Since Halloween tends to be a weeklong affair these days, we are betting that you have started seeing some repeat costumes out there. What will be the most overdone Halloween costume of 2013? Our money is on twerking Miley Cyrus, what do you think?
Good riddance summer! The weather is cooling, leaves are turning color (well, actually they don’t really do that here in LA) and we’re all planning which obscure pop culture reference we can turn into a Halloween costume this year.*
*Do you think I’d look good as a Sharknado?
To commemorate one of our favorite seasons here around the Ranker office, we’re loving this list of The Best Things About Fall. Autumn leaves are currently ranked #1 in the overall list, so we’re feeling a little left out–but Crisp Air, Comfy Clothes and Halloween are not too far behind.
We were curious about how people’s favorite things about fall differed depending on where they live, so we sorted the votes by geographic region and took a look at the data.
Turns out that us weather-obsessed West Coasters voted for 1) The Weather, 2) Crisp Air and 3) Comfy Clothes before 4) Autumn Leaves. In case you’re wondering, this chart was made by looking at the Top 10 items that people from the west coast voted on the most from this list. The size of the pie slices represent the number of upvotes each item got, as compared to the others.
Our friends in South voted slightly differently. Looks like they’re all about 1) Autumn Leaves, 2) Crisp Air, 3) Halloween and 4) The Weather. Check out the breakdown of their Top 10:
Well, it looks like we are definitely in agreement about the weather! But while us West Coasters are looking forward to snuggling in our comfy clothes, it looks like people from the South would rather be watching pro football or partaking in one of fall’s main holidays.
What about our readers from the east coast? (This may surprise you.)
They are the ones with the pretty autumn leaves and they barely even care! Our voters from the east coast most love 1) Comfy Clothes, 2) Halloween, 3) Pumpkin Pie and 4) The Weather! They’re also, apparently, really into their desserts. Anyone from the east coast have a recipe for apple crumble they’d like to share with us? We’re serious. Tell us in the comments or email firstname.lastname@example.org.
What about you? Be sure to visit the list: The Best Things About Fall and chime in while the fall season is still upon us.
Ranker has a new program where we offer a polling widget to partner sites who want the engagement of a poll in list format (as opposed to the standard radio button poll). Currently, sites that use our poll (e.g. TheNextWeb or CBC) are seeing 20-50% of visitors engaging in the poll and an increase in returning visitors who want to keep track of results. We also give partners prominent placement on Ranker.com (details of that here), but a benefit that is less obvious is the potential insights from one’s users that one can gain from the data behind a poll. To illustrate what is possible, I’m going to use data from one of our regular widget users, Phish.net, who posted this poll on Phish’s best summer concert jams.
One piece of data that Ranker can give partners is a regional breakdown of voters. Unsuprisingly, there were strong regional differences in voting behavior with voters from the northeast often choosing a jam from their New Jersey show, voters from the west coast often choosing a jam from their Hollywood Bowl show, voters from the south often choosing a jam from their Maryland show, voters from the midwest often choosing a jam from their Chicago show, and voters from the mountain region often choosing a jam from their show at The Gorge. However, the interesting thing to me was that the leading jam in every region was Tweezer – Lake Tahoe from July 31st. As someone who believes that better crowdsourced answers are produced by aggregating across bias and who has only been to 1 Phish concert, I’m definitely going to have to check out this jam. Perhaps the answer is obvious to more experienced Phish fans, but the results of the poll are certainly instructive to the more casual music fan who wants a taste of Phish.
Below are the results of the poll in graphical format. Notice how the shows cluster based on venue and geography except for Tweezer – Lake Tahoe which is directly in the center of the graph.
If you’re interested in running a widget poll on your site, the benefits are more clearly spelled out here and you can email us at “widget at ranker.com”. We’d love to provide similar region based insights for your polls as well.
– Ravi Iyer
Wickey, Wickey Widget! Welcome to The Ranker Widget 2.0. With the Ranker Widget, publishers can use Ranker’s tools to build unique, votable lists and publish them on their site. To date, over 1,600 third party websites have used Ranker’s widget to engage with their readers. Based on feedback from our users, we’ve just released an updated version that has some nifty new features.
