by   Ranker
Staff
in prediction

Ranker Predicts Jacksonville Jaguars to have NFL’s worst record in 2014

Today is the start of the NFL season and building on our success in using crowdsourcing to predict the World Cup, we’d like to release our predictions for the upcoming NFL season.  Using data from our “Which NFL Team Will Have the Worst Record in 2014?” list, which was largely voted on by the community at WalterFootball.com (using a Ranker widget), we would predict the following order of finish, from worst to first.  Unfortunately for fans in Florida, the wisdom of crowds predicts that the Jacksonville Jaguars will finish last this year.

As a point of comparison, I’ll also include predictions from WalterFootball’s Walter Cherepinsky, ESPN (based on power rankings), and Betfair (basted on betting odds for winning the Super Bowl).  Since we are attempting to predict the teams with the worst records in 2014, the worst teams are listed first and the best teams are listed last.

Ranker NFL Worst Team Predictions 2014

The value proposition of Ranker is that we believe that the combined judgments of many individuals is smarter than even the most informed individual experts.  Our predictions were based on over 27,000 votes from 2,900+ fans, taking into account both positive and negative sentiment by combining the raw magnitude of positive votes with the ratio of positive to negative votes.  As research on the wisdom of crowds predicts, the crowd sourced judgments from Ranker should outperform those from the experts.  Of course, there is a lot of luck and randomness that occurs throughout the NFL season, so our results, good or bad, should be taken with a grain of salt.  What is perhaps more interesting is the proposition that crowdsourced data can approximate the results of a betting market like BetFair, for the real value of Ranker data is in predicting things where there is no betting market (e.g. what content should Netflix pursue?).

Stay tuned til the end of the season for results.

– Ravi Iyer

by   Ranker
Staff
in Data

Why Ranker Data is Better than Facebook’s and Twitter’s

 By Clark Benson (CEO, Ranker)

It’s unlikely you’ll be pouring freezing water over your head for it, but the marketing world is experiencing its own Peak Oil crisis.

Yes, you read correctly: we don’t have enough data. At least not enough good data.

Pull up to any marketing RSS and you’ll read the same story: the world is awash in golden insights, companies are able to “know” their customers in real time and predict more and better about their own market … blablabla.

Here’s what you won’t read: it’s really, really hard. And it’s getting harder, for the simple reason that we are all positively drenched in … overwhelmingly bad data. Noisy, incomplete, out of context, approximate, downright misleading data. “Big Data” = (Mostly) Bad Data as it tends to draw explicit behavior from implicit and noisy sources like social media or web visits.

Traditional market research methods are getting less reliable due to dropping response rates, especially among young, tech-savvy consumers. To counteract this trend, marketing research firms have hired hundreds of PhDs to refine the math in their models and try to build a better picture of the zeitgeist, leveraging social media and implicit web behavior. This has proven to be a dangerous proposition, as modeling and research firms have fallen prey to statistics’ number one rule: garbage in, garbage out.

No amount of genius mathematical skills can fix Bad Data, and simple statistical models on well measured data will trump extensive algorithms on badly measured data every single time. Sophisticated statistical models might help in political polling, where people are far more predictable based on party and demographics, but they won’t do anything to help traditional marketing research, where people’s tastes and positions are less entrenched and evolve more rapidly.

Parsing the exact sentiment behind a “like”, a follow or a natural language tweet is extremely difficult, as analysts often lack control over the sample population they are covering, as well as any context about why the action occurred, and what behavior or opinion triggered it. Since there is no negative sentiment to use as control, there is no aibility to unconfound good with popular. Natural language processing algorithms can’t sort out sarcasm, which reigns supreme on social media, and even the best algorithms can’t reliably categorize the sentiment of more than 50% of Twitter’s volume of posts. Others have pointed out the issues with developing a more than razor-thin understanding of consumer mindsets and preferences based on social media data. What does a Facebook “Like” mean, exactly? If you “like” Coca-Cola on Facebook, does it mean that you like the product or the company? And does it necessarily mean you don’t like Pepsi? And what is a “like” worth? Nobody knows.

This is where we come in. We at Ranker have developed a very good answer to this issue: the “opinion graph”, which is a more precise version of the “interest graph” that advertisers are currently using.

Ranker is a popular (top 200 website, 18 million unique visitors and 300 million pageviews per month) that crowdsources answers to questions, using the popular list format.  Visitors to Ranker can view, rank and vote items on around 400,000 lists. Unlike more ambiguous data points based on Facebook likes or twitter tweets, Ranker solicits precise and explicit opinions from users about questions like the most annoying celebrities, the best guilty pleasure movies, the most memorable ad slogansthe top dream colleges, or the best men’s watch brands.

