I did some analysis of epidemiology curves for coronavirus. This particular curve plots out the cumulative proportion of cases over time for a number of countries:
Each point on the line is a proportion of the total — which is why they all touch at the far right; all countries are at their daily cumulative maximum (1.0) as of March 16th.
The graphs differ across counties in two important ways. First, they are shifted in time. This shows something we already know–that China and south-east Asia got hit with the infection first, and Western Europe and North America more recently.
More interesting is the shape of the curves. Notice that the rate of increase has been flattening out for China for some time. South Korea has is seeing a more recent flattening. Countries in Europe and North America are seeing a large increase now.
The most noteworthy line on this graph is Japan. Japan is seeing a slow and steady growth in cases, something that is typically not what infectious disease models predict. Usually growth, and often decline, tends to be nonlinear–a fast rise followed by a fast drop (and then a possible return with a lower amplitude). It’s hard to know what to make of this.
Is it because Japan is under-testing or under-reporting? Or is it that public health interventions were implemented very quickly and effectively in Japan? Only time will tell… Here’s the code for you to see for yourself.
I have created a daily updating web page with infection rates for the countries in the top 25 of total infections diagnosed, as well as Canadian provinces. Data are from Du and Gardner (see their Lancet publication here) but are ‘scraped’ automatically from their data on GitHub so that I don’t have to update it manually every day. However, this data source is not entirely up to date, so I am adding newer data sources over time, as well as doing some validation work, so bookmark it, sucka!
A halt to the NHL season
Professional sports leagues are shutting down. If the NHL cancels the season altogether, this means that fans of the Edmonton Oilers can confidently say that their team will not not make the playoffs this year! Go Oilers!
In the traditional sense of the word, a loser is simply someone that has not won. This is a descriptive and sometimes useful definition, but of course ‘loser’ is often used to imply a pathology of failure–someone who never wins, who can’t succeed at anything, and that we as a society don’t value.
I am not satisfied with this definition, so I offer an alternative:
Loser: a person for whom success is uncorrelated or negatively correlated with demonstrable merit.
Using this definition, a person can be a loser in two ways:
A person with great potential that is wasted.
A person with great success due to something other than demonstrated merit.
Both 1 and 2 require a little explanation. First, consider the concept of wasted potential. To waste potential means failing to live up to what one could have done had they tried. Trying and failing doesn’t make someone a loser. A loser is anyone who doesn’t make use of the talents they have due to things like fear of failure, sloth, or sense of entitlement.
Second, consider the concept of merit. Merit is ability, skill or talent (inherited or not) that is relevant to success. A great hockey player that records a best selling album of mediocre country music is a loser in the country music domain. His success in hockey doesn’t demonstrate his merit in other areas. So the hockey player is a loser in one domain and not a loser in the other. For merit to be meaningful, it has to be linked to success.
Why should you accept my definition?
Calling someone a ‘loser’ in the traditional sense is to ignore important many uncontrollable factors that contribute to failure and lack of success–like bad-luck. People should not be held responsible for bad luck. Bad luck doesn’t make a person a loser.
Undeserved success is economically inefficient. Merit-less success rewards people based on attributes that are not relevant to the creation of value. Labelling people with unmerited success as ‘losers’ is a way of knocking them off their roost (at least verbally), and perhaps making way for those more deserving of praise.
Fear of being thought of as a ‘loser’ in the traditional sense may discourage risk-taking. The world benefits from some risk-taking; it doesn’t make sense to condemn people for trying and failing. Trying and failing is taking one for the team. By my definition, trying and failing doesn’t make someone a loser.
If you accept my definition, you won’t know whether a person is a loser or not unless you get to know them, and compare their success against their merit. Nobody is a loser by default.
Here are some examples to help illustrate the meaning and usefulness of this term.
At least two of Donald Trump’s children are clearly losers (Jr. and Ivanka). As far as I am aware, they have not been tested in this world, yet they seem to wield great power and reap the rewards of financial success. They did not ‘make it’ in business or politics or anything in a way that demonstrates a competence commensurate with their positions. Losers.
Many elected officials are losers. People do not win elections by demonstrating an ability to craft useful legislation or make good decisions. They win through a mix of spectacle, popularity contests and back stabbing. The higher you go in political rank, the more likely you are a loser. So yah, Justin Trudeau is probably a a loser. On the one hand, he did earn his seat in Montreal through by hustling door-to-door, shaking hands, kissing babies and so on. On the other hand, as Prime Minister of Canada he is clearly the beneficiary of his father’s name and wealth. Sorry J.T., you’re a loser.
