Championship · England

Lincoln CityvPortsmouth

Saturday 22 August at 12:30LNER Stadiumlineups not announced

Referee

Tony Harrington

38 matches on record

Fouls per game

22.2

-4% vs league

League average

23.2

fouls per match, both teams

Angles

every priced line builder

formTerry Devlin over 1.5 tackles: 9/10 of his last 10, 9 in a row
Last 20: 16/20 · 3/4 away · strip 3 2 2 3 3 3 2 5 3 1 · market 67% · best 1.55 (BetWay) · bet365 1.25
formColby Bishop over 0.5 fouls: 9/10 of his last 10, 9 in a row
Last 20: 14/20 · 5/5 away · strip 1 1 1 3 3 2 1 2 1 0 · market 80% · best 1.29 (Bet365) · bet365 1.29
formEbou Adams over 1.5 fouls won: 8/10 of his last 10
Last 20: 16/20 · 4/5 away · strip 2 0 0 4 2 3 3 3 3 2 · market 71% · best 1.57 (Bet365) · bet365 1.57

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 — 2 live calls

Adrian Segečićover 0.50 Fouls1.40 at bet365 · fair 1.29+8.5%
Regan Pooleover 1.50 Fouls3.75 at bet365 · fair 3.50+7.1%

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.

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.
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.
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.
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

Fouls33 playersHighest: Ebou Adams, 1.41 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.

Player	Team	Model /90	Exp. mins	Projected	Over 1.5	n	Distribution
Ebou AdamsMID	Portsmouth	1.52	81provisional	1.41	41%	84	026% 133% 223% 311% 44% 51% 6+0%
Luke Le RouxMID	Portsmouth	1.51	59provisional	1.09	30%	14	036% 134% 219% 38% 43% 51% 6+0%
Colby BishopFWD	Portsmouth	1.16	83provisional	1.09	30%	75	035% 136% 220% 37% 42% 50% 6+0%
Callum ElderDEF	—	1.05	79provisional	0.95	25%	57	040% 136% 217% 36% 41% 5+0%
Andre DozzellMID	Portsmouth	1.03	81provisional	0.95	25%	76	040% 136% 217% 36% 41% 5+0%
Gustavo CaballeroFWD	Portsmouth	1.07	69provisional	0.88	22%	12	043% 135% 216% 35% 41% 5+0%
Florian BianchiniFWD	Portsmouth	1.31	50provisional	0.84	21%	46	046% 133% 215% 35% 41% 5+0%
Marlon PackMID	Portsmouth	1.18	59provisional	0.84	21%	75	047% 132% 214% 35% 41% 5+0%
Márk KosznovszkyMID	Portsmouth	1.32	45provisional	0.78	19%	15	049% 132% 213% 34% 41% 5+0%
Jacob BrownFWD	Portsmouth	1.38	42provisional	0.78	19%	44	049% 132% 213% 34% 41% 5+0%
Adrian SegečićFWD	Portsmouth	0.95	68provisional	0.77	19%	39	048% 134% 214% 34% 41% 5+0%
Jordan WilliamsDEF	Portsmouth	0.92	68provisional	0.74	18%	50	049% 134% 213% 34% 41% 5+0%
Connor OgilvieDEF	Portsmouth	0.75	86provisional	0.73	17%	77	049% 135% 213% 33% 41% 5+0%
Thomas WaddinghamFWD	Portsmouth	2.26	19provisional	0.72	16%	11	050% 133% 212% 33% 41% 50% 6+0%
Regan PooleDEF	Portsmouth	0.83	73provisional	0.70	16%	69	051% 133% 212% 33% 41% 5+0%
John SwiftMID	Portsmouth	0.92	61provisional	0.69	16%	65	052% 132% 212% 33% 41% 5+0%
Josh KnightDEF	Portsmouth	0.76	74provisional	0.66	15%	14	053% 132% 211% 33% 4+1%
Terry DevlinDEF	Portsmouth	0.82	68provisional	0.66	15%	73	053% 132% 211% 33% 41% 5+0%
Ryley TowlerDEF	Lincoln City	0.68	80provisional	0.62	13%	13	054% 132% 210% 32% 4+0%
Conor ChaplinFWD	Portsmouth	0.94	50provisional	0.60	13%	62	057% 130% 210% 33% 41% 5+0%
Zak SwansonDEF	Portsmouth	0.81	60provisional	0.59	13%	64	057% 130% 210% 32% 4+1%
Harvey BlairFWD	Portsmouth	1.14	35provisional	0.57	12%	27	058% 130% 29% 32% 4+1%
Ibane BowatDEF	Portsmouth	0.93	48provisional	0.57	12%	17	059% 129% 29% 32% 41% 5+0%
Abu KamaraFWD	Portsmouth	0.84	50provisional	0.54	11%	53	060% 129% 29% 32% 4+0%
Makenzie KirkFWD	Portsmouth	1.33	27provisional	0.54	11%	14	061% 128% 28% 32% 41% 5+0%
Yunus KonakMID	—	1.01	40provisional	0.53	11%	26	061% 127% 29% 32% 4+1%
Hayden MatthewsDEF	Portsmouth	0.66	66provisional	0.52	10%	19	061% 129% 28% 32% 4+0%
Tanto OlaofeFWD	Lincoln City	1.32	26provisional	0.52	10%	23	062% 128% 28% 32% 40% 5+0%
Keshi AndersonFWD	Portsmouth	0.87	41provisional	0.48	9%	26	063% 128% 27% 32% 4+0%
Josh MurphyFWD	Portsmouth	0.64	56provisional	0.44	8%	59	065% 127% 27% 31% 4+0%
Conor ShaughnessyDEF	Portsmouth	0.60	55provisional	0.41	7%	29	067% 125% 26% 31% 4+0%
Josef BursikGK	Portsmouth	0.01	80provisional	0.01	—	10	099% 1+1%
Nicolas SchmidGK	Portsmouth	0.00	88provisional	0.00	—	70	0100% 1+0%

All 33 players shown, ranked by projection. Show fewer ↥

Shots33 playersHighest: Adrian Segečić, 1.61 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.

