Author: SK

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.

How Python swallowed R

A week ago, I put a post on LinkedIn saying if someone else working in analytics / data science primarily uses R for their work, I would like to chat.

I got two responses, one of which was from a guy who strictly isn’t in analytics / data science, but needs to analyse large amounts of data for his work. I had a long chat with the other guy today.

Yesterday I put the same post on Twitter, and have got a few more responses from there. However, it is staggering. An overwhelming majority of data people who I know work in Python. One of the reasons I put these posts was to assure myself that I’m not alone in using R, though the response so far hasn’t given me too much of an assurance.

So why do most companies end up using Python for analytics, even when R is clearly better for things like data wrangling, reporting, visualisation, dashboarding, etc.? I have a few theories on this, and I think all of them come together to result in python having its “overwhelming marketshare” (at least among people I know).

Tech people clearly prefer python since it’s easier to integrate. So the tech leaders request the data science leaders to use Python, since it is much easier for the tech people. In a lot of organisations, data science reports into tech, so this request is honoured.

Even if it isn’t, if you recall, “data scientists” are generally tech facing rather than business facing. This means that the models they build need to be codified, and added to the company’s code base. This means necessarily working together with tech, and this means using a programming language that tech is comfortable with.

Then, this spills over. Usually, someone has the bright idea that the firm shouldn’t use two languages for what is essentially the same thing. And so the analytics people are also forced to use python for their analytics, even if it isn’t built for the purpose. And then it spreads.

Next is the “cool factor”. There is this impression that the more technical a solution is, the more superior it is, even if it has no direct business impact (an employer had once  told me, “I have raised money saying we are using machine learning. If our investors see the algorithms you’re proposing, they’ll want their money back”).

So a youngster getting into data wants to do “all the latest stuff”. This means machine learning. Deep learning. Reinforcement learning. And all that. There is an impression that this kind of work is “better work” compared to let’s say generating business insights using data. And in general, the packages for machine learning have traditionally been easier in Python than they are in R (though R is fast catching up, and in general python is far behind R when it comes to user friendliness).

Then, the growth in data and jobs associated with it such as machine learning or data engineering have meant that a lot of formerly tech people have got into data work. Python is fundamentally a programming language, with a package (pandas) added on to do data work. Techies find it far more intuitive than R, which is fundamentally a statistical software. On the other hand, people who are coming from a business / Excel background find it far more comfortable to use R. Python can be intimidating (I fall in this bucket).

So yeah – the tech integration, the number of tech people who are coming into data and the “cool factor” associated with the more techie stuff means that Python is gaining, at R’s expense (in my circle at least).

In any case I’m going to continue to use R. I’m at least 10X faster in R than I am in Python, and having used R for 12 years now, I’m too used to that way of working to change things up.

Concepts from The Obesity Code

Based on the recommendation of a friend who had once described his waistline as “changing more often than Britney Spears’s (?) bra size”, I read Jasun Fung’s The Obesity Code over the last couple of days. The book is stellar.

Here are my highlights from the book.

Anyway, fitness and nutrition is something I’ve been struggling with for a very long time in life now. I used to believe that I have my health numbers (primarily triglycerides) under control because of regular lifting of heavy weights, but a recent blood test called that assumption to question. Having got what I now think is bad advice about what to eat and what not to eat, getting better advice on food is something I’ve been fairly receptive to. And the book does a great job of it.

The basic idea is – your body weight is controlled by hormones. How much you eat and how much you exercise doesn’t really matter. Calorie counting just doesn’t work. Your body has a “natural weight”, and if you are above that the body will try to adjust it lower, and vice versa. And this “natural weight” is guided by the hormones, especially insulin. The higher the level of insulin in your blood, the more your “natural weight” will be.

So the idea is to keep the level of insulin in your blood low. The author builds up a stellar case with some rigorous presentation of research. There is NO RELATIONSHIP between the fat that you eat and risk of heart attacks. A high carb low fat diet will make you fat.

And what I liked about the book is the structuring – the first 220 pages is all about presenting the research on various topics, and not really “giving away” what you should or should not eat. And then in the last 20 pages, he puts it all together, with a broad plan on what is good to eat and what is not.

