Championship · England

Queens Park RangersvBolton Wanderers

Saturday 22 August at 15:00MATRADE Loftus Roadlineups not announced

Referee

Edward Duckworth

6 matches on record — under 15, so the model falls back toward the league mean

Fouls per game

21.8

-6% vs league

League average

23.2

fouls per match, both teams

Angles

every priced line builder

refereeEdward Duckworth: 21.8 fouls and 5.8 cards a game (league 22.3 / 3.4)
-0.4 fouls and +2.4 cards against his league's average over his last 6.

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

Amadou Mbengueover 1.50 Shots8.00 at bet365 · fair 4.00+100.0%
Harvey Valeover 2.50 Shots2.25 at bet365 · fair 2.10+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

Fouls35 playersHighest: Richard Kone, 1.25 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
Richard KoneFWD	Queens Park Rangers	1.57	67provisional	1.25	35%	43	032% 133% 221% 310% 44% 51% 6+0%
Amadou MbengueDEF	Queens Park Rangers	1.18	79provisional	1.06	29%	42	036% 136% 219% 37% 42% 50% 6+0%
Daniel BennieFWD	Queens Park Rangers	1.53	48provisional	0.94	25%	29	042% 133% 216% 36% 42% 51% 6+0%
Michael FreyFWD	Queens Park Rangers	1.73	38provisional	0.91	23%	42	044% 133% 215% 36% 42% 51% 6+0%
Alfie LloydFWD	Queens Park Rangers	1.73	37provisional	0.89	22%	28	045% 133% 215% 35% 42% 51% 6+0%
Jonathan VaraneMID	Queens Park Rangers	1.22	59provisional	0.88	23%	76	044% 133% 215% 35% 41% 5+0%
Jimmy DunneDEF	Queens Park Rangers	0.86	89provisional	0.85	21%	85	043% 136% 215% 34% 41% 5+0%
Paul SmythFWD	Queens Park Rangers	1.35	46provisional	0.83	20%	80	046% 134% 214% 35% 41% 5+0%
Kieran MorganMID	Queens Park Rangers	1.11	61provisional	0.82	21%	59	046% 134% 215% 35% 41% 5+0%
Tariq LampteyDEF	—	1.26	51provisional	0.82	20%	17	047% 133% 214% 35% 41% 5+0%

10 of 35 players shown, ranked by projection. Show all 35 →

Shots35 playersHighest: Ilias Chair, 2.12 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
Ilias ChairMID	Queens Park Rangers	2.67	68provisional	2.12	59%	44	016% 125% 224% 317% 410% 55% 6+4%
Richard KoneFWD	Queens Park Rangers	2.38	67provisional	1.85	52%	43	021% 127% 222% 315% 48% 54% 6+3%
Harvey ValeMID	Queens Park Rangers	2.02	74provisional	1.72	50%	32	021% 129% 224% 314% 47% 53% 6+1%
Daniel BennieFWD	Queens Park Rangers	2.69	48provisional	1.58	43%	29	028% 129% 220% 312% 46% 53% 6+2%
Rayan KolliFWD	Queens Park Rangers	2.36	53provisional	1.53	43%	43	026% 131% 222% 312% 46% 52% 6+1%
Karamoko DembéléMID	Queens Park Rangers	2.05	60provisional	1.47	42%	51	026% 132% 222% 312% 45% 52% 6+1%
Paul SmythFWD	Queens Park Rangers	2.16	46provisional	1.25	34%	80	033% 133% 219% 39% 44% 51% 6+1%
Zan CelarFWD	Queens Park Rangers	2.03	49provisional	1.21	33%	21	035% 132% 218% 39% 44% 51% 6+1%
Rumarn BurrellFWD	Queens Park Rangers	1.66	60provisional	1.18	33%	31	035% 132% 219% 39% 43% 51% 6+0%
Ryan HardieFWD	Bolton Wanderers	2.09	42provisional	1.11	30%	44	039% 132% 217% 38% 43% 51% 6+1%
Nicolas MadsenMID	Queens Park Rangers	1.37	62provisional	1.00	26%	69	040% 133% 217% 37% 42% 51% 6+0%
Alfie LloydFWD	Queens Park Rangers	1.98	37provisional	0.95	24%	28	044% 132% 215% 36% 42% 51% 6+0%
Michael FreyFWD	Queens Park Rangers	1.93	38provisional	0.95	24%	42	044% 132% 215% 36% 42% 51% 6+0%
Koki SaitoFWD	Queens Park Rangers	1.41	55provisional	0.93	24%	74	043% 133% 216% 36% 42% 51% 6+0%
Kwame PokuFWD	Queens Park Rangers	1.46	48provisional	0.86	22%	17	046% 132% 215% 35% 42% 50% 6+0%
Jimmy DunneDEF	Queens Park Rangers	0.84	89provisional	0.83	20%	85	044% 135% 215% 34% 41% 5+0%
Kieran MorganMID	Queens Park Rangers	0.94	61provisional	0.68	16%	59	053% 131% 212% 33% 41% 5+0%
Samuel Iling-JuniorFWD	Bolton Wanderers	1.41	38provisional	0.68	16%	53	055% 130% 211% 33% 41% 5+0%
Amadou MbengueDEF	Queens Park Rangers	0.74	79provisional	0.67	15%	42	052% 133% 211% 33% 41% 5+0%
Isaac HaydenMID	Queens Park Rangers	1.01	51provisional	0.62	14%	46	056% 130% 210% 33% 41% 5+0%
Steve CookDEF	Queens Park Rangers	0.75	68provisional	0.59	13%	61	057% 130% 210% 32% 4+1%
Jonathan VaraneMID	Queens Park Rangers	0.79	59provisional	0.56	12%	76	059% 129% 29% 32% 4+1%
Dennis CirkinDEF	—	0.87	50provisional	0.53	11%	47	062% 127% 28% 32% 40% 5+0%
Liam MorrisonDEF	Queens Park Rangers	0.56	79provisional	0.50	9%	31	062% 129% 28% 31% 4+0%
EsquerdinhaDEF	Queens Park Rangers	1.00	32provisional	0.43	8%	16	067% 125% 26% 31% 4+0%
Ronnie EdwardsDEF	Queens Park Rangers	0.46	83provisional	0.43	7%	51	066% 127% 26% 31% 4+0%
Tariq LampteyDEF	—	0.64	51provisional	0.39	7%	17	069% 124% 26% 31% 4+0%
Ben DaviesDEF	—	0.41	58provisional	0.28	4%	10	077% 119% 23% 3+1%
Jake Clarke-SalterDEF	Queens Park Rangers	0.33	69provisional	0.26	3%	26	077% 120% 23% 3+0%
Lewis BruntDEF	Bolton Wanderers	0.38	53provisional	0.24	3%	10	079% 118% 23% 3+0%
Akin FamewoDEF	Bolton Wanderers	0.33	55provisional	0.22	2%	29	081% 117% 22% 3+0%
Paul Nardi	Queens Park Rangers	0.21	89provisional	0.21	2%	62	081% 117% 22% 3+0%
Cyrus ChristieDEF	—	0.30	23provisional	0.10	1%	11	091% 19% 2+1%
Pierce CharlesGK	Queens Park Rangers	0.00	88provisional	0.00	—	28	0100% 1+0%
Joe WalshGK	Queens Park Rangers	0.00	88provisional	0.00	—	24	0100% 1+0%

