The gaming industry is financially larger than the entire music industry plus the NFL. If that isn’t a sign to grow interest I do not know what is. The once misunderstood industry developed rapidly with its original consumers. Ever since the Atari and the super Nintendo hit the shelves the gaming industry has grown exponentially. It has now become a industry that houses a high variety of generating revenue. There are examples of cash cow franchises that repeat formulas for easy money, there are games that rely on micro transactions, and there are those that rely on the actual entertainment the product provides.
For this post I intend to take a closer look at some sales data that spans the recent years of games. I want to take a look at the highs and lows of companies such as Nintendo, Sony, Blizzard, and the massive Activision.
There is a lot of interest in this industry. It’s size has allowed for incredible change. For example, millions of dollars could be thrown at a game that surrounds well known characters, hours of content, and a huge marketing budget. Then a team of twelve game developers come around and make a silly survival game for 15 dollars that grasps the entire gaming public for months and shoots to the top of Steam sales charts. While the sales data I am looking at holds a lot of information, the real intriguing piece of this comes from the reviews of the players. Learning what makes a good game and what lends to fun is what makes this industry entertaining to view. So let’s begin.
library(tidyverse)
library(readr)
library(dplyr)
library(httr)
library(tidytext) Warning: package 'tidytext' was built under R version 4.3.3library(textdata) Warning: package 'textdata' was built under R version 4.3.3library(rvest) Warning: package 'rvest' was built under R version 4.3.3Vgames<-
read_csv("https://myxavier-my.sharepoint.com/:x:/g/personal/harigb_xavier_edu/EfULn1-WQ5ZDkAiLf_zXOIABXofh9kma-mZd8z_zX9CryQ?download=1")
#Data Dictionary
Didictionary <-
read_csv("https://myxavier-my.sharepoint.com/:x:/g/personal/harigb_xavier_edu/EZgWe3SbnT9DsVR65ikv_vwBF-qrnhXVmLUlSvR_JjZ9cA?download=1")This data set contains the top 16,600 most sold video games from 1980 to 2016. While I would love to do a deeper dive on where games are now as its been 8 years, this is a fantastic data set and holds accurate date to many of the games within it. As these games were produced in the early 2000s and 2010s, their sales have nearly stagnated. This is the benefit of many sales based data sets.
We can see the Rank of each game, the company that produced it, and the platform it released on. Further more we of course have the year and the sales data broken up into North America regions, European Union, and Japan. Japan and the United states are of course home to the largest gaming conglomerate such as Microsoft, Nintendo, and Sony. These three are Juggernauts in the industry.
To begin, let’s talk about Nintendo games. Specifically two of their most notorious franchises of Mario and Zelda.
The legend of Zelda franchise pioneered adventure style games. Being one of the biggest titles released on the Famicom disk system which was a cartridge version of the original Super Nintendo. This successful launch spiraled into a franchise of adventure games that are frequently a hit. And of course we have Mario. The arcade game turned most successful game franchise of all time.
Vgames %>%
filter(Publisher %in% "Nintendo") %>%
ggplot(aes(x = Platform, y = Global_Sales))+
geom_col()
Vgames %>%
filter(grepl("Zelda", Name)) %>%
ggplot(aes(x = Platform, y = Global_Sales))+
geom_col()
Vgames %>%
filter(grepl("Mario", Name)) %>%
ggplot(aes(x = Platform, y = Global_Sales))+
geom_col()
From top to bottom we have Nintendo’s global sales across platforms as well as the Mario and Zelda sale across platform. From these visuals we can see just how massive the Mario franchise is. Mario sales make up nearly 25% of Nintendo’s entire revenue stream. There is rarely a bigger cash cow in the gaming industry than this. A brand that has stood the test to time and innovates constantly.
What is more interesting is the Zelda data. Now this data does not include the ground breaking Breath of the Wild, by far the most profitable installment, but it does include the incredible Ocarina of time and Majora’s mask. When considering the two franchises together, Mario is a platform and a racing game (Mario kart) while Zelda is a adventure game with puzzling and combat aspects. But if you were to ask people “What is the better game” very rarely will give the answer Mario.
The key to understanding Sales in gaming is Replay-ablility and Co-op. Mario games, whether platforming, racing, or Mario party, all of these games are highly repayable and either involve or allow the option to play with friends. Meanwhile the Legend of Zelda franchise is almost entirely solo adventures.
I do wish that this data set went up to now so that I could more accurately depict the current decline of this once incredibly strong franchise. Call of Duty originated as a First Person shooter that followed soldiers through their missions. It all changed when Call of Duty 4 Modern Warfare was released. A first person shooter that had online game play. Actively allowing people to compete with each other with fictional weaponry.