It’s very simple: instead of the vaguely positive act of “liking” a popular actor on Facebook, Ranker visitors cast 8 million votes every month and thus directly express whether they think someone is “hot”, “cool”, one of the “best actors of all-time”, or just one of the “best action stars”. Not only that, they also vote on other lists of items seemingly unrelated to their initial interest: best cars, best beers, most annoying TV shows, etc.

As a result, Ranker has been building since 2008 the world’s largest opinion graph, with 50,000 nodes (topics) and 20 million edges (statistically significant connections between 2 items). Thanks to our massive sample and our rich database of correlations, we can tell you that people who like “Modern Family” are 5x more likely to dine at “Chipotle” than non-fans, or people who like the Nissan 370Z also like oddball comedy movies such as “Napoleon Dynamite” and “Big Lebowski”, and TV shows such as “Dexter” and “Weeds”.

Our exclusive Ranker “FanScope” about the show “Mad Men” lays out this capability in more details below:

Mad Men Data

How good is it? Pretty good. Like “ we predicted the outcome of the World Cup better than Nate Silver’s FiveThirtyEight and Betfair” good.

Our opinion data is also much more precise than Facebook’s, since we not only know that someone who likes Coke is very likely to rank “Jaws” as one of his/her top movies of all time, but we’re able to differentiate between those who like to drink Coke, and those who like Coca-Cola as a company:

jaws chart

We’re also able to differentiate between people who always like Pepsi better than Coke overall, and those who like to drink Coke but just at the movie theater:

  • 47% of Pepsi fans on Ranker vote for (vs. against) Coke on Best Sodas of All Time
  • 65% of Pepsi fans on Ranker vote for (vs. against) Coke on Best Movie Snacks

That’s the kind of specific relationship you can’t get using Facebook data or Twitter messages.

By collecting millions of discrete opinions each month on thousands of diverse topics, Ranker is the only company able to combine internet-level scale (hundreds of thousands surveyed on millions of opinions each month) with market research-level precision (e.g. adjective specific opinions about specific objects in a specific context).

We can poll questions that are too specific (e.g. most memorable slogans) or not lucrative enough (most annoying celebrities) for other pollsters. And we use the same types of mathematical models to address sampling challenges that all pollsters (internet or not internet based) currently have, working with some of the world’s leading academics who study crowdsourcing, such as our Chief Data Scientist Ravi Iyer, and UC Irvine Cognitive Sciences professor Michael Lee.

Our data suggests you won’t be dropping gallons of iced water on your face over it. But if you’re a marketer or an advertiser, we predict it’s likely you will want to pay close attention.

by   Ranker
Staff
in prediction

Ranker World Cup Predictions Outperform Betfair & FiveThirtyEight

Former England international player turned broadcaster Gary Lineker famously said “Football is a simple game; 22 men chase a ball for 90 minutes and at the end, the Germans always win.” That proved true for the 2014 World Cup, with a late German goal securing a 1-0 win over Argentina.

Towards the end of March, we posted predictions for the final ordering of teams in the World Cup, based on Ranker’s re-ranks and voting data. During the tournament, we posted an update, including comparisons with predictions made by FiveThirtyEight and Betfair. With the dust settled in Brazil (and the fireworks in Berlin shelved), it is time to do a final evaluation.

Our prediction was a little different from many others, in that we tried to predict the entire final ordering of all 32 teams. This is different from sites like Betfair, which provided an ordering in terms of the predicted probability each team would be the overall winner. In order to assess our order against the true final result, we used a standard statistical measure called partial tau. It is basically an error measure — 0 would be a perfect prediction, and the larger the value grows the worse the prediction — based on how many “swaps” of a predicted order need to be made to arrive at the true order. The “partial” part of partial tau allows for the fact that the final result of the tournament is not a strict ordering. While the final and 3rd place play-off determined the order of the first four teams: Germany, Argentina, the Netherlands, and Brazil, other groups of teams are effectively tied from then on.  All of the teams eliminated in the quarter finals can be regarded as having finished in equal fifth place. All of the teams eliminated in the first game past the group stage finished equal sixth. And all of the 32 teams eliminated in group play finished equal last.

