Sunny Shen
COMM 240
Messaris
The Higher the Spending, The Higher the Earning:
The Effects of Budget on Box Office Success for YA Novel Movie Adaptations
Introduction
With so many movie successes as well as failures, box office success seems extremely
hard to predict precisely. It is not hard to see that franchises with many sequels are among some
of the most successful movies over the past decade. While some of these franchises are based on
superhero comics and horror stories, others are based on adaptations of popular young adult
(YA) novels. As such, filmmakers are often on the lookout for YA novels to be adapted for the
big screen, hoping to create the next Harry Potter or Twilight franchise.
This study is designed to analyze whether the allocated production budget for whether
movie adaptations of young adult novels will directly affect the movies’ box office success.
Many of these novels have fantasy and supernatural elements, such as magic and mythical
creatures. In producing these films, filmmakers often use CGI and other technologies to bring
these spectacles to life. Thus, it is likely that the higher the budget, the more realistic and
fantastic these effects will look on the big screen.
The target audience of these movies is usually teenagers, often considered as the most
technologically advanced generation. As such, it would seem that they look highly upon the
quality of special effects; they are so familiar with new technology that it is reasonable to believe
they also expect quality effects in the movies they watch. Considering this, it is likely that for
this audience, a bigger budget – which means better effects – will bring about higher box office
sales.
Method
First, data (Figure 1) 1 2 was gathered based on 46 YA novel movie adaptations from
2001-2013. These movies include both successes and failures and are chosen to be a
representative sample of YA novel movie adaptations since 2000. Within these movies are five
franchises, with a total of 16 sequels (highlighted in orange in Figure 1). Also included are
movies that could have led to multiple-sequel franchises but did not due to the lack of box office
success.
The data collected includes the production budget, total worldwide box office sales, and
release date in the US for each movie. Additionally, the % change in budget and box office sales
were calculated for each sequel compared to the respective preceding movie using this formula:
𝑋𝑠𝑒𝑞𝑢𝑒𝑙 −𝑋𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠
× 100%.
𝑋
𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠
Within the analysis, special consideration was given to whether the movie is a sequel or
not. Many sequels are given the green light because the first movie had been a huge success.
Thus, with different expectations and predictability of box office sales, it is possible that the
success of sequels may not follow the general trend of adaptations of a new novel or series. The
data is plotted, using all 46 movies or a subset of either no sequels or just sequels, and in cases,
logarithmic transformations were used to see if a stronger relationship could be formed between
production budget and box office success.
Results
1
2
“Box Office Mojo.” <www.boxofficemojo.com>
“International Movie Database.” <www.imdb.com>
Sunny Shen
COMM 240
Messaris
The first analysis includes all 46 movies, plotting Box Office vs. Budget (Figure 2). After
including a linear fit, there appears to be a positive linear relationship between Budget and Box
Office. The strength of the relationship, R2, equals 0.429538, which is fairly strong. Additionally,
the coefficient of the Budget (4.0179801 with a standard deviation of 0.698062) has a t Ratio of
5.76 and a Prob>|t| of <0.0001. This shows that the estimate of the coefficient is within any
reasonable confidence interval. In other words, these statistics show that the estimate of a
positive linear relationship is fairly reliable: for every dollar change in the budget, there will be
an estimated $4.02 change in box office sales.
Figure 2 - Bivariate Fit of Box Office By Budget
Linear Fit
Box Office = 55977946 + 4.0179801*Budget
RSquare
0.429538
Parameter Estimates
Term
Estimate
Std Error t Ratio Prob>|t|
Budget
4.0179801 0.698062 5.76
<.0001*
However, just looking at the data points, they seem to follow a linear relationship when
the budget is low. As the budget increases, the variability increases and the data points are more
spread out. This shows that with higher budgets, the predictability of box office success based on
production budget decreases, and the filmmakers are taking on higher risk. Thus, another plot is
constructed, fitting the Log10Budget against Log10Box Office (Figure 3). This graph showed a
slightly better linear relationship. The R2 using logarithms is 0.605238. The higher R2 shows that
this relationship is a relatively strong one, assuming that the relationship is indeed linear.
Figure 3 - Bivariate Fit of Log BO By Log Budget
Log BO = -1.768607 + 1.2884604*Log Budget
RSquare
0.605238
Parameter Estimates
Term
Estimate Std Error t Ratio Prob>|t|
Log Budget 1.2884604 0.156873 8.21
<.0001*
A similar result is seen when excluding the 16 sequels (Figure 4). In this case, R2 equals
0.499858. The coefficient of the Budget (3.7207923 with a standard deviation of 0.703364) has a
t Ratio of 5.29 and a Prob>|t| of <0.0001. The R2 statistic in the model excluding the 16 sequels
Sunny Shen
COMM 240
Messaris
is even higher than that in Figures 2 when all movies were analyzed. This result shows that when
just considering the movie adaptations of single novels and the first book in a series, regardless
of whether it later became a franchise, there is an even stronger relationship between the budget
and box office success.
Figure 4 - Bivariate Fit of Box Office By Budget
Box Office = -24507524 + 3.7207923*Budget
RSquare
0.499858
Parameter Estimates
Term
Estimate Std Error t Ratio Prob>|t|
Budget
3.7207923 0.703364 5.29
<.0001*
This is not the case when analyzing sequels separately. Figures 5, 6, 7, and 8 (plotting
either Budget or Budget % Change against Box Office or Box Office % Change) show that with
sequels, the budget does not determine box office success, whether independently or compared
to the preceding movie. With relatively low R2 statistics, almost 0 coefficients, and very high
Prob>|t|, it is unlikely that the budget can be used to accurately predict box office success. These
data points are spread out so much that a fitted line does not have a true interpretation and there
is no observable pattern in any of the four graphs.
Figure 5
Figure 6
Bivariate Fit of Box Office By Budget
Bivariate Fit of BO % Change By Budget
Figure 7
Figure 8
Bivariate Fit of Box Office By Budget % Change
Bivariate Fit of BO % Change By Budget % Change
Sunny Shen
COMM 240
Messaris
Also interesting to note is that most of the movies above the fitted line in Figure 4 lead to
franchises, as they yielded higher-than-average box office sales to budget ratio. These include
Harry Potter, Lord of the Rings, The Hunger Games and Twilight, some of the biggest movie
franchises over the past thirteen years. The higher return on the cost may be an important
deciding factor in whether or not to pursue sequels and create a franchise.
Conclusion
With this data and analysis, it seems that the budget of a movie adaptation for a YA novel
can help predict the box office success to a certain extent. This predictor seems to be more
accurate with lower budget adaptations of YA novels, as the data points are closer together. As
the budget increases, the data points are more spread out, with higher residuals, showing more
variability and thus unpredictability.
One limitation of this study is the limit on the size of the data set as there are not an
incredible number of YA novel adaptations. Further areas for research can include other movies
targeted towards teens, other large franchises, or specifically fantasy genre movies. However,
this study does show there seems to be some relationship between the production budget and box
office success.
Sunny Shen
COMM 240
Messaris
Figure 1 (sequels highlighted in orange)
Data gathered from: www.wikipedia.org, www.boxoffice.com