Pricing Framework Interactive
Market Making
Balance the book by adjusting prices based on order flow. The goal is to profit from the spread while minimizing directional exposure.
⚖️ The Market Maker's Role
📥
Take Both Sides
Offer odds on OVER and UNDER. Collect bets from both sides of the market.
⚖️
Balance Exposure
Move lines to attract bets on the lighter side. Goal: equal $ on both sides.
💰
Profit from Spread
With balanced book, keep the vig regardless of outcome. Minimize risk.
Market Settings
210 240
210 240
0.1 1
Bet Flow (# of bets)
0 100
0 100
Net Flow +20 OVER
📊 Line Movement
Opening Line 225.5
Current Line 226.5
Movement +1.0
Book Exposure
OVER Liability
$11,400
60 bets × $190 payout
UNDER Liability
$7,600
40 bets × $190 payout
Net Exposure
$3,800
Balance: 67%
60% / 40%
P&L Scenarios
If OVER Wins
+$7,400
If UNDER Wins
+$1,600
Balanced Book P&L
+$450
4.5% hold
Simulated Bet Flow & Line Movement
🔗 DeFi Parallel: AMMs
Traditional Market Making
- • Human/algo adjusts bid-ask based on flow
- • Move line to attract counter-bets
- • Goal: balanced book, profit from spread
- • Risk: directional exposure if unbalanced
Automated Market Maker (AMM)
- • Algorithm adjusts prices via bonding curve
- • x * y = k (constant product formula)
- • Goal: provide liquidity, earn fees
- • Risk: impermanent loss if prices diverge
Your hook: "I built AMM mechanics at Flipside—the same principles apply to sports market making."
R Code Equivalent
# Market making simulation
simulate_market <- function(opening_line, true_value, sensitivity = 0.5, n_periods = 20) {
current_line <- opening_line
over_bets <- 0
under_bets <- 0
lines <- c(current_line)
for (t in 1:n_periods) {
# Sharps bet toward true value
over_flow <- rpois(1, lambda = 5 + ifelse(current_line > true_value, -1, 1))
under_flow <- rpois(1, lambda = 5 + ifelse(current_line < true_value, -1, 1))
over_bets <- over_bets + over_flow
under_bets <- under_bets + under_flow
# Adjust line based on imbalance
imbalance <- over_bets - under_bets
current_line <- opening_line + imbalance * sensitivity * 0.05
lines <- c(lines, current_line)
}
return(list(lines = lines, over = over_bets, under = under_bets))
}
# Run simulation
result <- simulate_market(225.5, 224, 0.5)
cat(sprintf("Final line: %.1f\n", tail(result$lines, 1)))✅ Key Takeaways
- • Goal: balanced book = riskless profit from vig
- • Move lines to attract bets on light side
- • Sharp money provides price discovery
- • Line movement reveals market sentiment
- • Similar to DeFi AMM mechanics
- • Sensitivity controls reactivity to flow