by   Ranker
Staff
in Data Science, Pop Culture, prediction

Ranker Predicts Spurs to beat Cavaliers for 2015 NBA Championship

The NBA Season starts tonight and building on the proven success of our World Cup and movie box office predictions, as well as the preliminary success of our NFL predictions, Ranker is happy to announce our 2015 NBA Championship Predictions, based upon the aggregated data from basketball fans who have weighed in on our NBA and basketball lists.

Ranker's 2015 NBA Championship Predictions as Compared to ESPN and FiveThirtyEight
Ranker’s 2015 NBA Championship Predictions as Compared to ESPN and FiveThirtyEight

For comparison’s sake, I included the current ESPN power rankings as well as FiveThirtyEight’s teams that have the most percentage chance of winning the championship.  As with any sporting event, chance will play a large role in the outcome, but the premise of producing our predictions regularly is to validate our belief that the aggregated opinions of many will generally outperform expert opinions (ESPN) or models based on non-opinion data (e.g. player performance data plays a large role in FiveThirtyEight’s predictions).  Our ultimate goal is to prove the utility of crowdsourced data, as while something like NBA predictions is a crowded space where many people attempt to answer this question, Ranker produces the world’s only significant data model for equally important questions, such as determining the world’s best DJseveryone’s biggest turn-ons or the best cheeses for a grilled cheese sandwich.

– Ravi Iyer

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 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 Data Science, Opinion Graph, prediction, semantic search

Why Topsy/Twitter Data may never predict what matters to the rest of us

Recently Apple paid a reported $200 million for Topsy and some speculate that the reason for this purchase is to improve recommendations for products consumed using Apple devices, leveraging the data that Topsy has from Twitter.  This makes perfect sense to me, but the utility of Twitter data in predicting what people want is easy to overstate, largely because people often confuse bigger data with better data.  There are at least 2 reasons why there is a fairly hard ceiling on how much Twitter data will ever allow one to predict about what regular people want.

1.  Sampling – Twitter has a ton of data, with daily usage of around 10%.  Sample size isn’t the issue here as there is plenty of data, but rather the people who use Twitter are a very specific set of people.  Even if you correct for demographics, the psychographic of people who want to share their opinion publicly and regularly (far more people have heard of Twitter than actually use it) is way too unique to generalize to the average person, in the same way that surveys of landline users cannot be used to predict what psychographically distinct cellphone users think.

2. Domain Comprehensiveness – The opinions that people share on Twitter are biased by the medium, such that they do not represent the spectrum of things many people care about.  There are tons of opinions on entertainment, pop culture, and links that people want to promote, since they are easy to share quickly, but very little information on people’s important life goals or the qualities we admire most in a person or anything where people’s opinions are likely to be more nuanced.  Even where we have opinions in those domains, they are likely to be skewed by the 140 character limit.

Twitter (and by extension, companies that use their data like Topsy and DataSift) has a treasure trove of information, but people working on next generation recommendations and semantic search should realize that it is a small part of the overall puzzle given the above limitations.  The volume of information gives you a very precise measure of a very specific group of people’s opinions about very specific things, leaving out the vast majority of people’s opinions about the vast majority of things.  When you add in the bias introduced by analyzing 140 character natural language, there is a great deal of variance in recommendations that likely will have to be provided by other sources.

At Ranker, we have similar sampling issues, in that we collect much of our data at Ranker.com, but we are actively broadening our reach through our widget program, that now collects data on thousands of partner sites.  Our ranked list methodology certainly has bias too, which we attempt to mitigate that through combining voting and ranking data.  The key is not in the volume of data, but rather in the diversity of data, which helps mitigate the bias inherent in any particular sampling/data collection method.

Similarly, people using Twitter data would do well to consider issues of data diversity and not be blinded by large numbers of users and data points.  Certainly Twitter is bound to be a part of understanding consumer opinions, but the size of the dataset alone will not guarantee that it will be a central part.  Given these issues, either Twitter will start to diversify the ways that it collects consumer sentiment data or the best semantic search algorithms will eventually use Twitter data as but one narrowly targeted input of many.

– Ravi Iyer

by   Ranker
Staff
in Data Science, prediction

Combining Preferences for Pizza Toppings to Predict Sales

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.

by   Ranker
Staff
in Data Science, Market Research, Opinion Graph, Pop Culture

Why We Still Play Board Games: An Opinion Graph Analysis

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

by   Ranker
Staff
in Data Science, prediction

Recent Celebrity Deaths as Predicted by the Wisdom of Ranker Crowds

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.

Recent Celebrity Deaths and Predictions

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

by   Ranker
Staff
in Data Science, Market Research, Opinion Graph, Pop Culture

An Opinion Graph of the World’s Beers

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.

Ranker's Beer Opinion Graph

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

Ranker Uses Big Data to Rank the World’s 25 Best Film Schools

NYU, USC, UCLA, Yale, Julliard, Columbia, and Harvard top the Rankings.

