ℹ️ The Core Question: It's not enough to find edge — you need to know how much to bet, when to scale up, when to hedge, and when to walk away. This report covers four sizing models, maps each to specific market types, and builds the risk framework around your portfolio.

THE FOUR SIZING MODELS — AND WHEN TO USE EACH

Model 1: Many Small Bets ("The Domer Model")

Recommended for: Weather, Mentions, Long-Tail Niche Markets

The concept: Spread capital across dozens or hundreds of small positions. Each bet is 0.5–2% of bankroll. You're playing the law of large numbers — any single bet barely matters, but over hundreds of trades, a consistent 55–65% win rate compounds into significant returns.

Real-world proof: Domer (@ImJustKen on Polymarket) has ~10,000 predictions with $2.5M+ net profit. Of those, ~8,000 are small bets. He aims for 60% win rate: "If I'm right 60% of the time, I'm making a lot of money. If you lose 40% of the time, that's a lot of money out the door, but you have to look at it in the long term."

EventEdge sports bot: 2,324 trades, 56.7% win rate, average edge 6.5¢/trade. Win rate is under 50% on some days but Kelly sizing means wins pay more than losses cost → still profitable.

Example on $1,000 bankroll:
• 50 bets × $20 each = $1,000 deployed
• 60% win rate × $20 payouts = 30 wins × ~$15 avg profit = $450
• 40% loss rate = 20 losses × $20 = -$400
• Net: +$50 per cycle (5% return)
• Repeat daily/weekly → compounds

Why it works: Variance gets smoothed by volume. No single loss hurts. Fast feedback on whether your model actually has edge. Emotionally sustainable — you're never sweating a single bet.

Best for: Daily weather markets (7 cities × 6 brackets = 42 markets/day), mention markets, scanning hundreds of long-tail niche markets with small mispricings.

Model 2: Concentrated High-Conviction Bets ("The Thesis Trade")

Recommended for: Economic Indicators, Political Events

The concept: Fewer bets, bigger positions. You've done deep research, built a model, and you have high confidence that the market is mispriced by 10%+. You deploy 5–15% of bankroll on a single trade.

When it fits: Economic indicator markets are perfect for this. CPI releases, jobs reports, GDP — these happen monthly, resolve definitively, and you can build a model using publicly available leading indicators. When your model says 75% and the market says 55%, that's a 20-point edge worth concentrating on.

Example on $1,000 bankroll:
• Model says CPI will be above 3.0% with 80% probability
• Market price: $0.62 (62% implied probability)
• Edge: 18 points
• Position size: $100–150 (10–15% of bankroll)
• If right: $100 × ($1.00 - $0.62) / $0.62 = +$61.29 (61% return on position)
• If wrong: -$100
• Expected value: (0.80 × $61.29) - (0.20 × $100) = +$29.03

The risk: Any single trade can go wrong. With concentrated positions, a few losses hurt. This model only works if you genuinely have deep domain knowledge and your probability estimates are well-calibrated.

Best for: Monthly economic releases where you have leading indicator models, big political events where you've done research the crowd hasn't.

Model 3: Martingale / Doubling Down ("The Recovery Chase")

NOT Recommended — Including for Completeness

The concept: After each loss, double your bet size. When you eventually win, you recover all previous losses plus a small profit. Then reset to your base bet.

Martingale sequence on $1,000 bankroll, $10 base bet:
• Bet 1: $10 → Loss (-$10, total: -$10)
• Bet 2: $20 → Loss (-$20, total: -$30)
• Bet 3: $40 → Loss (-$40, total: -$70)
• Bet 4: $80 → Loss (-$80, total: -$150)
• Bet 5: $160 → Loss (-$160, total: -$310)
• Bet 6: $320 → Win (+$320, total: +$10)
• Six bets to make $10 profit. If bet 7 was needed: $640 (64% of bankroll on a single bet)

🚫 Why this kills bankrolls: The math is brutal. After just 7 consecutive losses (which WILL happen over enough trades), you need to bet 128× your base bet. On a $1,000 bankroll with a $10 base, that's $1,280 — more than your entire bankroll. You're bankrupt. The Martingale gives the illusion of consistency (lots of small wins) but hides catastrophic tail risk. Even with an edge, it increases your average loss vs flat betting.

