![]() ![]() It has an overall score of 441.3, which is 68 higher than the second studio.įigure 1. So, who is the winner?Īfter calculating the overall score for all studios, we got our winner: Toei Animation. Before adding up the four scores, we also standardized them so that they are on the same scale. To further elaborate on the four criteria: 1) Popularity score (the sum of audiences of all animes the studio produced divided by the number of anime studio produced) to see on average how popular the studio’s animes are 2) Loyal audience score (the total number of times animes being added to one’s favorite list divided by the sum of audiences of the animes produced by the studio) to see how successfully can the studio’s animes transform a normal audience to a loyal fan 3) Quality score (the average rating of all animes produced by the studio) to see the average quality of the studio’s work 4) Quantity score (the total number of animes produced by the studio). ![]() For each anime studio, a higher score means a higher rank. Then, in order to comprehensively analyze anime studios, we created a formula that covers different aspects: Overall studio score = popularity score+loyal audience score+ anime quality score+ anime quantity score. We also split columns that contain multiple genres using the same technique. After that, we used the pivot option to have separate these multiple studios into individual rows. Since the original dataset is based on anime, and one anime can be produced by multiple studios that have been stored in one cell, our first step was to split different studios into multiple columns. We mainly made use of the AnimeList dataset, which contains 31 columns such as anime name, anime ID, studio, genre, rating, favorite (how many times an anime was added to a user’s favorites list), popularity (how many people have watched the anime), etc. Overall, the dataset captures data from 302,675 unique users and 14,478 unique animes. There was also filtered data and cleaned data versions of these datasets available. This dataset was almost 2Gb in size and contained 3 sub-datasets: ‘Anime List, ‘User List’, ‘UserAnime List’. We were able to get an exhaustive dataset on anime from (MyAnimeList Dataset, 2018). To be more specific, we will be evaluating: 1) Which anime studio is the most successful studio 2) What are the characteristics of successful studios? Data source Therefore, we are inspired to fill the gap by objectively analyzing anime studio. There have been some anime studio rankings on the internet while they are only based on subjective online voting (Teffen, 2017 Lindwasser, 2019). Beyond that, the Association of Japanese Animations has been publishing annual reports about the Japanese Animation Industry, which has become one of the main sources of online analysis about the anime industry (AJA, 2019).Īlthough these works are insightful, none of them specifically looked at anime studios. Their works include creating anime rankings, analyzing the changing trend of preferred anime genres, and demographic characteristics of anime audiences (Bilgin, 2019 Rafiq, 2019 Overlytic, 2019). Based on our online research, some anime fans carried out analysis by themselves out of a passion for anime and the curiosity of the industry. This fast-growing industry has triggered people’s interest in studying it, and we are also part of them. In 2017, the anime market set a new sales record of $19.8 billion owing greatly to overseas demand (Jozuka, 2019). Nowadays, anime has become increasingly more mainstream and its market continues to expand all over the world.
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