The Wizardry of Good Movies

Analysis of magic spells that lead to high box office revenue

Abstract

The most expensive film ever produced is Star Wars: The Force Awakens, costing an estimated $447 million, but it ended up being a huge financial success grossing over $2 billion. What explains this success? One could think that obvious features such as the budget of the film are the main contributors to its success, but this is not always the case. In fact, many other factors influence the success of the film. We will explore the connection between the films’ success (which we measure by its revenue when we have enough data) and various less obvious factors such as the gender of the cast, the sentiment expressed in the plot etc. Our analysis aims to find out which factors are the most important and play an important role in contributing to a movies’ success.

Along this analysis, we look at movies from different angles, using a whole range of data analysis tools. On our journey we use regression, NLP and sentiment analysis, graphs, to make sure to discover all little secrets hiding behind movies success. Let us now delve into the magic behind the success of movies to understand the composition of the magic potion that leads a film to a high box office revenue. But first, we must process the data a little bit before exploiting it. Indeed, certain values of our dataset need a small update.

Accounting for Inflation: Revenue and Budget

In any analysis concerning financial figures over time, such as movie box office revenues and budgets, it’s crucial to consider the impact of inflation. Inflation erodes the value of money over time, meaning that a movie’s box office revenue or budget from decades ago doesn’t hold the same value as today. To compare the financial performance and investment across different eras fairly, we adjust these figures for inflation, ensuring a like-for-like comparison.

Why Adjust for Inflation?

Without accounting for inflation, we risk misinterpreting the financial success of older films or underestimating the scale of investment in past productions. A blockbuster from the 1980s, for instance, might have had a tremendous box office success in its day, but when compared to modern revenues without adjustment, it may seem less impressive. Similarly, the budget of an old classic, when adjusted for today’s value, might reveal it as a more significant investment than initially perceived. Adjusting for inflation allows us to appreciate the real financial scale and success of movies across different periods.

Adjusting Box Office Revenue and Budget

To normalize the box office revenue and budget figures, we use the Consumer Price Index (CPI), which provides a consistent measure to adjust values over time. The adjusted revenue and budget provide a more accurate reflection of a movie’s financial success and the investment put into it, respectively. The formula for adjustment is as follows:

Equation

Where:

  • Adjusted Value: The revenue or budget value adjusted for inflation.
  • Nominal Value: The reported revenue or budget at the time of the movie’s release.
  • CPI in Target Year: The CPI of the year to which we’re adjusting the value.
  • CPI in Release Year: The CPI of the year in which the movie was released.

By applying this formula to both the box office revenue and the production budget, we can analyze the real financial performance and investment in movies on a comparable basis, regardless of their release year.

1. Does the Period of Release of a Movie Have an Impact on Its Box Office Revenue?

In the intricate world of cinema, where countless variables intertwine to dictate a film’s success, the timing of its release emerges as a crucial yet often understated factor. As we transition from analyzing the impact of inflation, our focus now shifts to the temporal dimension of movie releases. This part of our analysis delves into how the specific timing of a film’s debut, from the broader spectrum of years down to the precision of days and months, might sway its box office performance. Our aim is to peel back the layers of this temporal enigma and reveal the patterns that could guide a film to financial triumph.

Unveiling the Temporal Mysteries Behind Box Office Success

Our initial journey through the annals of cinema uncovered a mild, yet statistically significant, negative correlation between a film’s release year and its inflation-adjusted earnings. While this suggests that the release year does exert an influence, the effect is not pronounced enough to overshadow other temporal aspects. Thus, we turn our analytical lens to the more detailed aspects of time — the months and days that might hold the keys to box office success. We pose critical inquiries: Which month offers the best prospects for maximizing box office returns? Are there particular days that promise greater success for film premieres? With these questions as our guide, we embark on a meticulous exploration to unravel the temporal secrets that might boost a film’s appeal and revenue, shedding light on the nuanced interplay between a movie’s release timing and its box office destiny.

