In-Play Sports Trading: Quantifying the Infrastructure Gap in the World's Largest Prediction Market
ForSyt Research | March 2026
Abstract
We present a quantitative analysis of the in-play (live) sports trading market and demonstrate that it constitutes the world's largest active prediction market by volume, participants, and event frequency — yet operates on infrastructure one to three orders of magnitude behind comparable financial exchanges. We define a formal framework of variables governing live sports price discovery, map established financial technical indicators to their sports equivalents, and quantify the infrastructure gap against NYSE, NSE/Zerodha, and Binance/Coinbase. We show that regulated sports trading volume ($112B, 2025) already rivals crypto exchange volume, and that inclusion of the unregulated credit market — exemplified by India's ₹8.3 lakh crore ($100B) IPL season alone — places total annual handle above $500B. The opportunity is not to create a new market. It is to build exchange-grade infrastructure for one that already exists.
1. Introduction
1.1 The Prediction Market Thesis
Prediction markets aggregate information through price discovery. Polymarket and Kalshi demonstrated this at scale in 2025, processing $44B in combined volume across political, economic, and cultural events. The sector is projected to reach $1T by 2030.
This paper argues that the largest prediction market already exists and has for decades: live sports trading. Every in-play sporting event is a continuous prediction market where participants express probabilistic views through positions, and prices (odds) adjust in real-time based on new information.
1.2 The Core Problem
The infrastructure serving this market has not evolved since Betfair launched the first betting exchange in 2000. Execution latency is measured in seconds. Risk management is manual. Settlement takes hours to days. Price discovery is fragmented across opaque bookmakers.
Compare this to equities (sub-microsecond execution, real-time risk, T+1 settlement) or crypto (sub-10ms execution, instant settlement) — and the gap becomes self-evident.
1.3 Scope
This paper:
- Quantifies market size across regulated and unregulated segments
- Defines a variable framework for live sports price discovery
- Maps financial technical indicators to sports equivalents
- Benchmarks infrastructure across four exchange classes
- Identifies the structural opportunity
2. Market Quantification
2.1 Regulated Market
| Metric | Value | Source |
|---|---|---|
| Global sports betting market (2025) | $112.26B | Precedence Research |
| Projected market (2035) | $325.71B | Precedence Research |
| CAGR (2025–2035) | ~11.2% | Precedence Research |
| In-play share of online sports betting | 62.35% | Grand View Research |
| Active participants worldwide | 1.4B+ | Industry estimates |
In-play is the dominant and fastest-growing segment. At 62% of online handle, live sports trading generates approximately $70B annually in the regulated market alone.
2.2 Unregulated / Credit Market
The regulated number understates the true market by 3–5x. Sports trading in India, Southeast Asia, the Middle East, and Africa operates primarily through credit-based agent networks — offline, unregulated, and largely unmeasured.
India case study (IPL 2024):
| Metric | Value |
|---|---|
| Total wagered during 60-day IPL season | ₹8.3 lakh crore (~$100B) |
| Duration | 60 days |
| Implied daily handle | ~$1.67B/day |
| Matches played | 74 |
| Implied handle per match | ~$1.35B |
For context: Polymarket's single biggest day (2024 US election) processed ~$300M. A single IPL match generates 4.5x that volume.
Conservative estimate for global unregulated sports handle: $300–500B annually.
2.3 Volume Comparison Across Prediction Markets
| Market | Annual Volume (2025) | Daily Average | Events/Year |
|---|---|---|---|
| Sports (regulated) | $112B | $307M | 100,000+ |
| Sports (total est.) | $500B+ | $1.37B+ | 100,000+ |
| Polymarket | $21.5B | $59M | ~50,000 |
| Kalshi | $17B | $47M | ~30,000 |
| All prediction markets | $44B | $120M | ~80,000 |
Sports trading is already 11x larger than all prediction markets combined in the regulated segment, and 25x+ larger when including the credit market.
