The mAR index of Hollywood films

UPDATE (March 2015): A revised version of this paper has now been published as Robust estimation of the mAR index of high grossing films at the US box office, 1935 to 2005, Journal of Data Science 12 (2) 2014: 277-291.  [The pdf of this article can be accessed here: 4.JDS-1181_final-1].

UPDATE: reviewing the methodology of the mAR index in general, Mike Baxter noted an error in the data whereby I had reported the exponent of the negative exponential function instead of the mAR index for films from the 1960s. I have now corrected this and redone the analysis and the graphs (which are still cool). This mainly effects the conclusions regarding differences between genres. Overall, it turns out that, as a result of this error, I had actually underestimated the difference between the classical and rank mAR indices. If anyone finds any other errors then feel free to add a comment to this post and I’ll try to correct it as soon as possible.

And so to finish the month as we started, looking at robust estimates of the mAR index of film style. Below is the first draft of a paper comparing the mAR index  based on the methods used by James Cutting, Jordan De Long and Christine Nothelfer to describe the clustering of shots in motion picture with a rank-based alternative that is resistant to outliers. Naturally, it features some pretty cool graphs.

Robust estimation of the modified autoregressive index for high grossing films at the US box office, 1935 to 2005 The modified autoregressive (mAR) index describes the clustering of shots of similar duration in a motion picture. In this paper we derive robust estimates of the mAR index for high grossing films at the US box office using a rank-based autocorrelation function resistant to the influence of outliers and compare this to estimates obtained using the classical, moment-based autocorrelation function. The results show that (1) The classical mAR function underestimates both the level of shot clustering and the variation in style among the films in the sample.; (2) there is a decline in shot clustering from 1935 to the 1950s followed by an increase from the 1960s to the 1980s and a levelling off thereafter rather than the monotonic trend indicated by the classical index, and this is mirrored in the trend of the median shot lengths and interquartile range; and (3) the rank mAR index indentifies differences between genres missed by the classical index.


About Nick Redfern

I am an independent academic with over 15 years experience teaching film in higher education in the UK. I have taught film analysis, film industries, film theories, film history, science fiction at Manchester Metropolitan University, the University of Central Lancashire, and Leeds Trinity University, where I was programme leader for film from 2016 to 2020. My research interests include computational film analysis, horror cinema, sound design, science fiction, film trailers, British cinema, and regional film cultures.

Posted on November 22, 2012, in Cinemetrics, Film Analysis, Film Studies, Film Style, Hollywood, Statistics, Time Series Analysis and tagged , , , , , . Bookmark the permalink. 1 Comment.

  1. Nick, enjoyed reading this and your other articles. Was wondering if your text “An introduction to using graphical displays for analysing the editing of motion pictures” is somewhere on this blog – would like to use in my Spring class.

    I just put online my paper “Visualizing Vertov” which presents my approach to “exploratory media analysis” using high-res visualizations created with free tools developed in my lab. (The concept of course is derived from “exploratory data analysis”). Would love to see your comments:

    Other recent articles on theory, methods, and examples of exploratory media visualization are here:

    Also, since you are clearly much better with time series analysis than me, would like to talk about some data we extracted from Vertov’s film (not shot lengths)?



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