CRASHMATH
TELEMETRY // ROUND STREAM:
[LATENCY TAX ENGINE // SLIPPAGE QUANTIFIER] • CM-TOOL-LATENCY

Latency Tax & Slippage Simulator

Explore hypothetical delay with exponential multiplier growth and fixed-target settlement. Actual game timing and payout rules may differ.

[LATENCY TAX ENGINE // SLIPPAGE QUANTIFIER] • CM-TOOL-LATENCY

# Latency Tax & Slippage Simulator

Explore a hypothetical delay under a declared exponential growth curve and fixed-target payout. These are model inputs, not measurements of a game or device.

2.00x
Quick Targets:
120 ms
200 ms
$
Multiplier Slippage (Δm = d)
+0.042x
Climbs during Δt window
Latency Wipeout Probability
1.02%
Crash inside lag window
Effective Manual House Edge
5.04%
vs 3.00% nominal edge
Excess Latency Loss (100 Rounds)
-$20.40
Pure latency penalty
Nominal Edge (Auto) 3.00%
Effective Edge (Manual) 5.04%
Edge Inflation: +68.0% casino advantage
Capital Preserved by Auto-Cashout:
+$20.40 / 100 rounds

Execution Architecture Comparison: Server-Side vs Manual Client Tap

Execution Mode Total Latency (Δt) Wipeout Risk Window Effective House Edge Session Capital Protection
● Server-Side Auto-Cashout Model baseline: no added delay 0.000% (Zero latency exposure) 3.00% Fixed-target reference calculation
● Manual Button Cashout 320 ms 1.02% 5.04% Depends on acceptance and payout rules
Mathematical Proof: The Physics of the Latency Tax

The extra loss below assumes accepted delayed wins still pay the original target m. With payout at the higher arrival multiplier, surviving delayed wins can compensate under this ideal model. Real game settlement and timing must be checked separately; server-side auto-cashout is not a universal zero-latency guarantee.

1win 1win / Stake

COMPARE CASHOUT CONTROLS IN THE CHOSEN GAME

Review the actual game rules and available cashout controls, then choose your platform. These model outputs do not certify network performance.

[CLOSED-FORM DERIVATION // NETWORK DRIFT]

Fixed-target delay model

Assume growth m(t)=exp(0.06t), delay tau = RTT + reaction, and d=m*(exp(0.06*tau)-1). The lost-win window is (1-E)*d/[m*(m+d)]. Extra house edge is m times this window only if surviving wins still pay m. Payment at the higher arrival multiplier can compensate in the ideal model. These are assumptions, not measured device or game performance.

P(m ≤ X < m + d) = ((1 - E) × d) / (m × (m + d)) ⇒ E_eff ≈ E + 2 × P_wipeout