From Jump Odyssey to Hyperstone
Not long ago, while working on balancing game parameters for our mobile project Jump Odyssey, we hit a wall. Manual testing was taking too much time, and we were never 100% certain if our settings were truly optimal.
Instead of guessing, we built a system that analyzes player behavior and recommends parameters in real-time. This experiment was so successful that it became the foundation for Hyperstone.
The parameters we optimized
For the initial test, we focused on five key configuration variables:
- ad_hp_restore — How many hearts a player gets after watching a rewarded ad.
- inter_to_revive — Whether to show an interstitial ad to revive a player.
- tutorial_jumps — The number of jumps defined as the tutorial phase.
- initial_hp — The starting number of hearts for each run.
- checkpoint_hp_restore — How much health is restored at checkpoints.
The results
The test has been running for over two weeks, and the results have been eye-opening. We managed to increase our Ad Revenue per User from $0.015 to $0.035.
While there is still work to be done, doubling the revenue per user purely through parameter optimization is a major milestone.
[!TIP] This approach allows you to stop guessing about IAP package sizes or ad frequency and let data-driven logic find the winner.
What Hyperstone optimizes now
Our system now handles recommendations for:
- IAP Prices & Bundles: Find the perfect combination of items and price points.
- Ad Placement & Frequency: Maximize revenue without hurting player retention.
- Offer Timing: Deliver the right offer at the magical moment to boost conversion.
- Player Progression: Balance difficulty, rewards, and enemy stats automatically.
- Onboarding: Identify the most effective tutorial flow for different player segments.
Join the beta
Hyperstone is currently in beta, and we are looking for a few more teams to join the testing phase. If you’re interested in applying advanced optimization algorithms to your game’s economy and progression, we’d love to have you.
Conclusion
Optimization shouldn’t be a manual grind. By moving from static balancing to dynamic “Multi-Armed Bandit” models, we’re helping developers find their game’s true potential.
