I have to admit that I thought it was a joke at first when I heard the news that Stephen Colbert is leaving The Colbert Report and is going to host the Late Show, currently hosted by David Letterman. The fact that he won’t be “in character” in the new show makes it more intriguing, even as it brings tremendous change to my entertainment universe. However, while it will take some getting used to, looking at Ranker data on the two shows reveals how the change really does make sense for CBS.
An octopus called Paul was one of the media stars of the 2010 soccer world cup. Paul correctly predicted 11 out of 13 matches, including the final in which Spain defeated the Netherlands. The 2014 world cup is in Brazil and, in an attempt to avoid eating mussels painted with national flags, we made predictions by analyzing data from Ranker’s “Who Will Win The 2014 World Cup?” list.
Ranker lists provide two sources of information, and we used both to make our predictions. One source is the original ranking, and the re-ranks provided by other users. For the world cup list, some users were very thorough, ranking all (or nearly all) of the 32 teams who qualified for the world cup. Other users were more selective, listing just the teams they thought would finish in the top places. An interesting question for data analysis is how much weight should be given to different rankings, depending on how complete they are.
The second source of information on Ranker are the thumbs-up and thumbs-down votes other users make in response to the master list of rankings. Often ranker lists have many more votes than they have re-ranks, and so the voting data potentially are very valuable. So, another interesting question for data analysis is how the voting information should be combined with the ranking information.
A special feature of making world cup predictions is that there is very useful information provided by the structure of the competition itself. The 32 teams have been drawn in 8 brackets with 4 teams each. Within a bracket, every team plays every other team once in initial group play. The top two teams from each bracket then advance to a series of elimination games. This system places strong constraints on possible outcomes, which a good prediction should follow. For example, Although Group B contains Spain, the Netherlands, and Chile — all strong teams, currently ranked in the top 16 in the world according to FIFA rankings — only two can progress from group play and finish in the top 16 for the world cup.
We developed a model that accounts for all three of these sources of information. It uses the ranking and re-ranking data, the voting data, and the constraints coming from the brackets, to make an overall prediction. The results of this analysis are shown in the figure. The left panel shows the thumbs-up (to the right, lighter) and thumbs-down (to the left, darker) votes for each team. The middle panel summarizes the ranking data, with the area of the circles corresponding to how often each team was ranked in each position. The right hand panel shows the inferred “strength” of each team on which we based our predicted order.
Our overall prediction has host-nation Brazil winning. But the distribution of strengths shown in the model inferences panel suggests it is possible Germany, Argentina, or Spain could win. There is little to separate the remainder of the top 16, with any country from the Netherlands to Algeria capable of doing well in the finals. The impact of the drawn brackets on our predictions is clear, with a raft of strong countries — the England, USA, Uruguay, and Chile — predicted to miss the finals, because they have been drawn in difficult brackets.
Happy spring! Here are the best lists you crazy kids have been upvoting this month:
Which Ex-Presidents Would You Want to Go on a Bender With? Did you know that some of the former presidents of the U.S. were huge drinkers and recreational drug users? Huge! Read about the scandalous habits of these former POTUSes (or would that be POTI?) and then vote for the ex-pres that you’d most want to get down with.
The Craziest Things Girls Will Do to Make You Like Them Quit playing games with my heart! Really. No, not really. We all can get a little crazy when it comes to romance. Whether they’re just flirting or looking to put a ring on it, some ladies will do some pretty insane things to get noticed.
Who Did These Eventually Famous Kids Grow Up To Be? When kids are young, they have the potential to be anything: astronauts, politicians, police officers… or they could go the way of Darth Vader and cross over to the Dark Side. From these vintage childhood photos, can you guess who turned in to whom?
20 Celebrities Who Have Had Hair Transplants We aren’t 100% certain how these balding men managed to halt the cruel hands of time… but we’re guessing that science had something to do with it, because most human beings don’t lose hair and then miraculously get it back.
““Most sites rely on simple heuristics like thumbs-up, ‘like’ or 1-5 stars,” stated Squerb founder and CEO Chris Biscoe. He added that while those tools offer a quick overview of opinion, they don’t offer much in the way of meaningful data.
Doesn’t Twitter already provide a pretty good ‘opinion network’? Alex thinks not. “The opinions out there in the world today represent a very thin slice. Most people are not motivated to express their opinion and the opinions out there for the most part are very chaotic and siloed. 98 percent of people never get heard,” he told Wired.co.uk.