In any case, I’m not going to reproduce the book here. You can go read it (it’s very very well written), or just read my highlights. The reason I started writing this post is to document my learnings from the book. I think I’d already internalised a lot of it, but some of it is new. This is how I plan to change my diet going forward:

  • Sometimes in recent times I’ve noticed this “heady feeling” upon eating certain foods. I used to think it’s due to eating too much sweet. Now, after reading, I think it’s the feeling of an insulin spike in my head. I’m not going to have any fruit juices. Fruits need to be eaten whole
  • I’ve largely eschewed added sugars for a while now (sometimes on and off). This will continue.
  • Artificial sweeteners also cause a spike in insulin. I didn’t know this. So no more coke zero. No more Muscle Blaze Whey Energy powder as well (I now need to find a whey powder that doesn’t contain any sweet or any sweetener). No energy bars. No “no added sugar” biscuits.
  • This is maybe the most important concept in the book – NO SNACKING. Eat exactly two or three times a day (I used to eat two a day, but nowadays I go to the gym in the mornings, so breakfast is necessary). Eat as much as you want at each meal, but don’t eat in between meals. The body needs lots of periods of time when insulin levels go low – so it doesn’t adjust to a higher natural level of insulin, which means a higher natural weight.
  • Dairy products have a high “insulin index” (produce lots of insulin once eaten), but also have high satiety – they keep you full for a very long time after eating. After my last cholesterol test, after a fight with the wife, I largely gave up on cheeses. I’m reversing that now. I love cheese, and it’s good for me. Calorie counting just doesn’t work (the book does a great job of explaining this).
  • Not doing keto. It’s unsustainable. And I love my fruits too much. Oh, and I need to eat my fruits along with my meals. Not as “snacks”
  • Processed carbohydrates are not good. So no more bread for me. I need to figure out if fried eggs + milk will be enough for breakfast. Or find a decent substitute.
  • I also need to figure out how good or bad basmati rice is. Definitely makes me feel better than sona masuri (which we used to eat before). Need to figure out if this feeling is justified.
  • Peanuts are good. Peanut butter is good. Other nuts are good as well. But need to eat them for breakfast. Not as a snack.

The hardest part for me, with this new regimen I plan to start, is “no snack”. I’d gotten so used to snacking that I think I eat far less than necessary during my main meals. And that results in a vicious cycle. I’ve attempted to start breaking out of that by supplementing my chapati-paneer curry with some curd rice tonight.

So far I’ve been feeling great. Let’s see how this goes.

The first heart attack

Gerard Houllier is no more. The man who led Liverpool to the “cup treble” in 2001 passed away following a heart operation. Supporters of the club might remember that he had had yet another heart operation when he was managing Liverpool, and the impact of that heart attack on the club was serious.

I’m reusing a graph that I’d put here a couple of years back. This shows Liverpool’s Elo Rating (as per clubelo.com) over the years, with managers’s reigns being overlaid on top.

Notice the green region towards the right – it says “Houllier”, and it has one massive up and one massive down. Actually I’m going to re-upload this graph to blow up the Premier League period.

Liverpool’s Elo Rating in the Premier League period

Now you can see that there are two separate regions marked “Gerard Houllier”, with a small gap that says “Phil Thompson”. This gap represented Houllier’s first heart operation. Notice how, before his heart operation, Liverpool had been on a massive upswing, on their way back nearly to the levels where they had started off in the Premier League (they had last won  the league in 1990; compare to the first graph here).

And then the heart attack, and heart operation happened. Houllier’s assistant Phil Thompson took over and held things (here is Thompson’s tribute to Houllier). And then Houllier came back and he and Thompson became joint managers (the “orange” region here). And Liverpool’s rally was gone. The 2001-2 season was gone.

Looking at this graph, with the full benefit of hindsight, Houllier’s sacking in 2004 (to be replaced by Rafa Benitez) seems fully justified. And then notice the club’s steep fall under Benitez after Xabi Alonso got sold in 2009.

I’ve said here before – these Elo graphs can be used to tell a lot of footballing stories.

Why I quit public policy

This is yet another of those posts that elaborates something I’ve put on twitter.

I remember getting interested in public policy sometime in 2005. I think that was around the time when I stopped solely talking about gossip (and random “life issues”) on this blog, and started commenting about random “issues” here.

That was also the time when Madman Aadisht introduced me to his blog circle that he called the “libertarian cartel”. Reading blogposts by this cartel (included the likes of Ravikiran Rao, Amit Varma, Gaurav Sabnis (who was once a libertarian), Nitin Pai, etc.), I was hooked. I too wanted in on this “libertarian cartel”.