Where people get seduced: Weather markets with daily resolution. You lose Monday, double Tuesday, double Wednesday... the streak of 7+ wrong predictions on temperature brackets is rare but not impossible. And when it happens, you're wiped.

Bottom line: Do not use this. Including it here because you asked about "doubling until we hit" — the math says no, unequivocally.

Model 4: Kelly Criterion ("The Mathematically Optimal Bet")

Recommended as the Core Sizing Engine

The concept: Kelly tells you exactly how much to bet as a function of your edge and the odds. It maximizes long-term geometric growth rate while naturally protecting against ruin.

Kelly Formula for binary prediction markets:

f* = (p × b − q) / b

Where:
• f* = fraction of bankroll to bet
• p = your estimated probability of winning
• q = 1 − p (probability of losing)
• b = payout odds (for a $0.60 contract: b = $0.40/$0.60 = 0.667)

Simplified for prediction markets:
f* = p − (market_price / (1 − market_price)) × (1 − p)

Or even simpler: f* = p − market_price (when contracts pay $1)
i.e., your edge IS your Kelly fraction (approximately).

Kelly in Practice — Worked Examples

Scenario	Your Prob	Market Price	Full Kelly	Half Kelly	Quarter Kelly
Weather: Strong model divergence	85%	$0.65	20%	10%	5%
CPI: Moderate conviction	70%	$0.55	15%	7.5%	3.75%
Politics: Slight edge from news	60%	$0.50	10%	5%	2.5%
Niche: Small mispricing	55%	$0.48	7%	3.5%	1.75%
Long shot: Cheap contract	20%	$0.08	12%	6%	3%

✅ Why Fractional Kelly is the Move: Full Kelly is mathematically optimal but emotionally brutal — it produces drawdowns of 50%+ regularly. Edward Thorp (the man who beat Vegas AND Wall Street) recommends Half Kelly as the practical standard. You capture ~75% of the growth rate with dramatically less volatility. At Quarter Kelly, you still capture ~50% of growth with a 1-in-213 chance of 80% drawdown (vs 1-in-5 at full Kelly). Start at quarter Kelly while learning, graduate to half Kelly when your model is proven.

MATCHING STRATEGY TO DOMAIN

Your intuition is exactly right — different domains need different approaches. Here's how it maps:

Domain	Sizing Model	Typical Bet Size	Trade Frequency	Why
Weather	Many Small Bets + Quarter Kelly	1–3% of bankroll	5–20 bets/day	Daily resolution, many markets, noisy data. Volume smooths variance. Your model will be wrong often but right more often than the market.
Economic Indicators	Concentrated + Half Kelly	5–10% of bankroll	2–4 bets/month	Monthly resolution, deep research per trade, high-conviction positions. Fewer at-bats but each one is well-modeled.
Political/News	Medium bets + Half Kelly	3–7% of bankroll	5–15 bets/month	Event-driven, variable frequency. Size scales with confidence in your news processing speed advantage.
TSA / Niche Data	Small Bets + Quarter Kelly	1–2% of bankroll	2–5 bets/week	Low liquidity caps position size anyway. Learning vehicle, not profit center.
Cross-Platform Arb	Fixed amount per opportunity	3–5% of bankroll per arb	Opportunistic	Near-zero risk if executed properly. Size limited by liquidity on smaller side. No Kelly needed — it's structural, not probabilistic.
Mentions	Many Tiny Bets	0.5–1% of bankroll	5–10 bets/week	High uncertainty, fun money. LLM analysis adds edge but outcomes are noisy.

