How to Master gold365 Green with Simple Math

1. 🧮 The Foundation: Probability & Implied Odds

A. Probability Calculation

Every event on Gold365—like the next wicket or boundary—has a certain probability. You estimate it using simple frequency:

If in the last 20 overs, there were 4 wickets in the 17th over, probability ≈ 4 ÷ 20 = 0.20 (20%).

B. Implied Odds from Market Prices

If the live platform shows odds of 4.0, implied probability is:

Implied Probability = 1 ÷ Odds = 1 ÷ 4.0 = 0.25 (25%).

C. Edge Calculation

Your edge = Your Probability – Implied Probability
If you see 20% vs. 25%, that’s a -5% edge (avoid this bet).
A positive edge means your probability estimate is higher than implied, giving you an advantage.

2. 💵 Bankroll Management with Percent Math

A. The Kelly Criterion (Simplified)

Bet size = Edge ÷ Odds

• With edge 0.05 and odds 4.0 → Bet 0.05 ÷ 4 = 0.0125 (1.25% of bankroll).

B. Flat-Fraction Model

If Kelly is too volatile:

• Use a fixed percent per bet—1–3% of bankroll is smart and safe.

C. Daily & Session Caps

• Max 5% Daily Loss: If you lose more, stop for the day to preserve capital.

3. 📊 Tracking ROI with Simple Metrics

For each bet:

• ROI per bet = (Payout – Stake) ÷ Stake
  E.g., ₹100 stake at odds 4.0 → Payout ₹400 → Profit ₹300 → ROI = 3.0 (300%).

• Overall ROI = (Total Profits ÷ Total Stakes)
  If ₹10,000 total staked and ₹1,000 earned → ROI = 10%.

Track these in a spreadsheet along with event type, odds, and payout.

4. ⏱️ Timing & Expected Value (EV)

A. Value Bets

A positive edge bet has:

EV = Edge × Odds – (1 – Implied Probability)

E.g., Edge = 0.10, Odds = 5.0 → EV = 0.10×5.0 – (1–0.20) = 0.50 – 0.80 = –0.30 → Bad bet!

True value bet example:

• Your Probability = 0.30

• Implied Odds = 1 ÷ 0.20 = 5.0

• Edge = 0.10 → EV = 0.10 × 5 – (1–0.20) = 0.50 – 0.80 = –0.30 — still negative!

• You need edge ≥ (1–p)/odds e.g., edge ≥ 0.16 (for EV ≥ 0).

B. Minimum Edge Threshold

Only bet when EV > 0.
A good rule: seek at least +5% positive EV, and odds of at least +10% better than implied.

5. 📉 Variance & the Law of Large Numbers

A. Expected Variance

Smaller bets reduce daily swings.
Variance (Volatility) ≈ Odds × √n (simplified).
If average edge is 10%, variance shrinks as you make more small bets.

B. Consistency Over Time

• Win rate alone is deceptive.

• More important: Win rate + average odds.

• Expect a period of losses—discipline and math protect your bankroll.

6. 🔁 Diversification of Events

A. Bet Types

Spread bets over:

• Next wicket

• Runs in over

• Extras

• Toss outcomes

Each has an expected value; diversify to smooth ROI.

7. ✍️ Simple Math in Workflow

Step 1: Estimate Probability

E.g., In a pitch with average 0.1 wicket per over, probability ≈ 0.10.

Step 2: Note Live Odds

Odds = 12.0 → Implied Probability = 1 ÷ 12 ≈ 8.3%.

Step 3: Calculate Edge

Edge ≈ 10% – 8.3% = 1.7%.

Step 4: Calculate Bet Size

• Kelly bet size = 0.017 ÷ 12 ≈ 0.14% of bankroll (tiny!)

• Flat model: bet 1%.

Step 5: Evaluate EV

EV = 0.017 × 12 – (1–0.083) = 0.204 – 0.917 = –0.713 (!), negative → skip.

8. 📝 Record, Review, Refine

Track:

• Event

• Prob Estimate

• Live Odds

• Edge

• Stake %

• Outcome

• ROI

Weekly: Calculate total stakes, P&L, average edge. Adjust your probability model accordingly.

9. ⏳ Mid-Match Adjustments

Let math guide real-time bets:

• Adjust probability dynamically as match unfolds.

• Recompute edge and bet size for each decision.

E.g., wicket rates rise mid-match → recalculate probabilities.

10. 🧠 Psychological Discipline

• No chase after loss—math over emotion.

• Set clear stop-loss and profit goals (~5% ROI per day).

• Only bet when edge meets your criteria.

🧭 Example Session (₹10,000 Bankroll)

Net: +₹20 on ₹1,000 stakes (2% daily ROI). Discipline kept losses in check and math led picks.

✅ Final Takeaways

1. Estimate probability, compare with market-implied.

2. Calculate edge, only bet positive.

3. Manage stakes via Kelly or flat %.

4. Track & refine each bet mathematically.

5. Diversify across bet types.

6. Iterate over time—math improves with data.

7. Stay disciplined, not emotional.