1. Increased Options for Customization. There are a ton of new options for adjusting the look and feel of your widget. You can find customization options in the Size, Header, List and Footer tabs of the Customize panel.
Height: Decide how many rows you would like to display at once. If you choose to display more rows than the amount of items on your list, they will all display at once.
You now have the option to show the list image, username and list criteria to your poll.
2. Social Share Buttons: Allow users to share your poll on through email, Facebook, Twitter and Google+ right on your site.
3. New List Stats Button: This new tab shows the number of voters, votes and items on your list. It also lists any reranks of your list by your users right there on your widget.
4. Users can more easily add new items to your list. If you’d like your readers to be able to add new items to a list in addition to voting on the items that are already there, you’re in luck. The new widget allows users to type in new list items right there at the bottom of your list.
Today we are unveiling the new Ranker Partnership Program! Ranker is partnering with select publishers and individual experts who regularly create high-quality lists on our site. We will feature our Partner Lists in a new section of our homepage and throughout our category pages.
Partner Lists will also have added space for branding, a link back to their own site and an “Official Partner” verification checkmark next to their name.
While believing in the wisdom of crowds is kind of our thing here at Ranker, we will admit that sometimes some people do know more about some topics than others. Hey, I won’t ask you about your favorite Kombucha flavors if you don’t ask me about my favorite GPS fitness-tracking apps. Capiche?
But you know who I would ask about GPS apps? The good people over at The Next Web, who are experts on Internet technology. So when a noted authority on a certain topic (that has an active online community who can make our rankings more accurate) makes a list about a topic that they know a lot about, we thought it’d be nice to call that out.
For this reason, we are unveiling some nifty new features that distinguish expert Partner Lists from user-generated lists. Keep an eye out around the site for these new lists. You will recognize them by their “Official Partner” verified checkmark.
Trusted publishers and individual experts have been making lists on Ranker.com for awhile now and we added these new features to make their lists pop and to highlight their expertise.
Many of our partners use Ranker’s killer interface to create a poll on their topic of choice to host on their very own site. Embedding a list is super easy–just look for the “embed list” button in the footer of any new or pre-existing Ranker list.
If you’d like to be considered for our Partnership Program, send us a note. If you want more details, visit this short guide first. In general, our partners are publications or experts who have their own active online communities and some expertise in a certain subject. Most of our partners produce at least one piece of high quality content a month. And remember, since these lists will be featured on our homepage we tend to prefer partners who produce content that appeals to our broad, diverse online community.
By the way, you don’t have to be a partner to use our widget or enjoy Ranker.com. For most of our users, the new Partnership Program won’t change a thing!
Did you know that Ranker is one of the top 100 web destinations for mobile per Quantcast, ahead of household names like The Onion and People magazine? We are ranked #520 in the non-mobile world. Why do we do better with mobile users as opposed to people using a desktop computer? I’ve made this argument for awhile, but I’m hardly an authority, so I was heartened to see Google making a similar argument.
This embrace of mobile computing impacts search behavior in a number of important ways.
First, it makes the process of refining search queries much more tiresome. …While refining queries is never a great user experience, on a mobile device (and particularly on a mobile phone) it is especially onerous. This has provided the search engines with a compelling incentive to ensure that the right search results are delivered to users on the first go, freeing them of laborious refinements.
Second, the process of navigating to web pages (is) a royal pain on a hand-held mobile device.
This situation provides a compelling incentive for the search engines to circumvent additional web page visits altogether, and instead present answers to queries – especially straightforward informational queries – directly in the search results. While many in the search marketing field have suggested that the search engines have increasingly introduced direct answers in the search results to rob publishers of clicks, there’s more than a trivial case to be made that this is in the best interest of mobile users. Is it really a good thing to compel an iPhone user to browse to a web page – which may or may not be optimized for mobile – and wait for it to load in order to learn the height of the Eiffel Tower?
As a result, if you ask your mobile phone for the height of a famous building (Taipei 101 in the below case), it doesn’t direct you to a web page. Instead it answers the question itself.
That’s great for a question that has a single answer, but an increasing number of searches are not for objective facts with a single answer, but rather for subjective opinions where a ranked list is the best result. Consider the below chart showing the increase in searches for the term “best”. A similar pattern can be found for most any adjective.