Delving into the Calendar: Monthly Patterns and Box Office Success

In our exploration of the film industry, we’ve looked beyond the usual suspects of big stars and compelling stories to focus on something less obvious but just as important: the month a movie is released. We’ve been analyzing how this timing affects a film’s earnings and have found that not all months are equal when it comes to box office success. Our investigation showed significant differences in average revenues depending on the month, revealing that the choice of when to release a film is more than a small detail; it’s a crucial decision that can have a big impact on its financial success.

The Quest for the Optimal Release Month

Understanding the crucial impact of release months, we delved into a thorough investigation to determine how each month influences movie revenues. By comparing the performance of movies released in specific months with the rest, we gained a detailed understanding of the trends. This wasn’t just a number game; it was a journey to pinpoint the most profitable times for releasing films.

We tailored our approach, giving a character to each month, from the cold start of January to the warmth of June, and beyond. In analyzing the data, we observed the rise and fall of box office returns across the year, identifying the months that tend to be more favorable for movies and those that are less so.

Following our statistical analysis, we now present a visual representation of the data that further illuminates our findings. The interactive graph below captures the essence of our results, emphasizing the temporal dimension of box office success.

June and December: The Crowned Months of Cinema

Our analysis pinpointed June as the top month for releasing movies, aligning with the warm, leisure-filled days of summer when audiences are more likely to seek out theater experiences. Similarly, December emerged as a key period, its festive atmosphere drawing families and friends to cinemas for shared moments of enjoyment and celebration.

This understanding extends beyond simple observation; it’s a strategic asset for filmmakers and producers. Scheduling releases to coincide with these prime vacation periods can markedly improve a movie’s box office appeal, transforming a well-timed debut into a resounding commercial victory.

Daily Dynamics: The Impact of Weekdays on Box Office Revenues

Exploring deeper into the world of cinema, we shift our attention to how the day of the week can significantly affect a movie’s box office performance. Beyond the broader trends of monthly releases, we recognize that each weekday carries its unique influence, shaping the success of films across various genres. This part of our analysis is dedicated to uncovering which days offer the most promising returns and how these patterns differ depending on the type of movie.

Understanding Box Office Performance: Weekday and Genre Analysis

Diving into the daily fluctuations, we look at the average box office revenues for each genre on every day of the week. This exploration is vital for identifying which days might hold an advantage for certain genres. To enhance our understanding and accuracy, we incorporate a Random Forest model into our analysis. This advanced method helps us interpret complex patterns and predict the most strategic days for releasing films. By combining this predictive modeling with our observations, we can suggest the best release timings that align with genre-specific trends and audience behaviors.

To encapsulate our findings, we present a heatmap that illustrates the average box office earnings for each genre across the weekdays. This visualization allows us to quickly discern which days are generally more lucrative for each type of film. It’s more than a representation of data; it’s a strategic tool that highlights the best days for movie releases. With this visual guide, filmmakers and distributors can better understand and leverage the subtle yet significant impact of weekdays on box office success, crafting release strategies that maximize audience engagement and revenue.

The Daily Rhythm: Selecting the Best Release Days

Our analysis showed that the success of a movie at the box office can vary significantly depending on the day it’s released. Different genres tend to perform better on specific weekdays, suggesting an underlying pattern in audience behavior. By combining rigorous data analysis with industry knowledge, we’ve identified which days might offer the best chance for a film’s successful release, providing a valuable guide for planning premiere dates.

2. What is the relationship between a movies’ genre and its success? Do Movie Genres Hold the Key to Success?

Embarking on a meticulous analysis into the financial dynamics within cinematic genres, our exploration pivots to an initial analysis: the comparative of mean budgets and revenues across diverse movie genres. Do certain genres stand as exemplars of financial efficiency, strategically managing budgets while making substantial revenues, or do other genres showcase more extravagant budgets that yield commensurate returns? Utilizing a rigorous analytical lens, we delve into the financials, seeking answers within the cinematic genres. How do genres navigate the interplay between budget allocation and revenue generation?