3. The Variable Framework
3.1 Core Variables
We define the following variables governing live sports price discovery:
| Variable | Symbol | Definition | Unit |
|---|---|---|---|
| Implied probability | P(t) | Market-implied probability of outcome at time t | [0, 1] |
| Odds | O(t) | Decimal odds, where O(t) = 1/P(t) | Decimal |
| Spread | S(t) | Back-lay spread: O_back(t) - O_lay(t) | Decimal |
| Matched volume | V(t) | Cumulative matched volume at time t | Currency |
| Volume velocity | dV/dt | Rate of volume change (liquidity flow) | Currency/min |
| Odds velocity | dO/dt | Rate of odds movement (price momentum) | Decimal/min |
| Depth | D(t, k) | Available liquidity at k price levels | Currency |
| Event signal | E(t) | Binary: 1 if discrete event occurred at t, 0 otherwise | {0, 1} |
| Signal magnitude | M(E) | Impact magnitude of event E on P(t) | [0, 1] |
| Recovery time | τ(E) | Time for market to reach new equilibrium post-event | Seconds |
| Information asymmetry | A(t) | Divergence between model probability and market probability | [0, 1] |
3.2 The Price Discovery Equation
At any point during a live event, the fair price is:
P_fair(t) = P_model(t | game_state, historical_data, external_signals)
The market price is:
P_market(t) = f(order_flow, liquidity, participant_mix)
Alpha exists when:
α(t) = |P_fair(t) - P_market(t)| > S(t) + transaction_cost
This alpha is transient — it decays as the market reprices. The decay rate is governed by:
α(t + Δt) ≈ α(t) · e^(-λ·Δt)
Where λ (repricing speed) depends on:
- Market liquidity (
V(t)) - Participant sophistication (sharp vs. recreational mix)
- Infrastructure latency (how fast orders can be placed)
Key insight: In equities, λ is extremely high (milliseconds). In sports, λ is low (seconds to minutes) — not because the information is slower, but because the infrastructure is slower. Better infrastructure increases λ, which paradoxically increases total volume by enabling more participants to trade more frequently at tighter spreads.
3.3 Liquidity Pockets
We define a liquidity pocket as a discrete time window [t, t + τ] where:
- An event signal fires:
E(t) = 1 - Information asymmetry spikes:
A(t) > A_threshold - Volume accelerates:
dV/dt > μ_V + 2σ_V(2 standard deviations above mean)
In a cricket T20 match (~120 deliveries per innings):
| Event Type | Frequency | Avg M(E) | Avg τ(E) | Pocket Duration |
|---|---|---|---|---|
| Wicket | 10/match | 0.15–0.40 | 30–90s | ~2 min |
| Boundary (4/6) | 25/match | 0.02–0.08 | 10–30s | ~45s |
| Over completion | 40/match | 0.01–0.05 | 5–15s | ~20s |
| Powerplay transition | 4/match | 0.05–0.12 | 20–60s | ~1.5 min |
| Strategic timeout | 2/match | 0.01–0.03 | 60–180s | ~3 min |
| Rain/weather | Rare | 0.10–0.50 | 300–600s | ~10 min |
A single T20 match generates ~80 tradeable liquidity pockets across ~3.5 hours. A Test match generates 300+. An IPL season of 74 matches produces ~6,000 liquidity pockets in 60 days.
4. Technical Indicators: Financial Markets → Sports Markets
4.1 Established Financial Indicators
In equity and crypto trading, participants use standardized technical indicators:
| Indicator | What It Measures | How It's Used |
|---|---|---|
| RSI (Relative Strength Index) | Momentum — speed of price changes, 0–100 scale | RSI > 70 = overbought (sell signal), RSI < 30 = oversold (buy signal) |
| MACD (Moving Average Convergence Divergence) | Trend — difference between short and long moving averages | MACD crosses above signal line = bullish; below = bearish |
| VWAP (Volume Weighted Average Price) | Fair value — average price weighted by volume | Price > VWAP = bullish; price < VWAP = bearish; institutional benchmark |
| Bollinger Bands | Volatility — price channel around moving average | Price near upper band = overbought; near lower = oversold |
| Order Book Depth | Liquidity — available buy/sell volume at each price level | Thin depth = slippage risk; deep = stable execution |
These indicators are computable, standardized, and automated. Every trading terminal — Bloomberg, TradingView, Zerodha Kite — provides them in real-time.
