Main draw starts Sunday 24 May. Polymarket's RG men's outright market sits over $23M volume with Sinner ~70% implied. Our model has live reads on the value below — see the RG field cracks open analysis for the names we're tracking. Workflows below apply directly.
Polymarket's tennis markets have exploded since 2024 — the 2026 French Open men's winner alone has over $23M in volume — but they're still slow. Information flows from Betfair Exchange to Polymarket with a 2-5 minute lag in-running, and a 24-hour-plus drift on outright markets where retail traders dominate. That gap is opportunity if you have a model that's right.
This is a practical guide to using fault.bet's three products — the daily Signals channel, the Premium dashboard, and the API tier — to trade Polymarket tennis. We'll cover three workflows depending on how hands-on you want to be: copy a daily pick, monitor cross-market spreads in a UI, or programmatically scan for edges with code. Real examples from tomorrow's Madrid Open women's final included.
Three things are true about Polymarket tennis markets that make a calibrated probability model genuinely useful:
1. Liquidity is uneven. Headline matches (Sinner final, Sabalenka final) trade in the millions. Round-of-16 matches between two top-50 players might trade $5,000 total. Thin markets mean prices can sit 5-10% off fair value for hours because nobody's there to push them.
2. Information arrives late. Betfair has serve-by-serve in-play feeds, broker desks, syndicates running automated bots. Polymarket has none of that natively — most of its tennis traders are watching the match on TV and reacting on a 30-second delay. When a player loses serve at 5-4 in the third, Betfair prices move within a single point. Polymarket prices catch up in 2-5 minutes. If you have the data, that delay is a structural edge.
3. Outright markets drift. Tournament-winner markets ("Who wins Rome 2026 Men's?") see daily price movement on Polymarket, but the price discovery is slow because the audience is making weekly decisions, not minute-by-minute. A player whose Betfair price has shortened 20% over 48 hours after winning their R16 might still be trading at the old price on Polymarket.
None of this is a guaranteed edge — you need a probability model that's actually right, not just one that disagrees with the market. That's what fault.bet provides: an isotonic-calibrated ensemble trained on 1.6 million ATP and WTA matches, validated walk-forward, with public CLV-tracked results at fault.bet/results.
The fault.bet products are designed to layer. Each one adds a layer of detail and automation:
| Product | What you get | Polymarket use case |
|---|---|---|
| Signals (£20/mo) | Daily pre-match picks via Telegram | Copy the pick, find the same match on Polymarket, take the better price |
| Premium (£37.50/mo) | Signals + live in-play alerts + dashboard | Monitor cross-market gaps in a UI; trade Polymarket while Betfair leads |
| API (£99/mo) | Full REST API access — matches, signals, odds, history | Build a bot that scans Polymarket vs the model in real time |
The next three sections walk through each. Pick the one that matches how you trade.
The simplest workflow. You're already paying £20/month for the daily Signals channel; you just route the picks through Polymarket instead of (or alongside) Betfair.
The flow each morning at 08:00 UTC:
This is "exchange shopping" with the model as your reference point. You're not arbitraging — you're just picking the best price available on either platform for a position the model has already validated. Over 100 picks, taking the best of two markets typically adds 1-2 percentage points to your annualised ROI compared to single-market trading.
The Premium tier (£37.50/mo) adds the live dashboard at app.fault.bet. The dashboard shows every match the model has scored today, including ones outside the Telegram signal cap (which only fires picks at 1.5-3.5 prices with edge ≥ 10%).
For Polymarket trading, this matters because the Telegram channel deliberately filters to the safest, most repeatable signals. The model often finds genuine edges at:
The dashboard surfaces these. A typical session: open the dashboard, sort matches by "edge", check the top 5 against Polymarket prices, take positions on the 1-2 where Polymarket is significantly off-side.
For Rome week (5-17 May 2026) we're publishing full draw previews with the model's view of every R1 matchup — Premium subs get the full value board, not just the daily picks. That's where Polymarket-shaped opportunities live.
The API tier (£99/mo) gives you read access to the full fault.bet data layer. If you're comfortable with Python or any HTTP-aware language, this is the workflow that scales.