Dictators are always losers; their power is not commensurate with demonstrated abilities in governing. They are usually good at employing violence, bullying and other skills best suited for other kinds of occupations.
Big lottery winners are all losers. Nobody that wins the lottery wins because of merit, just dumb luck. Lottery losers are losers too (in the traditional sense), so it would seem that playing the lottery is for losers.
Athletes are pretty well never losers; sports are great at demonstrating merit. People don’t win at sports by luck alone; any sport that you can win based on luck alone isn’t a sport. People who try and lose at sports aren’t losers either. To paraphrase 90% of the high school gym teachers who ever lived: the only losers are those who don’t try!
On the other hand, super famous actors and musicians are all kind of losers. We may love them, but demonstrating merit in the acting/music industries is pretty tricky. I firmly believe that any rock ‘n’ roll orchestra that is filthy, insanely successful in money and fame should be viewed with suspicion. Sure, Nickelback has sold millions of records, but they are clearly losers.
Mark Zuckerberg, Bill Gates, Steve Jobs, Jeff Bezos: mostly losers. They all have demonstrated some merit, but it is pretty weakly correlated with their success. Check out a recent computer simulation that makes this point beautifully. If they were multi-millionaires, they may not have been losers. But as billionaires, losers they be.
Doctors make a lot of money, and are highly regarded culturally, but most aren’t losers. Doctors are under close professional scrutiny, and it takes a lot of work to enter the profession–most of them have to work their butts off to get into and out of medical school, especially these days. People who are bad at these jobs don’t keep them for very long. Doctors are not losers. Of course, most people in health care aren’t losers. They have tough jobs and neither their pay or cultural success is excessive. The same can be said about school most teachers.
However, many University professors are kind of losers. Universities are not good at getting rid of under-performers, and success (in terms of salary and reputation) usually goes up over time irrespective of merit. There is evidence that the research system as a whole is not always great at allocating success (see the same simulation above). I’ll fully acknowledge that I am probably at least 37.5% loser (+/- 20% 19 times out of 20).
Conclusions: we’re mostly not losers
Most regular folks with jobs, and/or families and/or that contribute to their communities are not losers. In fact, 80-90% of the human population are probably not losers. Your neighbour who keeps you up at night with his banjo music and pool parties? Sorry, he’s probably not a loser, just annoying.
My younger brother is in a hard working rock ‘n’ roll orchestra who slave away for the love of it, so obviously they are not losers. Here is the video from their most recent album, Loser Delusions:
I wrote a bit of R code to explore the ‘parity question’ in the NHL; specifically, if all teams were more or less equal, what would we expect to see in terms of regular season point totals? You can find the code for generating a hockey schedule here and the simulation code here.
The scheduler is just a dirty optimizing algorithm that I came up with that is probably inferior to what the NHL uses, but it seems to work. You can set the schedule parameters and it seems to work fairly well provided the inputs result in a feasible solution; for example, don’t lower the max games too much or it won’t be able to solve. I had to create the scheduler to generate a realistic play schedule that the hockey simulator could use.
For a given schedule, the hockey simulator plays a season of hockey and sums up point totals. For the purpose of this experiment, I assume that all teams have an equal chance of winning. If a team loses, they have a x% chance at getting a loser point (by default, this is set at 11%).
I then run the simulation many times. One way to investigate the results is to plot out the distribution of top regular season team (the team with the most points). Here is a histogram of the maximum point totals over 1000 simulations:
How does this compare to real data? Well, it depends on the season, but generally speaking, the best teams in the real world do better than the best teams in the simulation. This suggests that the NHL has not yet reached the point of real parity. The far left values on the figure below tell us the mean, range (95%) and maximum (of the maximum points) over 1000 simulations. Tampa Bay outperformed the parity simulation even after 1000 simulations–suggesting that their regular season performance in 2018/2019 was far from a matter of luck.
Nevertheless, imagine if 2018/2019 Tampa Bay was an anomaly, and the trend of real NHL point maximums continues downward–where the best regular season team gets point totals that approach 107 or so in a few years. At that point, it might suggest that the NHL has reached some level of true parity, where the vagaries of year-to-year luck (like injuries) will play an increasingly large role in determining the success of a team, rather than true superiority of skill and tactics on the ice.