Wolves sit 20th in the Premier League with 17 points from 32 games. The expected-points models CURSD runs project that number closer to 31.93. It is the largest gap between reality and projection in the division.
That does not mean Wolves are secretly good. It means the ceiling on an already-bad season has been pushed lower by variance, and most of that variance has landed on one end of the pitch.
15 points that never arrived
Wolves' record reads 3-8-21. By the xG-based simulations that drive most serious football models, the distribution of chances they have created and conceded over 32 games projects to roughly 31.93 points on average. Not European. Not even comfortable. Still, almost double what is actually on the board.
The gap is 14.93 points, the widest in the Premier League. It has built up over 32 matches rather than a short stretch, and it is the largest single disagreement between the scoreboard and the underlying play in the division this season.
Where most of the curse lives
Roughly two-thirds of that gap sits on the defensive side of the ledger.
Opponents have scored 58 goals against Wolves from 49.96 expected goals. That is an 8-goal overperformance by opposing attacks, the biggest figure of its kind in the league. Just over one extra goal every four games, layered on top of what the defensive data already allowed for. Across a season, that translates directly into dropped points.
The attack has been mildly unlucky too. 24 goals from 28.63 xG is a finishing shortfall of roughly 4.6 goals. Not historic, but not nothing, and it compounds a defensive picture already tilted against them.
The caveat the Luck Index does not hide
Wolves have taken 299 shots and conceded 447. They are being pinned into their own half, comprehensively and often. Their xG differential is firmly negative. They are not a hidden top-half side wearing a 20th-place wig. They are a team that was set up for a difficult season on the underlying numbers, and then the variance landed on the wrong side of it.
This matters for how to read the luck score. A positive xG differential undone by poor finishing (the shape of Crystal Palace's cursed season, for instance) implies different things than a negative xG differential undone by defensive variance. The curses look different. So does what regression to the mean would actually buy them.
Luck-adjusted, they are still 19th
CURSD calculates a Luck-Adjusted Rank, which estimates where a team would sit if the variance had broken neutrally. Wolves' actual rank is 20th. Their luck-adjusted rank is 19th. The curse moved them from "fighting to survive" to "statistically already down." It did not move them from comfort into crisis.
Even in the universe where every rub of the ball went the other way, Wolves would almost certainly still be in the relegation conversation. The variance turned a difficult season into a finished one. It did not create the difficulty.
What the models know, and what they do not
The Luck Index describes the size of the gap between what has already happened and what the underlying chances suggest should have. It does not predict what comes next. Over the six matches Wolves have left, variance can and often does regress. That is mathematically likely, not guaranteed.
For Wolves to climb out of the relegation zone, two separate things would probably have to happen. The variance would have to regress, and the underlying performance would have to improve. The models can tell you which parts of a bad season are luck and which parts are not. They cannot tell you whether the non-luck parts are about to get fixed.
The numbers for Wolves say the gap is real, the cause is mostly defensive, and the ceiling the data implies is "slightly less bad" rather than "actually safe." Both of those statements come from the same dataset. Both are supposed to be there.