Shea Lacey
Over his last 3 Premier League games, Shea Lacey has hit 1+ shot on target in 33% (1 of 3), 1+ total shot in 100%, 1+ foul committed in 33%, and 1+ foul won in 33%. He has been booked in 0% (0 of 3). These are raw hit rates from completed matches, not odds.
All markets last 3 games
Recent matches
The last 3 games Shea Lacey featured in — minutes and the counts that settle these markets.
| Match | Min | SoT | Shots | Fouls | Won | Cards |
|---|---|---|---|---|---|---|
| @ Brighton24 May | 28 | 0 | 1 | 2 | 2 | 0 |
| @ Burnley7 Jan | 12 | 0 | 2 | 0 | 0 | 0 |
| @ Aston Villa21 Dec | 11 | 1 | 1 | 0 | 0 | 0 |
Frequently asked
How often does Shea Lacey have a shot on target?
Over his last 3 Premier League games, Shea Lacey has hit 1+ shot on target in 33% (1 of 3). That's the raw hit rate from completed matches; SharpXI's model turns it into a true probability by adjusting for the opponent and expected minutes, then de-vigs the bookmaker's price.
How many shots on target does Shea Lacey average?
Across his last 3 Premier League games, Shea Lacey is averaging 1.76 shots on target and 7.06 total shots per 90 minutes. SharpXI treats that as a starting rate, then adjusts for the opponent and expected minutes before pricing a 1+ or 2+ line.
How often does Shea Lacey get booked?
Shea Lacey is averaging 0.00 cards per 90 over his last 3 games. Bookings swing heavily on the referee, so the model multiplies a player's base rate by each official's card tendency — the biggest single lever in the to-be-carded market.
Does Shea Lacey commit or win more fouls?
Over his last 3 games Shea Lacey commits 3.53 fouls per 90 and wins 3.53 — about as many as he wins. Fouls committed and fouls won are separate markets, each modelled from the player's rate and the opponent he faces.
Which player markets does SharpXI model for Shea Lacey?
Shots on target, total shots, fouls committed, fouls won and to be carded — each as a true probability, de-vigged against the bookmaker's price. Shea Lacey plays as a forward.
Rates shown are raw counts from public match data for context; the model adds shrinkage, opponent and referee factors, and de-vigging before flagging value. 18+ — please gamble responsibly.