So if consumers are increasingly doing searches on mobile phones, requiring a concise list of potential answers to questions with more than one answer, they naturally are going to end up at sites which have ranked lists…like Ranker. As such, a lot of Ranker’s future growth is likely to parallel the growth of mobile and the growth of searches for opinion based questions.
– Ravi Iyer
The world’s most expensive pizza, auctioned for $4,200 as a charity gift in 2007, was topped with edible gold, lobster marinated in cognac, champagne-soaked caviar, smoked salmon, and medallions of venison. While most of us prefer (or can only afford to prefer) more humble ingredients, our preferences are similarly diverse. Ranker has a Tastiest Pizza Toppingslist that asks people to express their preferences. At the time of writing there are 29 re-ranks of this list, and a total of 64 different ingredients mentioned. Edible gold, by the way, is not one of them.
Equipped with this data about popular pizza toppings, we were interested in finding out if pizzerias were actually selling the toppings that people say that they want. We also wanted to see if we could predict sales for individual ingredients by looking at one list that combined all of the responses about pizza topping preferences. This “Ultimate List” contains all of toppings that were listed in individual lists (known as re-ranks) and is ordered in a way that reflects how many times each ingredient was mentioned and where they ranked on individual lists. Many of the re-ranks only list a few ingredients, so it is fitting to combine lists and rely on the “wisdom of the crowd” to get a more complete ranking of many possible ingredients.
As a real-world test of how people’s preferences correspond to sales, we used Strombolini’s New York Pizzeria’s list of their top 10 selling ingredients. Pepperoni, cheese, sausage and mushrooms topped the list, followed by: pineapple, bacon, ham, shrimp, onion, and green peppers. All of these ingredients, save for shrimp, are included in the Ranker lists so we considered the 9 overlapping ingredients and measured how close each user’s preference list was to the pizzeria’s sales list.
To compare lists, we used a standard statistical measure known as Kendall’s tau, which counts how many times we would need to swap one item for another (known as a pair-wise swap) before two lists are identical. A Kendall’s tau of zero means the two lists are exactly the same. The larger the Kendall’s tau value becomes, the further one list is from another.
The figure shows, using little stick people, the Kendall’s tau distances between users’ lists, and the Strombolini’s sales list. The green dot corresponds to a perfect tau of zero, and the red dot is the highest possible tau (if two lists are the exact opposite of the other). The dotted line is provided as a reference to show how likely each Kendall’s tau value is by chance (that is, how often different Kendall’s tau values occur for random lists of the ingredients). It is clear that there are large differences in how close individual users’ lists came to the sales-based list. It is also clear that many users produced rankings that were quite different from the sales-based list.
Using this model, the combined list came out to be: cheese, pepperoni, bacon, mushrooms, sausage, onion, pineapple, ham, and green peppers. This is a Kendall’s tau of 7 pair-wise swaps from the Strombolini list, as shown in the figure by the blue dot representing the crowd. This means the combined list is closer to the sales list than all but one of the individual users.
Our “wisdom of the crowd” analysis, combining all the users’ lists, used the same approach we previously applied to predicting celebrity deaths using Ranker data. It is a “Top-N” variant of the psychological approach developed in our work modeling decision-making and individual differences for ranking lists, and has the nice property of naturally incorporating individual differences.
This analysis is a beginning example of a couple of interesting ideas. One is that it is possible to extract relatively complete information from a set of incomplete opinions provided by many people. The other is that this combined knowledge can be compared to, and possibly be predictive of, real-world ground truths, like whether more pizzas have bacon or green peppers on them. It may never begin to explain, however, why someone would waste champagne-soaked caviar on pizza, as a topping.
It’s hard reading studies about people my age when research scientists haven’t agreed upon a term for us yet. In one study I’m a member of “Gen Y” (lazy), in another I’m from the “iGeneration” (Orwellian), or worse still, a “Millennial” (…no). You beleaguered and cynical 30-somethings had things easy with the “Generation X” thing. Let the record reflect that no one from my generation is even remotely okay with any of these terms. Furthermore, we all collectively check out whenever we hear the term “aughties”.
