The analysis of how COVID-19 impact on video game industry

Github Link: https://github.com/pengtongyang1/INST-414-Final-Medium

Our project is Analysis of Performance of Different Genres of Games During Times of Intense Anxiety. We have four group members in our group, Pengtong Yang, Ruoyi Liu, Yang Lu, and Liangshan Chen. Our main goal is to investigate the impact of COVID-19 on video game stocks, with a focus on the investors as the main stakeholder. In reaching our goal we would be able to answer the following questions: When was the best time to invest in video games during the COVID-19 pandemic? How do we know that is the optimal time? After we made sure about the research question, we started to make the purpose of it. Our prediction is to predict there will be a rise in console gaming stocks, such as Nintendo(NTDOY), Sony(SONY), etc., given that console games such as Animal Crossing: New Horizons were released around the time when lockdowns begin. From investigating these insights, investors can see the trend-wise, which companies will continue a steady growth, and which to not invest in. This project will be successful in getting the answer to our research question if it can answer the questions regarding video games and their sales overtime throughout the current Covid-19 period. The answer would most likely be US dollars for these potential questions, as the stock market is measured in USD. The purpose of our research is to see trends in the video game market and make financial decisions accordingly for investors or marketing decisions for corporations, where they can see periods of stock growth and why. For the group of people as our target audience, they are the people who always like to play video games, and the leader in the video game industry or the people who make the games will follow this project’s result.

The methods we use in this project are the Alpha Vantage, which will analyze the Stock API. The data collection and analysis from different websites, such as Kaggle. The data relating to the COVID-19 pandemic, also in the sense of game selling. The main language we used for our project is- python. We use Python to do the data analysis, which will help us to easily find out the useful information that we could use to analyze our project. Let’s talk about the details of each method we use and what we find in the following paragraphs.

As the project measures stock performance by their price, for the stock information collecting, we used Alpha Vantage API, with a focus on Sony, Tencent, and Nintendo, and Microsoft. Which are key companies in the video game field. As we are looking at the success of video game companies during times of high anxiety such as during the Coronavirus disease 2019 pandemic, our scope is between January 2020 to March 2022. We have listed two graphs of stock trends as examples in this report, the graphs are for SONY and Nintendo, or essentially, the Switch console against the Playstation console. The colored bars means closing lower than opening/decline and the uncolored bars means closing higher than opening/growth.

From these two graphs thus far, we see an evident growth between April 2020 to March 2021. The growth brings to question whether or not it correlates with COVID-19 “waves”. From a study by Gianfelice et al (2022), “The starting dates of COVID-19 multiple waves”, they concluded that “Multiple waves of contagion/deaths can be seen: the four most pronounced are in April 2020, December 2020, April 2021, and August 2021.” April 2020, December 2020, April 2021 are all points of growth for both stocks. Interestingly August 2021 was a point of decline, as both companies closed lower than their opening.

Looking at the impact of published video games by each company, Animal Crossing: New Horizons(ACNH) was released on March 28, 2020. It can be hypothesized that ACNH is impacting the growth for NTDOY in April 2020. For SONY, Final Fantasy vii Remake was released on April 10, 2020. However, more data and analysis is needed to make a definite conclusion on the strength of the impact newly released games have on the stock price, which is simply to say whether the games are correlation or causation factor.

Some previous approaches for handling the stock data obtained was the scope of data analyzed. Going by daily or weekly data provided a cluttered data visualization that offered no additional information compared to monthly. The surplus of data rather served as a distraction to the overall core of the data trend.

For the part of estimated sales data for games, I collected the data from the website of “vgchartz”, which is related to the different video game’s data collection. Such as Nintendo handhelds, Sony handhelds, Nintendo consoles, etc. The progress for this part towards the proposed goal is the data collection and some analysis to the result of estimated sales for games in the future. As we are looking for the success of video games companies during the covid-19 pandemic, we need to analyze the data from previous years. From the website “vgchartz”, in the part of the data analysis from sales for the Nintendo consoles, I can see the highest sales in March 2022 is Wii. It accounted for 26.2 percent of the total sales. Then, I looked at the SONY consoles at the same time period as the Nintendo consoles. The sales for PS2 is the highest sales, 32.6%. From the two different game consoles, we can analyze that even though the covid is very serious in recent years, the video games don’t have any impact on sales.

Looking at the different console hardware sales, the PS2 in Sony consoles, PS4 and 3Ds in 8th generation worked well. The PS4 is 64.4% of the total sales, and the 3DS is 82.4% of the total sales for the handhelds hardware sales. In the category of the Microsoft consoles, XBOX 360 is the best selling console in this category, which is 49.1% beyond the total sales. However, the sales for the Xbox Series S did not sell well, only 7.8% of people who played video games bought it. The reason may be because there are less games on the platform of Xbox.

