01 · 2 legs
Safe
- Reading to win51%
- Under 3.5 goals73%
EFL League OneSelect Car Leasing Stadium, Reading15°6,0 mm19 km/h
Läs på svenskaSofia Andersson · · The model's read on the match · How the predictions work
TEXT:
Notts County have faced Reading recently, but with home advantage and superior underlying numbers, Reading's form and expected goals data give a clearer picture than any head-to-head record. Both teams lean heavily on possession control, yet the hosts have displayed a sharper edge in turning possession into meaningful chances. This is reflected in their xG per match of 2.1, comfortably surpassing the visitors' 1.3. The model also tilts towards Reading, granting them a 51% chance of victory.
Despite their unbeaten streak in the last five league matches, Notts County's defensive frailties are clear. Conceding an xGA of 1.2 per match suggests vulnerability, especially when confronted with Reading's potent attacking output. Meanwhile, the hosts have a solid defensive structure, allowing just 0.86 xGA per match, which should keep the visitors' chances limited.
In terms of scoreline probabilities, the model nudges towards a 1-0 or 1-1 finish, both sharing a 12% likelihood. This indicates a match potentially tight in score, but not necessarily in chances. Reading's superior xG in moments of transition, at 0.42 within eight seconds of ball recovery, underscores their ability to capitalize on quick turnovers, a facet in which Notts County lag significantly with just 0.1.
The numbers offer an enticing betting angle. With Reading's defensive solidity and attacking prowess, a 1-0 home win aligns with both the expected goals and model probabilities. At this junction, Ladbrokes presents a tempting opportunity with odds of 1.90 on a Reading win. While over 2.5 goals sits right on the knife's edge at 49%, the safer lean might be towards under, given the hosts' defensive assurance and the visitors' struggle to convert possession into goals.
01 · 2 legs
02 · 3 legs
03 · 4 legs
No odds available yet.
Solid line = created (xG), dashed = conceded (xGA).
Last 5 matches. Circle size = pass volume, line width = combinations between players. Attacking left to right.
The same network weighted by offensive threat (xT): circle size = threat created by the player, line width = threat of the combinations. Last 5 matches, attacking left to right.
| Performance | Reading | Notts County |
|---|---|---|
| Points | 7 | 7 |
| xPoints | 8.5 | 8.1 |
| xG per match | 2.1 | 1.3 |
| xGA per match | 0.86 | 1.2 |
| xG within 8s of winning the ball | 0.42 | 0.1 |
| xGA within 8s of losing the ball | 0.07 | 0.24 |
| Playing style | Reading | Notts County |
|---|---|---|
| Build-up efficiency | 0.31 | 0.3 |
| Field tilt | 0.44 | 0.43 |
| xT per match | 1.2 | 0.75 |
| xTA per match | 0.88 | 0.91 |
| Won balls, offensive half | 24 | 26 |
| Pressing intensity | 0.24 | 0.22 |
| Pressing efficiency | 0.29 | 0.33 |
| Pressing efficiency, offensive half | 0.25 | 0.29 |
| Entries into the box per match | 13 | 12 |
| Entries into the box against | 11 | 13 |
| Pass completion % | 0.7 | 0.69 |
| Pass completion % under pressure | 0.62 | 0.68 |
| Passes per match | 291 | 254 |
| Passes against per match | 340 | 364 |
| Switches of play per match | 15.2 | 11.8 |
| Long balls per match | 26 | 22 |
| Set pieces | Reading | Notts County |
|---|---|---|
| xG from free kicks | 0.04 | 0.07 |
| Corners per match | 5.7 | 4.9 |
| Corners against per match | 3.5 | 5.3 |
| xG per corner | 0.06 | 0.05 |
| xGA per corner against | 0.03 | 0 |
| First touch, offensive corners % | 0.35 | 0.44 |
| First touch, defensive corners % | 0.79 | 0.81 |
| Other | Reading | Notts County |
|---|---|---|
| Throw-in control | 0.72 | 0.72 |
| The goalkeepers | Joel Pereira (Reading) | Lucas Ness (Notts County) |
|---|---|---|
| Saves | 7 | 11 |
| Save % | 58% | 100% |
| xG prevented | 56% | 100% |
| Claims | 0 (0%) | 0 (0%) |
The model gives Reading a 51% win probability. Full 1X2 picture: Reading 51%, draw 27%, Notts County 22%.
The model's most likely final score is 1-0 at 12% probability.
The model rates over 2.5 goals at 49% and under at 51%.
The probability of both teams scoring is 50% according to the model.
The same rules as the bookmakers’ builders: legs that contradict or already follow from your slip cannot be combined — for example correct score 2–1 with under 2.5 goals. The difference is that we don’t look the rules up in a table; we compute them straight from the score matrix.
Predictions come from PlaymakerAI's match model and reflect the model's assessment — not betting advice. Gamble responsibly.