Clermont Foot v AS Monaco
How the SharpXI model rated this Ligue 1 fixture — every call it made on shots, cards and fouls, set against what actually happened on the day.
Before Clermont Foot lost to AS Monaco 2–4, the SharpXI model made 14 confident calls on the player markets and 14 landed — the strongest being Wissam Ben Yedder for 1+ shots total (94%, had 3). Every probability here was published before kick-off, priced the same way as every other fixture — one game is noise, not a verdict.
Player markets — the model's calls
Each probability is the model's own number for the headline (1+) line, before any bookmaker margin. The result column is what the player actually recorded.
| Player | Market | Line | Model | Actual | Result |
|---|---|---|---|---|---|
| Wissam Ben Yedder | shots total | 1+ | 94% | 3 | ✓ |
| Vanderson | tackles | 1+ | 93% | 3 | ✓ |
| Maxime Gonalons | tackles | 1+ | 93% | 2 | ✓ |
| Mohamed Camara | tackles | 1+ | 93% | 1 | ✓ |
| Muhammed-Cham Saračević | shots total | 1+ | 92% | 2 | ✓ |
| Alidu Seidu | fouls drawn | 1+ | 91% | 4 | ✓ |
| Eliot Matazo | fouls committed | 1+ | 90% | 1 | ✓ |
| Takumi Minamino | fouls committed | 1+ | 90% | 1 | ✓ |
| Eliot Matazo | tackles | 1+ | 88% | 1 | ✓ |
| Soungoutou Magassa | tackles | 1+ | 88% | 7 | ✓ |
| Aleksandr Golovin | tackles | 1+ | 88% | 2 | ✓ |
| Caio Henrique | tackles | 1+ | 88% | 2 | ✓ |
| Soungoutou Magassa | fouls committed | 1+ | 87% | 4 | ✓ |
| Grejohn Kyei | shots total | 1+ | 87% | 1 | ✓ |
How these are modelled: Shots total Tackles Fouls drawn Fouls committed
Team & match markets
| Market | Line | Model | Actual | Result |
|---|---|---|---|---|
| BTTS | Yes | 59% | Yes | ✓ |
| Match goals | O2.5 | 56% | 6 goals | ✓ |
| Match cards | O4.5 | 36% | 4 cards | ✗ |
How these are modelled: BTTS Match cards
Dig deeper
Past meetings
- AS Monaco 4–1 Clermont Foot May 2024
- Clermont Foot 0–2 AS Monaco Feb 2023
- AS Monaco 1–1 Clermont Foot Oct 2022
Frequently asked
How did the SharpXI model do on Clermont Foot v AS Monaco?
14 of its 14 most-confident calls landed. The board on this page lists every one with the model's pre-kickoff probability next to what actually happened, hits and misses in the same table.
What did the model get wrong on Clermont Foot v AS Monaco?
Every one of its confident calls landed this time — a single strong game, not a verdict. When the model does miss, those rows sit in the same table as the hits, marked with a cross; we never hide them.
Are these probabilities hindsight?
No. Each one is the number the model produced from data available before kick-off, using the same settings as every other fixture on the site — nothing was refitted after the result. That is the only way a per-match report means anything.
Does one match tell you if the model is any good in the Ligue 1?
No, and we'd rather say so. A single fixture is a handful of calls and mostly noise; the season-long, market-by-market record — including the markets that fail to beat a naive guess — is on the track record page.
SharpXI models probabilities from public match data; it does not take bets or guarantee outcomes. Prices are entered per leg in the tool. 18+ — please gamble responsibly.