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Forest for the trees: Risk categorization criteria


Forest for the trees: Risk categorization criteria

Colin Whitlow


Over the last month I’ve been wandering through a forest of data - exploring and compiling various pieces of information related to 1350 films that received a US theatrical release in 2012 and 2013. This data will be used to train the initial rubrics that will guide my separation of films into four risk levels.  While I’m still in the process of finding and organizing this data, I wanted to reach out to readers to ask for your thoughts on my approach. As a reminder, I’m initially dividing the index into 4 sub-indices, each with a distinct level of risk. See my last post for more detail. My job, therefore, is to properly select criteria that correlate to risk (in terms of films’ financial returns). The 3 criteria I believe will be most important follow:

Marketing budget

I believe this is one of the most important factors contributing to a film’s success or lack thereof. You can tell a unique story through an amazing, well-crafted film, but if you don’t properly get the word out about it, it won’t be seen by many people. Unfortunately, I’m discovering that accurate information about films’ marketing budgets is quite difficult to find.

I’m planning to compile information from filmmakers, distributors, ad agencies and journalists to triangulate/approximate marketing budgets for specific titles. However, this may not be fruitful in a lot of cases. And it’s hardly a scalable approach. So, dear reader, where else might I look for information about marketing budgets? Extra points for accurate, scalable sources.

Production budget

A film’s budget impacts everything about it. If a story hasn’t been sufficiently supported through its budget, an amazing story and/or cast won’t shine through. Nor will a marketing budget be as effective, regardless of its size. This is not to say that all films have to have a large budget – many very low budget films have still performed very well, both critically and financially. While I believe it highly likely there will be strong correlation between film budget and ability to drive revenue, I also believe films with distinct profiles have distinct budget levels that will indicate propensity for success at the box office and beyond.

Sources like BoxOfficeMojo are fantastic aggregators of data like film budget. However, it’s not assured the recorded budgets are correct. Additionally, I’ve found sites like this don’t record budget for hundreds of low budget indies. Where might I find budgets for these smaller films? How might I confirm the accuracy of reports made by data aggregators like this?


Films are not commodities. Films’ casts, directors and producers undoubtedly have enormous impact on the finished product. Therefore, I’m positing that attachments themselves have significant impact on the potential performance of a film independent of its production budget or marketing budget.

The creative players in a film are some of the easiest pieces of information to discover with certainty. However, unlike budgets, attachments are not numerical in nature. To approximate a numerical system, I’m taking on the highly imperfect task of devising a scoring system within which to place key attachments, based on the financial success of films in which they previously participated. I am trying to create a science in something that is obviously unscientific. Any opinions about factors I might want to consider along the way are highly encouraged.


I should note that I will be tracking other sorts of data as I organize my training data. Critera like distribution strategy (wide, platform, limited, day-and-date, ultra-VOD, etc.), source (studio, mini-major, established indie filmmaker, unknown, etc.) and origin (existing blockbuster book, original material, etc.) are likely related to level of financial risk. However, these may end up being largely duplicative of the above, more complex criteria. Also, I’m limiting the data pool at this early point in hopes of not muddling other correlations that may prove strong.

I’m lucky to have had interested mathematicians and data scientists reach out to help me discover and organize meaningful conclusions from my raw data. Engaging with folks who read these posts has been incredibly helpful. I want to talk with even more people – so, please, comment on this post or email me directly at if you’d prefer. I’m open to all ideas and questions.