ISAs and Power Laws

There are a number of professions where incomes are distributed according to a power law. The most successful people in the professions corner a very large share of the income that people in the profession make, and unless you reach that very high level of success, you might even struggle to make a living wage.

Professions of this nature include the arts (movies, music, drama, standup comedy, painting, sculpture, etc.), sports, writing and entrepreneurship. The thing with such professions is that it needs some degree of “socialism” – if people are left to their own devices, then the 99% confidence payoffs will mean that few people will enter the profession, and when fewer people enter the profession, the overall quality of the profession goes down.

So what is required in this case is some sort of a safety net – people who are reasonably competent at the profession get paid a sort of regular basic income (could either be one-time, periodic or output-based) by “investors” in exchange for a cut of the upside. And this, for a talented but struggling beginner, is usually a good deal – they are assured a basic income to pursue what they love and think they are good at, and anything they have to pay in return is only probabilistic – contingent upon a heavy degree of success.

And in order for this kind of safety net to work, it is important that the investment be of the nature of “equity” rather than “debt” – the extreme power law nature of these professions is that only a small proportion of the people who get the safety net will be able to pay back, and those that are able to pay back will be able to pay disproportionately large amounts.

Entrepreneurship and film acting have sort of done well in terms of providing these safety net. Entrepreneurs get venture capital investment, which allows them to fund their business and take (nominal) salaries, while working on the thing they hope to make it big in. The venture capitalists make money even when a small proportion of their investments don’t fail.

The model in acting is a little different- studios hire actors on long term contracts at negotiated salaries. These salaries give actors the safety net to continue in the profession. And in case the actors become popular, the studios cash out essentially by “encashing the option” of using the actor at the pre-negotiated rate for the duration of the contract.

There are other examples of these safety nets as well – artist studios pay their artists a basic wage, in exchange for a cut on the sale of their paintings. However, the model is not as popular as it seems.

For sportspersons, for example, apart from things like the Ranji Trophy increasing match fees in a big way in the late noughties, this kind of a safety net has been absent. The studio model in acting hasn’t held on. Writers get advances but that doesn’t represent much of a “living wage”.

The good news is that this is changing. Investment in athletes in exchange for a cut of future earnings is gaining traction. And now we have this deal ($):

Taxes will cut into his new 14-year agreement with the Padres, of course. But Tatis also must pay off a previous obligation, a deal he made during the 2017-18 offseason, when he was turning 19 years old and preparing for his first full season at Double A.

It was then that Tatis entered into a contract with Big League Advance (BLA), a company that offers select minor leaguers upfront payments in exchange for a percentage of their future earnings in Major League Baseball. Neither Tatis nor BLA has revealed the exact percentage he owes the company.

The company’s president and CEO, former major-league pitcher Michael Schwimer, told The Athletic in April 2018 that BLA uses a proprietary algorithm to value every player in the minors. Players who receive offers can accept a base-level payout in return for 1 percent of their earnings, with the chance to receive greater incremental payouts and pay back a maximum of 10 percent. If a player never reaches the majors, he keeps the cash advance, with no obligation to pay it back.

This is an awesome thing. For a struggling potential sportsperson, a minor investment (in exchange for equity) can provide a huge boost in their chances of making it – hiring coaches, for example, or eating better food, or living more comfortably.

While the media attention will go to the small proportion of investments that do pay off (like how tech media gives disproportionate coverage, and quite rightly so, to startups that do well), arrangements like this mean that more people will play the sport, and the overall standard in the sport will improve.

We need to see if such arrangements start making a mark in the rest of the arts and writing as well.

Oh, and much has been made of income sharing agreements for professional colleges and “tuition centres”. I’m not sure that is the right model there – the thing is that if you are studying to be a software engineer, your payoffs don’t follow a power law. Yes, if you are successful, you make a few orders of magnitude more money than the less successful ones, but even an average software engineer can expect to make a fairly decent income.

From that perspective, selling equity in your future earnings to get paid to study engineering is not a great idea, and can lead to adverse selection on the part of the candidates (the better ones will prefer to get funding through debt, which their average salaries can help pay off). In that sense I prefer what the likes of MountBlue are doing, where the “training fees” get paid off by simply working for the company for a certain period of time.

 

 

More on Diversity and Inclusion

Diversity and Inclusion are words that are normally thrown around by people of a certain persuasion. In fact, they were among the key principles espoused by one of my earlier employers as well (to their credit, some of their diversity and inclusion sessions did a lot of help broaden my worldview).

