PER90

Championship · England

Preston North EndvWolverhampton Wanderers

Deepdalelineups not announced

Referee

Elliot Bell

17 matches on record

Fouls per game

22.6

-3% 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 — 3 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

Fouls52 playersHighest: Ali McCann, 1.40 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
Ali McCannMIDPreston North End1.5579provisional1.4040%56
026%
133%
223%
311%
44%
51%
6+0%
Liam LindsayDEFPreston North End1.4275provisional1.2335%62
032%
134%
221%
39%
43%
51%
6+0%
Callum LangFWDPreston North End1.4076provisional1.2335%46
031%
135%
221%
39%
43%
51%
6+0%
Andrew HughesDEFPreston North End1.2085provisional1.1532%79
032%
136%
221%
38%
42%
51%
6+0%
Andrew MoranMIDPreston North End1.3870provisional1.1432%46
034%
135%
220%
38%
43%
51%
6+0%
AndréMIDWolverhampton Wanderers1.2379provisional1.1231%69
034%
136%
220%
38%
42%
51%
6+0%
Marshall MunetsiMIDWolverhampton Wanderers1.4364provisional1.1030%28
036%
134%
219%
38%
43%
51%
6+0%
Yerson MosqueraDEFWolverhampton Wanderers1.1782provisional1.0930%33
035%
136%
220%
37%
42%
51%
6+0%
Jhon AriasFWDWolverhampton Wanderers1.6648provisional1.0328%23
040%
132%
217%
37%
43%
51%
6+0%
Jordan ThompsonMIDPreston North End1.5154provisional1.0327%44
039%
134%
217%
37%
42%
51%
6+0%