Soon enough, I started work and did one project that involved the study of some economic reforms. I soon quit that job but wrote about this, and other issues. I started getting into the “econ blogosphere”. Between the libertarian cartel, the opinion pages of the Business Standard (back when TN Ninan was the editor) and “econ blogs” (the likes of Marginal Revolution and EconLog), I got deeply interested in “policy issues”. And I thought I wanted to do public policy.

Of course, what public policy pays is nothing comparable to what post-MBA jobs pay, so I never explored it seriously as a career. I kept moving from one highly paid job to another, though I kept writing about “policy issues” on this blog, and then on Twitter (when I opened an account there in 2008). I even wrote on the “Indian Economy Blog”. And while the libertarian cartel never admitted me as a member, when they did form a mailing list, I got invited to join it soon enough (thanks to Aadisht once again).

“Policy work”, or “policy blogging” (which might be a more accurate term), in the late noughties was enjoyable because most people (at least those I bothered to read) were issue driven. So you had the aforementioned libertarians who analysed issues through a libertarian lens. You had leftists like the Jagadguru Krish and “Jihvaa”.  You had right wingers like SandeepWeb. Each class largely evaluated each issue based on their own philosophies, and commented about them. People avoided being partisan.

And so, in 2011, when I quit full time employment and decided to lead a portfolio life, I decided that public policy should be part of my portfolio. And the Takshashila Institution was kind enough to appoint me as its “resident quant” (for the most part, there were no formal responsibilities for the role and I wasn’t paid. However, we mutually enjoyed it, I would like to think).

That was a fantastic opportunity. I didn’t have to commit that much time, but got the optionality to participate in a large number of fairly interesting discussions with fairly interesting people. I did some work here and there, doing some research and teaching and course designing and lecturing, and it was most enjoyable. More enjoyable, of course, was the set of people I met through this assignment.

Somewhere down the line, maybe in 2015 or 2016 (or maybe even earlier), things changed. Basically policy became partisan. Out went the libertarians and totalitarians and right wingers and left wingers. In came the “Congressis” and “bhakts”, and republicans and democrats.

Output of policy analysis everywhere, except in academic journals (which I can’t comment on since I don’t bother reading them), became a function of the author’s political preferences. One year, an author might be favourable to the BJP and everything he/she wrote would nicely tally with the BJP’s view of the world. And then maybe the author would change political preferences, and there was a 180 degree turn on most issues!

On twitter, on mainstream media, on blogs, even on Instagram – “policy analysis” became rather predictable. Once you knew a person’s political preferences and leanings, it became clear what their view on any topic would be – it was identical to the view of their chosen party at that point in time. This partisanship meant there was “no information content” in any of this writing.

And that is how I started getting disillusioned. And the disillusionment grew over time, until a point when I started actively avoiding policy discussions (I’ve even muted the word “policy” on twitter).

I’m happy living my life, and doing my work, and earning my money, and paying my taxes. In the spirit of 2020, I’ll “leave public policy to the experts”.

Start the schools already

Irrespective of when you open the schools, there will be a second wave at that point in time. So we might as well reopen sooner rather than later and put children (and parents of young children) out of their misery.

OK, I admit I have a personal interest in this one. Being a double income, single kid, no nanny, nuclear family, we have been incredibly badly hit by the school shutdown for the last nine months. The wife and I have been effectively working at 50% capacity since March, been incredibly stressed out, and have no time for anything.

And now that I’ve begun a “proper job”, her utilisation has dropped well below 50%. This can’t last for long.

Then again, this post is not being driven solely by personal agendas or interests. The more perceptive of you might know that on my twitter account, I publish a bunch of graphs every morning, based on the statistics put out by covid19india.org . And every day, even when I don’t log into twitter, I go and take a look at the graphs to see what’s happening in the country.

And the message is clear – the pandemic is dying down in India. It is a pretty consistent trend. The Levitt Model might not really be true (my old friend’s comment that it is “random curve fitting” when I first came across it holds true, I would think), but it gives a great picture of how the pandemic has been performing in India. This is the graph I put out today.

In most states in India, the Levitt measure is incredibly close to 1, indicating that the pandmic is all but over. However, you might notice that the decline in this metric is not monotoniuc.

However, if you look at the Delhi numbers on the top right, notice how nicely the Levitt metric shows the three “waves” of the disease in the city. And you can see here that the third wave in Delhi is all but over. And while you see the clear effect of Delhi’s third wave in the Levitt metric, you can also see that it coincided with a second wave in Haryana, and a (barely noticeable) second wave in Uttar Pradesh and Rajasthan.

