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Helping bettors build
long-term profits.

8,295+ picks tracked since 2019. Every win, every loss, fully public. Check the record, then decide.

Verified since January 2019

Six Years of Tracked Results

Picks Tracked

8,295+

Every result logged and verifiable.

Data Points Per Game

1,000+

Injuries, form, rest, market context.

Win-Percentage

55.1%

Above 52.4% is profitable at standard juice.

Total Profit

+746.5

Cumulative profit, updated daily.

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WarriorsWarriors@JazzJazz· NBA · Mar 10
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CampbellCampbell@MonmouthMonmouth· NCAAB · Mar 9
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KnicksKnicks@ClippersClippers· NBA · Mar 10
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NuggetsNuggets@ThunderThunder· NBA · Mar 9
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FlamesFlames@CapitalsCapitals· NHL · Mar 9

The Process

How I Bet

Every play is backed by mathematical models, closing line analysis, and strict bankroll discipline. You don't need to understand every model. But knowing the process exists is what separates real edge from coin-flip gambling.

Expected Value (EV)

I only bet when the math says I have an edge. If the numbers are not there, I pass, no matter how good the game looks. No edge means no bet.

Closing Line Value (CLV)

Beating the closing line consistently is the strongest predictor of long-term profit. I track CLV on every pick as my core quality signal.

Quantitative Models

Sport-specific models process injuries, rest days, recent form, and market context, then update automatically when new data lands.

Risk & Bankroll Management

Flat 1-unit staking, no chasing losses, no 10-unit "lock" plays. Drawdown rules cut exposure early and protect long-term capital.

Timing & Execution

I monitor line movement and liquidity windows to avoid bad prices and enter at the strongest available number.

Performance Review Loop

Weekly reviews of edge quality, variance, and execution accuracy. Model filters only change when the data justifies it.

Under the Hood

See the Models Behind Every Pick

Most tipsters give you a pick. I show you the math. Every play runs through EV calculators, CLV trackers, and Monte Carlo simulations before it reaches your phone.

1import numpy as np
2from models.odds import implied_probability
3
4def calculate_expected_value(
5 true_probability: float,
6 odds: float,
7 stake: float = 1.0
8) -> dict:
9 "Calculate expected value for a bet."
10 implied_prob = implied_probability(odds)
11 edge = true_probability - implied_prob
12
13 ev = (true_probability * (odds - 1) * stake) - \
14 ((1 - true_probability) * stake)
15
16 roi = ev / stake * 100
17
18 return {
19 "edge": round(edge * 100, 2),
20 "expected_value": round(ev, 4),
21 "roi_per_bet": round(roi, 2),
22 "kelly_fraction": round(edge / (odds - 1), 4),
23 "is_positive_ev": ev > 0
24 }
25
26# Lakers -3.5 @ 1.91
27result = calculate_expected_value(
28 true_probability=0.558,
29 odds=1.91
30)
output

Model Output

Lakers -3.5 @ 1.91

Edge+3.45%
EV+0.035u
ROI/Bet+3.45%
Kelly1.03u
Verdict✓ Positive EV - BET

From the Desk

Analysis & Breakdowns

Free analysis on line value, model updates, and what's moving the market. Published weekly.

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