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Trent Crebbin

Tipster dossier — settled-bet performance through 2026-06-27 18:16 UTC

Verdict
STRUCTURALLY LOSING
Small sample (1 bets) — anything below ~100 settled bets means CIs are wide enough to be near-useless. Calibration is negative: actual win rate 0.0% trails market-implied 9.1% by -9.1pp — picks have negative skill relative to fair odds. Bayesian-shrunk ROI (regressed toward -13% population baseline by sample size) is -14.7% — a more honest expectation than the raw -100.0%.
Since last weekly snapshot: ROI: · Shrunk ROI:

Charts

Cumulative P/L (theoretical $1 stakes)

A straight upward line = consistent profitability. Step-jumps = single fortunate bets carrying the curve.

Monthly ROI %

Top 10 P/L outliers

ROI by odds bracket

Calibration — actual vs implied win rate by odds

Blue bars under orange = picks lose to market expectations.

ROI by day of week

At a glance

Total Bets
1
Wins
0
Win Rate
0.0%
Avg Odds
11.00
Total P/L ($1 stakes)
$-1.00
ROI
-100.0%
Bayesian-shrunk ROI
-14.7%
Implied Win % (1/odds)
9.1%
Calibration Gap
-9.1pp

What's driving the ROI?

Strip away the top winning bets and see how the picture changes — a stable record degrades gracefully, a lucky record collapses.

(insufficient data)

Recent trend

(insufficient sample to compare recent vs prior)

Monthly performance

MonthNWin %Avg OddsP/LROI
2026-0610.0%11.00$-1.00-100.0%

By odds bracket

OddsNWin %P/LROI
8.0-1510.0%$-1.00-100.0%

By pre-race rank

No pre-race rank data yet for this tipster.

Strategy Zone (rank ≤ 3 AND odds ≥ 5)

No rank data yet.

By source

(no source data)

By venue

(no venue data with sufficient samples)

By day of week

(no day-of-week data)

Place market

No place outcomes captured yet for this tipster.

Biggest wins

DateVenueSelectionOddsP/L
No wins

Biggest losses

DateVenueSelectionOddsP/L
2026-06-274. Takeko11.00$-1.00
Generated 2026-06-27 18:16 UTC. Stats based on theoretical $1 BACK stakes settled on Betfair. Place data uses captured Betfair Starting Price. ROI = total P/L divided by total bets — high variance at small samples.