After meticulously analyzing the mean revenues and budgets across various cinematic genres, distinct patterns in financial performances come to light.

Genres like Animation, Adventure, and Fantasy emerge as the frontrunners in generating substantial mean revenues, boasting impressive figures exceeding $100 million, indicative of their potential commercial strength. Conversely, genres like Drama, Western, Documentary, and Foreign genres reveal notably lower mean revenues, signaling potential challenges in garnering higher box office returns.

In tandem with revenue analysis, a scrutiny of mean budgets across genres showcases similar insights. Adventure, Animation, and Family genres tend to exhibit higher mean budgets, signaling considerable investments in production. Conversely, genres like Horror, TV Movie, and Mystery demonstrate relatively lower mean budgets, suggesting potentially restrained financial backing in their production.

However, amidst these revelations, a pivotal question arises: Should our assessment of movie success predominantly hinge on revenues alone, or should we delve deeper into net profit comparisons? The focus on net profit might present a more holistic view, considering that substantial revenues might not necessarily translate to profitability if excessive budgets have been allocated.

We can observe that genres such as Animation, Adventure, Family, and Fantasy emerge as frontrunners, showcasing higher mean profits, exceeding $100 million in the box office. These genres manifest considerable success in garnering profitable returns, reflecting robust performances in terms of financial gains.

Conversely, genres like Foreign, Western, Documentary, and History exhibit notably lower mean profits, signaling potential challenges in generating substantial financial gains in the box office. The lower figures suggest comparatively limited success in reaping profitable returns within these genres.

However, Should our evaluation of movie success solely pivot on net profits, or should we pivot towards the evaluation of Return on Investment (ROI)? Examining ROI might offer a more comprehensive view, considering that higher net profits may not inherently signify superior financial performance if substantial initial investments haven’t been factored in.

Upon analyzing the Return on Investment (ROI) for various cinematic genres, compelling insights into their financial efficiency and profitability emerge. Genres such as Documentary, Horror, Music, Animation, and War stand out, boasting notably high ROIs, surpassing 300%. These genres showcase exceptionally efficient utilization of financial resources, demonstrating substantial returns in comparison to their initial investments.

In contrast, genres like Crime, Action, and Foreign exhibit relatively lower ROIs, hovering around or below 220%. While these genres might generate profits, their ROI suggests that the returns might not proportionately align with the capital invested, potentially indicating less efficient financial performance.

3. Explore the relationship between movie plot sentiment and movie success

The third stage of our analysis will take us to a point that is crucial for humans and which therefore probably influences the success of movies. We have carried out a sentiment analysis of movie plots to explore the relationship between the emotions expressed in the plots and the success of the films in terms of box office revenue. We know that human beings are fond of feelings. This is one of the reasons why so many films in the drama genre are produced, because they express human interaction. Let’s start by explaining in a few words how our NLP pipeline works.

For each film we tokenised its plot and labelled the sentences in the plot as being either positive, negative or neutral on the basis of an emotional score. Here’s a small example with this wise quote from Frank the wizard:

“I got up in a good mood. I had a good night. I had an excellent breakfast. Unfortunately, I realised that I’d have to do the washing-up afterwards. I would have liked to use a dishwasher”

The first three sentences are labelled as positive because of their positive score assigned by our sentiment analyser. Therefore they are coloured in green. On the contrary, the forth sentence in red is labelled as negative by the analyser and the last one is considered as neutral. This is why they appear in red and blue, respectively.

Then we classified plots as follows. All plots containing more than 50% sentences associated with a particular sentiment are labelled as belonging to that sentiment. For example, if more than 50% of the sentences of a plot are negative, the plot will be considered negative. In the case of our previous example, the text would be labelled as positive because it contains three of positive sentences over five. Movies with a proportion of sentiment of less than 50% in all categories are left out because they cannot be correctly labelled. Let’s look at the result of this processing by visualising the number of films with plots appearing in each of the emotional categories for different genres:

Now that the data is available, we can ask our research questions to address them:

  • 1) Are films with emotional plots (positive or negative) more successful than films with neutral plots in terms of box office revenue?
  • 2) Focusing on movies with emotional plot, what emotion (between positive and negative) in the plot makes the movie more successful?