Today Call of Duty has become a cash grab full of micro transactions. It is an uninspired lack luster repetitive mess that undermines the people who originally enjoyed it. While it does suffer from its age, the lack of creativity from these massive budgeted companies like Activision only further highlights that how certain franchises have become more about the money than the content produced.
Vgames %>%
filter(Year>2007) %>%
filter(grepl("Call of Duty", Name)) %>%
ggplot(aes(x = Platform, y = Global_Sales))+
geom_col()+
labs(title = "Call of Duty by Platform") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) 
Vgames %>%
filter(Year>2008) %>%
filter(Global_Sales>1) %>%
filter(grepl("Call of Duty", Name)) %>%
ggplot(aes(x = Name, y = Global_Sales))+
geom_col()+ labs(title = "Call of Duty games total sales") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) 
Vgames %>%
filter(grepl("Call of Duty", Name)) %>%
ggplot(aes(x = Year, y = Global_Sales))+
geom_col()+
labs(title = "Call of Duty yearly sales") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) 
Vgames %>%
filter(grepl("Black Ops", Name)) %>%
ggplot(aes(x = Name, y = Global_Sales))+
geom_col()+
labs(title = "Black Ops yearly sales") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) 
Vgames %>%
filter(grepl("Modern Warfare", Name)) %>%
ggplot(aes(x = Name, y = Global_Sales))+
geom_col()+
labs(title = "Modern Warfare yearly sales") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Here we can see Call of Duty’s sales across platforms. It is important to note that the only COD games released on the ps4 consoles at this time were Call of Duty Ghost, Advanced Warfare, Black Ops III, and Infinite warfare. However, as we see below, the most popular installments of this franchise were on the PS3 and Xbox360.
The Call of Duty franchise would do scheduled releases alternating between two companies. There was Treyarch, owned by Activision, and there was Infinity Ward, also owned by Activision.
Infinity Ward gained an early start when they released COD 4, with its ground breaking multiplayer. However Treyarch followed suit with World at War. But nothing compared player for what would happen on the release of MW2. The beauty of this game came from a few things. The impact the campaign had on the fans, featuring shockingly well written characters. Far better map design and variety. And most importantly, the culture and community aspect of the game. People were more active than ever, be it aiding your team with call outs or the vast amount of chirping that happened in this time.
However, even more shocking was how the next installment became the highest selling Call of Duty game on record. Call of Duty Black Ops.
With the introduction of Black Ops, Call of Duty entered its true golden age of stellar games. Up until we see the first dip in sales with Call of Duty Ghosts. This is the game that cause 2013 to dip much lower than its previous year. A game that was known to have lack luster map design, and poor bugs. The distaste continued with Advanced warfare. A game that attempted to innovate on the copy paste of game play by adding advanced movement, leaving players jumping around whilst fighting. This left players with a bit too much to re-learn to enjoy.
But are these good games? And is that why they sold? If what made Zelda a good game was because of its adventure and story telling while Mario was fun because of its replay-ability and cooperative aspects, than surely Call of Duty was the former correct? I don’t think so.
Call of Duty was fun for it’s competitiveness and it’s community. The game-play was never anything more than a vessel for players to combat each other and further more interact socially. It never needed to be super flashy like Advanced warfare attempted, it just needed to be well produced and fun. What has happened today, and why I believe this franchise is dying, is that the life span of this game has just run its course. There have become far more options, and the community around COD has dispersed to more games that fit their niches. Additionally Activision has gotten lazy, only expediting the process. They realized that they make more money through in game micro transactions than the original sale. So why take the time and effort to create well designed maps if your community is diminishing when you can just seduce more money out of the players still buying. The problem with this franchise is not innovation, because they tried it and it hurt them. It is the lack of consistency that the previous iterations had that have undermined their community. The people made the games fun, not the developers. And it is for these reasons that we can see these sales numbers decrease over time.
Now let’s see how different demographics purchase. The North American region by far has the most consumers. So what is important here are the percentages.
Vgames %>%
filter(Genre %in% c("Racing","Shooter", "Sports", "Strategy", "Platform")) %>%
ggplot(aes(x = Genre, y = NA_Sales))+
geom_col()+
labs(title = "Popular genres in North America")
Vgames %>%
filter(Genre %in% c("Racing","Shooter", "Sports", "Strategy", "Platform")) %>%
ggplot(aes(x = Genre, y = EU_Sales))+
geom_col()+
labs(title = "Popular genres in European Union")
Vgames %>%
filter(Genre %in% c("Racing","Shooter", "Sports", "Strategy", "Platform")) %>%
ggplot(aes(x = Genre, y = JP_Sales))+
geom_col()+
labs(title = "Popular genres in Japan")
Vgames %>%
filter(Genre %in% c("Racing","Shooter", "Sports", "Strategy", "Platform")) %>%
ggplot(aes(x = Genre, y = Global_Sales))+
geom_col()+
labs(title = "Popular Geres Overall")
I selected these Genres in particular because of how simple they are to picture. Adventure can be fairly broad and even things like Puzzle can be categorized a bit uniquely in the gaming industry. These five are simple to comprehend.
