Betting Strategy v2 — Sport-Specific Framework

After backtesting 20 NBA playoff games, the data showed:

My analysis returned +39% ROI but underperformed blind favorite-betting (+43.5%)
The 4 losses all came from overriding the line with narrative factors
The wins came from agreeing with the line on clear favorites

Core rules of v1

Only bet when analysis AGREES with the line direction (no contrarian picks)
Heavier weight on talent / seed / record gaps (these predict)
Lower weight on narrative factors (must-win, pivotal home, etc.)
SKIP coin-flip games (50-55% probability range)
Stake by confidence: STRONG $8 · MEDIUM $5 · SKIP $0
Track outcomes religiously to validate / invalidate

What actually happened in MLB

The v1 strategy applied to 20 MLB games (May 12-14): lost money. -11.7% ROI. 7-6 record.

Worse: STRONG-confidence bets went 4-4 (-19.4% ROI). MEDIUM bets went 3-2 (+8% ROI). When I was "most certain," I was wrong more often.

2. Why a "strategy" alone isn't enough

A betting strategy without sport-specific data is incomplete.

What works in NBA (favorites winning ~65-70%) does NOT work in MLB (favorites winning ~55-58%) because the vig kills you at lower hit rates. A -180 MLB favorite needs to win 64% of the time just to break even — that's an unrealistic bar for daily baseball.

Each sport needs:

Sport-specific factors that actually predict
Data sources beyond season records (especially MLB starting pitchers)
Calibrated confidence levels validated by results

The strategy framework needs sport-specific models, not one universal approach.

3. Strategy v2 framework

Universal rules (apply to all sports)

Track every prediction with explicit reasoning (in /bets/prediction-log/)
Score outcomes honestly — not just W/L but why
Don't trust any "edge" until 100+ samples
Lower stake sizes until proven track record
Calibrate confidence by actual results, not feel

🏀 NBA Playoffs

$5-8 per bet · target: 65%+ hit rate

What works:

Favorites with talent / seed gaps win consistently
Heavy home favorites in elimination games
Road favorites in dominant series (talent travels)
Series-closing G7 home court

Avoid: pivotal-G5 narratives (priced in), "must-win" home underdogs, overriding talent gap with situational stories.

⚾ MLB Regular Season

$3-5 per bet · target: 56%+ hit rate

What works (with caveats):

MUST include daily starting pitcher matchup — season records alone aren't enough
Bullpen quality for late-game scenarios
Travel / rest patterns affect performance
Bounce-back at home after upset loss (small sample, validate)

Avoid: heavy MLB favorites (-150+) without clear pitcher advantage. Daily variance and shutouts are real. Stakes smaller until pitching-data pipeline is built.

⛳ Golf Majors

$2-5 per long-shot · payout 50:1+ matters more than hit rate

What works:

Course fit > player ranking for specific events
Recent form matters more than career stats
Use the SameSHOT Framework for value (3+ factor stack)
Diversified portfolios across outcome types (outright + top-10 + cut)

Avoid: single big stake on one outright. Diversify across 5+ small tickets.

🏒 NHL / ⚽ Soccer / Other

$0 — don't bet yet · build the framework first

Don't bet what we don't know yet.

Build sport-specific factor list + backtest 20+ games BEFORE risking money. Avoid the "but this game looks good" trap.

4. Confidence calibration — the MLB lesson

From the MLB backtest:

Confidence	Record	ROI
STRONG	4-4	-19.4%
MEDIUM	3-2	+8.0%

When I think I'm most certain, I'm probably wrong.

Possible reasons:

Confirmation bias — when "sure", I ignore counter-evidence
Heavy favorites have terrible payouts (risk $8 to win $4)
The market knows what I know — "obvious" picks have no edge

New rule — inverse stake sizing (or flat)

STRONG conviction: bet SMALL ($3)
MEDIUM conviction: bet NORMAL ($5)
MAYBE conviction: bet NORMAL or skip

Or, honestly: $5 flat across all bets until calibration is proven over 30+ games.

5. How we actually improve

The real strategy is the tracking system

Make predictions with explicit reasoning
Wait for game outcome
Score honestly — was the pick right? Were the factors right?
Track which factors actually predict over many games
After 100 games: update strategy based on what actually worked

This is the difference between gambling and forecasting. Most casual bettors do step 1. Professionals do all 5. We aim for all 5.

📌 Sam's weekly bet budget: $25-50/week for analysis testing. Treat as learning expense, not income source. After 100+ games tracked, reassess.

Pending strategy v3 triggers

Update strategy v3 when:

100+ games tracked in the prediction log
Specific bet type shows ≥55% hit rate over 30+ samples
Specific factor shows ≥65% accuracy over 20+ scored outcomes
Starting-pitcher data pipeline is integrated for MLB

📐 Betting Strategy v2 — Sport-Specific Framework

1. Strategy v1 — "Line Agreement + Talent Gap"