This wave was due to increased pollution, primarily on the account of crop burning in Punjab and Haryana in October-November. The reason the second waves in Uttar Pradesh and Rajasthan (as seen in terms of the Levitt measures) were small is that they are rather large states, and the areas affected by the bad pollution was fairly small.

And along with this, consider the serosurveys in Karnataka (both the government one and the IDFC-sponsored one), which estimated that the number of actual infections in the state are higher than the official counts of infections by a factor of 40 to 100 (we had initially assumed 10-20 for this factor). In other words, an overwhelmingly large number of cases in India are “asymptomatic” (which is to say that the people are, for all practical purposes, “unaffected”).

In other words, we know cases only when someone is affected badly enough to get themselves tested, or has a family member affected badly enough to get themselves tested. And what happened in Delhi and surrounding states in October-November was that with higher pollution, everyone who got affected got affected more severely than they would have otherwise.

Some people who might have otherwise been unaffected showed symptoms and got themselves tested. Some people who might have not been affected seriously enough ended up in hospital. Pollution meant that some people who might have recovered in hospital ended up dying. And as the crops finished burning and pollution levels dropped, you can see the Levitt metric dropping as well.

And lest you argue that I’m making an argument based on a mostly discredited metric, here is the actual number of known cases in the most affected states in the country. The graph is a Loess smoothing, and the points can be seen here.

See the precipitous decline in Delhi (green line) and Karnataka (orange) and Andhra Pradesh (pink) in the last couple of months. The pandemic has pretty much burnt through in most states. We can start relaxing, and opening schools.

You might be tempted to ask, “but won’t there be a second wave when schools reopen?”. That is a very fair concern, since people who have so far been extremely conservative might relatively relax when the schools open. The counterpoint to that is, “irrespective of when you open the schools, there will be a second wave at that point in time“.

It doesn’t matter if we reopen the schools now, or in April, or in August, or in next December. There will always be a few vestigial (possibly unaffected) cases going around, and there will be a spike in known cases at that point. And by quickly dialling up and down, we can control that.

So I hereby strongly urge the state governments (especially looking at you, Government of Karnataka) to permit schools to reopen. A few vocal and overly conservative parents should not be able to hold the rest of the country (or state) to ransom.

Ending on a high

Now that I have a “proper job” I don’t get that much of an opportunity to post during the week. So I might dump “ideas gathered during the week” each weekend. Hopefully quality won’t suffer. Also, I should add that all opinions here are my own and don’t reflect that of any organisation(s) I’m associated with. 

My lifting had suffered massively during the lockdown. In the first week of March, just before the lockdown had hit, I had managed all-time personal bests in front squat, back squat and bench press. And then the gym shut for six months.

Both physical and mental health suffered. Physical because I wasn’t lifting, and so wasn’t burning as much calories as I used to, and so I lost muscle, and put on fat, and triglycerides and other things.

Mental because I wasn’t lifting, so I wasn’t sure any more what or how much I could eat. I would be anxious about every little thing I ate, or didn’t eat (after considering eating). All the mental models I had built up over time of what is good or bad for me went for a toss, meaning I had to make decisions on the fly. And that wasn’t easy at all.

So when the gym reopened in the middle of August, I was among the first to get back. Yes, the risk of catching the disease of 2020 was there, but that got counterbalanced by the prospect of vastly improved physical and mental health.

I restarted slowly, at about half the weights I had left off at in March. I had expected it to take a year to reach my previous highs. The guy who runs the gym thought it will take a couple of months. He had the better prediction – in the beginning of November I managed to deadlift twice my body weight (I had done that once before, in September 2019, but for post-lockdown, this was a massive high).

And then things went for a toss. Maybe I started going to the gym too often. Maybe I started sleeping too little. Maybe the diet I went on (after the elevated levels of triglycerides in my blood got confirmed due to a blood test) ended up reducing my strength.

The following week, I attempted 5 kg above twice my body weight. Failure. A week later (I do “normal deadlifts” once a week, and “sumo deadlifts” once a week), I tried 2X my body weight again. Failure again. And yet again. And three continuous weeks of failure was a bit too much to take. And it didn’t help that in my usual program, the deadlift is the last exercise before I wind up. Irrespective of how much I had lifted before, ending the workout with a failure wasn’t a great thing to do.

