Seesaw Mechanics

The seesaw mechanic is the engine that drives LTB token circulation within the Lotto Balls ecosystem.

The Seesaw Concept

Imagine a playground seesaw with the game contract on one side and players on the other:

    Players              Contract
       ↑                    ↑
       |    ←→ LTB ←→      |
       ↓                    ↓
    ═══════════════════════════
              ∧
              Balance Point

Tokens flow back and forth based on how players choose to bet.

Token Flows

Flow 1: USDC Play → Tokens Flow OUT

When players bet with USDC:

• Players receive LTB rewards (tokens leave the contract)

• Contract balance decreases

• Future rewards become smaller (self-limiting)

Contract Balance: 1B LTB
USDC Player wins with 2 matches
Reward: 124,320 LTB flows to player
New Balance: 999,875,680 LTB

Flow 2: LTB Play → Tokens Flow IN

When players bet with LTB:

• Players pay LTB to the contract (tokens enter the contract)

• Contract balance increases

• Future rewards become larger

Contract Balance: 999,875,680 LTB
LTB Player bets
Cost: 99,988 LTB flows to contract
New Balance: 999,975,668 LTB

Equilibrium Dynamics

The seesaw naturally seeks equilibrium through player behavior:

When Contract Balance is HIGH

• LTB cost is capped at 100 MLTB (relatively cheap)

• LTB rewards are generous (attractive)

• Player incentive: Play with USDC for big LTB rewards

• Result: Tokens flow out, balance decreases

When Contract Balance is LOW

• LTB cost is floored at 10 MLTB (affordable)

• LTB rewards are small (less attractive)

• Player incentive: Play with LTB (win USDC without big token expenditure)

• Result: Tokens flow in, balance increases

No Burning, No Minting

A key aspect of the seesaw model:

• LTB is never burned when used for bets

• LTB is never minted for rewards

• All tokens are transferred between players and the contract

This creates a closed-loop system where the total supply remains constant, but distribution changes based on play patterns.

Mathematical Balance

For the system to remain sustainable, outflows must balance inflows over time:

Outflows (USDC plays, per play)

Expected LTB outflow per USDC play:

E[Outflow] = P(0)×R(0) + P(1)×R(1) + P(2)×R(2) + P(3)×R(3)

Where:
P(n) = Probability of n matches
R(n) = Reward for n matches

Inflows (LTB plays, per play)

Inflow = getLtbCost()  (transferred to contract)

Equilibrium Condition

The system is balanced when:

(USDC plays × Average outflow) ≈ (LTB plays × Cost)

Player Strategy Implications

The seesaw creates strategic considerations:

For Value Maximizers

• Monitor contract balance

• Play with USDC when balance is high (better rewards)

• Play with LTB when balance is low (cheaper entry)

For Simplicity Seekers

• Either payment method works

• The system self-balances regardless of individual choices

• Choose based on which tokens you hold

Visual Flow

                    USDC Players
                         │
                         │ LTB Rewards
                         ↓
┌─────────────────────────────────────────┐
│                                         │
│        LOTTO BALLS CONTRACT             │
│           (LTB Balance)                 │
│                                         │
└─────────────────────────────────────────┘
                         ↑
                         │ LTB Bets
                         │
                    LTB Players

Monitoring the Seesaw

Track the current state:

// See current contract balance
function ltbBalance() public view returns (uint256)

// See what it would cost to play with LTB now
function getLtbCost() public view returns (uint256)

// See current reward levels
function getLtbRewards() external view returns (...)

Changes in ltbBalance() reflect the seesaw in action—increasing when LTB bets dominate, decreasing when USDC bets dominate.