Naive analysis

A naive approach to answer our questions on the impact of sentiment in movie plots on box office revenues would be the following: we could simply compare the box office revenue averages for each plot category: emotional vs neutral and positive vs negative. This can be done using a t-test. Let’s look at what this would give us:

Looking at the plot above we observe that in both cases, the confidence intervals of box office revenues are not overlapping whether looking to the left bar plots for question 1) or to the right bar plots for question 2). In addition, the p-values of t-test are the following:

  • p-value = 0.0202 for question 1) comparing mean box office revenues between emotional versus non-emotional plot
  • p-value = 9.18e-08 for question 2) comparing mean box office revenues between positive and negative emotional plot

So, in both cases the difference of mean box office revenues is statistically significant (at a significance level of 0.05) as both p-values are smaller than the significance level. For 1), if we look at the value of the t-statistic or simply observe the plot above, we can see that films with emotional plots have a higher average box office revenue than neutral plots! Thanks to the low p-value we can reject the fact that the mean box office revenues are the same for both categories. This is the same for question 2). As the p-value is below 0.05 we can conclude that the difference of mean box office revenues is statistically significant between movies with negative emotional plot and positive ones. Films with negative emotional plots have a higher average box office revenue than neutral plot.

Matched experience

But are those conclusions trustworthy? No! In the case of observational analyses, we have to take account of important factors in our data which can influence box office revenues by category in different ways! These are the terrible confounders… In our analyses we consider that the genre of a movie might have an effect on whether the plot is sentimental or not. For example, we would expect to have a higher number of emotional plots for drama and maybe only a few emotional plots for action movies. This may impact our analysis on box office revenues as the two genres may not have similar distributions of revenues.

The same applies to budgets: it is obvious that if we consider the money generated as output, we must care about the money at the input i.e. the budget as the later has probably a big impact on the revenues. A movie with a higher budget will generally reach a bigger audience (higher budget means it will probably lead to a greater marketing, be translated into more languages, have better special effect…) and consequently generate a higher box office revenue.

To get rid of this merciless enemy, we did some matching. In our analysis, we select for each genre the same number of movies with an emotional plot as with a non-emotional plot for question 1). For question 2) we also select for each genre the same number of movies with positive emotional plot as with negative plot. To match the dataset on budgets, it is very unlikely that two movies have the exact same budget. So, we match movies between the treatment and the control group whose diffence in budget is not more than 20% of the smallest one i.e.:

To compare the box office revenues distributions, we performed t-test as before.

But what about the results of our analysis? Here they are:

  • For question 1) on the link between box office income and plot emotionality (whether they are neutral or emotional) it turns out that, against all expectations, there is no statistically significant difference (significance level of 0.05) between the box office averages of the two categories! In fact, by performing a t-test between the two averages, we can see that the p-value (0.402) is above the significance threshold. We also observe in the figure below that the 95% confidence intervals overlap considerably. We might have expected the emotion ingredient in movie plots to contribute to the potion of success. But because of the low p-value, we cannot refute the hypothesis that the two averages are similar unlike the naive analysis told us.
  • Regarding question 2), if the plot is considered to be emotional, it turns out that this time we can reject the null hypothesis stating that the two sets of movies box office revenues have identical means (at a significance level of .05) as the p-value (0.0324) is below the significance level. Based on the t-statistic or on the plot below, we can say that the mean of box office revenues for movies with a negative emotional plot is higher. This is interesting to observe that films with negative emotional plot generally convey a greater success looking at the box office revenues. One would probably have thought that humans would be more sensitive to movies with positive emotional plot rather than negative emotional ones. “The heart has its reasons which reason knows not.” to quote the famous french mathematician Blaise Pascal.

