PER90

Premier League · England

Newcastle UnitedvLiverpool

St. James' Parklineups not announced

Referee

Stuart Attwell

48 matches on record

Fouls per game

21.7

-6% vs league

League average

23.2

fouls per match, both teams

Facts with sample sizes, not predictions. The market % beside a line is the books’ median price with margin in, and the over market runs hot.

Value — 4 live calls

Published at the price shown and scored against the closing line whatever happens next — the same rows, with staking context, are on the value board and in the track record.

What these numbers are

Not tips and not prices. Each row is the model's expected count for one player in this match — how many fouls, tackles or shots it thinks he records — plus the full spread of outcomes around it. Nothing here has been compared to a bookmaker.

  1. Step 1

    His own record

    Every completed match in the dataset, as a rate per 90 minutes. The n column is how many matches that rests on.

  2. Step 2

    Pulled toward his position

    A thin record gets pulled hard toward other players in his position, in his league — a goalkeeper toward goalkeepers, not toward the outfielders around him. How hard is fitted per market, not chosen: tackles pull about four times harder than shots on target.

  3. Step 3

    This fixture's conditions

    How much of the stat the opponent concedes, what the referee's matches look like, and home or away. This is where a projection stops being a season average.

  4. Step 4

    Scaled to expected minutes

    A rate becomes a count only once you say how long he plays. 60 minutes carries two-thirds the exposure of 90, and the distribution is built at that exposure.

How to read each column
Model /90
The model's rate for this match with the minutes taken back out — steps 1 to 3 already applied. It moves with the opponent and the referee, so it is not his raw career rate; the builder shows career and model rates side by side.
Exp. mins
Minutes the model expects. Confirmed means the lineup is out; provisional means it is guessing from his start rate, and is the widest of the three.
Projected
The expected count in this match, at those minutes. This is the number a line is set against.
Over 1.5, etc.
Read straight off the distribution beside it, not off how often he has beaten that line before. It answers what the model thinks, not what has happened.
n
Matches behind his own rate. Under 30 it turns amber: the projection is mostly his position group speaking, and §6.3 will not publish an edge on it.
Distribution
The whole spread, not just the average. The dashed amber cut is the line; jade bars beat it. An expected 1.8 fouls made of 2 every week and an expected 1.8 made of a 0 and a 5 are different bets, and this is where you see which one you have.

Counts are modelled as negative binomial rather than Poisson, because fouls and tackles are overdispersed and a Poisson would systematically underprice the tails — which is exactly where over bets live. Every market on this page cleared its calibration gate before it was allowed to produce a projection: when the model says 60%, it lands within a point or so of 60% out of sample.

Fouls v20260816-1555 · Shots v20260816-1555 · Shots on target v20260816-1555 · Fouls won v20260816-1555 · Saves v20260816-1229

Model projections

Fouls46 playersHighest: Joelinton, 1.50 expectedshow

What drives it Who referees the match. A strict official lifts every player on the pitch, which is the part most books price off a season average. See what has actually happened.

PlayerTeamModel /90Exp. minsProjectedOver 1.5nDistribution
JoelintonMIDNewcastle United1.6579provisional1.5044%56
023%
133%
224%
312%
45%
52%
6+1%
Stefan BajceticMID1.6172provisional1.3739%14
027%
134%
223%
311%
44%
51%
6+0%
Alexis Mac AllisterMIDLiverpool1.4377provisional1.2636%72
030%
134%
222%
310%
43%
51%
6+0%
Ryan GravenberchMIDLiverpool1.2384provisional1.1833%73
031%
136%
221%
38%
43%
51%
6+0%
Dominik SzoboszlaiMIDLiverpool1.2581provisional1.1632%72
032%
135%
221%
38%
43%
51%
6+0%
Hugo EkitikéFWDLiverpool1.5064provisional1.1532%61
035%
134%
219%
38%
43%
51%
6+0%
Bazoumana TouréFWD1.2373provisional1.0528%43
036%
135%
219%
37%
42%
50%
6+0%
Cody GakpoFWDLiverpool1.2271provisional1.0227%71
038%
135%
218%
37%
42%
50%
6+0%
Víctor MuñozFWD1.0777provisional0.9625%36
039%
136%
217%
36%
42%
5+0%
Conor BradleyDEFLiverpool1.3059provisional0.9425%34
041%
135%
217%
36%
42%
5+0%

10 of 46 players shown, ranked by projection. Show all 46 →

Shots46 playersHighest: Hugo Ekitiké, 2.05 expectedshow

What drives it The player’s own shot rate, shrunk toward others in his position, then adjusted for how many shots the opponent concedes. See what has actually happened.

