The document presents a data analysis and balancing overview in game design, highlighting the importance of adjusting game variables for enhancing player experience. It discusses two case studies focusing on balancing elements in an arcade game and a city builder game, demonstrating how proper analysis can improve player retention and game success. In conclusion, successful free-to-play games require effective balancing informed by both gameplay and user data.

1. Data Analysis & Balancing
Coupart Thibault - 10/04/2015

2. Presentation
Data Analyst / Game Economy Designer
- Master in engineering/town planning
- DU in Game / Level Design
- Complementary Formation in Hadoop/Programmation skills
- Internship data analyst at Corexpert
- Data analyst at Adictiz
- Current- data analyst at 505 Games

3. Plan
1. Definitions & concepts
2. Case Study 1 - Arcade Game
3. Case Study 2 - City Builder Game
4. Conclusion

4. Intro
Current mobile game trend (subjective opinion)
?

5. Definition & concepts

6. Definition & concepts
A few important words :
- The balancing fundamentally is about “tweaking” existing game variables in order to increase the game
experience.
- You always want to have the reward accorded with the difficulty, the investment accorded with the quality, the
cost accorded with the power.
- Data analysis helps a lot to spot areas of the balancing that need improvements, whereas a better balancing
usually increase the commercial success of the game
- This statement is even more true in the world of Free-to-Play, where it is all about convincing the player that the
ing-game purchase worth his real money.

7. Definition & concepts
A simple way of visualizing the balancing in a free-to-play Economy
Not good
(too easy)
Not good
(too hard)
Good !

8. Case Study 1 - Arcade game

9. Case study 1 - Arcade game
2D casual Games where you need to reach the highest distance possible with your dog ! (tap to fly)

10. Case Study 1 - Arcade Game
Unlock
Buy
Accelerate Revival Reroll
PLAY
Content
BUY
VIRALIZE

11. Case Study 1 - Arcade Game
Distribution of players score for each levels of the Game

12. Case study 1 - Arcade game
Retention by level and fail rate

13. Case study 1 - Arcade game
Gate 1 where you need to pay 500 coins
Tutorial
Most important
retention losses
Retention by level/steps and Retention as percent from previous

14. Case study 1 - Arcade game
Changing the wheel reward balance is also a part of the balancing.
25% 25
45% 45
5% 100
20% 5
50% 50
5% 100
Original values
New values
(expected gain / roll : 31) (expected gain / roll : still 31)

15. Case study 1 - Arcade game
+4 pts post lvl 3
+8 pts post lvl 7
+3 pts post lvl 12

16. Case study 1 - Arcade game
Introducing the “bad” roll on the wheel
with the 2.5 release
Average Rerolls used by players

17. Case study 1 - Arcade game
+5 points retention at Day +1 and after

18. Case Study 2 - Builder game

19. Case study 2 - Builder Game

20. Case study 2 - Builder Game
Percentage from First - Economy Variables

21. Case study 2 - Builder Game
DPS/ Health of Units unlocked throughout the game

22. Case study 2 - Builder Game
DPS/ Health of Turrets unlocked throughout the game

23. Case study 2 - Builder GamePOWER
PROGRESSION
Defender > Attacker; hard to
progress easily at this point in the
game; correspond to HQ lvl 2 / 3

24. Case study 2 - Builder Game
Retention of users according to Campaign Mission with Fail Rate- February 2015
Most important drop

25. Case study 2 - Builder Game
Researching
Negative side effect - the investment is a deception

26. Case study 2 - Builder Game
Supplies invested in each units for each users who unlocked the said unit - February 2015
(Total number of Purchases * Unit Price) / Distinct users who bought it at least once)
Underused

27. Conclusion

28. Conclusion
- The balancing has became a big topic in the free-to-play economy, and pretty much every gameplay needs a
decent balancing now to succeed
- A data analyst will have many benefits by matching balancing data with user data, and the opposite is true : a
game designer / balancer will use user data to orient his balancing !
- QUESTIONS

29. Thanks for your attention!
Contact
Coupart Thibault
Mail : thibault.coupart69@gmail.com
Linkedin : https://www.linkedin.com/hp/?dnr=oiFedA9QkZ4bzJnRoqEvqAHABQ43iJ4WcI2W&trk