HEDGING & RISK MITIGATION

Strategy 1: Natural Hedges Within Market Categories

Weather markets have a built-in hedge structure. If you're betting on temperature in NYC, the six brackets sum to ~100%. You can buy the bracket you think will hit AND sell (or buy NO on) the adjacent bracket as a hedge. Example:

NYC forecast: 78°F. Brackets: 74-76, 76-78, 78-80, 80-82.

Primary bet: Buy YES on 76-78 at $0.40 → pays $1.00 if right
Hedge: Buy YES on 78-80 at $0.35 → pays $1.00 if right
Total cost: $0.75. You win $1.00 if temp is anywhere in 76-80 range. You lose $0.75 only if temp is outside both brackets.
This is essentially a spread bet — you're buying a wider range at reduced payout but much higher probability.

Strategy 2: Correlated Event Hedges

Some markets are logically correlated. If you're long "CPI above 3%" you can hedge with a position on "Fed raises rates" — these are directionally correlated. If CPI comes in hot and the market overreacts on Fed expectations, your Fed position captures that overreaction even if your CPI bet was slightly off.

⚠️ Warning from regulators: Concentration risk can snowball if you pyramid dozens of correlated positions. If you're long "inflation high" on CPI, PCE, Fed rates, AND bond yields, a single data print can hit ALL positions simultaneously. Diversify across uncorrelated categories (weather + economics + politics) rather than stacking within one theme.

Strategy 3: Time-Based Position Management

Prediction market contracts can be traded before they resolve. This means you can:

Scale in gradually: Take a small position early when uncertainty is high, add size as your model's confidence grows with new data. On CPI, start a position 2 weeks out, add as regional data comes in.
Take profits early: If you bought at $0.40 and the market moves to $0.70, you can sell your contracts for a 75% gain without waiting for resolution. Locks in profit, eliminates remaining risk.
Cut losses: If your model was wrong and new data confirms it, sell at $0.30 (25% loss) instead of riding to $0.00 (100% loss). Active position management, not buy-and-hold.
Hedge with opposing bets late: If you have a YES position that's looking shaky, buy some NO contracts to cap your downside. The combined position locks in a narrower range of outcomes.

Strategy 4: Bankroll Segmentation

🏦 The Three-Bucket System

Split your total capital into three buckets with different risk profiles:

Core (60% of bankroll): Conservative positions. Half Kelly or less. Data-driven markets (weather, economics) where you have model edge. The bread and butter.
Tactical (30% of bankroll): Medium-conviction event-driven trades. Political repricing, news events. Half to three-quarter Kelly. Higher variance but still disciplined.
Speculative (10% of bankroll): Long-shot plays, new market categories, experimental strategies. Quarter Kelly or flat small bets. This is your learning budget — you expect to lose some but it generates data for future edge.

Rule: Never let one bucket borrow from another. If Speculative hits zero, stop spec trading until the next bankroll rebalance.

HARD RULES — NON-NEGOTIABLE RISK MANAGEMENT

⚠️ These protect us from ourselves:

Maximum single bet: 10% of bankroll. No exceptions. Even if the model screams 95% confidence, never put more than 10% on one outcome. Models can be wrong in ways we don't anticipate.
Daily loss limit: 15% of bankroll. If we lose 15% in a day, all trading stops for 24 hours. Cool off, review what went wrong, adjust models.
Weekly loss limit: 25% of bankroll. Same — stop, review, recalibrate.
Maximum concurrent exposure: 50% of bankroll at risk. Always keep 50% in cash/reserve. This lets us survive a worst-case scenario where multiple correlated positions move against us.
No martingale, ever. Doubling down after losses is the path to ruin. Kelly criterion already handles position sizing optimally.
No trading on tilt. After a big loss, the temptation to "make it back" is real. The 24-hour cooling period is mandatory.
Log every trade with reasoning. If you can't articulate why you're taking the position, you don't take it. "It feels right" is not a reason.
Review weekly. Every Sunday: P&L by category, model accuracy vs. claimed probability, biggest wins/losses and why.