However, as I’ve argued earlier on this blog, in a lot of cases, arguments on diversity and inclusion are (literally) only skin deep – people go big on diversity of sex, sexuality, skin colour, nationality and so on while giving short shrift to things like diversity of thought, which in my opinion plays a larger role in building a more successful team.

I’ve also mentioned earlier on this blog about how some simple acts of inclusion can go a long way – for example, I’d mentioned about how building a pedestrian walkway, or pedestrian crossing with signals, would help make one of the roads in Bangalore more inclusive towards pedestrians (a class of people the usual proponents of diversity and inclusion don’t care about).

I was reminded of diversity and inclusion when the recent hoopla about messaging apps happened. A number of my contacts said they were leaving WhatsApp and moving to Telegram or Signal. Others said they weren’t going anywhere and were sticking to WhatsApp, and that Facebook’s new privacy rules were nothing new.

From my personal point of view, since I didn’t have a view on this messaging apps issue, the best solution turned out to be “inclusion”.

I’m on all apps. I’m on Signal, and Telegram, and WhatsApp, and iMessage, and good old SMS. However you choose to reach me, I’m there to receive your message and respond to you. In that sense, when you don’t have a strong opinion, the best thing to do is to be inclusive.

Of late I’ve realised it’s the same with language. Since I now work for a company that is headquartered in Gurgaon, a number of colleagues instinctively speak in Hindi. Initially I used to be a bit snobbish, and tell them that my Hindi sucked, and when they spoke Hindi, I would reply in English.

Over time, however, I’ve realised that I’m only being an asshole by refusing to be inclusive. Since I know Hindi (I got more marks in Hindi in Class 10 board exams than I did in English – not that that says anything), I should let the people decide whether I’m worth talking to in Hindi at all. I’ll talk to them in my broken Hindi, and if they think it’s too broken they can choose to switch to a language I’m more comfortable in.

And a week ago, Pranay and Saurabh of the Puliyabaazi podcast asked me if I’m willing to go on their (Hindi) podcast to talk about logical fallacies and “how not to use data”. I immediately accepted, not only because it’s a great podcast to be on (they’re fun to talk to), but it also gives me an opportunity to show off my broken Hindi.

The episode dropped on Thursday. You can listen to it here:

I realised while I was recording that my Hindi has become really rusty, and I found myself struggling for words many times. I also realised after the episode dropped that I don’t even understand what the title means, yet I’ve been happily sharing it around in my office! (a colleague kept asking me if I knew this word and that word, and I realised the answer to all that was no. Yet I had made assumptions and gone on with the podcast – another example of my own “inclusiveness”!)

Henceforth I’m never telling a colleague that I don’t know Hindi. However, if I find that someone overestimates my level of Hindi I might inflict this podcast on them. Even then, if they choose to speak to me in Hindi, so be it! I’m going to make an attempt to be more inclusive, after all.

 

Apolitical fake news

For the last 4-5 years, the ills of “political fake news” have been well documented – documented well enough that I don’t even need to link to them (I think). However, there is another kind of fake news that doesn’t get the sort of (negative) attention it deserves – unbiased or apolitical fake news.

Before we describe such news, a couple of frameworks. Firstly, there are two kinds of media publications – periodicals and perennial. Periodicals deliver news at a certain periodicity – daily or weekly or monthly or whatever. Their job is to tell the reader what happened in the world (or the subset of the world that the publication focuses on) since the previous edition. Examples of periodicals include newspapers and magazines and the 9 o’clock (or whenever) news on Doordarshan.

The other side is perennials, which are “always on”. When some news breaks, their mandate is to break it to their audience as quickly as possible. When there is no breaking news, they need to make up something, or analyse, or have talk shows and shouting matches, or whatever. Examples of perennial publications include 24×7 TV channels and twitter.

The second framework is something I’ve written about a fair bit – on finite and infinite games. This was introduced by the late NYU philosopher James Carse. The basic concept is that the objective of a finite game is to win. There is a particular end point. In an infinite game, there is no concept of “winning”. The objective is to just continue playing. I think it’s a rather profound theory, and has consequences in lots of facets of life.

Including media. My argument is that periodicals play a finite game and perennials play an infinite game.

The objective of a periodical is to make each issue good enough that the reader/viewer continues the subscription until the next issue. This might, at face value, appear like an infinite game, but from the point of view of a single edition, it is a finite game. If the reader/viewer continues subscription (however you define it) till the next issue, you have “won”.

