Customize match algorithm for large matches

Add an algorithm component to the rule set, if one doesn't already exist. Set the following properties.

• strategy (required) – Set the strategy property to “balanced”. This setting triggers FlexMatch to do additional post-match checks to find the optimal team balance based on a specified player attribute, which is defined in the balancedAttribute property. The balanced strategy replaces the need for custom rules to build evenly matched teams.

• balancedAttribute (required) – Identify a player attribute to use when balancing the teams in a match. This attribute must have a numerical data type (double or integer). For example, if you choose to balance on player skill, FlexMatch tries to assign players so that all teams have aggregate skill levels that are as evenly matched as possible. The balancing attribute must be declared in the rule set's player attributes.

• batchingPreference (optional) – Choose how much emphasis you want to put on forming the lowest latency matches possible for your players. This setting affects how match tickets are sorted prior to building matches. Options include:

  • Largest population. FlexMatch allows matches using all tickets in the pool that have acceptable latency values in at least one location in common. As a result, the potential ticket pool tends to be large, which makes it easier to fill matches more quickly. Players might be placed in games with acceptable, but not always optimal, latency. If the batchingPreference property isn't set, this is the default behavior when strategy is set to "balanced".

  • Fastest location. FlexMatch pre-sorts all tickets in the pool based on where they report the lowest latency values. As a result, matches tend to be formed with players that report low latency in the same locations. At the same time, the potential ticket pool for each match is smaller, which can increase the time needed to fill a match. In addition, because a higher priority is placed on latency, players in matches may vary more widely with regard to the balancing attribute.

The following example configures the match algorithm to behave as follows: (1) Pre-sort the ticket pool to group tickets by location where they have acceptable latency values; (2) Form batches of sorted tickets for matching; (3) Create matches with tickets in a batch and balance the teams to even out the average player skill.

"algorithm": {
    "strategy": "balanced",
    "balancedAttribute": "player_skill",
    "batchingPreference": "largestPopulation"
},