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SharpXI©
Premier League 23/24
GA

Gabriel Osho

Wide centre-back 52% of 21 recent starts Cardiff CityPremier League 23/24

Also in: ChampionshipLigue 1

Last games
max 21 on record
What a window counts

Hit rates count the most recent completed games, newest first — reaching back into earlier seasons when the current one is short, and into games for a previous club, including in another league we cover.

All markets last 10 games

Compared with a Premier League wide centre-back, 25/26, 214 appearances

Bet line
MarketHit rateModel
Shots on target0.10 per 90
10%
1 of 100.8×
Total shots0.91 per 90
60%
6 of 101.5×
Fouls won1.11 per 90
80%Hot
8 of 102.3×
Fouls committed1.31 per 90
80%Hot
8 of 101.6×
Tackles2.02 per 90
100%Hot
10 of 101.4×
0%
0 of 10
Cards0.20 per 90
20%
2 of 101.2×
Goals0.10 per 90
10%
1 of 10
form only
Assists0.00 per 90
0%
0 of 10
form only
Hit rate, per-90 and the market price are free. Switch the bet line above — each carries its own price where the bookmaker offers one. What’s paid is the model’s edge: whether that price is wrong. Rare tails the model can’t beat, like 3+ on target, stay hit-rate-only.

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Picked from 20 selections across 10 matches on this week’s slate, each with its record and the market price.

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Over the last 10 Premier League games, Gabriel Osho has hit 1+ shot on target in 10% (1 of 10), 1+ total shot in 60%, 1+ foul committed in 80%, 1+ foul won in 80%, and been booked in 20% (2 of 10). These are raw hit rates from completed matches, not odds.

last 10

Comparable centre-backs

Gabriel Osho has no Premier League record yet, so this profile is drawn from the Championship 26/27 season. These are the closest 5 Premier League centre-backs by standing within their own league — not by matching output, which does not carry across leagues. A reference for the rates above, not a forecast.

PlayerAppsShots/90Fouls/90
Gabriel OshoCardiff CityChampionship 70.00 0.58
William SalibaArsenal 310.17 0.76
Olivier BoscagliBrighton & Hove Albion 120.11 0.77
Hjalmar EkdalBurnley 190.35 0.29
Bafodé DiakitéBournemouth 180.07 0.56
Maxime EstèveRB Leipzig 340.31 0.40
These 5, combined 0.23 0.54

Recent matches

The last 10 games Gabriel Osho featured in — minutes and the counts that settle these markets.

MatchMinSoTShots FoulsWonTckCards
Fulham19 May 90 0 1 1 1 2 0
@ West Ham11 May 90 0 0 1 0 3 0
Everton3 May 90 0 1 2 2 2 0
@ Wolves27 Apr 90 0 0 0 2 1 0
Brentford20 Apr 90 0 0 0 2 2 0
@ Crystal Palace9 Mar 81 0 1 2 1 3 0
@ Liverpool21 Feb 90 0 0 3 0 1 0
Man Utd18 Feb 90 0 3 1 1 2 1
Sheffield United10 Feb 90 0 2 1 1 2 0
@ Newcastle3 Feb 90 1 1 2 1 2 1

Frequently asked

How often does Gabriel Osho have a shot on target?

Over the last 10 Premier League games, Gabriel Osho has hit 1+ shot on target in 10% (1 of 10). 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 compares it with the bookmaker's price as offered.

How many shots on target does Gabriel Osho average?

Across the last 10 Premier League games, Gabriel Osho is averaging 0.10 shots on target and 0.91 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 Gabriel Osho get booked?

Gabriel Osho is averaging 0.20 cards per 90 over the last 10 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 Gabriel Osho commit or win more fouls?

Over the last 10 games Gabriel Osho commits 1.31 fouls per 90 and wins 1.11 — committing more than winning. 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 Gabriel Osho?

Shots on target, total shots, fouls committed, fouls won and to be carded — each as a true probability, compared with the bookmaker's price as offered. Gabriel Osho plays as a defender.

Rates shown are raw counts from public match data for context; the model adds shrinkage, opponent and referee factors before flagging value, and compares against the bookmaker's price as offered — player markets are priced one side only, so that price still carries the bookmaker's margin. 18+ — please gamble responsibly.