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Serie A 25/26
PO

Pol Lirola

Wide centre-back 38% of 21 recent starts Hellas VeronaSerie A 25/26

Also in: La LigaLigue 1

Last games
max 31 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 Serie A wide centre-back, 25/26, 1,005 appearances

Bet line
MarketHit rateModel
Shots on target0.00 per 90
0%
0 of 10
Total shots0.28 per 90
20%
2 of 100.6×
Fouls won0.28 per 90
20%
2 of 100.5×
Fouls committed0.69 per 90
40%
4 of 100.6×
Tackles1.66 per 90
60%
6 of 100.9×
0%
0 of 10
Cards0.14 per 90
10%
1 of 100.7×
Goals0.00 per 90
0%
0 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.

Five selections from the player report, free in your inbox

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 Serie A games, Pol Lirola has hit 1+ shot on target in 0% (0 of 10), 1+ total shot in 20%, 1+ foul committed in 40%, 1+ foul won in 20%, and been booked in 10% (1 of 10). These are raw hit rates from completed matches, not odds.

last 10

Comparable full-backs

The closest 5 profiles to Pol Lirola in Serie A, 25/26 — drawn from 30 full-backs with 450 or more minutes. A reference for the rates above, not a forecast.

PlayerAppsShots/90Fouls/90
Pol LirolaHellas Verona 60.00 0.86
Ulisses GarciaOlympique de Marseille 130.19 1.05
Sebastian WalukiewiczSassuolo 340.22 1.10
Josh DoigSassuolo 300.51 0.75
Pierre KaluluJuventus 370.36 0.93
Lorenzo De SilvestriBologna 120.00 1.61
These 5, combined 0.31 0.99

Recent matches

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

MatchMinSoTShots FoulsWonTckCards
Lecce25 Apr 11 0 0 0 0 0 0
AC Milan19 Apr 69 0 0 0 0 1 0
Pisa6 Feb 20 0 0 1 0 0 0
@ Cagliari31 Jan 58 0 0 1 0 0 0
Udinese26 Jan 68 0 0 1 0 2 1
@ Cremonese19 Jan 87 0 0 0 0 2 0
Udinese26 May 85 0 0 0 0 2 0
@ Monza19 May 90 0 1 2 1 4 0
Inter10 May 71 0 0 0 0 0 0
@ Empoli5 May 90 0 1 0 1 1 0
Officials on record
Antonio Rapuano 3 games Pol Lirola never booked 4.75cards/game 1.13×vs league
Daniele Doveri 3 games Pol Lirola never booked 3.34cards/game 0.80×vs league

Raw counts from every match on record, across 2 current Serie A officials — the model applies each official's overall card tendency, not this split. All Serie A referees

Frequently asked

How often does Pol Lirola have a shot on target?

Over the last 10 Serie A games, Pol Lirola has hit 1+ shot on target in 0% (0 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 Pol Lirola average?

Across the last 10 Serie A games, Pol Lirola is averaging 0.00 shots on target and 0.28 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 Pol Lirola get booked?

Pol Lirola is averaging 0.14 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 Pol Lirola commit or win more fouls?

Over the last 10 games Pol Lirola commits 0.69 fouls per 90 and wins 0.28 — 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 Pol Lirola?

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. Pol Lirola 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.