The hottest toy of the 1985 Christmas season was an Extraordinary Video Robot that could play games through your television.
Except it wasn’t.
The “robot” in that 1985 commercial was R.O.B. (Robot Operating Buddy), which shipped with early copies of the Nintendo Entertainment System (NES) to test markets in New York and Los Angeles. The NES proved to be a big hit in those test markets, and so the console rolled out throughout the U.S. over the next year – but without R.O.B. Why?
The best way to explain R.O.B. is to show him in action. It’s a collector’s item today, available on eBay for anywhere from $20 to $500 depending on its condition. To use R.O.B., power it on, plug in the second NES controller into the base unit, and load onr of the two games that works with it, Gyromite or Stack-Up.
The youtube channel videogamecollector shows what it looks like running, and it’s very cool:
…but beneath all the spinning and blinking and robot noises, here’s what R.O.B. was really doing: pressing the select button on the second controller. Which means that you could play the same two games much more easily without R.O.B., simply by pressing the select button on the second controller.
Except They Weren’t: An occasional series about people who are Not What They Seem Part 1: Joe Magarac
Why would Nintendo design something so complicated and so utterly pointless? It wasn’t a robot, it was a trojan horse. R.O.B.’s entire purpose was to hide the fact that the NES was a video game system.
And why would Nintendo of America want to hide the fact that their beautifully designed video game system was a video game system? Remember that this was fall 1985, before the wild success of the NES. But for a full answer, we need to look back even farther into the history of video games.
The first commercially successful video game was Pong, released in 1972 and still bizarrely addictive today. Following Pong, many other of these new “video games” were released to the new video arcades that were springing up all over the United States. As arcades became more and more popular, video game makers began to wonder how to bring the experience into their customers’ homes. After a few false starts, the first massive hit was the Atari 2600, released in 1977.
Manufacturers created hundreds and hundreds of games for the Atari system – and that was the problem. With so many titles clogging store shelves, and with a medium so new that there were not yet any reviews, customers had no way to tell good games from bad games.
The final indignity was the officially licensed E.T. video game, released in time for Christmas 1982. Atari spent millions to acquire the rights to what was at the time the highest-grossing movie of all time, and millions more on marketing, but left the game to a single developer to rush out in six weeks. The results were famously terrible:
Millions of American children woke up on Christmas morning to a shiny new copy of Atari’s E.T., only to have their joy turn to despair within minutes of starting up the boring, bug-filled mess above. Word quickly spread, sales dried up, and retailers were stuck with millions of unsold copies sitting on shelves. They sent the cartridges back to Atari, who had no choice but to take the loss and bury them in a New Mexico landfill.
It wasn’t just E.T.; every other game and even every other video game system completely dried up. Atari nearly went bankrupt, staying in business only by reorganizing and selling off its software division. Industry analysts declared that the fad was over; there was no more consumer demand for video games. That state of affairs continued for years. And that was the situation that Nintendo of America found itself in in fall 1985.
Nintendo had good reason to believe that video games would take off again – there was no video game industry crash in Japan, and their Famicom system had sold steadily there since 1983. And they thought they knew the cause of the crash and what to do about it. They would have strict quality control over all the games on their system, made possible by a licensing agreement and enforced by a lockout chip preventing unlicensed games from playing. They would create an in-house magazine to offer reviews, previews of future games, and strategy information to players. But even with all these efforts in place, they still had a major hurdle to overcome to get their new system to consumers.
This was 1985, years before online shopping was even a dream. To even get the chance to sell to customers, Nintendo knew it first had to sell to retailers who were understandably skeptical of video games after the crash of 1983.
So how do you sell a video game system to people who don’t like video games? Tell them it’s totally not a video game system! It’s an ENTERTAINMENT SYSTEM! And R.O.B. was a key part of that strategy. It looked like a toy, so retailers concluded that it must be a toy – and they marketed it like any other toy. Nintendo proved to be absolutely right about the NES. And as you can see below, thirty-five years later: the rest is history.
Unfortunately, COVID-19 has affected so many people in so many places that my spreadsheet has gotten too big to work with. Going from the raw data provided by the Corona Data Scraper citizen science project to my graphs now literally takes about 4 hours of constant attention to Excel. I’ll have to rethink my approach.
Most likely I’ll do the preprocessing in Python, a simple but powerful programming language used by scientists all over the Universe. I’ll try to provide step-by-step instructions on how to how to run Python, and to document my programs extensively, so you can try them yourself. Even though Python can make graphs, I plan to continue making the graphs in Excel, because it’s a simple tool that so many of you already know how to use.
It might be a few days until my next COVID-19 update. In the meantime, I’ll keep posting other things on my usual Monday-Wednesday-Friday schedule, including something coming later today.
Graphs day 181, pandemic day 188, day 258 since the first cases were diagnosed.
I took some time off to work on some other projects, including a very cool study of federal banking data with my JHU colleague and friend Mac McComas (you’ve heard from him here before). What’s the biggest factor in who gets loans in Baltimore City and who does not? Spoiler alert: it’s race.
But the virus never takes a day off, and since I made my last graph, another nearly 7 million people have been diagnosed with COVID-19, and more than 120,000 have died. We are rapidly closing on one million deaths. Not bad for a virus that we are pretty sure had not infected even a single human one year ago.