4.2 Sports Equivalents
Every financial indicator has a direct sports analog. None are currently formalized or automated:
| Financial Indicator | Sports Equivalent | Variable | Computation |
|---|---|---|---|
| RSI | Momentum Index | MI(t) | Rate of odds change over rolling window. MI > 70: team overbought (odds too short). MI < 30: oversold (value). |
| MACD | Form Convergence | FC(t) | Short-term performance (last 3 overs) vs. long-term (match average). Crossover = momentum shift. |
| VWAP | Volume-Weighted Odds | VWO(t) | Average odds weighted by matched volume. Current odds vs. VWO = market positioning signal. |
| Bollinger Bands | Odds Volatility Bands | OVB(t) | Standard deviation channel around rolling average odds. Breakout = significant event or mispricing. |
| Order Book Depth | Market Depth | D(t, k) | Available back/lay liquidity at k price levels on exchange. Identical concept, same mechanics. |
| Volume Profile | Session Volume | SV(t) | Volume distribution across match phases. Identifies where smart money enters (powerplay, death overs, etc.). |
| ATR (Average True Range) | Odds Range | OR(t) | Average odds swing per over. High ATR = volatile match, wider spreads needed. |
4.3 The Gap
In equities: a retail trader on Zerodha sees RSI, MACD, VWAP, and Bollinger Bands computed and rendered in real-time on every stock chart. One click to execute.
In sports: nobody computes these. Nobody renders them. Nobody provides the infrastructure to act on them. The analytical framework is identical — the tooling doesn't exist.
5. Infrastructure Benchmark
5.1 Exchange Comparison
| Metric | NYSE | NSE (India) | Binance | Coinbase | Betfair |
|---|---|---|---|---|---|
| Asset class | Equities | Equities + Derivatives | Crypto | Crypto | Sports |
| Daily volume | ~$50B | ₹90K Cr equity (~$10.7B) | $93B (Q1 '25 avg) | $5.5B | ~$70M |
| Annual volume | ~$12.5T | ₹40,000T derivatives | ~$23T | ~$1.4T | ~$26B |
| Execution latency | 50μs (HFT) / <20ms (retail) | <10ms (DMA) | 5–10ms | 10–20ms | 1,000–3,000ms |
| Settlement | T+1 | T+1 | Instant | Instant | Hours–Days |
| API | FIX + Streaming | FIX + REST | REST + WebSocket | REST + WebSocket | REST (rate-limited) |
| Risk tools | Full suite | Full suite | Stop/TP/Margin | Stop/TP | None |
| Uptime (2025) | 99.999% | 99.99% | 99.95% | 99.9% | 99.997% |
5.2 Latency Gap
NYSE: ████ 0.05ms
NSE: █████████ 10ms
Binance: █████████ 10ms
Coinbase: ████████████████ 20ms
Betfair: ████████████████████████████████████████████████████████████ 2,000ms
Betfair is 100x slower than Coinbase, 200x slower than NSE, and 40,000x slower than NYSE.
For context: when a wicket falls in a cricket match, the market reprices within 2–5 seconds. On Betfair, order placement alone consumes 1–3 seconds. By the time your order reaches the exchange, the price has already moved. This is the equivalent of trying to trade an earnings announcement on a 1990s dial-up connection.
5.3 Volume vs. Infrastructure Investment
| Exchange | Est. Annual Infrastructure Spend | Volume Served | $/Volume Ratio |
|---|---|---|---|
| NYSE (ICE) | ~$1.5B | $12.5T | $0.00012 |
| NSE | ~$200M | $4.5T+ (derivatives notional) | $0.00004 |
| Binance | ~$500M | $23T | $0.00002 |
| Betfair (Flutter) | ~$100M | $26B | $0.0038 |
Betfair spends 30–100x more per dollar of volume than comparable exchanges. This isn't because sports is harder — it's because the infrastructure hasn't been rebuilt since 2005.
5.4 The Zerodha Parallel
Zerodha is instructive. Before 2010, Indian retail equity trading was dominated by full-service brokers charging ₹500–800 per trade. Infrastructure was legacy. Latency was high. Participation was low.
Zerodha built modern infrastructure (direct market access, sub-10ms execution, real-time risk), cut commissions to ₹20/trade, and grew to 12M+ customers handling 15% of India's retail volume.
The parallel to sports is exact. Legacy infrastructure, high costs (Betfair charges 2–5% commission + formerly 60% Premium Charge on winners), and low retail participation relative to market size. Modern infrastructure unlocks participation, volume, and price efficiency.
