Automating Sportsmanship : AI for Behavior Score Analytics

A gaming community doesn’t need gamers who have child-like behavior exhibiting extreme toxicity. If you have a game that has a massive community and to balance their emotions, you need a regulatory solution. A solution that could curb their toxicity, analyze trolls, filter out BMs (Bad Manners), and eliminate cyberbullying. A solution like a sportsmanship score, conduct, player reputation, or Fairplay score, and here we are calling it Behavior Score Analytics.

What is Behavior Score Analytics?

Every game has always been on the radar of undetermined independent entities who score the effect of those games on users. The effect can vary; it can alter their behavior towards personal as well as professional life. Hence, the behavior score is used to assess the impact.

It also provides information regarding the player’s toxicity level and the way they treat their fellow teammates or in-game players. The score also validates the fairness of the player; cheating, scripting, and tampering with the game rules often lead to deactivation of the account. The score also provides insights into how the game is progressing with the community.

A Good Behavior Score: A player is reputable, has a good rank, and makes the gaming community a better place for other players.

A Mediocre Score: This is the first strike; you might want to consider reviewing your [All Chat] messages and conversations.

A Bad Behavior Score:  You might get banned for extreme toxicity or bullying or be placed in low ranks. This will impact your overall match-making factor, too.

The behavior score analytics scale is integrated into the game to make the experience wholesome. Friendly banter is okay, but if you don’t have control over your temper and start Chatting in a full rage, you might disrupt the gaming experience of other players, too.

So, we found another valid reason why a proficient game developer would introduce behavior score analytics into the game. Here, a skilled game developer will prioritize leaving the analytics and judging on an autonomous & unbiased entity. An AI-based Behavior Score analytics. It reduces the cost of manual moderators, implements efficient tacking, and has the ability to scale as well as adapt the game mechanics.

Can AI track Gamer Conduct?

Yes, developing an AI-based Behavior score tracking system is feasible. However, it sure takes a lot of planning, and the process is not as easy as it seems. Integrating Artificial Intelligence with a Behavior score tracker involves utilizing natural language and speech processing at full capacity. The system needs to process the way players verbalize, chat, and grief (Spoiling the game experience through irrelevant game plays that affect team rankings and score).

There are a lot of in-game metrics that need to be studied, analyzed, and labeled, and then we need to determine a result. Sure, one can moderate metrics like in-game chat, and normal communication. But a few metrics like reports, action, and gameplay that lead to reputation scores need to be thoroughly evaluated. Here, AI can be proven to be a primary contributor for game developers. Let’s learn what AI-integrated Fairplay score analytics can do and how to develop one.

What Behaviour can AI Track?

We explore the importance of behavior scores, but let's study what the system can do for the game. A game developer integrates a Fairplay scoring system to ensure quality across the game and its community. It mitigates the risk of a ‘maybe’ possibility of being put under investigation. Here are the following five common factors tracked by the AI score analysis system.

1. Teamplay: In gameplay, a team is sometimes assigned to respective roles. The system can monitor if the players are following the guidelines of their respective roles or not. Not doing so may disrupt the gameplay and, in pro scenes, can result in match-fixing for tilting the betting odds.

   
   

2. Sportsmanship: A true sportsman understands that sportsmanship matters. Every esports player is currently a well-known figure. The way they interact and behave in the game affects the thought process of the community, too. So AI can lawfully judge whether the player is being a true sportsman or not.

   
   

3. Cheating:-  They can also track aimbots, scripters, lag switches, and much more. Many players run scripts to climb up the rank or smurf using low-level secondary accounts.

   
   

4. Griefing:- Many in-game actions by players can lead to grief. Actions such as taunting, bullying, Bad manners, irrelevant playstyle, etc. It leads to boiled-up hatred, and the other players can report the person through the system.

   
   

5. Communication:- In-game communication is the best way for shot callers to make game-breaking plays. But on the other hand, it can be used to abuse words. AI solutions can track this and either mute the player or score them accordingly.

