Luan Gadegbeku
Over the last 3 Ligue 1 games, Luan Gadegbeku has hit 1+ shot on target in 0% (0 of 3), 1+ total shot in 67%, 1+ foul committed in 67%, 1+ foul won in 100%, and 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 Luan Gadegbeku featured in — minutes and the counts that settle these markets.
| Match | Min | SoT | Shots | Fouls | Won | Cards |
|---|---|---|---|---|---|---|
| @ Nice29 May | 84 | 0 | 1 | 0 | 1 | 0 |
| Nice26 May | 79 | 0 | 0 | 2 | 1 | 0 |
| Rodez AF15 May | 89 | 0 | 1 | 1 | 3 | 0 |
Frequently asked
How often does Luan Gadegbeku have a shot on target?
Over the last 3 Ligue 1 games, Luan Gadegbeku has hit 1+ shot on target in 0% (0 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 Luan Gadegbeku average?
Across the last 3 Ligue 1 games, Luan Gadegbeku is averaging 0.00 shots on target and 0.71 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 Luan Gadegbeku get booked?
Luan Gadegbeku is averaging 0.00 cards per 90 over the 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 Luan Gadegbeku commit or win more fouls?
Over the last 3 games Luan Gadegbeku commits 1.07 fouls per 90 and wins 1.79 — winning more than committing. Fouls committed and fouls won are separate markets, each modelled from the player's rate and the opponent faced.
Which player markets does SharpXI model for Luan Gadegbeku?
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. Luan Gadegbeku plays as a midfielder.
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.