It is different with perennials because there is no discrete “next edition”. The next edition of a next edition is the next minute. And that makes the “game” mentioned in the earlier paragraph hard to play. Instead, running a perennial media house is like playing an infinite game, where your objective is to make sure that the viewer/reader “continues to play the game”, or continues to watch without switching channels or diverting attention.

In other words, the objective of a perennial media house (like a 24×7 media channel, or twitter) is to make sure users stay on the platform. Which is good.

Except that, over a period of time, some of these media houses have figured out that one surefire way of retaining viewership and viewer interest is by stoking viewer anxiety. When a viewer is anxious about something, they want to get as much information as possible about the thing they are anxious about, and continue to hunt for information. This means that they are going to continue to hang around the channel (or social media platform) in the hope of resolving their anxieties. Which means that these channels or platforms “win” the infinite game of retaining audience attention.

And how do these channels create anxiety? By creating outrage. By creating sensationalism. By resorting to fake news, of the kind that is certain to cause anxiety among viewers, in the hope that they will continue to watch (and consume the intervening ads).

I clearly remember the Kaveri riots in Bangalore in 2016 (the week my daughter was born), when Kannada 24×7 news channels took to showing the riots and arson live on TV. And giving reports in a rather sensational voice on how the riots were only going to increase and things are going to get worse. This wasn’t “fake” per se, but sensational and anxiety causing (we kept the TV on one whole afternoon wondering if it was safe to go to the obstetrician’s clinic (300m away from home) ).

And the Kannada 24×7 channels were at it again in 2020 during the covid-19 induced lockdown. One day (in May) suddenly one of them claimed that “all of Bangalore would get sealed down because of increasing cases”. It turned out that two small neighbourhoods were “sealed down” because of a high density of cases there. The rumours of “seal down” were clearly fake news, that clearly created anxiety among the viewers.

I’m only quoting one such instance from this period, but news channels kept at this business of fostering anxiety by saying things that weren’t true (I don’t normally watch these channels, but kept getting informed about these fake “news” by elderly relatives who as a rule keep watching news all the time).

What I’m disappointed by is that this kind of fake news gets no attention at all, compared to the more political sort of fake news which is easy to see through for someone with an iota of brain cells. Then again, the platforms that give footage to the ills of political fake news (twitter, some whatsapp groups, etc) are also perennial news sources themselves and so it doesn’t make sense to call out people of their own ilk.

Monetising volatility

I’m catching up on old newsletters now – a combination of job and taking my email off what is now my daughter’s iPad means I have a considerable backlog – and I found this gem in Matt Levine’s newsletter from two weeks back  ($; Bloomberg).

“it comes from monetizing volatility, that great yet under-appreciated resource.”

He is talking about equity derivatives, and says that this is “not such a good explanation”. While it may not be such a good explanation when it comes to equity derivatives itself, I think it has tremendous potential outside of finance.

I’m reminded of the first time I was working in the logistics industry (back in 2007). I had what I had thought was a stellar idea, which was basically based on monetising volatility, but given that I was in a company full of logistics and technology and operations research people, and no other derivatives people, I had a hard time convincing anyone of that idea.

My way of “monetising volatility” was rather simple – charge people cancellation fees. In the part of the logistics industry I was working in back then, this was (surprisingly, to me) a particularly novel idea. So how does cancellation fees equate to monetising volatility?

Again it’s due to “unbundling”. Let’s say you purchase a train ticket using advance reservation. You are basically buying two things – the OPTION to travel on that particular day using that particular train, sitting on that particular seat, and the cost of the travel itself.

The genius of the airline industry following the deregulation in the US in the 1980s was that these two costs could be separated. The genius was that charging separately for the travel itself and the option to travel, you can offer the travel itself at a much lower price. Think of the cancellation charge as as the “option premium” for exercising the option to travel.

And you can come up with options with different strike prices, and depending upon the strike price, the value of the option itself changes. Since it is the option to travel, it is like a call option, and so higher the strike price (the price you pay for the travel itself), the lower the price of the option.

This way, you can come up with a repertoire of strike-option combinations – the more you’re willing to pay for cancellation (option premium), the lower the price of the travel itself will be. This is why, for example, the cheapest airline tickets are those that come with close to zero refund on cancellation (though I’ve argued that bringing refunds all the way to zero is not a good idea).

Since there is uncertainty in whether you can travel at all (there are zillions of reasons why you might want to “cancel tickets”), this is basically about monetising this uncertainty or (in finance terms) “monetising volatility”. Rather than the old (regulated) world where cancellation fees were low and travel charges were high (option itself was not monetised), monetising the options (which is basically a price on volatility) meant that airlines could make more money, AND customers could travel cheaper.