10 of 52 players shown, ranked by projection. Show all 52 →

Shots52 playersHighest: Adam Armstrong, 1.97 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
Adam ArmstrongFWDWolverhampton Wanderers2.3473provisional1.9756%80
017%
127%
224%
316%
49%
54%
6+3%
Alfie DevineMIDPreston North End1.8974provisional1.6146%46
023%
130%
223%
313%
46%
52%
6+1%
Raúl JiménezFWDWolverhampton Wanderers2.8745provisional1.5942%75
029%
128%
219%
311%
46%
53%
6+3%
Mateus ManéMIDWolverhampton Wanderers1.8571provisional1.5143%29
026%
130%
222%
312%
46%
52%
6+1%
Callum LangFWDPreston North End1.7176provisional1.4843%46
026%
132%
223%
312%
45%
52%
6+1%
Delano BurgzorgFWDPreston North End1.8560provisional1.3036%83
033%
131%
220%
310%
44%
52%
6+1%
Marshall MunetsiMIDWolverhampton Wanderers1.5664provisional1.1732%28
036%
132%
219%
39%
43%
51%
6+0%
Tolu ArokodareFWDWolverhampton Wanderers2.7133provisional1.1529%33
040%
131%
216%
37%
44%
52%
6+1%
Milutin OsmajicFWDPreston North End1.4756provisional0.9926%63
042%
132%
216%
37%
42%
51%
6+0%
Fer LópezMIDWolverhampton Wanderers1.3560provisional0.9625%44
042%
133%
216%
36%
42%
51%
6+0%
Yerson MosqueraDEFWolverhampton Wanderers1.0182provisional0.9324%33
041%
135%
217%
36%
41%
5+0%
Rodrigo GomesMIDWolverhampton Wanderers1.4454provisional0.9324%48
044%
132%
215%
36%
42%
51%
6+0%
Jordan JamesMID1.6445provisional0.9123%34
046%
131%
214%
36%
42%
51%
6+0%
Hee-chan HwangFWDWolverhampton Wanderers1.2257provisional0.8321%47
047%
132%
214%
35%
41%
5+0%
Brad PottsDEFPreston North End1.2058provisional0.8321%45
047%
133%
214%
35%
41%
5+0%
Andrew MoranMIDPreston North End1.0070provisional0.8120%46
047%
134%
214%
34%
41%
5+0%
Michael SmithFWDPreston North End1.8234provisional0.8019%78
049%
131%
213%
35%
42%
51%
6+0%
Angel GomesMIDWolverhampton Wanderers1.3744provisional0.7518%12
051%
131%
213%
34%
41%
5+0%
Jhon AriasFWDWolverhampton Wanderers1.2048provisional0.7117%23
053%
130%
212%
34%
41%
5+0%
Liam LindsayDEFPreston North End0.8175provisional0.6916%62
052%
132%
212%
33%
41%
5+0%
Ladislav KrejciDEFWolverhampton Wanderers0.7186provisional0.6915%58
051%
134%
212%
33%
41%
5+0%
Thierry SmallDEFPreston North End0.9956provisional0.6715%42
054%
131%
211%
33%
41%
5+0%
Robbie BradyMIDPreston North End1.3140provisional0.6615%34
055%
130%
211%
33%
41%
5+0%
Emmanuel AgbadouDEFWolverhampton Wanderers0.6783provisional0.6314%30
054%
133%
211%
32%
4+1%
Ali McCannMIDPreston North End0.6979provisional0.6213%56
055%
132%
210%
32%
4+1%
AndréMIDWolverhampton Wanderers0.6779provisional0.6113%69
055%
132%
210%
32%
4+0%
Harry ClarkeDEF0.6878provisional0.6013%32
056%
131%
210%
32%
4+0%
Stefán Teitur ThórdarsonMIDPreston North End1.1142provisional0.5813%54
059%
128%
29%
33%
41%
5+0%
Jean-Ricner BellegardeMIDWolverhampton Wanderers1.2137provisional0.5813%61
059%
129%
29%
33%
41%
5+0%
Andrew HughesDEFPreston North End0.5785provisional0.5411%79
059%
131%
29%
32%
4+0%
Jordan ThompsonMIDPreston North End0.7854provisional0.5110%44
062%
128%
28%
32%
4+0%
Tommy DoyleMIDWolverhampton Wanderers1.2530provisional0.5010%63
063%
126%
27%
32%
41%
5+0%
Jordan StoreyDEFPreston North End0.5286provisional0.509%79
061%
130%
28%
31%
4+0%
Will KeaneFWDPreston North End1.1232provisional0.479%29
065%
126%
27%
32%
40%
5+0%
Mads Frøkjaer-JensenMIDPreston North End0.8940provisional0.458%56
066%
126%
27%
31%
4+0%
Hugo BuenoDEFWolverhampton Wanderers0.6648provisional0.387%35
070%
123%
25%
31%
4+0%
Santiago BuenoDEFWolverhampton Wanderers0.4279provisional0.386%58
069%
125%
25%
31%
4+0%
Lewis GibsonDEFPreston North End0.4572provisional0.376%64
070%
124%
25%
31%
4+0%
Pedro LimaDEFWolverhampton Wanderers0.9724provisional0.325%13
074%
120%
24%
31%
4+0%
TotiDEFWolverhampton Wanderers0.3669provisional0.294%51
076%
120%
23%
3+0%
David Møller WolfeDEFWolverhampton Wanderers0.3561provisional0.263%23
078%
119%
23%
3+0%
Ki-Jana HoeverDEFWolverhampton Wanderers0.5733provisional0.253%23
079%
118%
23%
3+0%
Pol ValentínDEFPreston North End0.3462provisional0.253%68
078%
119%
23%
3+0%
Kieran TrippierDEFWolverhampton Wanderers0.2770provisional0.222%47
081%
117%
22%
3+0%
Jackson TchatchouaDEFWolverhampton Wanderers0.3253provisional0.202%68
082%
116%
22%
3+0%
Jamal Piaras LewisDEFPreston North End0.3245provisional0.182%14
084%
114%
22%
3+0%
Odeluga OffiahDEFPreston North End0.3047provisional0.172%43
085%
113%
22%
3+0%
Andrija VukcevicDEFPreston North End0.3144provisional0.172%30
085%
113%
22%
3+0%
José SáGKWolverhampton Wanderers0.0089provisional0.0052
0100%
1+0%
Sam JohnstoneGKWolverhampton Wanderers0.0088provisional0.0019
0100%
1+0%
Daniel IversenGKPreston North End0.0088provisional0.0037
0100%
1+0%
David CornellGKPreston North End0.0069provisional0.0018
0100%
1+0%

