something that we weren’t able to fundamentally comprehend.

All this is okay but the problem starts when we ignore the assumptions that were made while building the model, and instead treat the model as completely representative of the phenomenon it is supposed to represent. While this may allow us to build on these models using easily tractable and precise mathematics, what this leads to is that a lot of the information that went into the initial formulation is lost.

Mathematicians are known for their affinity towards precision and rigour. They like to have things precisely defined, and measurable. You are likely to find them going into a tizzy when faced with something “grey”, or something not precisely measurable. Faced with a problem, the first thing the mathematician will want to do is to define it precisely, and eliminate as much of the greyness as possible. What they ideally like is a model.

From the point of view of the mathematician, with his fondness for precision, it makes complete sense to assume that the model is precise and complete. This allows them to bringing all their beautiful math without dealing with ugly “greyness”. Actual phenomena are now irrelevant.The model reigns supreme.

Now you can imagine what happens when you put a bunch of mathematically minded people on this kind of a problem. And maybe even create an organization full of them. I guess it is not hard to guess what happens here – with a bunch of similar thinking people, their thinking becomes the orthodoxy. Their thinking becomes fact. Models reign supreme. The actual phenomenon becomes a four-letter word. And this kind of thinking gets propagated.

Soon the people fail to  see beyond the models. They refuse to accept that the phenomenon cannot obey their models. The model, they think, should drive the phenomenon, rather than the other way around. The tails wagging the dog, basically.

I’m not going into the specifics here, but this might give you an idea as to why the financial crisis happened. This might give you an insight into why obvious mistakes were made, even when the incentives were loaded in favour of the bankers getting it right. This might give you an insight as to why internal models in Moody’s even assumed that housing prices can never decrease.

I think there is a lot more that can be explained due to this love for models and ignorance of phenomena. I’ll leave them as an exercise to the reader.

Apart from commenting about the content of this post, I also want your feedback on how I write when I write with pencil-on-paper, rather than on a computer.