The model we used to make our predictions involved three sources of information. The first was the ranks and re-ranks provided by users. The second was the up and down votes provided by users. The third was the bracket structure of the tournament itself. As we emphasized in our original post, the initial group stage structure of the World Cup provides strong constraints on where teams can and cannot finish in the final order. Thus, we were interested to test how our model predictions depended on each sources of information. This lead to a total of 8 separate models

  • Random: Using no information, but just placing all 32 teams in a random order.
  • Bracket: Using no information beyond the bracket structure, placing all the teams in an order that was a possible finish, but treating each game as a coin toss.
  • Rank: Using just the ranking data.
  • Vote: Using just the voting data.
  • Rank+Vote: Using the ranking and voting data, but not the bracket structure.
  • Bracket+Vote: Using the voting data and bracket structure, but not the ranking data.
  • Bracket+Rank: Using the ranking data and bracket structure, but not the voting data.
  • Rank+Vote+Bracket: Using all of the information, as per the predictions made in our March blog post.

We also considered the Betfair and FiveThirtyEight rankings, as well as the Ranker Ultimate List at the start of the tournament, as interesting (but maybe slightly unfair, given their different goals) comparisons. The partial taus for all these predictions, with those based on less information on the left, and those based on more information on the right, are shown in the graph below. Remember, lower is better.

The prediction we made using the votes, ranks, and bracket structure out-performed Betfair, FiveThirtyEight, and the Ranker Ultimate List. This is almost certainly because of the use of the bracket information. Interestingly, just using the ranking and bracket structure information, but not the votes, resulted in a slightly better prediction. It seems as if our modeling needs to improve how it benefits from using both ranking and voting data. The Rank+Vote prediction was worse than either source alone. It is also interesting to note that the Bracket information by itself is not useful — it performs almost as poorly as a random order — but it is powerful when combined with people’s opinions, as the improvement from Rank to Bracket+Rank and from Vote to Bracket+Vote show.

by   Ranker
Staff
in Data Science, Pop Culture, prediction

Comparing World Cup Prediction Algorithms – Ranker vs. FiveThirtyEight

Like most Americans, I pay attention to soccer/football once every four years.  But I think about prediction almost daily and so this year’s World Cup will be especially interesting to me as I have a dog in this fight.  Specifically, UC-Irvine Professor Michael Lee put together a prediction model based on the combined wisdom of Ranker users who voted on our Who will win the 2014 World Cup list, plus the structure of the tournament itself.  The methodology runs in contrast to the FiveThirtyEight model, which uses entirely different data (national team results plus the results of players who will be playing for the national team in league play) to make predictions.  As such, the battle lines are clearly drawn.  Will the Wisdom of Crowds outperform algorithmic analyses based on match results?  Or a better way of putting it might be that this is a test of whether human beings notice things that aren’t picked up in the box scores and statistics that form the core of FiveThirtyEight’s predictions or sabermetrics.

So who will I be rooting for?  Both methodologies agree that Brazil, Germany, Argentina, and Spain are the teams to beat.  But the crowds believe that those four teams are relatively evenly matched while the FiveThirtyEight statistical model puts Brazil as having a 45% chance to win.  After those first four, the models diverge quite a bit with the crowd picking the Netherlands, Italy, and Portugal amongst the next few (both models agree on Colombia), while the FiveThirtyEight model picks Chile, France, and Uruguay.  Accordingly, I’ll be rooting for the Netherlands, Italy, and Portugal and against Chile, France, and Uruguay.

In truth, the best model would combine the signal from both methodologies, similar to how the Netflix prize was won or how baseball teams combine scout and sabermetric opinions.  I’m pretty sure that Nate Silver would agree that his model would be improved by adding our data (or similar data from betting markets that similarly think that FiveThirtyEight is underrating Italy and Portugal) and vice versa.  Still, even as I know that chance will play a big part in the outcome, I’m hoping Ranker data wins in this year’s world cup.

– Ravi Iyer

Ranker’s Pre-Tournament Predictions:

FiveThirtyEight’s Pre-Tournament Predictions:

by   Ranker
Staff
in Data

Men and Women Both Lie—But They Do It For Different Reasons

lying-girlfriend-1085358-TwoByOne

We all tell white lies now and then (yes you do, don’t lie!) but did you know that men and women lie for different reasons? The data from our list of Things People Lie About All the Time shows a pattern that may hint at this difference.

The poll lists 49 common lies and asks respondents to vote “yes” if they’ve lied about that in the past 6 months or “no” if they have not. According to votes cast by over 350 people, women are more likely to lie about things that “keep the peace socially” while men are more likely to lie over matters of “self-preservation.”

On the list, women are 8 times more likely than men to lie about “being too swamped to hang out” and 4 times more likely to claim that their “phone died.” These results imply that women may be more likely to feel guilty about canceling on friends or having alone time.