I think more and more people who try to parse Facebook and Twitter data for deeper Netflix AltGenre-like opinions will realize the limitations of such data, and attempt to collect better opinion data. In the end, I think collecting better opinion data will inevitably involve the list format that Ranker specializes in. Lists have a few important advantages over the methods that Squerb and State are using, which include slick interfaces for tagging semantic objects with adjectives. The advantages of lists include:
Lists are popular and easily digestible. There is a reason why every article on Cracked is a list. Lists appeal to the masses, which is precisely the audience that Alex Asseily is trying to reach on State. To collect mass opinions, one needs a site that appeals to the masses, which is why Ranker has focused on growth as a consumer destination site, that currently collects millions of opinions.
Lists provide the context of other items. It’s one thing to think that Army of Darkness is a good movie. But how does it compare to other Zombie Movies? Without context, it’s hard to compare people’s opinions as we all have different thresholds for different adjectives. The presence of other items lets people consider alternatives they may not have considered in a vacuum and allows better interpretation of non-response.
Lists provide limits to what is being considered. For example, consider the question of whether Tom Cruise is a good actor? Is he one of the Best Actors of All-time? one of the Best Action Stars? One of the Best Actors Working Today? Ranker data shows that people’s answers usually depend on the context (e.g. Tom Cruise gets a lot of downvotes as one of the best actors of all-time, but is indeed considered one of the best action stars.)
In short, collecting opinions using lists produces both more data and better data. I welcome companies that seek to collect semantic opinion data as the opportunity is large and there are network effects such that each of our datasets is more valuable when other datasets with different biases are available for mashups. As others realize the importance of opinion graphs, we likely will see more companies in this space and my guess is that many of these companies will evolve along the path that Ranker has taken, toward the list format.
January is almost over. Good riddance! Have you given up on your New Year’s Resolutions yet? Trust us, you’ll feel much better once you just let go. For your enjoyment, here are the most popular lists that people have been upvoting on Ranker this month. Enjoy!
If you grew up in the ’90s, odds are that in between playing Pogs or watching reruns of “Saved by the Bell”, you were telling your mom to “talk to the hand” and that her cooking was Da Bomb…Not! Looking back, slang from the ’90s involved giving people a lot of attitude and tricking them.
Matthew McConaughey lost 47 pounds for his role in Dallas Buyer’s Club. Jared Leto lost nearly 40. Christian Bale packed on 43 pounds and a huge beer belly for American Hustle. Those aren’t even the most extreme cases! See the shocking before and after pictures of actors who completely changed their bodies for a movie role.
You instantly thought of at least one person when you saw the name of this list, didn’t you? Odds are that you have at least one special person in your life that is a major Facebook offender. Take heart, it happens to the best of us.
Embarrassing selfies happen when a sexy guy or gal is trying just a little too hard to look good for the camera. They are obviously sucking it in (Chris Pratt, Justin Bieber) or showing a bit too much skin that no one wants to see (Lindsay Lohan). We would feel bad…but these celebs did post these photos to their own social media.
Similar to getting drunk and singing at the top of your lungs, being naked is fun (!!) if not always appropriate. Whether an activity involves sharp, flying objects, extreme heat or compromising positions, there are some things that you should just never do naked. Ever.
That’s it! Stay in touch and we hope you’re having a great month!
Bruno Mars is #98 out of the 468 worst bands of all time, as voted on by over 12.5k voters. But it turns out that older people dislike him way more than younger people.
Super Bowl XLVIII is upon us! Woop, woop! Time to prepare for head-spinning sensory overload: brawny men knocking each others’ lights out, sassy cheerleaders, flashy TV ads… it’s almost too much to handle. And what about the crown jewel of the day’s entertainment: the halftime performer, Bruno Mars?
Will Peter Gene Hernandez, aka Bruno Mars, have the charisma to command a crowd of 100,000 screaming fans, not to mention the 110 million Americans who are expected to watch the game from home?
According to our data, it’s not looking good.At least not on the surface. Bruno Mars is currently ranked #98 on our list of The Worst Bands of All Time.
As of right now, 12,627 people have voted on this list, which means that there are a whole lot of haters out there. Compound that with the fact that people always complain about the Super Bowl halftime performer, and it’s looking like Bruno Mars may not get a lot of love for his performance.
1. Not to be taken lightly: Bruno Mars Has Got Some Serious Dance Moves.
Even though he was voted as one of the worst bands of all-time, people also acknowledge that he’s got some moves, which bodes well for him as a performer—especially in a situation where he is expected to wow the crowd. He was voted #29 out of 54 on this list of the best dancing singers.
2. Young people like Bruno Mars way more than this list would have you believe.
Statistically Speaking: If we isolate the votes coming from only young people—people ages 30 and under, that is, Bruno Mars would drop all the way down to #381 on the list of worst bands of all time.
Only 5 out of 12 young people who voted on this list upvoted Bruno Mars. That’s about 40% who agree that he should be considered one of the worst bands of all time.
