There is perhaps no other game feature as easily misused as the leaderboard. Not all leaderboards are created equally. The right one can transform a boring experience into a tense competition, while the wrong one can transform a pleasant experience into an affront. And the difference between the two extremes is unfortunately subtle.
Most visualization preferences don’t have much impact on a leaderboard’s effectiveness, but some do. Below are three of six such variables to think about when designing a leaderboard: centering, filtering, and aggregation. These can significantly affect a leaderboard’s effectiveness. (I’ll discuss the other three in a separate post.)
What is a leaderboard?
Let’s define what we’re talking about: A leaderboard is a list of participants, ordered by a metric.
- A leaderboard must be visualized.
- A leaderboard must be ordered.
- A leaderboard must be consistent (use the same metric for all participants).
Your Klout score by itself, for example, isn’t a leaderboard. A score only becomes a leaderboard if it is compared against others in a linear visualization. The actual layout of a leaderboard can vary, though. A leaderboard could be arranged horizontally or vertically. It could have three entries or three million entries. It could show three entries at once or fifty at once. It could be fixed, scrolling, or paginated. It could show ranks, scores, or both. It could refresh in real time, hourly, or daily.
Centered (a.k.a. “Local”) Leaderboards
Imagine a leaderboard with 10,000 players ranked on it. Does the average player see himself? If you said “no,” you’re probably picturing a leaderboard that defaults to showing the top performers. If you said “yes,” you’re likely picturing one that centers on the current user. A centered leaderboard provides two benefits:
- It makes it easy for a user to find him/herself
- It sets obtainable objectives
“Sets obtainable objectives” means that a player sees himself in relation to the other users that he has a chance of passing in the short term. It’s likely only a few points separate him from the “top” of a centered leaderboard. Once he moves up, the board will re-center, and he’ll see a new group of players just above him. Providing such “baby step” objectives to motivate behavior change is one of the primary tenets of gamification.
A leaderboard that isn’t centered probably requires the user to scroll down to find herself. There are tricks for making this easier (such as always locking the player on the bottom of the leaderboard for reference) but none of them solve the “obtainable objectives” problem. A leaderboard that defaults to the top-ranked players is discouraging for anyone not at the top. This isn’t much of an issue with small communities, but with larger groups it can be significantly demotivating.
![a bottom-locked leaderboard]()
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Left: logged-in user is highlighted at the bottom, even when the leaderboard isn’t scrolled to her location
Right: Fixed leaderboard showing top players above and fixed leaderboard showing local players below.
It’s also possible for a leaderboard not to scroll at all. This is further demotivating because users who don’t make the top page of a non-scrolling leaderboard may not be able to view their status or progress.
Filtered Leaderboards
The simplest leaderboards include everyone; for example, if your leaderboard measured test scores of everyone in a college class, everyone in the class would show up on the board. A filtered leaderboard reduces the participant pool. For example, you could filter to show only undergrads. The advantage of this is that by reducing the competitive field, filtered leaderboards increase the chances of an individual user reaching the top. And users who get near the top are going to be more motivated by the leaderboard than users who don’t.
When considering filtering, think about the attributes that are relevant to your users. For example, leaderboards are highly engaging if the participant knows everyone else on the leaderboard — think about users’ friends and social contacts. Other possible filters include physical proximity (e.g., a regional leaderboard), time (leaders for the week or year), cohort, experience level, office, and team.
Overall, you might be noticing a trend:
- Players want to focus on themselves.
- Players want to feel like they are near the top of something.
The more you can feed these two desires, the more effective your leaderboards will be.
Aggregated (a.k.a. “Team”) Leaderboards
An alternate strategy to filtering participants is aggregating them, or “rolling up” everyone who shares an attribute into a single entry on the leaderboard. For example, everyone in the San Francisco office contributes to a single score, while everyone in the New York office contributes to a different single score. San Francisco and New York then both show up as participants. There are a number of advantages to aggregated leaderboards:
- They lead to less “shame” in losing
- They encourage cooperation
By “shame” I mean that players near the bottom of a board feel bad about the public humiliation of being identified as the “worst” at something. In some cases, shame can be a motivating force, but in other cases, it drives people away. Hiding anonymously behind a team removes an individual’s shame. Aggregated leaderboards also significantly reduce the number of entries on the board (just like filtering) and therefore improve everyone’s likelihood of “winning.” Their biggest advantage, though, is that they encourage cooperation: everyone on the same “team” works together to move to the top.
Don’t miss the other three visualization preferences that have a major impact on leaderboard effectiveness in my second post.