Performance tracking
Forecast statistics without hiding the risk
We show both sides of the model: flat betting performance as a strict control line, and Martingale simulation as a recovery-based approach with explicit bankroll requirements.
Total forecasts
59
Wins
14
Losses
16
Pending
21
Martingale recovery simulation
$70.00
Required bank
$198.41
The Martingale block shows how completed betting cycles behave when the next stake is adjusted to recover previous losses and target a fixed cycle profit.
This is not presented as a guarantee. The key limitation of any recovery model is bankroll pressure during long losing streaks.
Completed series profit
$70.00
Required bank
$198.41
Longest loss streak
4
Cycle profit target
$10.00
Flat betting control line
-47.10%
Flat betting assumes the same stake on every forecast. It is useful for transparency because it shows the raw quality of picks without recovery mechanics.
A negative flat result is expected on many betting models because bookmaker margin and variance work against equal-stake betting. We keep it visible as the honest baseline.
All time
-47.10%
Year
-47.10%
Month
-47.10%
Week
-47.10%
Last 100
-47.10%
Last 1000
-47.10%
How to read these numbers
Why flat betting may be negative
Flat betting is the strictest view of the model: every forecast receives the same stake. It does not try to recover losses, so bookmaker margin, variance and losing streaks are fully visible. This makes it a useful honesty check, not the most flattering presentation.
What Martingale shows
Martingale simulation groups forecasts into recovery cycles. After a loss, the next stake is adjusted using the actual odds so that a later win can recover previous losses and add the target profit for the cycle.
Why loss streak length matters
The longest loss streak is the main stress test for any recovery model. A short streak makes the required bank modest; a long streak can increase the needed bankroll quickly.
Required bank is the risk number
Required bank is calculated from the actual losing streak and odds structure. It shows how much capital the recovery model would have needed to survive the worst completed sequence in the tracked data.