I’m whining about the nomenclature only because there’s a clear need for distinction between my generation and those who have/will come before/after us. This isn’t just from a cultural standpoint (although calling us “Generation Spongebob” might be the most ubiquitous touchstone you could get), but from a technical one. If this Kaiser Family Foundation study is to be believed (via NYT), 8-18 year olds today are the first to spend the majority of their waking hours interacting with the internet.
Yet despite this monumental change, there are still many childhood staples that have not been forsaken by an increasingly digital generation. One of the most compelling examples of this anomaly lies in board games. In a day and age where Apple is selling two billion apps a month (Apple), companies peddling games for our increasingly elusive away-from-keyboard time are still holding their own. For example, Hasbro’s board-and-card game based revenue grew to $1.19b dollars over the course of the last fiscal year (a 2% gain from last year).
What drove this growth? Hasbro’s earnings reports primarily accredits this growth to three products: Magic: The Gathering, Twister, and Battleship. All of these products have been mainstays of their line-up for quite some time (prepare to feel old: if Magic: The Gathering was a child, it could buy booze this year), so what’s compelling people to keep buying? Fortunately, Ranker has some pretty in-depth data on all of these products, based on people who vote on it’s best board games list, which receives thousands of opinions each month, as well as voting on other Ranker lists.
Twister’s continuous sales were the easiest to explain: users who expressed interest in the game were most likely to be a fan of other board games (Candy Land, Chutes and Ladders, Monopoly and so forth). Twister also correlated with many other programs/products with fairly universal appeal (Friends, Gremlins). This would seem to indicate that the chief reason for Twister’s continued high sales lies in its simplicity and ubiquity. The game is a cultural touchstone for that reason: more than any other game on the list, it’s the one hardest to picture a childhood without.
Battleship’s success lies in the same roots: our data shows great overlap between fans of the game and fans of Mouse Trap, Monopoly, etc. But Battleship has attracted fans of a different stripe, interest in films such as Doom, Independence Day, and Terminator were highly correlated with the game. In all likelihood, this is due to the recent silver-screen adaptation of the game. Although the movie only faired modestly within the United States, the film clearly did propel the game back into the public consciousness, which translated nicely into sales.
Finally, Magic: The Gathering’s success came from support of another nature. Interest in Magic correlated primarily with other role-play and strategy games (Settlers of Catan, Dominion, Heroscape). Simply put, most fans of Magic are likely to enjoy other traditionally “nerdy” games. The large correlation overlap between Magic and other role-playing games is a testament to how voraciously this group consumes these products.
The crowd-sourced information we have here neatly divides the consumers of each game into three pools. With this sort of individualized knowledge, targeting and marketing to each archetype of consumer is a far easier task.
– Eamon Levesque
At the end of each year, there are usually media stories that compile lists of famous people who have passed away. These lists usually cause us to pause and reflect. Lists like Celebrity Death Pool 2013 on Ranker, however, give us an opportunity to make (macabre) predictions about recent celebrity deaths.
We were interested in whether “wisdom of the crowd” methods could be applied to aggregate the individual predictions. The wisdom of the crowd is about making more complete and more accurate predictions, and both completeness and accuracy seem relevant here. Being complete means building an aggregate list that identifies as many celebrity deaths as possible. Being accurate means, in a list where only some predictions are borne out, placing those who do die near the top of the list.
Our Ranker data involved the lists provided by a total of 27 users up until early in 2013. (Some them were done after at least one celebrity, Patti Page, had passed away, but we thought they still provided useful predictions about other celebrities). Some users predicted as many as 25 deaths, while some made a single prediction. The median number of predictions was eight, and, in total, 99 celebrities were included in at least one list. At the time of posting, six of the 99 celebrities have passed away.
One way to measure how well a user made predictions is to work down their list, keeping track of every time they correctly predicted a recent celebrity death. This approach to scoring is shown for all 27 users in the graph below. Each blue circle corresponds to a user, and represents their final tally. The location of the circle on the x-axis corresponds to the total length of their list, and the location on the y-axis corresponds to the total number of correct predictions they made. The blue lines leading up to the circles track the progress for each user, working down their ranked lists. We can see that the best any user did was predict two out or the current six deaths, and most users currently have none or one correct predictions in their list.