Unlike the early years when there were a lot of platform exclusives, such as Sony’s The last of us, Microsoft’s Halo series, and Nintendo’s Mario series, more and more manufacturers began to develop games on different platforms and sell them at the same time in order to increase the sales of games. Moreover, there are a number of games that are simultaneously released on two generations of the same brand consoles. For example, Sony’s Spider-Man series first came out of the ps4 version, and then it was released in a picture-quality enhanced version after the PS5 release. Specifically, there are exclusive games that are developed by the first party and released on the PC platform after the game becomes profitable, such as Sony’s God of War and Detroit Become Human.

The analysis I have run for the part of estimated sales for games shows the analysis I made above. The lowest sales type of console should open their market to put more new games on the platform. Since those games are only on the specific consoles, the users may like to play them at the beginning. Then, the company should consider putting those games on the different consoles in their company, or the company may need to research and develop the new games on the platform as Xbox Series S.

The data collection process used libraries in Python. We attempted to web scrap dataset from the Worldometers website and Vgchartz website using beautiful soup, but the process of gathering the dataset was way more advanced with the majority of the dataset being encrypted. We were forced to manually scrape the dataset from the Worldometers website and the Vgchartz website. We figured that the total covid-19 cases worldwide were too broad for analysis and data visualization. We decided to split into smaller countries or even smaller areas to conduct better, accurate data results to be present in reports and in graphs. The covid-19 dataset included 26 observations and 4 variables. The 4 variables in the covid-19 dataset are Case_ID; which gives each observation or row a number for more consistent data manipulation, Date: the dates of covid-19 cases in each month, Country/Area: the country or the area of covid-19 cases, and Cases: the number active covid-19 cases in each particular month. The game sales dataset also had 26 observations and 4 variables. The 4 variables are Game_ID: which represents the ID of each game sales, Date: the date of the month for the game sales, Platform: the platform of the games of most recent generation consoles, Sales_Total: the total numbers of sales of each individual month.

We also used Python for the data cleaning, and data manipulation process. Pandas library, DateTime from date library are being used for the data cleaning. The covid-19 dataset in XLSX file format and the Game Sales dataset in CSV file format is being imported to Python using PD.read_excel and pd.read_csv. We used the merge method in the pandas library to combine both the covid-19 cases dataset and the game sales dataset to prepare for the data visualization graphs.

We created models to support the insights of trends of gaming stocks, game sales during the covid-19 pandemic, and the time for companies to invest in games or stocks based on the visualization that our team has created. We used the seaborn library, matplotlib library, and matplotlib inline for the display style of our graph visualizations. We used Plt for matplotlib library to x-axis label, y-axis label, title name, and visual adjustments. We used sns for seaborn library to create bar graphs and line graphs for covid-19 cases, game sales, and the combination of covid-19 cases versus game sales.

According to the graph created based on the total active covid-19 cases in the USA from February 2020 to March 2022. The peak of the total cases is the February of 2022, and of course, the lowest active cases are the first few months of covid-19 outbreak February 2020 and March 2020 as in millions. As you can see the total active cases of covid-19 cases haven’t reached millions at this point. The trend of total active cases only increased for the year of 2020, for the year of 2021 and 2022, the total active cases had a trend going upward and downward for every 2–3-month period of time.

According to covid-19 data collected from the Worldometer website, we had fulfilled the sufficient amount of information needed for the game stocks and game sales related to covid-19 cases throughout the pandemic. We used the covid-19 data beautiful soup from the Python library to scrape the covid-19 data. We created graphs about the for covid-19 cases from February 2020 to March 2022. We are in the progress of testing on comparison graphs between covid-19 cases and sales number, covid-19 cases and stocks trend.

According to the graph created based on the game sales in the USA from February 2020 to March 2022 in five different platforms: PS4, PS5, Xbox One, Xbox Series X, and Nintendo Switch. The peak of game sales for each platform are around January, 2021 and January 2022. The rest of the game sales in other months remained constant. The most selling game platform of all times are Nintendo Switch.

According to the line graph of “Relationship between Sales Total and Covid Cases”, as covid cases increase from 0 to 15,000,000 the trend tends to increase upward, which means as covid cases increases the game sales also increases in this range. As covid cases start to increase from 15,000,000 to 20,000,000, the trend tends to decrease downward, which means as covid cases increase the game sales decrease in this range. These graphs will help the investor to decide when: the best time in the month, platform: the associated game platforms in PS4, PS5, Xbox Series X, Xbox One or Nintendo Switch, and game totile: as of which game title in this particular month and with these platforms to invest in based on the trends provided. The visualizations and reports were provided as reference to make the correct decisions on investments.