6. The Credit Market: India as Case Study
6.1 Structure
The Indian cricket trading market operates on a credit-based agent hierarchy:
Platform Owner
└── Master Agent (MA)
└── Agent
└── Sub-Agent
└── Player
Each level extends credit downward and aggregates risk upward. Settlement happens weekly or after tournament completion. Risk management is manual (phone calls, WhatsApp, spreadsheets).
6.2 Scale
| Metric | Value |
|---|---|
| IPL 2024 total handle | ₹8.3 lakh crore (~$100B) |
| IPL duration | 60 days, 74 matches |
| Daily handle (IPL season) | ~$1.67B |
| Per-match handle | ~$1.35B |
| Year-round cricket handle (est.) | $200–300B |
| All sports (India, est.) | $300–500B |
6.3 Comparison
| Market | Daily Volume |
|---|---|
| NSE equity cash market | $10.7B |
| IPL season daily handle | $1.67B |
| Binance daily (Mar 2026) | $12.9B |
| Coinbase daily (Mar 2026) | $2.8B |
| Betfair daily | ~$70M |
Indian cricket trading alone produces 24x the daily volume of Betfair and 60% of Coinbase's daily volume — with zero infrastructure.
6.4 What Zero Infrastructure Means
- No real-time position tracking → agents don't know their exposure until settlement
- No automated settlement → disputes, defaults, cash flow gaps
- No price discovery → odds are copied from Betfair with manual markups
- No risk controls → agents blow up regularly during volatile matches
- No audit trail → no compliance, no transparency, no data
This is a $300–500B market running on WhatsApp and trust. The infrastructure opportunity is not incremental — it is foundational.
7. Why Now
-
Prediction markets validated. Polymarket ($21.5B, 2025) and Kalshi ($17B) proved that event-based trading at scale works. Sports is the same thesis, 25x larger.
-
Betfair's moat is eroding. Premium Charge abolished (2025). Volume declining in key markets. REST API in 2026 is indefensible.
-
AI enables formalization. The sports technical indicators defined in Section 4 are now computationally trivial. Real-time probability models, pattern recognition, and liquidity analysis can be embedded in the platform.
-
The credit market is ready to digitize. 1.2B smartphone users in India. UPI processes 14B transactions/month. The infrastructure for digital finance exists — it just hasn't been applied to sports trading.
-
Infrastructure cost has collapsed. Sub-10ms matching engines, real-time WebSocket streaming, global edge deployment — what cost $100M in 2010 costs <$1M today.
8. Conclusion
In-play sports trading is the world's largest prediction market by every measurable dimension — volume, participants, event frequency, and information density. It generates $500B+ in annual handle across regulated and unregulated segments, dwarfing the $44B combined volume of all blockchain-based prediction markets.
Yet it runs on infrastructure that is 40,000x slower than equities, has no standardized technical indicators, no real-time risk management, and no exchange-grade execution. The $300–500B credit market has literally zero technology.
The variables are defined. The indicators are mappable. The volume already exists. The only missing piece is infrastructure.
Definitions
| Term | Definition |
|---|---|
| In-play / Live | Trading during an active sporting event |
| Liquidity pocket | Time window where information asymmetry spikes and volume accelerates post-event |
| Back | Buying an outcome (equivalent to going long) |
| Lay | Selling an outcome (equivalent to going short) |
| Handle | Total amount wagered (gross volume before settlements) |
| Credit market | Agent-based system where participants trade on credit, settled periodically |
| Sharp | Sophisticated trader with consistent edge; moves the market |
| Exchange | Peer-to-peer matching platform (Betfair model) vs. bookmaker (house takes other side) |
Sources
- Precedence Research, "Sports Betting Market Size," 2025
- Grand View Research, "Sports Betting Market Report," 2025
- About96, "IPL Betting Market: Size, Growth, and Industry Dynamics," 2024
- Flutter Entertainment Q2 2025 Earnings Release
- CoinLaw, "Binance vs. Coinbase Statistics," 2026
- NSE India, "Business Growth — Derivatives Turnover," 2025
- Betfair Developer Program, Exchange API Documentation, 2026
- Gambling Insider, "Prediction Markets Statistics," 2026
- The Block, "Polymarket and Kalshi Volume," 2025