All 35 players shown, ranked by projection. Show fewer ↥

Shots on target35 playersHighest: Ilias Chair, 0.79 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
Ilias ChairMID	Queens Park Rangers	1.00	68provisional	0.79	53%	44	047% 134% 214% 34% 41% 5+0%
Richard KoneFWD	Queens Park Rangers	0.89	67provisional	0.70	48%	43	052% 132% 212% 33% 41% 5+0%
Rumarn BurrellFWD	Queens Park Rangers	0.81	60provisional	0.58	42%	31	058% 130% 29% 32% 4+1%
Rayan KolliFWD	Queens Park Rangers	0.84	53provisional	0.54	41%	43	059% 130% 29% 32% 4+0%
Daniel BennieFWD	Queens Park Rangers	0.93	48provisional	0.54	39%	29	061% 128% 29% 32% 40% 5+0%
Harvey ValeMID	Queens Park Rangers	0.63	74provisional	0.54	40%	32	060% 130% 29% 32% 4+0%
Paul SmythFWD	Queens Park Rangers	0.83	46provisional	0.48	36%	80	064% 127% 27% 32% 4+0%
Karamoko DembéléMID	Queens Park Rangers	0.62	60provisional	0.45	35%	51	065% 127% 27% 31% 4+0%
Ryan HardieFWD	Bolton Wanderers	0.82	42provisional	0.44	34%	44	066% 125% 26% 31% 4+0%
Zan CelarFWD	Queens Park Rangers	0.72	49provisional	0.43	33%	21	067% 125% 26% 31% 4+0%

10 of 35 players shown, ranked by projection. Show all 35 →

Fouls won35 playersHighest: Paul Smyth, 1.23 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
Paul SmythFWD	Queens Park Rangers	2.18	46provisional	1.23	34%	80	034% 133% 219% 39% 44% 51% 6+1%
Karamoko DembéléMID	Queens Park Rangers	1.71	60provisional	1.21	34%	51	033% 134% 220% 39% 43% 51% 6+0%
Rayan KolliFWD	Queens Park Rangers	1.90	53provisional	1.21	34%	43	033% 133% 220% 39% 43% 51% 6+0%
Koki SaitoFWD	Queens Park Rangers	1.63	55provisional	1.06	29%	74	039% 132% 217% 37% 43% 51% 6+0%
Jonathan VaraneMID	Queens Park Rangers	1.29	59provisional	0.89	23%	76	045% 132% 215% 36% 42% 50% 6+0%
Amadou MbengueDEF	Queens Park Rangers	0.89	79provisional	0.79	19%	42	046% 134% 214% 34% 41% 5+0%
Alfie LloydFWD	Queens Park Rangers	1.67	37provisional	0.78	19%	28	050% 131% 212% 34% 41% 50% 6+0%
Harvey ValeMID	Queens Park Rangers	0.82	74provisional	0.70	16%	32	051% 133% 212% 33% 41% 5+0%
Kwame PokuFWD	Queens Park Rangers	1.18	48provisional	0.68	16%	17	053% 131% 212% 33% 41% 5+0%
Richard KoneFWD	Queens Park Rangers	0.86	67provisional	0.66	15%	43	054% 131% 211% 33% 41% 5+0%

10 of 35 players shown, ranked by projection. Show all 35 →

Saves2 playersHighest: Pierce Charles, 2.88 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
Pierce CharlesGK	Queens Park Rangers	2.93	88provisional	2.88	53%	28	07% 117% 222% 320% 415% 59% 6+9%
Joe WalshGK	Queens Park Rangers	2.61	88provisional	2.56	46%	24	010% 121% 224% 320% 413% 57% 6+6%

2 of 7 markets have no projection for this fixture.