Paris FC v Nantes
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 Paris FC lost to Nantes 1–2, the SharpXI model made 14 confident calls on the player markets and 9 landed — the strongest being Matthis Abline for 1+ shots total (78%, had 3). Its clearest miss: Pierre Lees Melou for 1+ fouls committed (79%, 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 |
|---|---|---|---|---|---|
| Pierre Lees Melou | fouls committed | 1+ | 79% | 0 | ✗ |
| Matthis Abline | shots total | 1+ | 78% | 3 | ✓ |
| Mostafa Mohamed | fouls drawn | 1+ | 78% | 1 | ✓ |
| Jean-Philippe Krasso | shots total | 1+ | 77% | 0 | ✗ |
| Ilan Kebbal | fouls drawn | 1+ | 77% | 2 | ✓ |
| Youssef El Arabi | shots total | 1+ | 76% | 1 | ✓ |
| Mostafa Mohamed | shots total | 1+ | 75% | 0 | ✗ |
| Ilan Kebbal | shots total | 1+ | 74% | 5 | ✓ |
| Bahereba Guirassy | shots total | 1+ | 72% | 3 | ✓ |
| Pierre Lees Melou | fouls drawn | 1+ | 72% | 3 | ✓ |
| Jonathan Ikoné | shots total | 1+ | 72% | 0 | ✗ |
| Jean-Philippe Krasso | fouls drawn | 1+ | 71% | 1 | ✓ |
| Mostafa Mohamed | fouls committed | 1+ | 71% | 0 | ✗ |
| Maxime López | fouls committed | 1+ | 70% | 1 | ✓ |
How these are modelled: Fouls committed · Shots total · Fouls drawn
Team & match markets
| Market | Line | Model | Actual | Result |
|---|---|---|---|---|
| BTTS | Yes | 55% | Yes | ✓ |
| Match goals | O2.5 | 53% | 3 goals | ✓ |
| Match cards | O4.5 | 34% | 5 cards | ✓ |
How these are modelled: BTTS · Match cards
Dig deeper
Past meetings
- Nantes 1–2 Paris FC Jan 2026
Frequently asked
How did the SharpXI model do on Paris FC v Nantes?
9 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 Paris FC v Nantes?
The most confident call it got wrong was Pierre Lees Melou at 79% for fouls committed — 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.