With users spending more time than ever in sports, sports companies became an integral part of the global entertainment industry. These companies are masters of integrated entertainment as they excel in reviving social morality, art, and sharing. The global gaming market will reach $ 115.8 billion by 2018 with 45% mobile phone seizures, Newzoo said in its Global Games Market report.
It’s all about smartphones and social media now and this idea has brought down the market for video game consoles like PlayStation, Xbox, or Nintendo. Not only international countries like Electronic Arts (EA), Sony, or Microsoft but also developers and developers are entering the gaming business as a promising field.
By playing to set foot on social media, there are a lot of details for sports companies to deal with. Gambling company, Zynga has launched games such as Zynga Poker, Farmville, Chess with Friends, Speed Guess Something, and Words with Friends on social media. This has brought a lot of user connections and produces big data sets.
This is where the gaming industry needs data science to use this data collected from players across all social networks. Data analysis gives gamers a compelling, novel distraction to stay ahead of the race! One of the most interesting uses of data science is within the features and processes surrounding game development.
Game Collection and Data Collection
Games are used as a data collection method. The main idea is to track the data entered in the form of a periodic click and to record the user input of that image frame. This data is then used to draw the final result, for example – the final points.
From creating artificial intelligence systems to finding transferable algorithms, Data Scientists use data sources from in-depth learning games. This type of learning is appealing from a data scientist’s point of view as it helps in finding common and common ways to use not only the current sports project but also the games and future programs.
Game Data Scientist
A data scientist develops and investigates, designs and uses ideas, and organizes tests to test a variety of ideas. They are also responsible for creating mathematical models and automated analytics tools for identifying points and using the game. If you want to join the gaming industry as a data scientist and are interested in playing games, data mining, and modeling, then it is an added benefit for you.
To be part of the Advanced Mathematics Team, an in-depth data learning scientist needs to extract large amounts of data and perform extensive data analysis, and develop descriptive, predictable, and descriptive models using in-depth / machine learning algorithms. If you have a passion for problem-solving, creative solutions, and continuous learning, then this is the role you want to play.
Game Data Analytics
Data analytics is responsible for evaluating and visualizing service performance and user conversion data to find opportunities to improve user engagement and user retention. They use data analysis techniques to identify logical relationships, patterns, styles, and user behavior models from complex data sets to guide road maps and build automated detection systems and track their performance.
Identifying an object
Realistic graphics, the use of artificial ingenuity, and pressing the boundaries of graphic realities are now among the key functions of game developers and designers. Image recognition technology is predicted to transform the gaming industry. Along with object acquisition models, they are used by the engineer to create a natural transformation of scenes and movements in the real gaming space.
For example, these models are often used to distinguish players from different teams and to give instructions to a particular character within a team. The difference between forms, objects, obstacles, and figures becomes easier and faster for the player. In addition, object identification models and algorithms are used to identify body movements in order to transmit and display these actions on the interactive game screen.
Real-life Use Case: Activision uses Data Science to stop game hacker or cheaters
Activision, the main engineer of the first video game series for people Call of Duty (COD), is one company that uses big data to improve their games.
Game Science Division (GSD), which is in charge of collecting and analyzing Big Gaming Data at Activision, had to deal with the issue of empowerment among its players in COD.
In speaking of players, empowerment refers to the attempt to increase someone else’s sports scores by negative means such as meeting one player better than the other players and throwing the game on both sides where one side deliberately loses to allow the other side to win.
It sounds like cheating when you think about it and magnification has its consequences. Not only does a growing player gain unprecedented fame, but the strategy of promoting and can affect other players ’ratings and the balance of the prize systems.
That’s why augmentation is looked down upon by many players and needs to be addressed before it can be played by innocent players.
The GSD of Activision creates programs based on machine learning to detect power boosting, identifies key indicators for increasing COD, and tracking those indicators. The two references are players to a list of friends that end up in different teams equally after the game and birthplaces where death is found after a very short time and again and again.
After analyzing the verification indicator data, GSD then communicates with its users on Twitter about expanding and collecting data across all COD tweets to help make decisions about game and player base features.
Personalized Marketing And Ads
Customized marketing is actively used in various industries to avoid useless, annoying, and ineffective advertising. Both marketers and game developers are interested in customer-focused interactions and lead to the creation of meaningful marketing messages and send them to the right people. In any case, video game providers are collecting data that will help attract more audiences.
Personalized marketing in games helps to increase user activity and at the same time attract new ones. This can be achieved due to the specific adjustment of the advertising message. To ensure that your ads are clearly visible you need to know which players are responding to the ad and which are not.
Data-driven ads in Games
Suppose we are playing a collaborative mobile game called an Episode, in which we help the main character of the story choose a course of action or a sentence to say, which ultimately determines what happens later in the story. The story may contain a number of episodes and the World needs to include each episode.
In order for game developer Pocket Gems to earn money automatically, they play two 30-second ads every time We complete an episode. And since we get four Passes every few hours, we would complete the Passes by inserting four episodes in a row and we have to wait a few hours to renew the Passes.
Another way to wait is to buy Passes for real money, which not everyone wants to do. Pocket Gems may have found this as an opportunity to earn money through multiple ads by giving players the opportunity to play the ad to cut the waiting time by half an hour.
Ads are usually made by companies or personalities based on my country, the game shows us ads based on our location or nationality.
All actions and decisions in the sports world are fast. The high speed of all these processes raises the issue of high interest in fraudsters. Therefore, companies face the need to prevent fraudulent activity, yet to maintain a high level of customer satisfaction. Safety issues are a challenge for all industries.
Various player verification solutions are widely used in gaming. The point is that those game developers are obliged to use player certification legally. Also, various verification strategies allow for the detection of questionable accounts and actions in advance. In addition, these strategies are used to prevent identity theft, which is widespread in the visual gaming world.
Payment fraud is also very common in sports. Fraudsters often create special bots to obtain the required payment details. Therefore, gaming companies need to ensure maximum security for personal information about the activity and transactions made.
Machine learning algorithms help game companies. Their application allows for quick identification of suspicious activity. They make fraud detection more automated and effective because of the amount of data they can process.
Game design has become an art with the rapid development of modern technology. In addition, game design has become an amazingly popular site for building successful developers’ work. It is a complex process that requires a variety of programming, visualization, and animation skills.
The use of good visual effects is no longer to keep players engaged. Gambling data details and engineering innovations help create a complex interactive game environment. Data from game analytics is used to obtain specific information about what a player wants, predicting game issues, consultations, and time. New game concepts, news stories, and machines are built using previously acquired data.
The gaming industry makes full use of data analytics. Technology, finance, game, marketing, strategy, just say the domain of statistics and you will find it works with the revenue in the gaming industry.
Thanks For Reading!
Hey! I am Sharvari Raut and I love to write!
Connect with me on:
Technical Writer🤓 | AI Developer😎| Coder 👩💻 | Avid Reader 📖 | Data Science ❤️ | Open Source Contributor 🌍
The media shown in this article on Data Science in Gaming Industry are not owned by Analytics Vidhya and is used at the Author’s discretion.