Here we can see just how different other regions consumed. What shocks me the most is seeing how profitable the “Sports” category is. However, i guess with long running franchises like FIFA, Maden, and 2k, that release almost every year you’re going to see incredible numbers like this.
Japan is by far my favorite market here. One would guess that the home of Mario would have Platform be their highest purchased genre. I am shocked to see Shooters outpaced by Strategy, the lowest of these five genres I selected overall.
I was expecting to see racing ranked higher in the EU. In my own mind I pictured Formula one being the main reason why this happens.
Now I would like to talk about the most shocking part of this data set. The top 15 ranked games in this entire data set.
Vgames %>%
filter(Rank<16) %>%
ggplot(aes(x = Name, y = Global_Sales, color = Platform))+
geom_col()+
labs(title = "Top 15 highest sold games")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Vgames %>%
filter(Global_Sales> 7) %>%
ggplot(aes(x = Publisher, y = Global_Sales))+
geom_col()+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
These are the top 15 highest sold games on record. All of them are Nintendo, every single one. If you look at the top 25, 20 of those are Nintendo. Nintendo has cracked the code on gaming.
This is how you become a company valued at 10 trillion dollars. You deliver. Year after year Nintendo puts out games that become someone favorite thing they have ever played. Pokemon, Mario, Kirby, Super Smash brothers, Legend of Zelda. They have the formula for every single genre. They conquered Platformers, they nail Adventure, and they have heavy hitters in Sport, Fighting, and Racing. They cannot be beat.
Why do we see this? Why are these games so good? Is it the history? The replay ability? The Communities that surround them? Certainly this must vary game by game. Lets look at the graph on the right that shows just how many Publishers there are contending with Nintendo. And none of them even come close. Even if you consider removing the top five highest selling games, none of these companies even come close to them. Why can’t these companies capture the hearts of gamers like Nintendo has?
The answer to that is not in Sales Data.
Before we move on I’d like to show another graph. This graph is not meant to be legible. So if it hurts your eyes I apologize.
Vgames %>%
filter(Global_Sales < .02) %>%
ggplot(aes(x = Publisher, y = Global_Sales))+
geom_col()+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Look at this mess. This is the amount of publishers that have decided to compete with Nintendo. They have little money, but all the motivation and creativity that started Nintendo. This graph was filtered to Publishers with less than 200,000 global sales. Start ups, small dev teams, and dreamers. These are the people that played Nintendo and hope to emulate them. Maybe they’ll catch lightning, maybe they’ll be bought our by Activision and have their creativity stolen.
Now I would like to look at some sentiments from a personal favorite games series of mine. The Borderlands series.
First lets begin setting up the scrape
set_config(user_agent("Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; Googlebot/2.1; +http://www.google.com/bot.html) Chrome/W.X.Y.Z Safari/537.36"))
link <- c("https://steamcommunity.com/app/49520/positivereviews/?p=1&browsefilter=toprated",
"https://steamcommunity.com/app/49520/negativereviews/?p=1&browsefilter=toprated",
"https://steamcommunity.com/app/261640/positivereviews/?p=1&browsefilter=toprated",
"https://steamcommunity.com/app/261640/negativereviews/?p=1&browsefilter=toprated")BorderFull <- data.frame()
ben_fun <- function(url) {
link <-
read_html(url)
Sys.sleep(2)
review <-
link %>%
html_elements("div.apphub_CardTextContent") %>%
html_text()
opinion <-
link %>%
html_elements("div.title") %>%
html_text()
hoursplayed <-
link %>%
html_elements("div.hours") %>%
html_text()
helpful <-
link %>%
html_elements("div.found_helpful") %>%
html_text()
date <-
link %>%
html_elements("div.date_posted") %>%
html_text()
game <-
link %>%
html_elements("div.apphub_AppName.ellipsis") %>%
html_text()
### Name combine
name1 <-
link %>%
html_elements("div.apphub_CardContentAuthorName.online.ellipsis") %>%
html_text()
name2<-
link %>%
html_elements("div.apphub_CardContentAuthorName.offline.ellipsis") %>%
html_text()
name3 <-
link %>%
html_elements("div.apphub_CardContentAuthorName.in-game.ellipsis") %>%
html_text()
BL_df <- data.frame("Posted" = date, "Review" = review, "Opinion" = opinion,
"Hours Played" = hoursplayed, "Number of helpful vote" = helpful,
"Title" = game, "Names" = c(name1,name2,name3))
return(BL_df)
}
## Loop
for (i in seq_along(link)){
BorderFull <-
ben_fun(link[i]) %>%
bind_rows(BorderFull)}Lets separate the words from each review so we can conduct the sentiment analysis
tidy_Pandora <-
BorderFull %>%
unnest_tokens(word, Review) %>%
anti_join(stop_words)Joining with `by = join_by(word)`tidy_Pandora %>%
group_by(Names, word) %>%
summarize(n = n()) %>%
arrange(-n)`summarise()` has grouped output by 'Names'. You can override using the
`.groups` argument.bing <-
get_sentiments("bing")
Bandit_counts <-
tidy_Pandora %>%
group_by(Title, word) %>%
summarize(n = n()) %>%
inner_join(bing)`summarise()` has grouped output by 'Title'. You can override using the
`.groups` argument.