4. Opting for a Single Oscar-Nominated Actor? No. Choosing Two or More? Yes.

As a movie producer, you might wonder: “Is it really worth it to hire big-name actors? and if so, how Manny should I hire ?” for your next block buster. Sure, they bring star power, but they also bump up your movie’s budget. It’s a classic dilemma — balancing the sparkle of famous faces with the realities of filmmaking costs.

First, we had to decide what a big-name actor is. There are several ways to do so, for the purpose of our study, we decided to use Oscar nominations as the benchmark. Essentially, if an actor has received an Oscar nomination at any point in their career, we categorize them as a big-name actor.

The Sweet Spot

We grouped our movies into four categories based on the number of Oscar nominated actors that participated in the casting to find if there is any relationship between the number of nominated actors and the movie’s box office revenue. As you can see the four bar plots, we can observe that the more Oscar nominated actors you have in your casting, the better your movie will perform in the box office revenue 🤑. The non-overlapping confidence intervals between the no Oscar actor group and the one Oscar actor group clearly says that having one Oscar actor increases your mean box office revenue than having no Oscar actor at all.

But we are not dumb because we took the ADA class ! Our analysis so far has been naïve, not considering that many confounders may affect the box office revenue. To try to isolate the Oscar actor effect on revenue, we have performed a paired matching. By completing the paired matching, we try to compare movies with similar probability of receiving the treatment, which in our case is having at least one Oscar nominated actor. We used logistic regression to compute the propensity score based on the budget of the movie and the number of actors of the movie. We then perform the T-test for the mean box office revenues of two independent groups. This is a test for the null hypothesis that 2 independent samples have identical average (expected) values. Unfortunately we obtain a p-value of 0.42, which is greater than the rejection threshold of 0.05, which basically means that we cannot reject the null hypothesis that the two groups come from different distributions. Also, the confidence intervals between the two groups are clearly overlapping. We have to dig more deeply into our data !

Maybe let us modify a little bit the treatment assignment definition and re define the treatment by “having two or more Oscar nominated actors in your casting” and let us redo the exact same study to see if it changes anything. When re-doing the study, we are again separating the movies between a treatment and a control group where the treatment group consists of movies who have more than a single Oscar nominated actor in their cast. And guess what ? this time we got a 0 p-value and non-overlapping confidence intervals suggesting that having two or more Oscar nominated actors in your cast will really increase your chances of performing well in the box office !

Who were the most important actors during the past five decades ?

Do you feel like you’re always seeing the same actors, such as Brad Pitt, Emma Watson, and Robert De Niro, when you go watch blockbusters to the movies? Are certain actors more prominently featured in such films? This is what we are gonna explore, so let’s get started.

We are going to run a little graph study to explore the links between actors. To do so, we are going to consider the set of actors as a social network where each node is an actor and where two actors are connected to each other if and only if they played together in a movie, then the strenght of the link between the two actors will be the mean of the box office revenues of the movies they played together.

Since we only wanted to consider blockbuster movies, we had to define what a blockbuster is. For this purpose, we only selected movies with box office revenues greater or equal to 90% of all other movies of our dataset.

We then conduct a weighted degree centrality analysis on our graphs for each of the past five decades and sort the actors by their centrality measure, hoping that would give us some insights and some cool stuff to tell you . Let’s see what we get !

The seventies : a deceny marked by an emblematic movie, The godfather.

Joe Spinell, Talia Shire, James Caan, Marlon Brando, Richard Bright, Diane Keaton, Al Pacino, Robert Duvall, John Cazale and Peter Donat, these are the 10 most emblematic actors of the seventies. You may ask yourself what do all these actors have in common ? did they perform singularly each in a movie or do they have a common movie that kind of boosted their carreer ?

