
The Dog Pound Results
All results shown below are based on live testing using a fully automated setup via BF Bot Manager. A 2% Betfair commission has been applied to all figures.
This is a high-variance strategy operating at average odds of approximately 12.5 with a strike rate around 11%. Performance should therefore be evaluated over large sample sizes and complete cycles rather than isolated short-term periods.
Headline Performance (Live as at May 2026)
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Total Bets: 1,919
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Total Profit: +461.07 units
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ROI: ~24%
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Strike Rate: ~11%
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Max Drawdown: ~−136 units
300-Bet Cycle Performance
The strategy is best understood through 300-bet cycles, which help smooth out short-term variance and provide a clearer view of long-term expectancy.
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Cycle 1: +277.10 units
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Cycle 2: +115.61 units
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Cycle 3: +85.57 units
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Cycle 4: +63.08 units
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Cycle 5: −10.89 units
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Cycle 6: −61.60 units
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Cycle 7: In progress
Cycle 6 represented the most difficult completed cycle in the live dataset so far, highlighting the true variance profile of a high-odds, low strike rate strategy.
Monthly Performance
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March 2025: +198.64 units
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April 2025: +59.06 units
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May 2025: +7.21 units
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June 2025: +134.55 units
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July 2025: +27.77 units
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August 2025: +63.95 units
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September 2025: +28.34 units
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October 2025: −3.61 units
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November 2025: +1.63 units
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December 2025: +14.80 units
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January 2026: −3.75 units
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February 2026: −27.94 units
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March 2026: +6.26 units
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April 2026: −68.99 units
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May 2026: +23.16 units
Total: +461.07 units
April was the most challenging month in the live dataset so far. May then delivered a more stable profile, finishing positive and helping reinforce the importance of assessing performance over longer time horizons rather than reacting emotionally to short-term variance.
Key Observations
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The strategy remains strongly profitable overall despite the recent drawdown
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Variance can be significant, including extended losing periods
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The edge appears to remain structurally intact following the difficult Cycle 6 period
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Performance should be judged over complete cycles and large sample sizes
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Discipline and consistency in execution remain critical