The pattern: pull today's matches and model probabilities from fault.bet, pull Polymarket's current prices via their public CLOB API, find the gaps. Below is a runnable Python sketch — ~25 lines of real code.
import requests
API_KEY = "fb_your_api_key_here" # from your fault.bet dashboard
HEADERS = {"Authorization": f"Bearer {API_KEY}"}
# GET /matches/today returns model probs + Betfair prices for every match the model has scored
matches = requests.get("https://api.fault.bet/matches/today", headers=HEADERS).json()
for m in matches["matches"][:3]:
print(f"{m['p1']} vs {m['p2']}: model {m['model_prob_p1']:.3f}, BF {m['bf_price_p1']}")Output (real format, fictional prices):
Mirra Andreeva vs Marta Kostyuk: model 0.523, BF 1.68
Jannik Sinner vs Alexander Zverev: model 0.650, BF 1.19
Casper Ruud vs Stefanos Tsitsipas: model 0.510, BF 2.05Polymarket's CLOB (central limit order book) API is public and free. Each tennis match has a "condition ID" you can look up by tournament + player names. The response gives you the current best bid/ask for each side.
# Polymarket's public CLOB API — no auth needed for read
POLY_API = "https://clob.polymarket.com"
def get_polymarket_price(condition_id, outcome="YES"):
r = requests.get(f"{POLY_API}/book", params={"market": condition_id})
book = r.json()
side = "bids" if outcome == "YES" else "asks"
return float(book[side][0]["price"]) # top-of-book price in dollars
# Look up the Andreeva/Kostyuk Madrid final — condition_id from Polymarket UI or events API
poly_andreeva = get_polymarket_price("0x1234...", "YES")
print(f"Polymarket Andreeva YES: ${poly_andreeva:.2f}")
# → Polymarket Andreeva YES: $0.55Now you have model probability, Betfair price, and Polymarket price. Compute the implied probability for each market price, compare to the model, and rank by edge.
def implied_prob(decimal_price):
return 1.0 / decimal_price
def polymarket_implied(yes_price):
return float(yes_price) # YES price IS the implied probability
def kelly_edge(model_prob, decimal_price):
return model_prob * decimal_price - 1.0
for m in matches["matches"]:
bf_imp = implied_prob(m["bf_price_p1"])
poly_imp = polymarket_implied(get_polymarket_price(m["poly_condition_id"]))
bf_edge = kelly_edge(m["model_prob_p1"], m["bf_price_p1"])
poly_edge = kelly_edge(m["model_prob_p1"], 1.0/poly_imp)
best = "Polymarket" if poly_edge > bf_edge else "Betfair"
print(f"{m['p1']}: BF edge {bf_edge:+.1%}, Poly edge {poly_edge:+.1%}, best={best}")Run that on cron every 30 minutes during a tournament and you have a basic Polymarket-Betfair-model triangulation engine. The matches where Polymarket is meaningfully better than Betfair are the ones to size into.
To make this concrete, here's the exact three-market spread for Saturday's Madrid Open women's final between Mirra Andreeva and Marta Kostyuk, captured at 22:55 UTC on 1 May 2026:
| Market | Andreeva implied | Kostyuk implied |
|---|---|---|
| Betfair Exchange | 59.5% | 42.7% |
| Polymarket | 55.0% | 45.0% |
| fault.bet model | 52.3% | 47.7% |
The model has the match closer to a coin flip than either market. Polymarket is between the model and Betfair — closer to the model, oddly. Backing Kostyuk on Betfair @ 2.34 returns +11.6% expected. Backing Kostyuk YES on Polymarket at $0.45 returns +5.9% expected (a smaller edge because Polymarket has already partially corrected).
The edge IS bigger on Betfair this time. But on a different match — or a different day — the order would flip. The discipline is to check both every time, not assume one market is always sharper.
Full breakdown of the model's view on this match: Andreeva vs Kostyuk — Madrid Final Preview.
If you're starting from zero, here's the full path to running any of the three workflows:
Rome 2026 starts main draw 6 May. We're publishing two mega-previews early next week — full ATP and WTA draw breakdowns, model probabilities for every R1 match, and the cross-market value board comparing Betfair vs Polymarket vs the model on each. Premium subscribers get the full value board the moment it's compiled. Free Telegram gets the headline picks.
Tennis is the prediction-market category that pays the most attention to model output, because the structural setup — binary outcomes, fast turnover, public match data, in-running — rewards informed traders. Polymarket is the venue where that's most visibly true right now. The fault.bet stack is built to find the gaps.
Trade tennis with fault.bet's stack on Polymarket and Betfair from £20/month.
Daily signals, premium dashboard, full API access. Every signal publicly tracked.
See pricing →