Julio Díaz
Over his last 4 La Liga games, Julio Díaz has hit 1+ shot on target in 0% (0 of 4), 1+ total shot in 25%, 1+ foul committed in 75%, and 1+ foul won in 50%. He has been booked in 50% (2 of 4). These are raw hit rates from completed matches, not odds.
All markets last 4 games
Recent matches
The last 4 games Julio Díaz featured in — minutes and the counts that settle these markets.
| Match | Min | SoT | Shots | Fouls | Won | Cards |
|---|---|---|---|---|---|---|
| @ Valencia2 May | 90 | 0 | 0 | 0 | 0 | 0 |
| @ Elche22 Apr | 45 | 0 | 0 | 1 | 0 | 1 |
| @ Sevilla11 Apr | 86 | 0 | 0 | 2 | 1 | 1 |
| @ Real Oviedo28 Feb | 90 | 0 | 1 | 1 | 2 | 0 |
Frequently asked
How often does Julio Díaz have a shot on target?
Over his last 4 La Liga games, Julio Díaz has hit 1+ shot on target in 0% (0 of 4). 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 Julio Díaz average?
Across his last 4 La Liga games, Julio Díaz is averaging 0.00 shots on target and 0.29 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 Julio Díaz get booked?
Julio Díaz is averaging 0.58 cards per 90 over his last 4 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 Julio Díaz commit or win more fouls?
Over his last 4 games Julio Díaz commits 1.16 fouls per 90 and wins 0.87 — more than 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 Julio Díaz?
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. Julio Díaz 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.