Betcipline

Sample report

An anonymised Betcipline audit

This is an anonymised sample generated from a sanitized public-history fixture with the same production methodology as a live report. It is not your wallet, not a case study, and not a success story. Betting involves risk. Past performance does not guarantee future results.

Fact

Analysed records

10 decisions (1 open, 9 closed) and 12 activity events.

This is an anonymised sample generated with the same V1 audit engine as a live report. It is not a live wallet import and not a success story. Analyzed: 9 closed positions, 1 open position, 12 activity events.

Biggest pattern in this sample

The largest 3 losing position(s) account for 93.3% of realised losses in this sample.

Sample size 6 (minimum 5). Confidence: low.

Largest 3 closed losses by magnitude divided by the sum of all negative closed realised P&L. Historical comparison in this imported sample. It does not prove that size or category caused the result, and it is not a forecast.

What this sample does well

Strongest eligible group in this sample: crypto (+13.3% closed return).

Sample size 3 (minimum 3). Confidence: low.

Highest closed return among 3 eligible groups (category, else market). Groups with fewer than 3 closed sized decisions are ignored. Historical comparison in this imported sample. It does not prove that size or category caused the result, and it is not a forecast.

Where risk concentrates

Weakest eligible group in this sample: sports (-41.2% closed return).

Sample size 3 (minimum 3). Confidence: low.

Lowest closed return among 3 eligible groups (category, else market). Groups with fewer than 3 closed sized decisions are ignored. Historical comparison in this imported sample. It does not prove that size or category caused the result, and it is not a forecast.

Fact

Realised P&L

-$71.00

Sample size 9 (minimum 1). Confidence: high.

Sum of realised P&L across 9 decision(s) that include a realised P&L value. Open mark-to-market is excluded from this total.

Fact

Return on closed stake

-30.9%

Sample size 9 (minimum 5). Confidence: low.

Closed realised P&L (-71) divided by summed closed stake (230). Past return is not a forecast.

Fact

Category performance

-41.2%

Sample size 3 (minimum 3). Confidence: low.

Lowest closed ROI among eligible categories is sports (3 decisions). Highest is crypto. Categories with fewer than 3 closed decisions are ignored.

Fact

Concentration risk

75.5%

Sample size 10 (minimum 5). Confidence: medium.

sports accounts for 75.51% of total stake across 10 sized decisions. High concentration increases single-theme risk.

Fact

Losing streak

6

Sample size 9 (minimum 5). Confidence: low.

Longest consecutive closed-loss run is 6 on a timestamp-ordered series of 9 closed decisions.

Supporting scores

Overall

43

Discipline

50

Concentration / Risk

30

Consistency

50

Coarse 0–100 integers from the documented V1 methodology. Supporting context only — not a ranking, percentile, or profit guarantee.

Evidence

Where do I actually make or lose money?

  • Realised P&L

    -$71.00

    Sum of realised P&L across 9 decision(s) that include a realised P&L value. Open mark-to-market is excluded from this total.

  • Closed realised P&L

    -$71.00

    Sum of realised P&L on 9 closed decision(s). Kept separate from open exposure.

  • Open exposure

    $15.00

    Sum of current exposure on 1 open decision(s). This is not closed-result P&L.

  • Return on closed stake

    -30.9%

    Closed realised P&L (-71) divided by summed closed stake (230). Past return is not a forecast.

Evidence

Does my sizing help or hurt historically?

  • Average stake

    $24.50

    Arithmetic mean of 10 positive stake values (open and closed).

  • Median stake

    $10.00

    Median of 10 positive stake values. Compared with the mean to flag skew from a few large positions.

  • Stake consistency

    99.3%

    Sample standard deviation of stake (24.32) divided by mean stake (24.5). Higher values mean less consistent sizing.

Supporting scores

Consistency

50

Evidence

Where am I overconcentrated?

  • Concentration risk

    75.5%

    sports accounts for 75.51% of total stake across 10 sized decisions. High concentration increases single-theme risk.

  • Largest positions

    $80.00

    Largest stake is 80 (32.6531% of total stake). Supporting ids are the top three positions.

Supporting scores

Concentration / Risk

30

Evidence

Which behavioural patterns are supported by the data?

  • Rapid-repeat sessions

    1

    1 of 8 timestamped sessions contain 4+ events with gaps ≤ 15 minutes. Rapid repeats can indicate impulsive activity.

  • Losing streak

    6

    Longest consecutive closed-loss run is 6 on a timestamp-ordered series of 9 closed decisions.

  • Activity timing

    58.3%

    58.3333% of timestamped events fall in the UTC 12:00–15:00 window. Weekend (UTC Sat/Sun) share is 8.3333%. Timezone of the user is unknown, so this is descriptive only.

Evidence

Where does that P&L sit by theme?

  • Category performance

    -41.2%

    Lowest closed ROI among eligible categories is sports (3 decisions). Highest is crypto. Categories with fewer than 3 closed decisions are ignored.

Evidence

How did drawdowns and sessions look?

  • Max drawdown

    $75.00

    Largest peak-to-trough decline on a running sum of 9 timestamped closed P&L values, starting from 0. Missing timestamps are excluded.

  • Rapid-repeat sessions

    1

    1 of 8 timestamped sessions contain 4+ events with gaps ≤ 15 minutes. Rapid repeats can indicate impulsive activity.

Process

Which process guardrails are worth considering?

  • Cap how much of total stake sits in one category until that share is lower.
  • Avoid letting a single position dominate total stake.
  • Add a pause between clustered decisions instead of stacking activity in one burst.
  • After a losing streak, stop and review rather than immediately making another decision.
  • Keep stake sizes in a tighter band relative to typical size. Do not use this as a reason to raise stakes.

Method

How was this calculated?

Metrics, scores and personal patterns come from the documented V1 audit methodology. Missing rows are omitted. This sample is a bounded, anonymised fixture — not an implied all-time wallet history, not causation, and not a profit forecast.

Metrics are descriptive of imported history only. They are not a forecast or a betting recommendation.

Betting involves risk. Past performance does not guarantee future results. Betcipline is decision-support analytics, not a licensed gambling operator.

Sample anonymised audit report | Betcipline