PlayerTeamModel /90Exp. minsProjectedOver 1.5nDistribution
Hugo EkitikéFWDLiverpool2.7564provisional2.0556%61
019%
125%
222%
316%
49%
55%
6+4%
Mohamed SalahFWDLiverpool2.4073provisional2.0156%65
018%
126%
223%
316%
49%
55%
6+3%
Víctor MuñozFWD2.2477provisional1.9757%36
017%
127%
225%
317%
49%
54%
6+2%
Cody GakpoFWDLiverpool2.1971provisional1.8052%71
020%
128%
224%
315%
48%
53%
6+2%
Alexander IsakFWDLiverpool2.2063provisional1.6246%48
026%
128%
221%
313%
47%
53%
6+2%
Bazoumana TouréFWD1.8973provisional1.5846%43
024%
131%
223%
313%
46%
52%
6+1%
Dominik SzoboszlaiMIDLiverpool1.7281provisional1.5846%72
023%
131%
224%
313%
46%
52%
6+1%
Harvey BarnesFWDNewcastle United2.4052provisional1.5242%70
028%
130%
220%
311%
46%
53%
6+2%
William OsulaFWDNewcastle United2.3447provisional1.3236%38
037%
127%
217%
310%
45%
52%
6+1%
Yoane WissaFWDNewcastle United2.3445provisional1.2934%54
037%
129%
217%
39%
45%
52%
6+1%
JoelintonMIDNewcastle United1.4279provisional1.2836%56
030%
134%
222%
310%
43%
51%
6+0%
Nick WoltemadeFWDNewcastle United1.6266provisional1.2535%62
032%
132%
220%
310%
44%
51%
6+0%
Rio NgumohaFWDLiverpool2.0649provisional1.2233%19
037%
130%
218%
39%
44%
52%
6+1%
Jacob MurphyFWDNewcastle United1.7654provisional1.1632%68
036%
132%
219%
38%
43%
51%
6+0%
Florian WirtzMIDLiverpool1.7854provisional1.1531%64
037%
132%
218%
38%
43%
51%
6+1%
Alexis Mac AllisterMIDLiverpool1.3177provisional1.1532%72
034%
134%
220%
38%
43%
51%
6+0%
Fabian SchärDEFNewcastle United1.1582provisional1.0629%50
036%
135%
219%
37%
42%
51%
6+0%
Harvey ElliottMIDLiverpool2.5432provisional1.0627%23
042%
131%
215%
37%
43%
51%
6+1%
Ryan GravenberchMIDLiverpool0.8184provisional0.7718%73
047%
135%
213%
34%
41%
5+0%
Lewis HallDEFNewcastle United0.8380provisional0.7518%57
049%
134%
213%
34%
41%
5+0%
Joe WillockMIDNewcastle United1.4740provisional0.7518%56
051%
131%
212%
34%
41%
5+0%
Curtis JonesMIDLiverpool1.0064provisional0.7518%67
050%
132%
213%
34%
41%
5+0%
Anthony ElangaFWDNewcastle United1.3940provisional0.7117%70
053%
130%
211%
34%
41%
5+0%
Jacob RamseyMIDNewcastle United0.9662provisional0.7016%57
051%
133%
212%
33%
41%
5+0%
Federico ChiesaFWDLiverpool2.9714provisional0.6715%32
054%
132%
210%
33%
41%
50%
6+0%
Virgil van DijkDEFLiverpool0.6789provisional0.6615%75
052%
133%
211%
33%
4+1%
Malick ThiawDEFNewcastle United0.6684provisional0.6313%57
054%
132%
210%
32%
4+1%
Sven BotmanDEFNewcastle United0.7173provisional0.6013%33
056%
131%
210%
32%
4+0%
Lewis MileyMIDNewcastle United0.7563provisional0.5512%37
060%
128%
29%
32%
4+1%
Jeremie FrimpongDEFLiverpool0.7857provisional0.5311%54
061%
128%
29%
32%
4+0%
Jamaal LascellesDEFNewcastle United0.6766provisional0.5110%11
062%
128%
28%
32%
4+0%
Dan BurnDEFNewcastle United0.5371provisional0.438%66
066%
126%
26%
31%
4+0%
Ronald AraujoDEF0.7149provisional0.428%36
067%
125%
26%
31%
4+0%
Stefan BajceticMID0.5072provisional0.417%14
067%
126%
26%
31%
4+0%
Conor BradleyDEFLiverpool0.5559provisional0.396%34
069%
125%
25%
31%
4+0%
Konstantinos TsimikasDEFLiverpool0.7432provisional0.315%36
075%
121%
24%
31%
4+0%
Milos KerkezDEFLiverpool0.3867provisional0.294%72
075%
121%
24%
3+0%
Joe GomezDEFLiverpool0.5741provisional0.294%30
076%
119%
24%
3+1%
Tino LivramentoDEFNewcastle United0.3180provisional0.284%54
076%
121%
23%
3+0%
Giovanni LeoniDEF0.2771provisional0.222%17
080%
117%
22%
3+0%
Wataru EndoMIDLiverpool0.4128provisional0.152%28
087%
112%
21%
3+0%
Emil KrafthDEFNewcastle United0.3237provisional0.151%13
087%
112%
21%
3+0%
Nick PopeGKNewcastle United0.0489provisional0.0455
096%
1+4%
AlissonGKLiverpool0.0089provisional0.0054
0100%
1+0%
Giorgi MamardashviliGKLiverpool0.0088provisional0.0044
0100%
1+0%
Freddie WoodmanGKLiverpool0.0088provisional0.0040
0100%
1+0%