Building Blocks For a Behaviour Score Tracker

Let's explore the building blocks of a common AI-based behavior score system. We have divided the whole structure into five primary needs and wants of services required.

1. Game Data Collection Pipeline: Games are full of chats, voice messages, and communication pings. Building a game data collection pipeline is one of the important stages. You can utilize AI here for automated data collection as well as the security of the data gathered.

   
   

2. Data Annotation: Now, we need to label the data that was collected. Every string of data needs to be labeled either positive, negative, or neutral. The data annotation will help train your machine-learning model and provide results once the equation is run through the model.

   
   

3. Applied AI/ML Model Development: here, the automation kicks in; you now have a labeled data set; you just need to analyze it and build a classification system. The classifiers will automatically anatomize the whole data set in small details. The data set includes players’ behavior, speech, raw chats, messages, and behavior during the gameplay.

   
   

4. A Cloud infrastructure: The infrastructure will help you to deploy huge data streaming pipelines conveniently and in an efficient manner.

   
   

5. End Product Development: UI/UX design and mobile/web app development to build an easily accessible product. This is required to provide end product experience to users, like more precise data visualization, allowing them to view data insights, and ultimately a two-way data viewing channel for both users as well as manual moderators, just in case.

Top Three Case Studies

Automated sportsmanship analytics may be complex for a beginner or a person who is not aware of the system. So here are the top three case studies that we have explained in short to clear your understanding.

Note: These case studies are not ranked; we are not saying these are the most perfect in the market. But looking at the pro scene, this system has been praised for its analytics and much more.

Case 1: Riot Games uses an AI-powered behavior score system called Valorant Credit Score. The system is used to track reports, AFK-ing, Cheats, and Toxicity levels.

Case 2: Counter Strike 2 uses Overwatch and the Trust Factor system. It has contributed to making the community a better place. Players can report toxic behavior and tampering.

Case 3: Dota 2 has always been the king of esports games due to its massive prize pool. But in 2019, it experienced a rapid drop in its users. This was due to toxicity. Valve unveiled Overwatch and the Trust Factor system for Dota 2, too. It allowed players to file a report, and the gameplay was reviewed by an expert esports Dota 2 community. The conduct score system has so far helped not only retain their players but also enabled them to make the community a wholesome battleground for Newbies.

In all the cases, there was one single approach: to combine a natural language processing system with a scoring system. AI has already begun to develop and is at its prime, and the game development industry is not backing from leveraging its evident benefits.

Boosting Wholesome Experience in Mobile Games

Did you know that a report concluded that in 2023, there was a 21.7% increase in the number of mobile game users? The same report suggested that the segment will reach an all-time high of 1.9 billion users by 2028. So you may ask a simple question: can there be an AI-based Behavior Score just for mobile games? The answer is yes, although there would be a few things that need to be considered before creating it, such as:

• API Integration to tap into chats, gameplays, and reporting systems.
• Efficient Model that can work with low latency and high throughput.
• A data-protecting system that allows players to reliably transfer data from mobile to cloud.
• Cross-platform support for more convenience and accessibility.
• Mobile native data viewing and UI/UX design.

Epilogue

An AI-based Behavior Score System can help game developers build games that are not subjected to spreading toxicity. A positive community needs moderation strategies, and AI can help automate those tasks. Sure, there are ample benefits an AI-based Behavior score system can provide and along comes complexity.

However, due to the introduction of Generative AI, a variety of things are automated, such as dataset augmentation, pattern recognition, context evaluation, and stimulating gaming environments that might reduce toxicity. Still, it is a long way to go.

However, one should not back down from introducing their ideas in the market due to complexity. Explore how games are being developed in modern times, the use of AI/ML, and primary research about Generative AI tools and technologies. It will surely help you develop your idea, even mobile games with AI-based behavior score systems. Thank you for reading, and remember the best way to play games is responsibly.