Joining with `by = join_by(word)`Bandit_counts %>%
group_by(Title,sentiment) %>%
summarize(n = n()) %>%
arrange(-n)`summarise()` has grouped output by 'Title'. You can override using the
`.groups` argument.Bandit_counts %>%
group_by(Title) %>%
filter(n>3) %>%
mutate(n = ifelse(sentiment == "negative", -n, n)) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n)) +
geom_col() +
coord_flip() +
facet_wrap(~Title, ncol = 2) +
geom_text(aes(label = signif(n, digits = 3)), nudge_y = 8) +
labs(title = "Positive and Negative Words for Borderlands 2 and Borderlands 1.5",
subtitle = "Only words appearing at least 3 times are shown")
Here we can see some of the sentiments from each game. This is the most crucial observation we will see in this post as this seems to be the most accurate. Something interesting to look at is the negative words. This being a shooter, and a game known for its characters, words like enemy, loot, and damage have completely different conotations. Enemy and enemies could go either way. As the reviewer could be talking about the wide variety of enemies or about how the enemies are uninspired and tedious. Damage and loot are more than likely to be positive comments in this context. As damage is important in a shoot and loot because a main part of the combat is “Shoot cool gun at bad guy, big number shows up and health bar goes down”, its a satisfying thing to watch seeing as you level up in game you do more damage. Loot is CERTAINLY positive. The guns are what make these games fun. However, take it with a pinch of salt. There’s a chance that the context is the lack of loot or even that loot is hard to obtain. for this context lets assume its a positive.
In the visualization itself we can see a few things. Whats important here is seeing the negative words that appear for the Pre-Sequel that don’t appear for Borderlands 2. While there are positive terms that appear more frequently, we are at the mercy of the reviewers rhetoric. So seeing that there are more net negative words appearing for th Pre-Sequel is a strong indicator of which game is favored. Additionaly, negative comments tend to be written with strong emotion than positive ones. It is a fair assumption, and is accurate reading the full reviews, to say that negative reviews will have more content(words) than positive ones. This means with this basic bing dictionary, we will get skewed data. And with this in mind we will view the other visualizations.
Vgames %>%
filter(grepl("Borderlands", Name)) %>%
filter(Global_Sales>.15) %>%
ggplot(aes(x = Name, y = Global_Sales))+
geom_col()+
labs(title = "Borderlands") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
This visualization depicts four games. The original, the second installment along with the Pre-sequel, and the Handsome collection which features both games on one disk with all DLC attatched.
Here we can see some of the sentiments reflected in Sales. As it eventually panned out, the Pre-Sequel was labeled a lack luster unfinished game. It certainly carried much of the charm of Borderlands 2, but ultimately felt like an extended DLC. Much of its faults were due to hinging on the popularity of one character from the second installment. The villain, Handsome Jack. The word “handsome” actually appears multiple times in our scraped data, and it could only be referring to one character in particular. Our main antagonist. A narcissistic corporate overlord that seeks to harness the powers of celestial beings and rid the planet Pandora of our presence. An amazing antagonist with fantastic voice acting. His part in the second game may be the primary purpose for its success. The Pre-Sequel’s story surrounded his rise to power, while you work under his employ. An idea that sold many initial copies, but fell flat on execution when the game was released with too much filler and not enough screen time for Jack.
Initial hype is not enough to sell games. There are many players who don’t buy games based on the ones before it. A game needs to have an intrinsic value that it gives the players, a sense of achievement or wonder that is more than just bright colors. This could be through story, community, cooperation, or in some cases mindless fun. Borderlands is a series that gives story and action while also entertaining players with incredible amount of hidden “easter-eggs” in the game. Whether it’s a movie reference or an enemy named after an actor, these games provide value. But consistency is required to keep a franchise afloat.
A good game is like a piece of art. It needs to be enjoyed by those who play them for it to be worth anything. Whether it is by an individual or a group. Without an audience it’s just code on a screen. Regardless, it has been fun trying to analyse this topic a bit deeper.