All 52 players shown, ranked by projection. Show fewer ↥

Shots on target52 playersHighest: Adam Armstrong, 0.70 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
Adam ArmstrongFWDWolverhampton Wanderers0.8373provisional0.7049%80
051%
133%
212%
33%
41%
5+0%
Raúl JiménezFWDWolverhampton Wanderers1.1145provisional0.6242%75
058%
128%
210%
33%
41%
5+0%
Mateus ManéMIDWolverhampton Wanderers0.6871provisional0.5541%29
059%
130%
29%
32%
4+0%
Callum LangFWDPreston North End0.6376provisional0.5541%46
059%
130%
29%
32%
4+0%
Milutin OsmajicFWDPreston North End0.7056provisional0.4736%63
064%
127%
27%
32%
4+0%
Alfie DevineMIDPreston North End0.5374provisional0.4536%46
064%
128%
27%
31%
4+0%
Tolu ArokodareFWDWolverhampton Wanderers0.9933provisional0.4232%33
068%
124%
26%
31%
4+0%
Delano BurgzorgFWDPreston North End0.5960provisional0.4133%83
067%
125%
26%
31%
4+0%
Marshall MunetsiMIDWolverhampton Wanderers0.5164provisional0.3831%28
069%
124%
25%
31%
4+0%
Hee-chan HwangFWDWolverhampton Wanderers0.4757provisional0.3227%47
073%
122%
24%
3+1%

10 of 52 players shown, ranked by projection. Show all 52 →

Fouls won52 playersHighest: Callum Lang, 1.56 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
Callum LangFWDPreston North End1.8176provisional1.5645%46
024%
131%
224%
313%
46%
52%
6+1%
Mateus ManéMIDWolverhampton Wanderers1.8771provisional1.5244%29
026%
130%
222%
312%
46%
52%
6+1%
Yerson MosqueraDEFWolverhampton Wanderers1.3982provisional1.2836%33
030%
134%
222%
310%
43%
51%
6+0%
Delano BurgzorgFWDPreston North End1.7160provisional1.1933%83
036%
131%
219%
39%
44%
51%
6+0%
Jordan ThompsonMIDPreston North End1.7554provisional1.1230%44
037%
133%
218%
38%
43%
51%
6+0%
Jean-Ricner BellegardeMIDWolverhampton Wanderers2.4037provisional1.1230%61
039%
132%
217%
38%
43%
51%
6+1%
Andrew MoranMIDPreston North End1.3070provisional1.0428%46
038%
134%
218%
37%
42%
51%
6+0%
AndréMIDWolverhampton Wanderers1.1379provisional1.0227%69
038%
135%
218%
37%
42%
50%
6+0%
Milutin OsmajicFWDPreston North End1.3756provisional0.9124%63
045%
132%
215%
36%
42%
51%
6+0%
Fer LópezMIDWolverhampton Wanderers1.2460provisional0.8822%44
045%
133%
215%
35%
42%
5+0%

10 of 52 players shown, ranked by projection. Show all 52 →

Saves4 playersHighest: Sam Johnstone, 3.14 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
Sam JohnstoneGKWolverhampton Wanderers3.2088provisional3.1458%19
06%
115%
221%
320%
416%
510%
6+12%
José SáGKWolverhampton Wanderers3.1689provisional3.1358%52
06%
115%
221%
320%
416%
510%
6+12%
Daniel IversenGKPreston North End2.6388provisional2.5746%37
010%
121%
224%
320%
413%
57%
6+6%
David CornellGKPreston North End2.4169provisional1.9331%18
021%
126%
222%
315%
49%
54%
6+3%

2 of 7 markets have no projection for this fixture.