Reading v Queens Park Rangers
How the SharpXI model rated this Championship fixture — every call it made on shots, cards and fouls, set against what actually happened on the day.
Before Reading drew with Queens Park Rangers 2–2, the SharpXI model made 14 confident calls on the player markets and 7 landed — the strongest being Ilias Chair for 1+ shots total (81%, had 2). Its clearest miss: Mamadou Loum for 1+ fouls committed (82%, 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 |
|---|---|---|---|---|---|
| Mamadou Loum | fouls committed | 1+ | 82% | 0 | ✗ |
| Ilias Chair | shots total | 1+ | 81% | 2 | ✓ |
| Chris Willock | shots total | 1+ | 79% | 0 | ✗ |
| Junior Hoilett | fouls committed | 1+ | 78% | 1 | ✓ |
| Ethan Laird | fouls drawn | 1+ | 77% | 3 | ✓ |
| Shane Long | fouls committed | 1+ | 76% | 1 | ✓ |
| Tyler Roberts | shots total | 1+ | 76% | 3 | ✓ |
| Lyndon Dykes | shots total | 1+ | 76% | 4 | ✓ |
| Andy Carroll | fouls committed | 1+ | 75% | 0 | ✗ |
| Tyler Roberts | fouls drawn | 1+ | 75% | 1 | ✓ |
| Andy Carroll | shots total | 1+ | 74% | 0 | ✗ |
| Tim Iroegbunam | fouls drawn | 1+ | 74% | 0 | ✗ |
| Jamal Lowe | shots total | 1+ | 74% | 0 | ✗ |
| Lyndon Dykes | fouls committed | 1+ | 73% | 0 | ✗ |
How these are modelled: Fouls committed · Shots total · Fouls drawn
Team & match markets
| Market | Line | Model | Actual | Result |
|---|---|---|---|---|
| Match cards | O4.5 | 42% | 4 cards | ✗ |
How these are modelled: Match cards
Dig deeper
Past meetings
- Queens Park Rangers 2–1 Reading Oct 2022
Frequently asked
How did the SharpXI model do on Reading v Queens Park Rangers?
7 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 Reading v Queens Park Rangers?
The most confident call it got wrong was Mamadou Loum at 82% 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 Championship?
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.