THE RECOMMENDED APPROACH — PUTTING IT ALL TOGETHER

🎯 The Playbook (on $1,000 starting capital)

Week 1-2: Paper trade only. Run the models, calculate what we'd bet, track results. Zero real money. Kalshi has a demo environment for this.
Week 3-4: Quarter Kelly, Core bucket only. $600 in Core (weather + economics). $10-15 per weather bet, $30-60 per economic indicator bet. Log everything.
Month 2: Add Tactical bucket. If Core is profitable after 4 weeks, deploy $300 into Tactical (political/news repricing). Half Kelly sizing.
Month 3: Add Speculative + scale up. If overall profitable, add $100 to Speculative (long-tail, mentions, experiments). Consider adding capital if ROI warrants it.
Ongoing: Weekly reviews, model tuning, category expansion.

What the Bot Needs to Implement

The position sizing engine needs these inputs for every trade:

Input	Source	Used For
Current bankroll	Kalshi API (account balance + open positions)	Kelly calculation base
Model probability (p)	Our data model output	Kelly numerator
Market price	Kalshi API (current YES price)	Kelly denominator + edge calculation
Market category	Trade metadata	Bucket assignment (Core/Tactical/Spec)
Kelly fraction (λ)	Config per bucket (0.25 / 0.50 / 0.25)	Position size scaling
Current exposure	Sum of open positions	50% max exposure check
Daily/weekly P&L	Trade log	Loss limit circuit breakers
Minimum edge threshold	Config (e.g., 5%)	Only trade if edge exceeds this

Position Sizing Algorithm (pseudocode):

edge = model_probability - market_price
if edge < MIN_EDGE_THRESHOLD: skip trade

full_kelly = edge (simplified for binary markets)
fractional_kelly = full_kelly × KELLY_FRACTION[bucket]
position_size = bankroll × fractional_kelly

# Apply hard caps
position_size = min(position_size, bankroll × 0.10) # max 10% per trade
position_size = min(position_size, max_exposure_remaining) # 50% total cap
position_size = min(position_size, bucket_remaining_allocation) # bucket cap

if daily_loss > bankroll × 0.15: HALT_ALL_TRADING
if weekly_loss > bankroll × 0.25: HALT_ALL_TRADING

execute_trade(market, "YES", position_size, limit_price=market_price)

KEY INSIGHT FROM THE PROS

"Trading correctly is 90% money and portfolio management. A trader with mediocre strategy and a great risk model will become fairly successful. A trader with a great strategy and a mediocre risk model will become bankrupt."

— Michael W. Covel, via Nick Yoder's Kelly Criterion analysis

"I make a lot of small predictions. Of those 10,000, something like 8,000 are not very big. I kind of pick my spots."

— Domer ($2.5M+ net profit on Polymarket), via MetaMask

"By always betting 30% of the Kelly-optimal bet size, a gambler reduces the chance of dropping to 20% of his peak bankroll from 1-in-5 to 1-in-213, but still keeps 51% of the growth."

— Wikipedia, Kelly criterion, citing Thorp (2008)

The consistent message from everyone who's done this successfully: smaller bets than you think, more diversification than feels exciting, and rigid discipline on the risk rules. The edge comes from the model — the survival comes from the sizing.

Report prepared by Chief | Betting Strategy Playbook | March 18, 2026
Sources: Kelly (1956), Thorp (2008), Wikipedia Kelly Criterion, Investopedia, Nick Yoder (Quantitative Trading), MetaMask/Domer interview, Reddit r/SaaS (EventEdge), Corporate Finance Institute, Berkeley Statistics

This report is for research and planning purposes. Not financial advice. Prediction market trading involves real risk of loss.

🎲 Betting Strategy Playbook — Part 3