All 46 players shown, ranked by projection. Show fewer ↥

Shots on target43 playersHighest: Mohamed Salah, 0.81 expectedshow

What drives it Shot volume first, conversion second — literally: the projection is the shots model thinned by the player’s own on-target rate, so a high-volume low-accuracy shooter and a sniper price differently at the same shot count. See what has actually happened.

PlayerTeamModel /90Exp. minsProjectedOver 0.5nDistribution
Mohamed SalahFWDLiverpool0.9673provisional0.8153%65
047%
133%
214%
34%
41%
5+0%
Víctor MuñozFWD0.8377provisional0.7351%36
049%
134%
213%
33%
41%
5+0%
Hugo EkitikéFWDLiverpool0.9464provisional0.7048%61
052%
132%
212%
33%
41%
5+0%
Alexander IsakFWDLiverpool0.8963provisional0.6546%48
054%
131%
211%
33%
41%
5+0%
William OsulaFWDNewcastle United1.0547provisional0.5940%38
060%
127%
210%
33%
41%
5+0%
Bazoumana TouréFWD0.7073provisional0.5943%43
057%
131%
210%
32%
4+0%
Cody GakpoFWDLiverpool0.7071provisional0.5843%71
057%
131%
29%
32%
4+0%
Nick WoltemadeFWDNewcastle United0.7066provisional0.5440%62
060%
129%
29%
32%
4+0%
Harvey BarnesFWDNewcastle United0.8552provisional0.5439%70
061%
128%
29%
32%
4+1%
Yoane WissaFWDNewcastle United0.9745provisional0.5338%54
062%
126%
28%
32%
41%
5+0%

10 of 43 players shown, ranked by projection. Show all 43 →

Fouls won46 playersHighest: Víctor Muñoz, 1.29 expectedshow

What drives it How much a player runs at defenders. Dribble volume drives this far more than the referee does. See what has actually happened.

PlayerTeamModel /90Exp. minsProjectedOver 1.5nDistribution
Víctor MuñozFWD1.4877provisional1.2937%36
030%
134%
222%
310%
44%
51%
6+0%
Lewis HallDEFNewcastle United1.3380provisional1.2034%57
032%
134%
220%
39%
43%
51%
6+0%
Ryan GravenberchMIDLiverpool1.1284provisional1.0729%73
036%
136%
219%
37%
42%
51%
6+0%
JoelintonMIDNewcastle United1.1879provisional1.0628%56
036%
135%
219%
37%
42%
51%
6+0%
Conor BradleyDEFLiverpool1.4259provisional0.9926%34
040%
134%
217%
36%
42%
51%
6+0%
Cody GakpoFWDLiverpool1.1671provisional0.9425%71
041%
134%
217%
36%
42%
5+0%
William OsulaFWDNewcastle United1.6947provisional0.9425%38
047%
128%
214%
37%
43%
51%
6+0%
Jeremie FrimpongDEFLiverpool1.3157provisional0.8823%54
046%
132%
215%
36%
42%
50%
6+0%
Nick WoltemadeFWDNewcastle United1.1566provisional0.8823%62
044%
133%
215%
35%
41%
5+0%
Stefan BajceticMID1.0572provisional0.8722%14
043%
135%
215%
35%
41%
5+0%

10 of 46 players shown, ranked by projection. Show all 46 →

Saves4 playersHighest: Giorgi Mamardashvili, 3.51 expectedshow

What drives it Opponent shot volume. A keeper’s own record carries so little that this market shrinks harder than any other on the board. See what has actually happened.

PlayerTeamModel /90Exp. minsProjectedOver 2.5nDistribution
Giorgi MamardashviliGKLiverpool3.5688provisional3.5165%44
04%
112%
218%
320%
417%
512%
6+17%
Freddie WoodmanGKLiverpool3.1088provisional3.0456%40
07%
116%
221%
320%
415%
510%
6+11%
AlissonGKLiverpool3.0689provisional3.0356%54
07%
116%
221%
320%
415%
510%
6+11%
Nick PopeGKNewcastle United3.0389provisional3.0055%55
07%
116%
222%
320%
415%
510%
6+10%

2 of 7 markets have no projection for this fixture.