It’s like money was being created out of thin air. And that was because we monetised volatility.

I had the same idea for another part of the business, but unfortunately we couldn’t monetise that. My idea was simple – if you charge cancellation fees, our demand will become more predictable (since people won’t chumma book), and this means we will be able to offer a discount. And offering a discount would mean more people would buy this more predictable demand, and in the immortal jargon of Silicon Valley, “a flywheel would be set in motion”.

The idea didn’t fly. Maybe I was too junior. Maybe people were suspicious of my brief background in banking. Maybe most people around me had “too much domain knowledge”. So the idea of charging for cancellation in an industry that traditionally didn’t charge for cancellation didn’t fly at all.

Anyway all of that is history.

Now that I’m back in the industry, it remains to be seen if I can come up with such “brilliant” ideas again.

The Office!

For the first time in nearly ten years, I went to an office where I’m employed to work. I’m not going to start going regularly, yet. This was a one off since I had to meet some people who were visiting. On the evidence of today, though, I think i once again sort of enjoy going to an office, and might actually look forward to when I start going regularly again.

Metro

I had initially thought I’d drive to the office, but white topping work on CMH Road means I didn’t fancy driving. Also, the office being literally a stone’s throw away from the Indiranagar Metro Station meant that taking the Metro was an easy enough decision.

The walk to South End Metro station was uneventful, though I must mention that the footpath close to the metro station works after a very long time! However, they’ve changed the gate that’s kept open to enter the station which means that the escalator wasn’t available.

The first order of business upon entering the station was to show my palm to one reader which took my temperature and let me go past. As someone had instructed me on twitter, I put my phone, wallet and watch in my bag as I got it scanned.

Despite not having taken the metro for at least 11 months, the balance on my card remained, and as I swiped it while entering, I heard announcements of a train to Peenya about to enter the station. I bounded up the stairs, only to see that the train was a little distance away.

In 2019, when I had just moved back to Bangalore from London, I had declared that the air conditioning in the Bangalore Metro is the best ever in the city. Unfortunately post-covid protocols mean that the train is kept at a much warmer temperature than usual. So on the way to the office, I kept sweating like a pig.

The train wasn’t too crowded, though. On the green line (till Majestic), everyone was comfortably seated  (despite every alternate seat having been blocked off). I panicked once, though, when a guy seated two seats away from me sneezed. I felt less worried when I saw he was wearing a mask.

The purple line from Majestic was another story. It felt somewhat silly that every alternate seat remained blcoked off when plenty of people were crowding around standing. I must mention, though, that the crowd was nothing like what it normally is. In any case, most of the train emptied out at Vidhana Soudha, and it was a peaceful ride from there on.

40 minute from door to door. Once office starts regularly, I plan to take the metro every day.

The Office

While the office was thinly populated, it felt good being back there. I was meeting several of my colleagues for the first time ever, and it was good to see them in person. We sat together for lunch (ordered from Thai House), and spoke about random things while eating. There was an office boy who, from time to time, ensured that my water glass and bottle were always filled up.

In the evening, one colleague and I went for coffee to the darshini next door. That the coffee was provided in paper cups meant we could safely socially distance from the little crowd at that restaurant. The coffee at this place is actually good – which again bodes well for my office.

And then some usual office-y things happened. I was in a meeting room doing a call with my team when someone else knocked asking if he could use the room. I got into a constant cycle of “watering and dewatering”, something I always do when I’m in an office. The combination of the thin attendance and the office boy, though, meant that there was no need to crowd around the water cooler.

I guess this is what 2020 has done to us. Normally, going to office to work should be the “most normal and boring thing ever”. However, 2020 means that it is now an event worth blogging about. Then again, I don’t need much persuasion to write about anything, do I?

Railways and the military: an evening spent in ToK

Sometime this afternoon, when both the wife and I figured it was impossible for us to nap, she said that she wanted to “go on a drive to a part of town she hasn’t seen”. After some thinking I said that we could go to the “cantonment area” or the “towns” (Frazer Town, Cox Town, etc.), which we knew are not too far off from town, but where we had hardly been to.

Sometime back I had tried to imagine “symmetries” around the centre of Bangalore, whatever that means. It had started when I wondered which other areas in Bangalore are similar to Jayanagar, where I live. Having ruled out Banashankari and Rajajinagar, other areas I’ve lived in, because they are “too far from the centre of town”, I started looking at other areas that are nice and residential but not far from the MG Road area.

