Clermont Foot v Nice
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 Nice 0–1, the SharpXI model made 14 confident calls on the player markets and 13 landed — the strongest being Maxime Gonalons for 1+ tackles (95%, had 5). Its clearest miss: Jean-Clair Todibo for 1+ tackles (88%, had 0). 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 |
|---|---|---|---|---|---|
| Maxime Gonalons | tackles | 1+ | 95% | 5 | ✓ |
| Melvin Bard | tackles | 1+ | 93% | 1 | ✓ |
| Pablo Rosario | tackles | 1+ | 93% | 2 | ✓ |
| Gaëtan Laborde | shots total | 1+ | 92% | 4 | ✓ |
| Evann Guessand | shots total | 1+ | 92% | 1 | ✓ |
| Terem Moffi | shots total | 1+ | 90% | 1 | ✓ |
| Evann Guessand | fouls committed | 1+ | 90% | 3 | ✓ |
| Jean-Clair Todibo | tackles | 1+ | 88% | 0 | ✗ |
| Yohann Magnin | tackles | 1+ | 88% | 2 | ✓ |
| Romain Perraud | tackles | 1+ | 88% | 1 | ✓ |
| Hicham Boudaoui | tackles | 1+ | 88% | 2 | ✓ |
| Alidu Seidu | tackles | 1+ | 88% | 2 | ✓ |
| Maximiliano Caufriez | tackles | 1+ | 88% | 2 | ✓ |
| Cheick Oumar Konaté | tackles | 1+ | 88% | 2 | ✓ |
How these are modelled: Tackles Shots total Fouls committed
Team & match markets
| Market | Line | Model | Actual | Result |
|---|---|---|---|---|
| BTTS | Yes | 52% | No | ✗ |
| Match goals | O2.5 | 48% | 1 goals | ✗ |
| Match cards | O4.5 | 38% | 2 cards | ✗ |
How these are modelled: BTTS Match cards
Dig deeper
Past meetings
- Nice 0–0 Clermont Foot Feb 2024
- Nice 1–2 Clermont Foot Apr 2023
- Clermont Foot 1–0 Nice Aug 2022
Frequently asked
How did the SharpXI model do on Clermont Foot v Nice?
13 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 Nice?
The most confident call it got wrong was Jean-Clair Todibo at 88% for tackles — shown in the same table as the hits, marked with a cross. A record that hides its misses isn't a record, so we don't.
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.