Well, just by having a quick look at their wikipedia page, we found out that 8 out of 10 : Marlon Brando, Al Pacino, James Caan, Robert Duvall, John Cazale, Diane Keaton, Talia Shire, and Richard Bright all appeared in “The Godfather” (1972) directed by Francis Ford Coppola.

The Eighties: The Era of “Star Wars” and “Back to the Future”

Again the eighties top actors fall into two groups :

The Star Wars cast where :

  • Harrison Ford played Han Solo.
  • Billy Dee Williams portrayed Lando Calrissian.
  • Carrie Fisher was famous for her role as Princess Leia.
  • James Earl Jones provided the voice for Darth Vader.
  • Frank Oz voiced Yoda.
  • Mark Hamill starred as Luke Skywalker.

and the Back to the future cast where :

  • Christopher Lloyd played Dr. Emmett “Doc” Brown.
  • James Tolkan was known for his role as Mr. Strickland.
  • Frances Lee McCain played Stella Baines, Lorraine’s mother.

The early twenty first century : The era of fantasy movies

The early 21st century actors are known for their roles in iconic film series, like Harry Potter or the Lord of the rings which are celebrated for bringing the magical worlds of J.K. Rowling’s and J.R.R. Tolkien’s to life on the big screen.

“Harry Potter” Series Connection:

  • John Cleese played Nearly Headless Nick.
  • Robbie Coltrane portrayed Rubeus Hagrid.
  • Brendan Gleeson appeared as Alastor “Mad-Eye” Moody.
  • Gary Oldman was Sirius Black.
  • Alan Rickman played Severus Snape.
  • Warwick Davis took on multiple roles, including Professor Flitwick and Griphook.
  • Maggie Smith starred as Professor Minerva McGonagall.

“The Lord of the Rings” / “The Hobbit” Series Connection:

  • Hugo Weaving played Elrond.
  • Orlando Bloom portrayed Legolas.
  • Bruce Spence appeared in “The Lord of the Rings: The Return of the King” as the Mouth of Sauron (extended edition).

Note however that as this analysis might give us good insights on the importance of actors for the past five decades, the way we construct our graph gives the same importance to all kinf of actors and secondary actors might be considered as important as primary ones. We could have had done a better analysis by giving more weights to primary actors to primary actors but we did lack of data in order to do so.

5. Are longer movies more likely to be more successful or are they just more boring to watch?

As Benjamin Franklin once said “Remember that time is money”, but how does this apply to the world of movies? Movie runtime, measured in minutes is not simply the duration of a movie but also an element that can affect the audience’s interest and attention. As humans we dislike being under the impression that we are wasting our time, so this plays a big role in choosing how long a movie should be. In this part we will investigate the relationship between a movie’s runtime and it’s box office revenue in order to gage the influence of the duration of the movie on it’s success. So without wasting any time let us continue!

Starting off by looking at the scatter plot of movie runtimes and the movie revenues we might expect to see a clear relationship, but sadly we do not. But all hope is not lost! We will delve deeper into the analysis using statistical testing and linear regressions.

Using the magical power of linear regressions we investigate this question further

Analysing the results of our linear regression between movie runtime and box office revenue we find an R-squared value of 3.1%, meaning that movie runtime explains almost no variance in the movie revenue and with a p-value close to 0 it looks like move runtime is a poor predictor of our movies success.

When we are looking at continuous revenues, it appears that runtime does not explain well the variance in box office revenunes. But what about looking at 2 distinct revenue categories, do they have a significant difference in runtime? To accomplish this, we will be looking at block-buster movies (movies that have a revenue of over $400 million) and non block-buster movies.

By using a student test and doing exact matching on the genres to mitigate an important confounder in our results we find that the average runtime for block-buster movies is significantly higher than for non-blockbuster movies, and our result is statistically significant due to the p-value being close to 0.

Despite having a low value of R-squared between movie runtime and revenue we were able to expose a significant difference in movie runtimes between block-busters and non block-busters. This underlines the nuanced role that timing plays on a movies success. As Gandalf wisely said, “A wizard arrives precisely when he means to.”

