Tylon Smith
Over the last 3 Championship games, Tylon Smith has hit 1+ shot on target in 0% (0 of 3), 1+ total shot in 0%, 1+ foul committed in 67%, 1+ foul won in 33%, 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 Tylon Smith featured in — minutes and the counts that settle these markets.
| Match | Min | SoT | Shots | Fouls | Won | Cards |
|---|---|---|---|---|---|---|
| @ Ipswich2 May | 45 | 0 | 0 | 0 | 2 | 0 |
| Swansea City21 Apr | 29 | 0 | 0 | 1 | 0 | 0 |
| Middlesbrough8 Mar | 8 | 0 | 0 | 1 | 0 | 0 |
Frequently asked
How often does Tylon Smith have a shot on target?
Over the last 3 Championship games, Tylon Smith 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 Tylon Smith average?
Across the last 3 Championship games, Tylon Smith is averaging 0.00 shots on target and 0.00 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 Tylon Smith get booked?
Tylon Smith 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 Tylon Smith commit or win more fouls?
Over the last 3 games Tylon Smith commits 2.20 fouls per 90 and wins 2.20 — an even split. 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 Tylon Smith?
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. Tylon Smith plays as a defender.
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.