Balancing novices and experts
Once again, another question that came in via Quora. The issue at hand is, what do you do to balance experts and novices in a game? Especially if there are persistent elements like leaderboards in the design, which tend to cement experts towards the top?
This is a big issue as games become more persistent and emphasize multiplayer aspects more heavily. Single-player games now swim in a soup of constantly connected profiles with all sorts of achievement and expertise data, effectively rendering them all multiplayer via the addition of a metagame. And we should not forget: the average player is below average; or to be more precise, the median player will have a win-loss record that is lower than the mean or average win-loss record, because the high-skill players win a disproportionate percentage of the match-ups. This results in the mode for the win-loss record curve being “loss.” (For more on how Pareto curves manifest in this sort of persistent environment, I refer you to my 2003 talk on “Small Worlds” [PDF]).
This sort of accumulated record of expertise can serve as a huge disincentive to participate. Novices will look at high ratings and consider the game hopeless. Nobody likes feeling inadequate. And of course, once in an actual game session in any sort of competitive scenario, it is rare for the match to actually be between perfectly matched opponents. It doesn’t even take a significant skill gap for an accumulated win-loss record between a novice and a ninja squirrel to begin to look pretty dismal. And of course, in skill-based systems that lack infrastructure, people can try to hide their ratings — that’s the basis behind being a pool shark.
There is no way known to solve this issue. In fact, balancing arbitrary teams, for example, is an NP-Hard problem. Fortunately, there’s a pretty standard grab-bag of tricks to ameliorate the issue:
Handicapping before starting a match. In general, this is done by providing the less skilled player with material advantages in the form of greater assets. In grammar terms, these assets might manifest as…
A larger amount of whatever currencies the game supports (more money, or time to make a move). This could even go as far as providing a conceptually “infinite” amount of that currency; for example, it is common for novice players in an MMORPG to have “infinite armor class” or whatever to give them invulnerability from other players’ attacks until they are no longer of a very low level. This is usually actually implemented as a flag, but the principle remains.
More assets. For example, in chess we remove assets from the more skilled players, as when we “spot the new player a rook.” In grammar terms, an asset often actually translates into a verb.
More verbs. For example, we might permit a novice to use wildcards in poker, while not allowing the advanced player to do so.
More information. We might adjust things like fog of war, visibility of opponent’s hands, or the like. In tabletop gaming, this often manifests as outright giving a new player advice whilst they play.
Pairing up competitors of comparable estimated skill. The basic premise here is to avoid putting the less skilled in a match with the highly skilled. This ranges from simple stuff like using levels in cumulative-character games, to advanced systems like Elo ratings used in chess and other competitive scenarios.
Levels in an RPG-style system tend to suffer from the fact that actual skill is not reflected. Instead, you rely on gating use of verbs, currencies, etc, based on pure devotion to the game. This works well if your game admits of relatively little skill (e.g., emphasizes in-game assets as the metrics of power). It works poorly in a skill-based scenario. And of course, a cumulative character system by definition causes rich-get-richer syndrome.
Ratings work better in that scenario, but of course suffer from some flaws. Standard Elo systems don’t account for inactivity, so people can choose not to participate to try to keep their ratings up.
Leagues are more or less a combo of the above two: a crude approximation of rating, that you advance through via a levelling process.
Dynamic difficulty adjustment systems. These are more or less described as “handicaps that occur during play.” The intent behind these systems is to keep the competition tight and close.
Give more benefits to people who are losing. It’s most visible in racing games, which often improve your car’s handling when you are losing.
Give more penalties to people who are winning. In kart-style racers, it’s common for the leader to get hit with more negative pick-ups.In oer racers, AI cars will start driving worse to allow you to catch up.
Randomness. In general, a more random game will equalize the playing field, reducing the value of skill. The random drops in a kart racer are one obvious example, but consider classic children’s board games, which are heavily reliant on dice. Snakes and Ladders uses this mechanism to put even very young children on an even footing.
Upsetting permanent tracking. This is to prevent “rich get richer” syndrome, and can be seen in how championships cycle. If the Superbowl’s results carried over year on year, it would be less and less likely that a team who had never won before would climb in the standings. Classic high score tables have this issue; at any given venue, a machine’s top scores would basically freeze into place, possibly even consisting of a single individual’s many high-scoring games.
Wipes are how sports handle it. After a tourney cycle, standings are reset. On MUDs, it was common to do periodic player wipes as the upper ranks not only calcified, but became dull as everyone reached caps and the game economy started to break under the impact of mudflation. It’s also what unplugging the arcade cabinet could do!
Time-bounded and social-tie-bounded leaderboards. This means leaderboards that show best this week, best among your friends, etc. This is the commonest means of handling this issue these days. This also works against “rich get richer” syndrome. However, it really is just a delaying tactic; given time, it still falls prey to the overall issues.
Erosion. Some types of persistent game require the advanced player to participate on a regular basis, or their accumulated rewards atrophy. Sometimes the atrophy is in the form of attacks being permitted against them while offline; we see this a lot in persistent world strategy games. Sometimes we see systems that literally consume resources while the player is not around; examples have included “rent” systems in MMORPGs and MUDs, withering or disaster events in social games, and many more.
Multiple ladders. Offering a wide array of ways to consider yourself a winner, or many separate possible achievement ladders to climb, mitigates the shame factor in doing poorly in any given one, while maximizing the opportunity for each player to achieve glory. (Please, do see Jonathan Baron’s classic “Glory and Shame” essay and presentations!)
Multiplicity of goals. Players will usually invent these if they are not provided (cf speedruns as a classic example), but things like secrets, achievement or badge systems, narrative branches and completionist rewards, and so on are all alternate goals for the same game system, permitting that many more separate ways to compare players.
Roles are a powerful way to allow players to feel special, even if not actually on separate achievement ladders. Classes in an RPG or positions on a team are one way to do this. Typically roles all aim towards one purpose or goal.
Orthogonality is even more powerful. If you create diverse activities within one game framework which have completely different goals, effectively different games to play, then you can accommodate a much wider array of play styles and player psychologies. This then permits a far greater percentage of participants to feel valued. Crafters versus fighters in an MMO; modders versus players in a shooter…
None of these techniques are perfect. But you can go a long way by using any one, or indeed all of them, within one game.