Compare that to say, Justin Bieber who received 16 upvotes for every 19 people who voted on him, which is close to 85%.
*Or, in Plain English if You’re Starting to Get a Headache: Being #381 on a ‘worst bands’ list is way better than being #1. Young people voting him down on this list means that a lot of them do not think that he is the worst.
3. Old people hating on Bruno Mars is making him rise in the ‘worst of’ rankings.
Let’s look at how old people (over the age of 50) feel about our buddy Bruno. If we strip out all of the young and middle-aged voters, Bruno Mars would climb up to #82 on the list of worst bands of all time. Remember, getting closer to #1 on a ‘worst’ list is not a good thing.
The ratio for this demographic is much higher. 3 out of 5 old-timers who voted on this list upvoted Bruno Mars as the worst band of all time. That’s 60% for those of you who are keeping track.
While we can crunch the numbers for preferences according to age, it must be noted that we do not have a specific reason as to why people voted this way. Why do people over 50 dislike Bruno Mars? Is it his cavalier attitude, his voluminous hair, his sexy lyrics? His widely-publicized cocaine bust?
Either way–we’d bet that Bruno would rather be pleasing young’uns than winning the hearts of old folks. They are the ones, after all, who will be more likely to pay to see him perform live (they’ll also be way more likely to pirate his music, but that’s another conversation).
So, while you are eating your triple beany cheese nachos and downing Bud heavies this weekend and one of your friends starts to complain about how much he hates Bruno Mars…you can think to yourself (or gently point out) that maybe he’s just too old to understand him.
Enter Ranker’s Rankings API, which is currently now in beta, as we’d love the input of potential users’ of our API to help improve it. Our API returns aggregated opinions about specific movies, people, tv shows, places, etc. As an input, we can take a Wikipedia, Freebase, or Ranker ID. For example, below is a request for information about Tom Cruise, using his Ranker ID from his Ranker page (contact us if you want to use other IDs to access). http://api.ranker.com/rankings/?ids=2257588&type=RANKER
In the response to this request, you’ll get a set of Rankings for the requested object, including a set of list names (e.g. “listName”:”The Greatest 80s Teen Stars”), list urls (e.g. “listUrl”:”http://www.ranker.com/crowdranked-list/45-greatest-80_s-teen-stars” – note that the domain, www.ranker.com, is implied), item names (e.g. “itemName”:”Tom Cruise”) position of the item on this list (e.g. “position”:21), number of items on the list (e.g. “numItemsOnList”:70), the number of people who have voted on this list (e.g. “numVoters”:1149), the number of positive votes for this item (e.g. “numUpVotes”:245) vs. the number of negative votes (e.g. “numDownVotes”:169), and the Ranker list id (e.g. “listId”:584305). Note that results are cached so they may not match the current page exactly.
We can provide this information, not just about actors, but also about politicians, books, places, movies, tv shows, bands, athletes, colleges, brands, food, beer, and more. We will tend to have more information about entertainment related categories, for now, but as the domains of our lists grow, so too will the breadth of opinion related information available from our API.
Our API is free and no registration is required, though we would request that you provide links and attributions to the Ranker lists that provide this data. We likely will add some free registration at some point. There are currently no formal rate limits, though there are obviously practical limits so please contact us if you plan to use the API heavily as we may need to make changes to accommodate such usage. Please do let me know (ravi a t ranker) your experiences with our API and any suggestions for improvements as we are definitely looking to improve upon our beta offering.
Its hard to resist a cold treat on a hot summer afternoon, and frozen yogurt shops with their array of flavors and toppings have a little of something for everyone. Once you’re done agonizing over whether you want new york cheesecake or wild berry froyo (and trying a sample of each at least twice), its time for the topping bar. But which topping should you choose? We asked people to vote for their favorite frozen yogurt toppings on Ranker from a list of 32 toppings, and they responded with over 7,500 votes.
But let’s be honest, who can just choose just ONE topping for their froyo? Using Gephi and data from Ranker’s Opinion Graph, we ran a cluster analysis on people’s favorite froyo topping votes to determine which toppings people like to eat together (click on graph to enlarge). In the graph, larger circles mean more likes with other toppings. Most of the versatile toppings were either a syrup (like strawberry sauce) or chocolate candy (like Reese’s Pieces).
Using the modularity clustering tool in Gephi, we were then able to sort toppings into groups based on which toppings people were most likely to upvote together. We identified 4 kinds of froyo topping lovers:
1. Fruit and Nuts (Blue): This cluster is all about the fruits and nuts. These people love Strawberry sauce, sliced almonds, and Marschino cherries.