And that thought process had taken me to the “towns”  – Frazer and Cox and Richards and all that. I hadn’t thought much about it then. And I hadn’t wondered much about what sets these “towns” apart from Jayanagar. Today’s drive gave me the answer.

There are two defining features of the “cantonment” or “towns” area – the military and the railways. As we journeyed east from Frazer Town (the one part of this part of Bangalore we are vaguely familiar with) all the way to Kammanahalli, and the outer ring road, and Banaswadi, and then back towards Indiranagar (more on that later), we kept encountering large swathes of military lands, and railway lines.

Along the way, we saw roads and areas we had only heard about but never seen. For the most part, we didn’t use Google Maps, but just kept driving along the big roads we could find. So we saw Frazer Town. We saw what we first thought was Banaswadi, but later figured is some Ramaswamy Palya or something. And then suddenly, we decided we had heard about Kammanahalli, but never knew where it was, and decided to drive towards that. Halfway up a railway bridge, we saw a signboard to a detour that would take us to Kammanahalli.

And so we went there, and drove through it. Nothing spectacular. And then I had this “flash of inspiration” that this part of town wasn’t actually very far from Indiranagar, and so we could return home via a dinner stop in Indiranagar. So I entered the address of my office (which I haven’t been to yet, but which is in Indiranagar), and let Google Maps take over.

It took us to the Outer Ring Road. And seemed to suggest a route that was going through KR Puram. “Ring roads are boring to drive on”, I declared, and seeing a detour that was “7 minutes longer” I went off the outer ring road. This took us through Banaswadi, and the drive was great (the road was great).

In any road trip, there is a point where you think you are having so much fun by exploring. And then soon after you suddenly feel tired and exhausted, and start wondering what the hell you were thinking when you decided that this drive was a good idea. Soon after we had passed Banaswadi, we had this moment. And this had to do with the railways and the military.

We had driven past Banaswadi, and encountered the Baiyyappanahalli station (with 16 platforms) that is still being renovated. This was the time when we were still feeling excited, that we were seeing parts of town that weren’t too far, but we had normally not seen.

And then we hit a mud road, and a dead end (literally. Not a T-junction). “I don’t get a good feeling here”, my wife said. I turned around and took a nearby road. This took us to a railway gate.

It is the highlighted route here. The red section near the railway line. It’s interesting that Google has coloured it red, because the section just doesn’t exist now. Maybe as part of the work done to revive the Baiyyappanahalli metro station, a new railway overbridge is being built there. That means the road itself has been closed.

This, we figured after we had crossed the railway line (this happened after a 10 minute wait for the Mysore-Kochuveli Express to pass). We crossed the line and found that the road didn’t exist after that. Everyone was going left there, but the road didn’t look good so on a whim I turned right. The road was decent.

What I hadn’t anticipated was that the other defining feature of cantonment Bangalore would come in our way – military areas. No sooner had I turned right after getting past the railway line that Google suddenly upped the time and distance estimates to Indiranagar. Soon there was a military gate to the left. “Trespassers will be fired upon”, said a board nearby. We drove on.

The size of the military area there meant that we had to go all the way back to Ulsoor Lake before going to Indiranagar. On the way, we passed a funeral procession that occupied the entire road (with lots of singing and dancing and flower throwing). We had a close shave trying to pass an auto rickshaw at an especially narrow stretch of road. At another point, we had to wait for two minutes for a cow to give us right of way.

And then, somewhere along the way, as we neared Assaye Road, I said something like “Ok, we are getting back to civilisation. Close to town now”.

The daughter, seated next to me, and supremely bored as we went round and round without stopping, asked “had we gone to a different state, appa?”.

“Yes”, I replied. “We had gone to ToK” (a tongue in cheek expression pioneered by Thejaswi Udupa (link possibly paywalled now). It can stand for either “Tamil Occupied Karnataka” and “Telugu Owned Karnataka”).

Social Media Regulation

To use (and abuse) my good friend Sangeet Paul Choudary‘s framework, Twitter is both a pipe and a platform. Whether it is a pipe or a platform depends on how you use it.

I always use Twitter in the “latest tweets” mode, which means that tweets from people I follow are shown to me in the order in which they happen, with most recent tweets on top. Twitter has no role in showing what tweets I see or not see. Someone I follow says something, it will come in its appointed place. This is the twitter in its “pipe avatar”. It is no different from reading blogs through an RSS feed. Twitter is just a pipe to convey these tweets to me.