In contrast, men were 2 times more likely to admit to saying things like “Oh yeah! That makes sense!” when they did not understand something and 5 times more likely to say, “No officer, I do not know why you pulled me over,” when, presumably, they did know why. These types of lies could point to men’s desire to show themselves in the best possible light and cover up wrongdoing.

Differences aside, both men and women voted similarly on many items on this list. In fact, the top 3 most popular lies were the same for both men and women.

The Top 3 Lies for BOTH Men and Women Are:

1. I’m Fine

2. I’m 5 Minutes Away

3. Yeah, I’m Listening.

Which goes to show that men and women may be able to see eye-to-eye after all… just as long as they don’t ask each other how they are doing, where they are and whether or not they are listening.

by   Ranker
Staff
in prediction

Predicting the Movie Box Office

The North American market for films totaled about US$11,000 million in 2013, with over 1300 million admissions. The film industry is a big business that not even Ishtar, nor Jaws: The Revenge, nor even the 1989 Australian film “Houseboat Horror” manages to derail. (Check out Houseboat Horror next time you’re low on self-esteem, and need to be reminded there are many people in the world much less talented than you.)

Given the importance of the film industry, we were interested in using Ranker data to make predictions about box office grosses for different movies. The ranker list dealing with the Most Anticipated 2013 Films gave us some opinions — both in the form of re-ranked lists, and up and down votes — on which to base predictions. We used the same cognitive modeling approach previously applied to make Football (Soccer) World Cup predictions, trying to combine the wisdom of the ranker crowd.

Our basic results are shown in the figure below. The movies people had ranked are listed from the heavily anticipated Iron Man 3, Star Trek: Into Darkness, and Thor: The Dark World down to less anticipated films like Simon Killing, The Conjuring, and Alan Partridge: Alpha Papa. The voting information is shown in the middle panel, with the light bar showing the number of up-votes and the dark bar showing the number of down-votes for each movie. The ranking information is shown in the right panel, with the size of the circles showing how often each movie was placed in each ranking position by a user.

This analysis gives us an overall crowd rank order of the movies, but that is still a step away from making direct predictions about the number of dollars a movie will gross. To bridge this gap, we consulted historical data. The Box Office Mojo site provides movie gross totals for the top 100 movies each year for about the last 20 years. There is a fairly clear relationship between the ranking of a movie in a year, and the money it grosses. As the figure below shows, a few highest grossing movies return a lot more than the rest, following a “U-shaped” pattern that is often found in real-world statistics. If a movie is the 5th top grossing in a given year, for example, it grosses between about 100 and 300 million dollars. if it is the 50th highest grossing, it makes between about 10 and 80 million.

We used this historical relationship between ranking and dollars to map our predictions about ranking to predictions about dollars. The resulting predictions about the 2013 movies are shown below. These predictions are naturally uncertain, and so cover a range of possible values, for two reasons. We do not know exactly where the crowd believed they would finish in the ranking list, and we only know a range of possible historical grossed dollars for each rank. Our predictions acknowledge both of those sources of uncertainty, and the blue bars in the figure below show the region in which we predicted it was 95% likely to final outcome would lie. To assess our predictions, we looked up the answers (again at Box Office Mojo), and overlayed them as red crosses.

Many of our predictions are good, for both high grossing (Iron Man 3, Star Trek) and more modest grossing (Percy Jackson, Hansel and Gretel) movies. Forecasting social behavior, though, is very difficult, and we missed a few high grossing movies (Gravity) and over-estimated some relative flops (47 Ronin, Kick Ass 2). One interesting finding came from contrasting an analysis based on ranking and voting data with similar analyses based on just ranking or just voting. Combining both sorts of data led to more accurate predictions than using either alone.

We’re repeating this analysis for 2014, waiting for user re-ranks and votes for the Most Anticipated Films of 2014. The X-men and Hunger Games franchises are currently favored, but we’d love to incorporate your opinion. Just don’t up-vote Houseboat Horror.

by   Ranker
Staff
in Data, Game of Thrones

Game of Thrones: Don’t Get Too Comfortable

[Spoiler Alert: This post contains references to Seasons 1-4, Episode 3 of the show. There are no references to the books. If you're all caught up on the show, then you are safe!]

fans react

Have you recently found yourself unreasonably happy about a certain child’s death? Excited, even, to watch the bile frothing out of his mouth and the blood streaming from the far corners of his eyes? Have you rationalized incest-rape and chalked it up to the pressures of the times? Rejoiced as a small girl murders a man in cold blood? (Something wrong with your leg, boy?)