2. Chocolate (purple): This cluster encompases all things chocolate. These people love Magic Shell, Brownie bits, and chocolate syrup.
3. Sugar candy (green): This cluster is made up of pure sugar. These people love gummy worms, Rainbow sprinkles, and Skittles.
4. Salty and Cake (Red): This cluster encompasses cake bites and toppings that have a salty taste to them. These people like Snickers, Cheesecake bits, and Caramel Syrup.
Some additional thoughts:
Banana was a strange topping that was only linked with Snickers.
People who like nuts like both fruit and items from the salty category.
People who like blueberries only like other fruits.
People who like sugar items like gummy worms also like chocolate, but don’t particularly like fruit.
I recently got sent this Atlantic article on how Netflix reverse engineered Hollywood by a few contacts, and it happens to mirror my long term vision for how Ranker’s data fits into the future of search personalization. Netflix’s goal, to put “the right title in front of the right person at the right time,” is very similar to what Apple, Bing, Google, and Facebook are attempting to do with regards to personalized contextual search. Rather than you having to type in “best kitchen gadgets for mothers”, applications like Google Now and Cue (bought by Apple) hope to eventually be able to surface this information to you in real time, knowing not only when your mother’s birthday is, but also that you tend to buy kitchen gadgets for her, and knowing what the best rated kitchen gadgets that aren’t too complex and are in your price range happen to be. If the application was good enough, a lot of us would trust it to simply charge our credit card and send the right gift. But obviously we are a long way from that reality.
Netflix’s altgenre movie grammar (e.g. Irreverent Werewolf Movies Of The 1960s) gives us a glimpse of the level of specificity that would be required to get us there. Consider what you need to know to buy the right gift for your mom. You aren’t just looking for a kitchen gadget, but one with specific attributes. In altgenre terminology, you might be looking for “best simple, beautifully designed kitchen gadgets of 2014 that cost between $25 and $100” or “best kitchen gadgets for vegetarian technophobes”. Google knows that simple text matching is not going to get it the level of precision necessary to provide such answers, which is why semantic search, where the precise meaning of pages is mapped, has become a strategic priority.
However, the universe of altgenre equivalents in the non-movie world is nearly endless (e.g. Netflix has thousands of ways just to classify movies), which is where Ranker comes in, as one of the world’s largest sources for collecting explicit cross-domain altgenre-like opinions. Semantic data from sources like wikipedia, dbpedia, and freebase can help you put together factual altgenres like “of the 60s” or “that starred Brad Pitt“, but you need opinion ratings to put together subtler data like “guilty pleasures” or “toughest movie badasses“. Netflix’s success is proof of the power of this level of specificity in personalizing movies and consider how they produced this knowledge. Not through running machine learning algorithms on their endless stream of user behavior data, but rather by soliciting explicit ratings along these dimensions by paying “people to watch films and tag them with all kinds of metadata” using a “36-page training document that teaches them how to rate movies on their suggestive content, goriness, romance levels, and even narrative elements like plot conclusiveness.” Some people may think that with enough data, TripAdvisor should be able to tell you which cities are “cool”, but big data is not always better data. Most data scientists will tell you the importance of defining the features in any recommendation task (see this article for technical detail on this), rather than assuming that a large amount of data will reveal all of the right dimensions. The wrong level of abstraction can make prediction akin to trying to predict who will win the superbowl by knowing the precise position and status of every cell in every player on every NFL team. Netflix’s system allows them to make predictions at the right level of abstraction.
The future of search needs a Netflix grammar that goes beyond movies. It needs to able to understand not only which movies are dark versus gritty, but also which cities are better babymoon destinations versus party cities and which rock singers are great vocalists versus great frontmen. Ranker lists actually have a similar grammar to Netflix movies, except that we apply this grammar beyond the movie domain. In a subsequent post, I’ll go into more detail about this, but suffice it to say for now that I’m hopeful that our data will eventually play a similar role in the personalization of non-movie content that Netflix’s microtagging plays in film recommendations.
Oh, hey there! You may have noticed that we got a whole new look for 2014. We’ve built some cool new features and streamlined the look of our editing interface. Making lists on Ranker has never been easier.
Here’s what’s new:
New YES/NO switches make it a breeze to customize your list.
Searching and finding stuff is now 10x faster.
Reranking is easier than ever thanks to our improved list suggestion tool.
Now you can create a new list or add items to other peoples’ lists on the go. Hello, mobile! Imagine creating your grocery list on your phone, allowing your roommates to vote and then buying only the most popular items for the party you’re throwing that weekend. Wow, you sure are nice… are you looking for new roommates?
Take a look around, make a new list or two, and drop us a line if you have any feedback. We’d love to hear what you think. Oh, and happy new year!