Why massed marines beat massed zerglings

There is a very common phenomenon in RTS games that’s often forgotten: massed ranged units tend to beat massed melee units. Even more so, good massed ranged units will beat inferior massed ranged units. The benefit from massing ranged units is their ability to lower the firepower of opposing units before they can inflict harm. For instance, a pair of zerglings will beat a marine, but 24 zerglings will lose to 12 marines.

During my time as a volunteer balance developer for a mod, I had to understand and apply core game mechanics. Below is an instrument with a very basic model that I call the Effective Range Firepower (ERF). It measures how well a cluster of units perform. We can interpret X as simply the number of units. Alpha would be a measure of well the unit performs when massed. In Starcraft, this is the unit’s attack range. In a more complex game such as Company of Heroes, this is the unit’s attack range, accuracy, cooldown/reload modifiers, etc.

There are two assumptions that the model makes. 1) There is no limit to the number of units aggregated. This is generally true until your units start physically blocking one another from attacking. If that’s the case, their ERF simply comes to a plateau. 2) There are no stacking penalties. In certain games, units actually debuff each other.

In Company of Heroes Opposing Fronts, there is actually an upgrade for the Panzer Elite faction that makes aggregating units even better. Although it’s only speculations, the fact that the design team behind that title was inexperienced leads me to believe that whoever designed that ability didn’t have a good understanding of how massed ranged units function. Subsequently, there was a huge host of problems associated with the blobbing of Panzer Grenadiers who inherently already had pretty good ERF.

When balancing these effects, we have to take many other variables into account. Generally speaking, we ask ourselves how easy is it for players to pull off unit aggregations. We don’t want to make the unit so expensive that only the best players can make use of the unit. At the same time, if we price the unit so low so that even the weakest players can use the unit, we risk having skilled players maximizing the unit’s ERF. In the worst case scenarios, we employ hard caps which effectively stops the unit’s ERF at a certain value. My favorite solutions tend to be the introduction of abilities or other units that punish unit aggregations. These things can range from the ensnare ability on the queen from Starcraft, or something like the machine gun unit in Company of Heroes that gets more accurate as the enemy gets more units together. These creative solutions have their own dangers of introducing even more variables to a game, but the risk is often worth the freedom that it will ultimately grant players.

The Billy Beanes of competitive gaming

Michael Lewis’ book, Moneyball, could be summarized as the failure of competitive Major League Baseball teams to compete in a competitive environment. Among this great fiasco of a market failure is the general manager of the Oakland Athletes, Billy Beane, who applied statistical analysis to build a low cost team of “ugly ducklings” that gave the best bang for the buck. It is the classic story of the underdog who through ingenuity and sufficient cleverness managed to beat Goliath. Lewis attributed the failure of baseball teams to emotional and irrational decision-making that undervalued statistically good players and overvalued the statistically bad ones.

In the arena of competitive real time strategy gaming, an almost diametrically opposite environment has emerged. Every competitive gamer can be considered a “Billy Beane.” Gamers tend to think at the margin, and they think to win. Strategies are played, and counter-strategies are quickly developed. Players then adapt, revise, and rethink their moves until they reached a point where it’s hard to deviate. Gamers called this the metagame; economists would have called it an equilibrium.

Competitive gamers are cost minimizing and benefit maximizing. They also happen to have an encyclopedia of every unit’s statistics in their head. A Starcraft player decides to train a marine much in the same way a baseball general manager like Billy Beane decides to draft a pitcher.