Don’t get too comfortable.

Now that you’re fully immersed in the world of the Seven Kingdoms, your capacity for moral relativism may surprise you. You may feel like nothing on the show could totally shock or upset you anymore. Now that you’re sort of OK with incest-rape, should you just hang up your hat and quit? Can’t anything feel uncomfortable or shocking anymore?!

Don’t worry: If we’ve learned anything about this series so far, there will be plenty of horrific incidents to come. And according to our data, there is pretty much something on the show to upset every sensibility.

We were looking at our list of The Most Uncomfortable Game of Thrones Moments again (weird, we know — we like to keep the wounds fresh) and noticed an interesting pattern in our data that gives us an insight on what makes certain viewers uncomfortable. So far, over 1,000 people have voted on this list an average of 5 times. There are 18 uncomfortable moments to choose from, and they are ordered from jaw-dropping to ain’t-no-thing. (Vote if you haven’t already. It’s fun!)

As more and more people vote, some interesting correlations have emerged.

TheonFor example, Ranker users who said that they were very uncomfortable when Theon Greyjoy lost his “most prized possession” were far more likely to also feel uncomfortable when Jaime Lannister’s right hand was cut off. Jaime
A particular distaste for bodily harm, it would seem.

[In plain English: The majority of people who hated watching that first scene also hated watching the second. Most people who didn't mind the first also didn't mind the second.]

But there’s more. Two main “camps” of voters emerged in our data. We’ll call them “Camp Emotional” and “Camp Physical.

People who voted for one thing that could be considered emotionally distressing — witnessing an incest scene between brother and sister, for example – were highly likely to also vote for other moments that can be associated with emotional distress: Lysa Tully’s disturbing breastfeeding scene and Viserys Targaryan’s willingness to whore out his own sister in exchange for power both come to mind.

Similarly, people who voted on one item in “Camp Physical” were more likely to vote on other physically revolting scenes. Viserys Targaryen getting “crowned,” Khaleesi eating a horse heart, and the execution of Eddard Stark were all positively correlated.

Disgusting GoT Tastes Graph smaller 500

The “Game of Thrones” show creators certainly have their bases covered as far as upsetting every sensibility.

Don’t mind a six year-old suckling on the teat of his mother? Maybe your favorite character will be brutally executed. Don’t think the gory stuff is that big of a deal? Maybe a character you thought you trusted will double-cross his sister, have sex with his mother, and steal the crown for himself. This is all just speculation, of course, but we’re just saying: no one is safe. Not even you. 

by   Ranker
Staff
in Market Research, Opinion Graph, Pop Culture, Rankings

Can Colbert bring young Breaking Bad Fans to The Late Show?

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.

Despite the ire of those who disagree with The Colbert Report’s politics, CBS is definitely addressing a need to compete better for younger viewers, who are less likely to watch TV on the major networks.  Ranker users tend to be in the 18-35 year old age bracket and The Colbert Report ranks higher than the Late Show on most every list that they both are on including the Funniest TV shows of 2012 (19 vs. 28), Best TV Shows of All-Time (186 vs. 197), and Best TV Shows of Recent Memory (37 vs. 166).  Further, people who tend to like The Colbert Report also seem to like many of the most popular shows around like Breaking Bad, Mad Men, Game of Thrones, and 30 Rock.  In contrast, correlates of the Late Show include older shows like The Sopranos and 60 Minutes.  There is some overlap as fans of both shows like The West Wing and The Daily Show, indicating that Colbert may be able to appeal to current fans as well as new audiences.

Colbert Can Expand Late Show's Audience to New Groups, yet Retain Many Current Fans.

I’ll be sad to see “Stephen Colbert” the character go.  But it looks like my loss is CBS’ gain.

– Ravi Iyer

by   Ranker
Staff
in Data Science, prediction, Rankings

World Cup 2014 Predictions

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.

– Michael Lee

by   Ranker
Staff
in interest graph, Opinion Graph, semantic search

Lists are the Best way to get Opinion Graph Data: Comparing Ranker to State & Squerb

I was recently forwarded an article about Squerb, which shares an opinion we have long agreed with.  Specifically…

““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.

It reminds me a bit of State, another company building an opinion graph that connects more specific opinions to specific objects in the world.  They too are built upon the idea that existing sources of big data opinions, e.g. mining tweets and facebook likes, have inherent limitations.  From this Wired UK article:

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.)
  • Lists are useful, especially in a mobile friendly world.

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.

– Ravi Iyer

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