In Case studies, Game design, Games Analytics

By Thibault Coupart, Senior Data Analyst, DR Studios/ 505 Games

Free to play (F2P) games have seen a growing part of their design focused on balancing. In this article, Thibault Coupart, Senior Data Analyst at DR Studios/505 Games, showcases how in-game data can be associated with user data, to improve the quality of the game experience and the game monetization, using real data examples from the deltaDNA platform.

Minimize iterations

Game balancing is about resolving game experience problems, by tweaking the existing game variables to improve the player experience. Data analysis helps to spot areas that require balance improvements, which in turn, increases the commercial success of the game. As there are so many variables to consider, it will never be balanced correctly at the first attempt, therefore, the goal isn’t to get it right first time, but rather to minimize the number of iterations it takes to get there.

Gameplay vs economic balancing

Balancing can be split into two broad fields of application: ‘gameplay balancing’ and ‘economic balancing’. The former is the balancing of units and items in terms of gameplay advantage. For example, for a game like Hearthstone, game balancing would focus on the card values ie their “mana” cost and the advantage gained during a battle. For a game like Age of Empire, it would be about the different civilizations and units of the game, and for a game like Candy Crush Saga, it would be about the difficulty of game levels. ‘Economic balancing’ refers to the values which define the economic system in which the player evolves. It’s about defining the different currencies of the game, what you can buy with them, what advantages they offer, and setting the optimum rate at which you sell them to the players for real money. The game’s economy is at the heart of the game’s reward structure and is crucial to its success. Depending on the nature of the product, both balancing applications are closely linked to each other. In the majority of cases, defining a price for an item cannot be done properly without considering its gameplay advantage. Using both economic and gameplay balancing in your analysis leads towards more actionable recommendations, instead of generic solutions. Balancing generates more personalized and tailored recommendations for a game.

Battle Islands

A good example of balancing can be seen in the WW2-themed battle strategy mobile game, Battle Islands. Inspired by Clash of Clans, it incorporates city building and tower defence gameplay. Using deltaDNA, we looked at the investment of players toward the units in the game in comparison with their progression.


Looking at the balancing data and comparing the cost of units with their military power, we can see interesting perspectives on the balancing issues of the game. For example, certain units seem to offer high military power (gunboat, fighter, rifleman etc) at a very moderate cost. However, this data alone is not enough to understand what the players are doing, therefore, a closer look at the user data is needed.


This chart shows the investment of players toward the different units in the game at all stages of progression (from level 1 to level 100). Very interestingly, we can see that the average gain per battle tends to stabilize after level 20, which means that players prefer to create armies of riflemen to target low-level players, instead of creating stronger units to attack the higher-level players. This prevents players from investing in new units, which is obviously detrimental to the game. Therefore, with this in mind, we were able to rebalance every unit in the game to match the power of the riflemen. Through this rebalance, the supply spend on upgrade units increased significantly from 40K to 80K a day (+100%) which demonstrates the success of the update. The balancing change meant that the higher-level units became more interesting to players, which in turn meant they monetized by upgrading units.

Looking at the retention of paying users, we could see, from the players who spend money in the game, 35% of them leave the game after just 3 days. We considered the point in the game where they were leaving, and could see that many paying players left the game at HQ4, without reaching HQ5. To re-balance this, we implemented a game feature which increased player gains at HQ4. This change was made to incentivize the player toward PVP, to give a sense of progression and make up for the steep cost curve of the game. A ‘division bonus system’ was unlocked at HQ4 in the game, which gave additional supplies to the player in case of a PVP victory, and which ramps-up with the progression of the player.


This patch had a significant impact on the retention of spenders. Previously, only 59% of spenders remained after day 8, and after the patch, this rose to an impressive 70%. By improving the retention of spenders, it led to a positive impact on the LTV of players with an increase of 16% in revenue.

The results

In summary, balancing a game is an ongoing challenge for developers and is a factor that can make or break a game. By paying close attention to the data, successful re-balancing can increase retention and monetization. It’s vital to consider balancing at all times during development and as an ongoing consideration, as changes can be found and implemented quickly to optimize the game for both the game makers and the players.

If you liked this article, you may also be interested in Dr Roseboom’s game analytics insight compendium, vol. 1.

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