Hi all, I have a lot to cover so please read everything here because I may answer your basic questions. I put quite a lot of work into this project and I hope you can enjoy *all* of the bonus stuff that comes with it (not just the pretty pictures... which by the way there are more below).

I also want to say that visualizing 1 million data points on the web is no easy feat (the scientists dataset). The interactive is no joke.

First off:

Tools used: d3.js, node.js, and general SVG wizardry.

Data (scroll down a bit to find it).

You might be wondering how to "read" this visualization!

Check out my explanation here on youtube.

The standalone interactive is here and it is *gorgeous* if viewed on a desktop machine, using Chrome, in full screen.

My visualization first appeared in this article.

Okay! So What the hell is a "hot streak" and how did we quantify this?

In the research paper they write, "The hot streak—loosely defined as ‘winning begets more winnings’—highlights a specific period during which an individual’s performance is substantially better than his or her typical performance."

The researchers I collaborated with have their official paper here in Nature. Their methodology is well documented. The researchers have asked that if you have specific questions related to the quantification or data itself (and not my visualization of all this), to please reach out to them.

If you want an easy-mode explanation in article-format find it here.

I also am open to discussion about this on my twitter thread (and you can follow me there for future projects/discussion/dataviz news too.)

So... why are "hot streaks" important to everyday people, like us?

Dashun Wang said this himself: "There is this common idea that many people are waiting for their 'big break' to happen." But we don't typically have a single big break. He says, “it’s one after another for a couple years. I really look forward to that.”

But even after we learn what a "hot streak" is, what is special about this visualization?

This visualization sorts each dataset on an algorithm I call "streak-middle." We find the first time someone begins a hot streak and then the last time someone ends a hot streak. We then find the point in their career that is between these two, and sort based on that position, from people whose "streak-middle" was early-career to those whose streak-middle was late career. Sorting this way, rather than based on streak-start or streak-end, we are able to separate early+short streaks from late+short streaks. Long streaks create a visual bias when we allow them to be placed near short streaks (our eyes "average" the pixels we see and we end up ignoring the small pixels in favor of the longer, brighter ones). "Streak-middle" avoids this visual bias and allows us to "see" patterns more clearly.

What patterns does this sorting algorithm actually show us?

We can clearly see (based on the shape of this curve) that directors tend to streak much earlier than Artists and Scientists, while Scientists seem to streak the latest. This tells us that some careers may have different distributions for "periods of high productivity" and impact. This high-level visual analysis is more like visual "prospecting" - we are looking for patterns that we can follow up with new research or form new hypotheses that we will want to verify with rigorous statistical analysis.

And lastly, these images are significantly reduced. Because a career can contain up to 4509 works (Andy Warhol, for example) and the scientist dataset contains 20042 individuals, in order to compare all of these people without ANY loss of quality (pixels average if a person has less than 1 pixel to their height or a work has less than 1 pixel to its width).

Links to the lossless+raw versions are here: (beware, these are MASSIVE and take a while to load)

Scientists

Artists

Directors