However, this “latest tweets” is not the default mode for Twitter. The default mode is what I think it calls “top tweets” or something. This is the algorithmic timeline that Twitter launched a few years back. Here, twitter’s algorithms determine what you should see. Whether a tweet gets shown to you at all, whether you follow someone whose tweets you are shown and what order tweets are shown to you in – none of these are under your control. It is twitter’s (rather, and understandably, opaque) algorithm that determines this. This is twitter operating in its “platform avatar”, since it, through its algorithms, is effectively controlling the content you see.

Why is it important if twitter is a pipe or a platform? It has to do with regulation. I understand that twitter and facebook have recently suspended Donald Trump’s account. Some people are saying this is unfair, and that it is a step too far for social media. Others are using this as an excuse for more social media regulation.

My contention is that whether social media should be regulated or not is guided by whether social media is a pipe or a platform.

If social media is a pipe, like twitter in its latest tweets (or “traditional”) format, then regulation is unnecessary. In this situation, people are served tweets only because they’ve chosen to receive them. If some account only tells lies, so be it. People follow parody accounts all the time. By censoring accounts, twitter is denying people the right to see the thing they have subscribed to see. Any regulation or censorship means that people are not getting what they have signed up for.

On the other hand, in the algorithmic timeline format, one can make a case for some kind of regulation or censorship. This is because the platform here, either implicitly or explicitly, chooses what the user sees. And if the platform’s algorithms mean that lies and hatred and outrage get amplified, then that is a problem. If a tweet from a parody account suddenly appears in my timeline, it can throw me off and drive me bonkers. And that is not “fair”.

Then again, while one can make a case for censorship in the “platform model”, I’m not advocating that regulation or censorship is necessary. Yes, the opaque algorithms can amplify bad shit, but how are you going to even regulate that?

You want algorithms to be passed by some central board? You want the platform to deplatform your opponents but not your folks? You want a profit-maximising (likely monopoly) private entity to determine what is “truth” and what is not? Irrespective of how the regulation or censorship is defined, it is rather easy for it to have consequences that the designers of the regulation or censorship have least expected.

In any case, these occasional cals for censorship or regulation or cancellation are the reasons why I put most of my better arguments on this blog, which gets delivered through this pipe called RSS feeds.

The Law is an Ape

I’ve always known that I have long arms relative to the size of the rest of my body. I think I discovered this sometime in the late 90s, around the time I both stopped growing vertically and started wearing full arm shirts. I remember being forced to buy shirts one size too large for my shoulders because otherwise the sleeves wouldn’t reach all the way down.

My father had the same problem as well, and so he wore shirts one size too large as well. Over time, I managed to find brands that fit both my shoulders and my arms properly (the Aditya Birla stable is good for this -Loius Phillippe, Van Heusen, etc. Arrow never fits me). And then I took to getting my formal shirts tailored. Last year I bought a bunch at Gap, after I found that they fit me well.

Only recently, while I was trying to analyse my performances at the gym, that I realised that my long arms might be affecting stuff apart from my attire as well. For the longest time now, I’ve been trying to learn to power clean, and have never quite managed it.

The power clean involves, among other things, holding the bar with your arms outstretched where it touches the fold in your waist (where your torso meets your groin). The idea is that as you pull the bar up past your thighs, you make it touch the fold in your waist while performing a “triple extension” and jumping, and that will power the bar up.

And I recently discovered that I can’t make my bar touch the fold of my waist unless I hold it really really wide, like you do for a snatch. “Maybe I have long arms”, I thought, and then remembered my troubles with buying shirts.

And then I started wondering if I could quantify if I actually had long arms. Looked around a little and found that there is the concept of the “wing span” or “arm span“. I figured how to measure it, and got my wife to measure it for me. It’s 192 cm. My height is between 179 and 180 cm. This means my arm span is 12-13 cm, or nearly 5 inches longer than my height.

Most humans have their arm spans about the same as their height, or just a little longer. According to this article, my long arms mean that I could have been an elite basketball player or a swimmer, since these sports are good for people with long arms. That perhaps explains why I was a decent defender in basketball in school, though I was among the least athletic people you could find.

I kept looking, and reading articles. I thought of myself as being “the Law” (long arms, get it?). And then I came across this measure where rather than subtracting your height from your arm span, you take the ratio. The ratio of your arm span to your height is called “ape index“.

Most humans have an ape index close to 1. NBA players have an average ape index of 1.06. My ape index is higher than 1.07. Shortly after she had measured my arm span, I told my wife about this. “Well, I always knew you were an ape”, she said.