6. Are movies with higher budgets more successful?

Does investing more money into a film guarantee higher returns? Is this a myth or is this reality? A priori one would expect that investing more would correlate to higher box office revenues. By doing a linear regression between the films budget and box office revenue, we find that the R-squared values is 0.165 meaning that 16.5% of the variance in box office revenue is explained by the movies budget! And the coefficient associated with the budget is positive indicating that an increase in budget leads to a direct increase in movie revenue. The associated p-value is also close to 0 indicating that our results are statistically relevant.

In the midst of all of this, one could wonder how profit evolves as a function of the budget. Do movies with higher budgets also have higher profits? Is this true? It sure looks like it! We have done a student test and found that the average profit for movies with high budgets (meaning a budget above $50 million) was indeed higher than movies with low budgets and the p-value was close to 0 indicating that the result is again statistically significant. This makes sense since we would expect movies with bigger budgets to also bring in more money so even if they cost more, they should on average make more money overall than smaller movies. But wait! Each genre might lead to different amounts of profit being made even if the budget is the same! For example if you spend $80 million on an action film or on a film about Frank the wizard, you are unlikely to yield the same net profit! To avoid this we have employed exact matching in order to mitigate the genres as a potential confounder.

By inspecting the image above we can see a big difference in the average net profit earned by low-budget movies and high-budget movies. Indeed showing us that to make the big bucks you need to invest the big bucks.

Wrap Up

Let us now quickly recap the main results of this analysis to be ready to produce good movie generating a looooooot of money.

In our thorough analysis of release timing and box office success, we’ve found a clear link between when a movie is released and how well it does financially. While we noticed gradual changes in revenue over time, reflecting evolving audience preferences and industry trends, our deeper examination particularly highlighted June and December as successful months, likely due to holiday seasons attracting more viewers. Our detailed daily analysis also pinpointed specific weekdays that favor different movie genres, offering valuable insights for planning optimal release dates. Overall, our study confirms the significant impact of release timing on a movie’s box office performance, providing industry professionals with essential knowledge to strategically schedule releases and maximize audience turnout and revenue.

Concluding the genre analysis, the findings offer valuable insights into movie success metrics. Metrics such as ROI, net profits, and mean revenues contribute significantly to understanding a genre’s financial performance. However, it’s important to note that these financial indicators, while crucial, do not singularly define a movie’s success. Other qualitative elements like storytelling, audience engagement, and critical acclaim play equally vital roles in shaping a genre’s impact within the film industry.

From our sentiment analysis question, it seems that there is no significant difference between the success of movies with emotional plot versus movies that have a non-emotional plot looking at the box office revenues. However, when movies plot starts to deal with emotions, it appears that movies with negative emotional plot encounter a greater success in terms of box office revenues than movies with positive emotional plot. So, when writing a plot, if you wish to increase your movie box office don’t forget to put negative emotions if the plot is emotional.

As far as the runtime analysis is concerned, we can conclude that although runtime does not play a significant role in determining a film’s subsequent success it still carries some weight in helping predict whether a film will be successful or not but we cannot base our result solely on duration to make any significant predictions. However when splitting films into blockbusters and non-blockbusters we find that there is a link between longer films and higher revenues, although this could also be due to various other factors such as budget or when the film is released for example.

As for the budget, it does have a significant impact on a movie’s revenue aswell as a movie’s net profit. Therefore film studios can also expect to make more or less money depending on how much they are prepared to invest. However it is important to remember that without the correct balance of runtime, budget, genre distribution and many other factors it would be difficult to make a real blockbuster that would tear down the charts.

Regarding the cast of your movie, it would be wiser to hire two or more oscar nominated actors because only having one won’t make the difference. Additionally, the conducted network analysis helped us uncover the fact that some movies like harry potter, the godfather, the lord of rings helped boost the careers of some worldwide known actors