Total cases of COVID-19 diagnosed worldwide: 28,524,156
Total deaths: 902,224
It took me literally most of the day to get the data cleaned and into my template, so I don’t have any new graphs to show you today. But here is an updated version of one I posted recently, showing COVID-19 case rates in various metro areas in Florida. Usual graph styles apply.
There is definitely cause for cautious optimism from this graph, but with two caveats. First, the case rate is still ahead of where it was during the first peak of the Florida epidemic in late March. Second, schools are a savage vector for virus transmission, and schools in many parts of Florida have been open for a little more than two weeks. So if there is going to be a third wave of infection that results, we should start seeing it riiiiiiiiiight about now. Let’s hope we don’t.
I obviously haven’t been living up to my initial plan to post an update every day of the pandemic, but why not start now? Another update tomorrow.
Want to give these graphs a try? Please do! Here, for the first time, is version 8 of my template. Beware, it’s big, it will take several minutes per calculation. Be sure you have Excel set to manual calculations.
Pandemic updates tomorrow. And probably not literally every day until the pandemic ends? But the virus is tenacious, and so am I.
Graphs day 154, pandemic day 161, day 231 since the first cases were diagnosed. Today, let’s go back to the data from the JHU Coronavirus Resource Center and look at national-level graphs (since my version 7 spreadsheet has a template already set up for that).
Total cases of COVID-19 diagnosed worldwide: 21,879,368
Usual graphs and labels for all five categories today. All five graphs are in the usual styles. The main graph is on the regular scale, from zero to 200 new cases diagnosed per day per million people – with the exception of the “getting worse” graph, which runs from zero to 300. Where there are smaller inset graphs, they are on the “Qatar scale,” which runs from zero to 700 cases per million people. Each country gets a unique color in each graph (although the colors can repeat across graphs). Line labels show the name of the region, and also the mortality rate (cumulative deaths per million people) in the region – cumulative because the dead stay dead. The thickness of the lines and the size of the labels depend on the cumulative case fatality rate – the number of people who died divided by the number diagnosed.
Regions where COVID-19 was quickly contained
After 101 days with no local transmission of COVID-19, New Zealand experienced its first local case on day 102. There’s a very slight uptick in cases there and in South Korea, but keep it in perspective: the total number of cases in both places is still very low.
Regions where COVID-19 is currently under control(-ish)
France is still ticking depressingly upward. If cases in France reach 52 per million people per day – half of their peak in mid-April – I will sadly move them into the Second Wave category.
Regions moving in the right direction(-ish)
Gabon is on this graph (purple line). Reported cases have gone up and down somewhat, but never above 75 cases per million people per day. Qatar had a bump within the past two weeks, but it appears to have passed for the moment. And unfortunately Sweden seems to be on an uptick again, but it’s still too early to tell if it’s a real increase or just random variation. Antlers crossed that Sweden’s herd immunity strategy is working, but it’s really not looking good at the moment.
Regions experiencing a second wave of COVID-19 cases
The second wave is maaaybe over in Australia, but keeps getting worse in Spain.
Regions where the first wave of COVID-19 continues to get worse
I showed some different regions today. I’m showing Georgia on the main graph, but I forgot to swap them in for Florida on the Qatar scale inset, so you get a bit of both today. This is where Indonesia goes also. Cases are clearly increasing in Indonesia, but they are increasing very slowly and are still at a quite low level.
Coming up tomorrow: a break from the COVID-19 graphs and a return of the series about people and things who are Not What They Seem: enjoy a new episode of Except They Weren’t.
Want to give these graphs a try? Please do! Here is version 7.3 of my spreadsheet, which is just like version 7.2 but is now updated with data up to yesterday.
Pandemic updates tomorrow, and every day until the pandemic ends or I do.
Graphs day 153, pandemic day 160, day 230 since the first cases were diagnosed.
Total cases of COVID-19 diagnosed worldwide: 21,389,903
Total deaths: 772,373
Today’s Excel graph update comes from my weird home state of Florida. We’ve been following cases there for quite a while, but with the county health department data collected by Corona Data Scraper, we can look at patterns around the state in much greater detail. We’ll look at five urban areas: Miami, Tampa, Orlando, Pensacola – and Clewiston, a town of 7,000 people on the south shore of Lake Okeechobee. (Technically the data we’re looking at for the first four is for their metropolitan statistical areas, and Clewiston’s is for its micropolitan statistical area.)
Graphs are in the usual format, on the “Miami scale,” which runs from zero to 1,000 cases reported per million people per day. Urban areas are color-coded and labeled. Line thicknesses and label sizes are proportional to case fatality rates.
I also looked at the Jacksonville metropolitan area, and it follows almost exactly the same trend as Orlando.
And speaking of Florida, the Wang Mansion has sold! It’s now listed as Pending on Zillow. Which one of you lucky readers purchased it? (If you don’t know what I’m talking about, see my post from July 2020, Wangception.)
Want to give it a try? Please do! Here is my new spreadsheet (version 8), although God help you until I document it better. The good news is that you should only need to change the worksheet called Graphs, and only refer to the sheet called Daily to get the codes for each country. Also be warned, it’s so big that it calculates sloooooooooooooooowly. You will probably want to go to Settings -> Calculation and change Calculation Options to Manual. Then the spreadsheet will only calculate its updated numbers when you tell it to, by pressing F5 on Windows or Shift-Enter on Mac.
Pandemic updates tomorrow, and every day until the pandemic ends or I do.