To try and find some wisdom in this crowd of users, we applied an approach to combining rank data developed as part of our general research into human decision-making, memory, and individual differences. The approach is based on classic models in psychology that go all the way back to the work of Thurstone in 1931, but has some modern tweaks. Our approach allows for individual differences, and naturally identifies expert users, upweighting their opinions in determining the aggregated crowd list. A paper describing the nuts and bolts of our modeling approach can be found here (but note we used a modified version for this problem, because users only provide their “Top-N” responses, and they get to choose N, which is the length of their list).
The net result of our modeling is a list of all 99 celebrities, in an order that combines the rankings provided by everybody. The top 5 in our aggregated list, for the morbidly curious, are Hugo Chavez (already a correct prediction), Fidel Castro, Zsa Zsa Gabor, Abe Vigoda, and Kirk Douglas. We can assess the wisdom of the crowd in the same way we did individuals, by working down the list, and keeping track of correct predictions. This assessment is shown by the green line in the graph below. Because the list includes all 99 celebrities, it will always find the six who have already recently passed away, and the names of those celebrities are shown at the top, in the place they occur in the aggregated list.
The interesting part assessing the wisdom of the crowd is how early in the list it makes correct predictions about recent celebrity deaths. Thus, the more quickly the green line goes up as it moves to the right, the better the predictions of the crowd. From the graph, we can see that the crowd is currently performing quite well, and is certainly about the “chance” line, represented by the dotted diagonal. (This line corresponds to the average performance of a randomly-ordered list).
We can also see that the crowd is performing as well as, or better than, all but one of the individual users. Their blue circles are shown again along with crowd performance. Circles that lie above and to the left of the green line indicate users outperforming the crowd, and there is only one of these. Interestingly, predicting celebrity deaths by using age, and starting with the oldest celebrity first, does not perform well. This seemingly sensible heuristic is assessed by the red line, but is outperformed by the crowd and many users.
Of course, it is only May, so the predictions made by users on Ranker have time to be borne out. Our wisdom of the crowd predictions are locked in, and we will continue to update the assessment graphs.
– Michael Lee
One of the strengths of Ranker‘s data is that we collect such a wide variety of opinions from users that we can put opinions about a wide variety of subjects into a graph format. Graphs are useful as they let you go beyond the individual relationships between items and see overall patterns. In anticipation of Cinco de Mayo, I produced the below opinion graph of beers, based on votes on lists such as our Best World Beers list. Connections in this graph represent significant correlations between sentiment towards connected beers, which vary in terms of strength. A layout algorithm (force atlas in Gephi) placed beers that were more related closer to each other and beers that had fewer/weaker connections further apart. I also ran a classification algorithm that clustered beers according to preference and colored the graph according to these clusters. Click on the below graph to expand it.
One of the fun things about graphs is that different people will see different patterns. Among the things I learned from this exercise are:
- •The opposite of light beer, from a taste perspective, isn’t dark beer. Rather, light beers like Miller Lite are most opposite craft beers like Stone IPA and Chimay.
- •Coors light is the light beer that is closest to the mainstream cluster. Stella Artois, Corona, and Heineken are also reasonable bridge beers between the main cluster and the light beer world.
- •The classification algorithm revealed six main taste/opinion clusters, which I would label: Really Light Beers (e.g. Natural Light), Lighter Mainstream Beers (e.g. Blue Moon), Stout Beers (e.g. Guinness), Craft Beers (e.g. Stone IPA), Darker European Beers (e.g. Chimay), and Lighter European Beers (e.g. Leffe Blonde). The interesting parts about the classifications are the cases on the edge, such as how Newcastle Brown Ale appeals to both Guinness and Heineken drinkers.
- •Seeing beers graphed according to opinions made me wonder if companies consciously position their beers accordingly. Is Pyramid Hefeweizen successfully appealing to the Sam Adams drinker who wants a bit of European flavor? Is Anchor Steam supposed to appeal to both the Guinness drinker and the craft beer drinker? I’m not sure if I know enough about the marketing of beers to know the answer to this, but I’d be curious if beer companies place their beers in the same space that this opinion graph does.
These are just a few observations based on my own limited beer drinking experience. I tend to be more of a whiskey drinker, and hope more of you will vote on our Best Tasting Whiskey list, so I can graph that next. I’d love to hear comments about other observations that you might make from this graph.
– Ravi Iyer