Does USC or NYU have a better film school?  “Big data” can provide an answer to this question by linking data about movies and the actors, directors, and producers who have worked on specific movies, to data about universities and the graduates of those universities.  As such, one can use semantic data from sources like Freebase, DBPedia, and IMDB to figure out which schools have produced the most working graduates.  However, what if you cared about the quality of the movies they worked on rather than just the quantity?  Educating a student who went on to work on The Godfather must certainly be worth more than producing a student who received a credit on Gigli.

Leveraging opinion data from Ranker’s Best Movies of All-Time list in addition to widely available semantic data, Ranker recently produced a ranked list of the world’s 25 best film schools, based on credits on movies within the top 500 movies of all-time.  USC produces the most film credits by graduates overall, but when film quality is taken into account, NYU (208 credits) actually produces more credits among the top 500 movies of all-time, compared to USC (186 credits).  UCLA, Yale, Julliard, Columbia, and Harvard take places 3 through 7 on the Ranker’s list.  Several professional schools that focus on the arts also place in the top 25 (e.g. London’s Royal Academy of Dramatic Art) as well as some well-located high schools (New York’s Fiorello H. Laguardia High School & Beverly Hills High School).

The World’s Top 25 Film Schools

  1. New York University (208 credits)
  2. University of Southern California (186 credits)
  3. University of California – Los Angeles (165 credits)
  4. Yale University (110 credits)
  5. Julliard School (106 credits)
  6. Columbia University (100 credits)
  7. Harvard University (90 credits)
  8. Royal Academy of Dramatic Art (86 credits)
  9. Fiorello H. Laguardia High School of Music & Art (64 credits)
  10. American Academy of Dramatic Arts (51 credits)
  11. London Academy of Music and Dramatic Art (51 credits)
  12. Stanford University (50 credits)
  13. HB Studio (49 credits)
  14. Northwestern University (47 credits)
  15. The Actors Studio (44 credits)
  16. Brown University (43 credits)
  17. University of Texas – Austin (40 credits)
  18. Central School of Speech and Drama (39 credits)
  19. Cornell University (39 credits)
  20. Guildhall School of Music and Drama (38 credits)
  21. University of California – Berkeley (38 credits)
  22. California Institute of the Arts (38 credits)
  23. University of Michigan (37 credits)
  24. Beverly Hills High School (36 credits)
  25. Boston University (35 credits)

“Clearly, there is a huge effect of geography, as prominent New York and Los Angeles based high schools appear to produce more graduates who work on quality films compared to many colleges and universities,“ says Ravi Iyer, Ranker’s Principal Data Scientist, a graduate of the University of Southern California.

Ranker is able to combine factual semantic data with an opinion layer because Ranker is powered by a Virtuoso triple store with over 700 million triples of information that are processed into an entertaining list format for users on Ranker’s consumer facing website, Ranker.com.  Each month, over 7 million unique users interact with this data – ranking, listing and voting on various objects – effectively adding a layer of opinion data on top of the factual data from Ranker’s triple store. The result is a continually growing opinion graph that connects factual and opinion data.  As of January 2013, Ranker’s opinion graph included over 30,000 nodes with over 5 million edges connecting these nodes.

– Ravi Iyer

by   Ranker
Staff
in Data Science, Market Research, Pop Culture, prediction

Predicting Box Office Success a Year in Advance from Ranker Data

A number of data scientists have attempted to predict movie box office success from various datasets.  For example, researchers at HP labs were able to use tweets around the release date plus the number of theaters that a movie was released in to predict 97.3% of movie box office revenue in the first weekend.  The Hollywood Stock Exchange, which lets participants bet on the box office revenues and infers a prediction, predicts 96.5% of box office revenue in the opening weekend.  Wikipedia activity predicts 77% of box office revenue according to a collaboration of European researchers.  Ranker runs lists of anticipated movies each year, often for more than a year in advance, and so the question I wanted to analyze in our data was how predictive is Ranker data of box office success.

However, since the above researchers have already shown that online activity at the time of the opening weekend predicts box office success during that weekend, I wanted to build upon that work and see if Ranker data could predict box office receipts well in advance of opening weekend.  Below is a simple scatterplot of results, showing that Ranker data from the previous year predicts 82% of variance in movie box office revenue for movies released in the next year.

Predicting Box Office Success from Ranker Data
Predicting Box Office Success from Ranker Data

The above graph uses votes cast in 2011 to predict revenues from our Most Anticipated 2012 Films list.  While our data is not as predictive as twitter data collected leading up to opening weekend, the remarkable thing about this result is that most votes (8,200 votes from 1,146 voters) were cast 7-13 months before the actual release date.  I look forward to doing the same analysis on our Most Anticipated 2013 Films list at the end of this year.

– Ravi Iyer

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