Game Balance by Brenda Romero

Author:Brenda Romero [Romero, Brenda]
Language: eng
Format: epub
ISBN: 9781032034003
Publisher: CRC Press
Published: 2021-06-09T12:00:00+00:00

Online Rating Systems

For Multiplayer Online Battle Arena (MOBA) games such as League of Legends, as well as some other competitive online-only games like Hearthstone and Dota Auto Chess, there are some additional considerations and innovations over former systems, based on what is known of player behaviors.

It is generally accepted that the most exciting games are those between players of similar skill level. If a novice and an expert play together, the expert is almost certainly going to win, which is neither surprising nor particularly engaging for either player. If the novice does manage to win, it’s probably from luck rather than skill, which is not something the novice can feel proud of, and surely frustrates the expert. As such, one goal of online games is to put players into matchups that are as even as possible.

To do this, most games have an MMR (Match Making Rating) for each player, which is generally updated similarly to Elo or TrueSkill™. When a player tells the system they would like to play a game, they are placed in a queue which attempts to match them against a player of similar MMR, but also attempts to match them quickly. If a suitable opponent cannot be found quickly, the acceptable difference in MMR increases, until eventually a match is found (even if that opponent is much better or worse than the player). MMR may or may not be displayed to players; often, it is purely internal, so as not to discourage players with low ratings. After all, if a player is winning about half of their games, they still feel like they are playing well, even if the reason is that they are bad at the game but matched against other weak opponents.

For team-based games, an ideal matchup involves teams where every player’s MMR is similar, but that is often not possible or practical. In particular, if players can choose to form a “party” with their real-life friends, skill disparity is probably going to be larger than if an automated system simply looked for the nearest matches. In this situation, some kind of average is taken of all MMRs on a team, and teammates or opponents are sought who have a similar MMR to that average.4 The game may even keep a separate MMR for each player when they enter into the game solo vs. when they group together with friends; players who play together regularly may be better at communicating and working together as a team, and therefore, a player’s MMR on a known team may be different from when that same player is grouped with complete strangers.

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