A T-shirt I bought recently

And so this week, I decided to reverse course. I still continued with the deadlift as my last lift of the day, but gave myself enough time for it (by changing my workout schedule) that there was time to “end on a high”.

So on Tuesday, I tried 2X my body weight once again. Failure. However, my schedule meant that I had time left over. I removed 5 kg, and tried again. Failure again. I wasn’t going to be done. I took off another 10kg and attempted again, and managed to complete 3 reps. I was done for the day.

It happened once again with the sumo deadlift yesterday, and with the overhead press three days back – giving myself more time meant that I had the time to scale back upon the end of my unsuccessful lift, and finish the day on a high, even if it is a lower high than what I wanted to end on before I started my workout.

Oh, and I should mention that in the last week, I’ve managed to hit all time personal bests (including pre-lockdown) in front squat, sumo deadlift and bench press. I think the “ending on a high” philosophy, combined with giving myself more time, have something to do with it.

PS: Ending on a failure, apart from ruining the rest of that day, also makes you more apprehensive the next time you want to lift, and might lead you to lift less than your potential next time.

Proper Job

For the first time in over nine years, I’m taking up one of these.

If someone, sometime, were to do a compendium of stories of people whose careers changed because of covid-19, then I might feature in it. To be very honest, my present career change had been in the works for a while now. However, a bunch of things that covid-19 forced upon me this year made it that much easier to take the plunge.

As the more perceptive of you might have observed by now, I quit full time employment to embark on a “portfolio life” in late 2011. Apart from getting control over my own time, this change allowed me to do a lot of interesting things apart from my “core work”, which I took on such that most of the work I did was things I was good at or interested in.

So over the last nine years, apart from doing a lot of very interesting consulting work around data and analytics and AI and ML and “data science” and all that, I did a lot of interesting stuff otherwise as well. I wrote a book. I wrote a column for Mint. I taught at IIMB. I did public policy work for Takshashila.

I met lots of people and had loads of interesting discussions. There were times, yes, when I went into every meeting or catchup with a “sales mindset”, trying to sell something to someone. Thankfully these times were infrequent, and short. At all other times, I enjoyed all these random catchups, without any expectation  that anything come out of it.

My network expanded like crazy during these years. For the first time in my life, I came to be known for something apart from entrance exams. I spent time living in other places. I “followed my wife” when she first went to Barcelona, and then to London. It was all smooth.

In any case, you might be wondering how the pandemic resulted in my transition to employment being easier. The main way in which it has eased this transition is by ruining my carefully constructed lifestyle of the last nine years.

I’ve loved going around and meeting people. On an average, I would meet two to three people a week, for things completely unrelated to work. That has come down to nearly zero in the last nine months.

I had grown used to having massive control of my time and schedule. The prolonged school shutdown has completely sent it for a toss, with shared childcare responsibilities. “If I don’t have control over my time any ways, I might as well take up a job”, went one line of my reasoning.

I sometimes think I have a fear of open offices (I’ve felt this even during my consulting times when some clients have asked me to do “face time” in their offices). I hate having other people looking at my screen when I’m working. Maybe it has to do with some bad bosses / colleagues I’ve had over the years. The pandemic means I start working from the comfort of my home. And by the time I go to an office I will have hopefully settled down in this job.

And speaking of offices, the pandemic has normalised remote or hybrid working to an extent that I applied to jobs without having the constraint that they necessarily need to have an office in Central Bangalore. The company I’m joining – I’m not sure I would have thought of them in a “normal job search”. As it happens, while they’re not primarily based here, they do have a small office not far from Central Bangalore, and I’ll be going there once it reopens.

Then, thanks to the pandemic, I have successfully concluded my jobhunt without stepping out of home. All interviews, with a big range of companies, happened through video conferencing. In terms of my personal experience, Zoom >> Teams >> Meet.

But yeah, the biggest impact of the pandemic has  been on my lifestyle. So many things that I craved, and took as given, have been taken away from my life, that changing lifestyle seems to have become far easier than I had imagined. It’s like the tube strike model. I got shaken out of my earlier local optimum, and that has enabled me to convince myself that this new lifestyle will work.

In any case, I hope this works out. Just before joining, I feel positive, and excited in a good way.

Oh, and I guess I need to add here, and maybe at the beginning of every subsequent post.

All opinions expressed here on this blog are mine, and only mine. They don’t reflect the thoughts or opinions or positions of any organisation(s) that I might be associated with. Also, none of what I write on this blog is to be taken as investment advice.