So yes, for my height I have really long arms. This means I find it hard to buy shirts that fit me. This also means I find it relatively easier to deadlift. Long arms also mean that I find movements where I have to lock out my hands upwards, like the bench press or the overhead press, really difficult. Maybe this explains why I have piddly bench and overhead numbers compared to my squat or deadlift? Long arms also make it harder to do pull ups, which possibly explains why completed my first ever pull up in life at 37.

You could think I am the law. You could also think I am an ape. Or maybe, the law is an ape?

Hinge koDaka

Being married to Marriage Broker Auntie means that I sometimes get to participate, either directly or indirectly, in some of her “experiments”. Her latest experiment was to get on to dating apps, to see what the hell they are all about, so that she can advise her clients better about them.

She has written about her experience on these apps in the latest edition of her newsletter. Oh, and you should totally subscribe to her newsletter if you haven’t already. You will get some very interesting relationship insights, which you can appreciate even if you aren’t looking for a relationship.

Anyways, once she started her latest experiment, I asked myself “why should girls have all the fun?”, and got curious to get on these apps myself. I spoke to her about it, and she suggested that I check out Hinge. “It’s the most decent among all the apps”, she said.

I mean, this wasn’t my first time on a dating app. Though they all appeared well after I had got married, I remember trying out Tinder a few years back, possibly as part of another of my wife’s experiments. I remember getting disillusioned by it and deleting it in less than a day. I had even forgotten about it, except that when I was searching for Hinge on the app store, I found that I had already “bought” Tinder in the past (I now realise I’d tried TrulyMadly in the past as well – yet another unmemorable experience).

Anyways, I quite liked Hinge. I spent a whole week on it, before I decided that people who don’t know what’s happening might think I’m a creep and deleted my account.

What makes Hinge so nice is the way it is structured and the user experience. For starters, there’s no easy swiping left or right – there are (fairly small) buttons to either like or dismiss a profile, and in case  there has been a mutual like, then there is a “match” and you can start chatting.

Also, from one little experiment (where the wife and I decided to like each other on Hinge), I found that Hinge has implemented something that I have always believed in – basically don’t tell both parties that there is a match immediately after the second person has liked. That way, the pair know who liked whom first and that can set an unhealthy prior in the relationship. Instead, if the app waits for a “random period of time” before announcing the match, you don’t know who liked whom first.

Back to Hinge – what I liked about it was how the profiles had been designed. You are asked to upload six photos of yourself doing different things, and also answer a few questions. The answers to these questions are displayed in bold on your profile, and this means that anyone who pays some amount of attention is likely to see these answers.

This means that you don’t need to impress your potential counterparties with your photos (or one photo) alone – you can show off your “well rounded personality” (if you have one that is). For example, I found this girl whose profile seemed unremarkable until I saw that she “got turned on by probability and maths”. That, of course, grabbed my attention and I immediately paid much more attention to her full profile. This kind of information (conveying your possibly unusual interests) is a little hard to get across on other dating platforms.

The other nice thing about Hinge is that you can choose what part of a person’s profile you want to like. You could choose one of the pictures, for example, or one of their answers to some question. Like if I were actually in the market (and not casually “researching”) I would have tried to start a conversation with the above mentioned person by liking (and possibly commenting on) her interest in probability.

This specific liking provides an automatic conversation starter. And in a congested market (see chapter 4 of my book here), anything that can help you distinguish yourself can be a sure winner. So it helps that you can write about your interest in probability. It helps that you can tell someone you like her for her interest in probability and not for her tattoo. In marketing jargon, it allows you to be “a qualified lead”.

I had fun for about a week. I must mention that I had used my real name (rather, my oldest nickname that everyone knows me by), and my real photo (my wife picked that one) on the platform. And then I got likes from two women (apart from the one from my wife).

Given that I’m not actually looking for a relationship, that made me feel like I’m doing something wrong. I felt horrible about myself for putting myself on a dating app when I’m not looking to date. There was also the thing that people who found me on the app and knew me would think of me as a creep (or get the wrong kind of ideas about my marriage). So I deleted it.

However, if you are in the market and looking to date, I strongly recommend Hinge. Among the apps that I’ve used, it’s easily among the best.

Should this have been my SOP?

I was chatting with a friend yesterday about analytics and “data science” and machine learning and data engineering and all that, and he commented that in his opinion a lot of the work mostly involves gathering and cleaning the data, and that any “analytics” is mostly around averaging and the sort.