Player	Team	Model /90	Exp. mins	Projected	Over 1.5	n	Distribution
Adrian SegečićFWD	Portsmouth	2.03	68provisional	1.61	46%	39	024% 130% 223% 313% 46% 53% 6+1%
Josh MurphyFWD	Portsmouth	2.36	56provisional	1.57	43%	59	028% 129% 220% 312% 46% 53% 6+2%
Gustavo CaballeroFWD	Portsmouth	1.77	69provisional	1.41	40%	12	028% 132% 222% 311% 45% 52% 6+1%
Colby BishopFWD	Portsmouth	1.47	83provisional	1.37	39%	75	027% 133% 223% 311% 44% 51% 6+0%
Conor ChaplinFWD	Portsmouth	1.79	50provisional	1.09	29%	62	039% 131% 217% 38% 43% 51% 6+0%
Florian BianchiniFWD	Portsmouth	1.77	50provisional	1.08	29%	46	039% 132% 217% 38% 43% 51% 6+0%
John SwiftMID	Portsmouth	1.49	61provisional	1.08	29%	65	038% 133% 218% 38% 43% 51% 6+0%
Jacob BrownFWD	Portsmouth	2.01	42provisional	1.07	28%	44	040% 132% 217% 37% 43% 51% 6+0%
Ebou AdamsMID	Portsmouth	1.10	81provisional	1.01	27%	84	038% 135% 218% 37% 42% 50% 6+0%
Abu KamaraFWD	Portsmouth	1.48	50provisional	0.90	23%	53	045% 132% 215% 36% 42% 51% 6+0%

10 of 33 players shown, ranked by projection. Show all 33 →

Shots on target33 playersHighest: Adrian Segečić, 0.50 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.

Player	Team	Model /90	Exp. mins	Projected	Over 0.5	n	Distribution
Adrian SegečićFWD	Portsmouth	0.63	68provisional	0.50	38%	39	062% 129% 28% 32% 4+0%
Colby BishopFWD	Portsmouth	0.53	83provisional	0.49	38%	75	062% 129% 27% 31% 4+0%
Josh MurphyFWD	Portsmouth	0.71	56provisional	0.47	36%	59	064% 127% 27% 32% 4+0%
Jacob BrownFWD	Portsmouth	0.84	42provisional	0.45	34%	44	066% 126% 27% 31% 4+0%
Gustavo CaballeroFWD	Portsmouth	0.55	69provisional	0.44	35%	12	065% 127% 26% 31% 4+0%
John SwiftMID	Portsmouth	0.53	61provisional	0.38	31%	65	069% 125% 25% 31% 4+0%
Florian BianchiniFWD	Portsmouth	0.62	50provisional	0.38	30%	46	070% 124% 25% 31% 4+0%
Makenzie KirkFWD	Portsmouth	0.98	27provisional	0.36	28%	14	072% 122% 25% 31% 4+0%
Abu KamaraFWD	Portsmouth	0.53	50provisional	0.32	27%	53	073% 122% 24% 3+1%
Conor ChaplinFWD	Portsmouth	0.49	50provisional	0.30	25%	62	075% 121% 24% 3+1%

10 of 33 players shown, ranked by projection. Show all 33 →

Fouls won33 playersHighest: Ebou Adams, 1.91 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.

Player	Team	Model /90	Exp. mins	Projected	Over 1.5	n	Distribution
Ebou AdamsMID	Portsmouth	2.09	81provisional	1.91	55%	84	017% 127% 225% 316% 48% 54% 6+2%
Josh MurphyFWD	Portsmouth	1.70	56provisional	1.11	30%	59	038% 132% 217% 38% 43% 51% 6+0%
Connor OgilvieDEF	Portsmouth	1.16	86provisional	1.11	30%	77	034% 136% 220% 38% 42% 51% 6+0%
Keshi AndersonFWD	Portsmouth	2.02	41provisional	1.03	27%	26	041% 132% 216% 37% 43% 51% 6+0%
Colby BishopFWD	Portsmouth	1.09	83provisional	1.02	27%	75	037% 135% 218% 37% 42% 50% 6+0%
Adrian SegečićFWD	Portsmouth	1.29	68provisional	1.01	27%	39	039% 134% 217% 37% 42% 51% 6+0%
Gustavo CaballeroFWD	Portsmouth	1.22	69provisional	0.96	25%	12	040% 134% 217% 36% 42% 50% 6+0%
Terry DevlinDEF	Portsmouth	1.16	68provisional	0.90	23%	73	044% 133% 216% 36% 42% 50% 6+0%
Yunus KonakMID	—	1.59	40provisional	0.78	20%	26	052% 128% 212% 35% 42% 50% 6+0%
Florian BianchiniFWD	Portsmouth	1.29	50provisional	0.77	19%	46	050% 131% 213% 34% 41% 5+0%

10 of 33 players shown, ranked by projection. Show all 33 →

Saves2 playersHighest: Nicolas Schmid, 3.39 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.

Player	Team	Model /90	Exp. mins	Projected	Over 2.5	n	Distribution
Nicolas SchmidGK	Portsmouth	3.46	88provisional	3.39	63%	70	05% 113% 219% 320% 416% 512% 6+15%
Josef BursikGK	Portsmouth	3.55	80provisional	3.25	60%	10	05% 114% 220% 320% 416% 511% 6+13%

2 of 7 markets have no projection for this fixture.