Based on the above data analysis, we give the following suggestions to the game line of stakeholders as a reference for the future development of the game industry.

First, the (self-)quarantine measures implemented as a result of the COVID 19 outbreak had a positive impact on the number of time players spent playing and the amount of money they spent on the game. Simply put, people spent more time on video games during the quarantine, which also resulted in more spending. We can also see from the data on console sales and covid diagnoses that the number of diagnoses is roughly proportional to console sales. This also means that when the outbreak is severe, people are more eager to stay at home and spend their days by consuming games. This can also give game players more insight into the development of multiplayer game genres, especially now that most games support trophies, or achievements, in their respective game consoles. Players are happy to share games with strangers in their networks or share their game achievements on social media. Developing games that enhance interaction between players like this will be the mainstream in the future.

Second, from Sony’s PlayStation 5 and Microsoft’s new Xbox sales can also be seen, that players are very active in buying new game consoles. However, subsequent to the impact of the outbreak on the industry chain caused by the lack of capacity and the lack of new games supported by the host, sales gradually decreased in the later stages. This also gives many game companies how to promote new game consoles in the need to prevent the primary problem.

Finally, video games have long been questioned by teachers and parents as to whether they can lead to addiction in players, especially teenagers and children. And the complexity and difficulty of preventing and controlling gaming disorders may increase as excessive gaming behavior may have many negative effects under the influence of the pandemic and inappropriate marketing campaigns. Game players may be at increased risk of developing gaming disorders, and early detection of problems may be more difficult. For example, under normal circumstances without the Covid 19 pandemic, a player’s behavior is often constrained by the regular school, work, family life, and social activities. In the case of prolonged home quarantine, however, the routine of daily life is shattered and the behavior of game users loses external constraints. Also, together with the increased access to computers, cell phones, and the Internet due to online classes and online offices, objectively provides the convenience of gaming, and individuals are more likely to develop unhealthy patterns of gaming behavior. For this reason, once people gradually return to school and work, it will be a great challenge for the gaming industry to continue to grow while preventing the emergence of a situation similar to game addiction in the post-COVID-19 era.

In progressing with this project, some key ideas from the course that we utilized in our project are API exploratory analysis through Python libraries such as pandas, numpy, and matplotlib, although we primarily used seaborn for the graph visualizations. This is because seaborn is better at handling data from data frames, which is how we processed our data. Pandas is used for the data frame data processing, and numpy is used for calculations. We also utilized beautifulsoup4 for parsing html data from websites such as the worldometer, and Vgchartz, since both websites did not have an available api to use. Unfortunately, as previously stated, the code was a bit complicated and we had to resort to manual scraping.

Overall, playing video games at home is likely to remain the preferred form of entertainment for many people for a longer period to come. Even after the pandemic eases or ends, many players may continue to maintain the patterns and intensity of gaming behavior developed during the home quarantine of the epidemic. Thus we would make recommendations for potential investors to invest in video game companies, as they tend to perform well in times of intense anxiety such as during the COVID-19 pandemic.

In conclusion， our data still have some limitations due to the time and the rough knowledge we have learned. In particular, many of the probability analyses learned in the second half of the semester were not well applied to the existing data of this project. This is a rather big regret. In addition, studying the correlation between the sales of the game and the covid pandemic requires a lot of huge and complex datasets. We had limited time to organize, filter, and clean up more clear data and visualize it. Also, as a fully online course, there was a time gap between each group member due to our conflicting schedules. Therefore, we encountered a lot of initial difficulties in arranging data collection and research directions. But all in all, our research exploration was practical and allowed us to recapitulate what we had learned this semester. It also gave us a better understanding of what teamwork is. Being able to learn this knowledge and apply it to actual data analysis through a completely online class will be very helpful in our future careers. In addition, the game industry as a global entertainment industry is now more profitable than the movie industry, hence the future is immeasurable. The available data also shows that the game industry will continue to grow rapidly in 2022, and such similar analysis is believed that in the future there will be many people to analyze and discuss. As previously discussed whether the game addiction problem will appear in the post-Covid era, we believe that no profitable product can be perfect. But it can be reduced as much as possible through data analysis. More importantly, the game industry can quickly promote millions of people in various industries to get the opportunity to work and get a profit, which especially plays a vital role in helping global economic recovery. We believe that this is our project as data analysis to give the industry’s greatest advice.