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What are some ways to use analytics to improve player pairing in online games?

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Player pairing, or matchmaking, is the process of finding and grouping players with similar skill levels, preferences, and goals in online games. It is crucial for creating engaging and satisfying gaming experiences, as well as retaining and growing the player base. However, player pairing is not a simple task, as it involves balancing various factors and constraints, such as player availability, latency, fairness, diversity, and feedback. In this article, we will explore some ways to use analytics to improve player pairing in online games.

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1 Data Sources

To design and evaluate effective player pairing systems, you need to collect and analyze data from various sources, such as game logs, user profiles, surveys, ratings, and reviews. These data can help you understand the characteristics, behaviors, preferences, and satisfaction of your players, as well as the performance and outcomes of your pairing algorithms. For example, you can use game logs to measure the skill level, activity, and retention of your players, user profiles to segment your players based on their demographics and interests, surveys to gather feedback and suggestions from your players, ratings to assess the quality and fairness of the matches, and reviews to identify the strengths and weaknesses of your pairing system.

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Christopher Anjos???

Creating Strategies, Games, and Mission-Driven Teams that Address Disruptive Trends w/YOU| TENCENT, EA & Activision | Inventor | Investor
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Ping should take precedent when matchmaking, along with other factors. 1. Ping is the time taken for data to travel from your device to a server (or another player's device) and then back to your computer, which when not taken as a priority in matchmaking can lead a poor player experience in the form of latency in the game. 2. Skill can also play a factor in ensuring different types of players can find games that best fit their player journey. Team Balance plays a factor to this as well, and a spread of win/loss ratio can be tuned for this area. 3. Play styles based on mapped out cohorts can be created, tracked and implemented.

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2 Pairing Methods

Various methods and techniques exist to pair players in online games, depending on the game genre, mode, and objective. The Elo rating system assigns a numerical rating to each player based on their performance in previous matches, which is used to pair players of similar skill level. Role-based pairing pairs players based on their preferred or assigned roles in the game. Meanwhile, preference-based pairing pairs players based on their expressed or inferred preferences in the game. Elo rating system is commonly used in competitive games such as chess and esports, while role-based pairing is often seen in cooperative games like MMORPGs and FPSs. Preference-based pairing is typically used in casual games like puzzle games and social games.

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3 Pairing Metrics

In order to measure and improve the effectiveness of your player pairing system, it is important to define and monitor key metrics that reflect the quality and impact of your matches. Matchmaking time is a key metric that indicates the efficiency and availability of your system, as well as the satisfaction and retention of your players. The goal is to minimize this metric without compromising the quality and diversity of matches. Match balance is another metric that indicates the accuracy and adaptability of your system, as well as the engagement and loyalty of your players. You want to maximize this metric without creating frustration or boredom for players. Lastly, match feedback is a metric that indicates the relevance and value of your system, as well as the advocacy and growth of your players. It should be optimized by listening to and acting on player feedback.

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Alexander Rehm

Director of Live Ops at People can Fly || Opinions are my own
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Ping is an additional metric that can be used: ping to the match-making server and the shard / world, as well as inter-player ping for 1v1, 2v2 or similar smaller scale matches. Tracking that metric will also enable you to understand where you may want to set up dedicated server locations in the future, as you learn where your core audience plays from, or where it will expand to as your game grows in audience

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4 Pairing Optimization

To optimize your player pairing system, you need to test and iterate on your methods, metrics, and data, using analytics and experimentation. A/B testing is a useful method to compare the performance and outcomes of two or more variants of your pairing system. Machine learning can use data and algorithms to learn and improve the pairing system over time, based on the feedback and behavior of your players. Additionally, user research can provide qualitative and quantitative methods to gather and analyze the opinions and insights of your players. All of these methods can help you identify the best variant for your system, create a more personalized and adaptive pairing system, as well as understand the motivations and expectations of your players.

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Tasos Flambouras

Managing Partner at One9Six
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To optimize player pairing in online games, think about tailoring the game to each player's likes and habits. It's like using a mix of their gaming history and real-time feedback to make matches more fun and engaging. We're also seeing cool tech from new companies that help understand player behavior better, like analyzing game videos to spot trends. By combining these insights, we can tweak pairings on the fly, keeping the game fresh and players happy. It's all about making the gaming experience more personal and responsive to what players actually want.

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5 Pairing Challenges

Analytics can be beneficial for improving player pairing in online games, however, there are several challenges and limitations that need to be addressed. Data quality is an important factor, as you need to guarantee that the data you collect and use is accurate and reliable. Additionally, data privacy must be taken into consideration, as you need to comply with the relevant laws and regulations regarding data protection. Lastly, data security is a challenge as you need to prevent and mitigate any threats or attacks that can compromise or damage your data or system. Appropriate measures such as encryption, authentication, or backup should be implemented to ensure data security.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Peter Stylianou

"Oh for Pete’s sake, you need my help!" | Art Director @Holyday Studios | 15+ Years in Creative Industries | Gaming, iGaming & Crypto Expert | Multidisciplinary Designer | Brand Strategist & Monetization Consultant
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Follow these three steps to elevate your matchmaking game: Step 1: Dive into player behavior data, unraveling gaming preferences and styles. Step 2: Fine-tune matchmaking with player feedback, keeping it in sync with ever-evolving gaming vibes. Step 3: Embrace predictive analytics to foresee player preferences, ensuring a match made in gaming heaven.

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Christopher Anjos???

Creating Strategies, Games, and Mission-Driven Teams that Address Disruptive Trends w/YOU| TENCENT, EA & Activision | Inventor | Investor
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A major feature to consider with so much data and how to implement, is the player experience. Some major themes: 1. Time it takes a player to find a match. The shorter the time it takes a player to find a match, the better (usually) 2. Measuring whether or not disbanding lobbies after every game helps or hurts the player experience

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What are some ways to use analytics to improve player pairing in online games?

1

2

3

4

5

6

1 Data Sources

2 Pairing Methods

3 Pairing Metrics

4 Pairing Optimization

5 Pairing Challenges

6 Here’s what else to consider

Gaming Industry

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What are some ways to use analytics to improve player pairing in online games?

1

2

3

4

5

6

1 Data Sources

2 Pairing Methods

3 Pairing Metrics

4 Pairing Optimization

5 Pairing Challenges

6 Here’s what else to consider

Gaming Industry

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