This reminded me of an old newsletter I’d written way back in January 2018, soon after I’d read Raphael Honigstein‘s Das Reboot. A short discussion ensued. I sent him the link to that newsletter. And having read the bit about Das Reboot (I was talking about how SAP had helped the German national team win the 2014 FIFA World Cup) and the subsequent section of the newsletter, my friend remarked that I could have used that newsletter edition as a “statement of purpose for my job hunt”.

Now that my job hunt is done, and I’m no more in the job market, I don’t need an SOP. However, for the purpose that I don’t forget this, and keep in mind the next time I’m applying for a job, I’m reproducing a part of that newsletter here. Even if you subscribed to that newsletter, I recommend that you read it again. It’s been a long time, and this is still relevant.

Das Reboot

This is not normally the kind of book you’d see being recommended in a Data Science newsletter, but I found enough in Raphael Honigstein’s book on the German football renaissance in the last 10 years for it to merit a mention here.

So the story goes that prior to the 2014 edition of the Indian Premier League (cricket), Kolkata Knight Riders had announced a partnership with tech giant SAP, and claimed that they would use “big data insights” from SAP’s HANA system to power their analytics. Back then, I’d scoffed, since I wasn’t sure if the amount of data that’s generated in all cricket matches till then wasn’t big enough to merit “big data analytics”.

As it happens, the Knight Riders duly won that edition of the IPL. Perhaps coincidentally, SAP entered into a partnership with another champion team that year – the German national men’s football team, and Honigstein dedicates a chapter of his book to this, and other, partnerships, and the role of analytics in helping the team’s victory in that year’s World Cup.

If you look past all the marketing spiel (“HANA”, “big data”, etc.) what SAP did was to group data, generate insights and present it to the players in an easily consumable format. So in the football case, they developed an app for players where they could see videos of specific opponents doing things. It made it easy for players to review certain kinds of their own mistakes. And so on. Nothing particularly fancy; simply simple data put together in a nice easy-to-consume format.

A couple of money quotes from the book. One on what makes for good analytics systems:

‘It’s not particularly clever,’ says McCormick, ‘but its ease of use made it an effective tool. We didn’t want to bombard coaches or players with numbers. We wanted them to be able to see, literally, whether the data supported their gut feelings and intuition. It was designed to add value for a coach or athlete who isn’t that interested in analytics otherwise. Big data needed to be turned into KPIs that made sense to non-analysts.’

And this one on how good analytics can sometimes invert hierarchies, and empower the people on the front to make their own good decisions rather than always depend on direction from the top:

In its user-friendliness, the technology reversed the traditional top-down flow of tactical information in a football team. Players would pass on their findings to Flick and Löw. Lahm and Mertesacker were also allowed to have some input into Siegenthaler’s and Clemens’ official pre-match briefing, bringing the players’ perspective – and a sense of what was truly relevant on the pitch – to the table.

A lot of business analytics is just about this – presenting the existing data in an easily consumable format. There might be some statistics or machine learning involved somewhere, but ultimately it’s about empowering the analysts and managers with the right kind of data and tools. And what SAP’s experience tells us is that it may not be that bad a thing to tack on some nice marketing on top!

Hiring data scientists

I normally don’t click through on articles in my LinkedIn feed, but this article about the churn in senior data scientists caught my eye enough for me to click through and read the whole thing. I must admit to some degree of confirmation bias – the article reflected my thoughts a fair bit.

Given this confirmation bias, I’ll spare you my commentary and simply put in a few quotes:

Many large companies have fallen into the trap that you need a PhD to do data science, you don’t.

Not to mention, I have yet to see a data science program I would personally endorse. It’s run by people who have never done the job of data science outside of a lab. That’s not what you want for your company.

Doing data science and managing data science are not the same. Just like being an engineer and a product manager are not the same. There is a lot of overlap but overlap does not equal sameness.

Most data scientists are just not ready to lead the teams. This is why the failure rate of data science teams is over 90% right now. Often companies put a strong technical person in charge when they really need a strong business person in charge. I call it a data strategist.

I have worked with companies that demand agile and scrum for data science and then see half their team walk in less than a year. You can’t tell a team they will solve a problem in two sprints. If they don’t’ have the data or tools it won’t happen.

I’ll end this blog post with what my friend had to say (yesterday) about what I’d written about how SAP helped the German National team. “This is what everyone needs to do first. (All that digital transformation everyone is working on should be this kind of work)”.

I agree with him on this.