Premier League · England

Tottenham HotspurvNewcastle United

Saturday 29 August at 17:30Tottenham Hotspur Stadiumlineups not announced

Referee

Not appointed

0 matches on record

Fouls per game

—

League average

22.6

fouls per match, both teams

Value

No edges published on this fixture. An edge is published only when a bookmaker's price beats the model's fair price by that market's full bar — most fixtures never produce one. The value board explains every gate a candidate has to clear.

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 v20260813-1451 · Tackles v20260813-1451 · Shots v20260813-1451 · Fouls won v20260813-1451 · Cards v20260813-1451 · Saves v20260813-1451

Model projections

Fouls41 playersHighest: Joelinton Cassio Apolinário de Lira, 1.75 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
Joelinton Cassio Apolinário de LiraMID	Newcastle United	2.02	78provisional	1.75	51%	52	020% 129% 225% 315% 47% 53% 6+1%
Conor GallagherMID	Tottenham Hotspur	1.63	74provisional	1.35	39%	15	028% 133% 222% 311% 44% 51% 6+0%
Dejan KulusevskiMID	Tottenham Hotspur	1.56	75provisional	1.29	37%	27	031% 133% 221% 310% 44% 51% 6+0%
Dominic SolankeFWD	Tottenham Hotspur	1.35	76provisional	1.15	32%	36	035% 133% 220% 38% 43% 51% 6+0%
Mohammed KudusFWD	Tottenham Hotspur	1.18	81provisional	1.06	29%	18	035% 136% 219% 37% 42% 50% 6+0%
Dan BurnDEF	Newcastle United	1.12	84provisional	1.04	28%	62	037% 135% 219% 37% 42% 50% 6+0%
Rodrigo Bentancur ColmánMID	Tottenham Hotspur	1.36	68provisional	1.03	28%	46	039% 133% 218% 37% 42% 51% 6+0%
Pape Matar SarrMID	Tottenham Hotspur	1.73	53provisional	1.02	28%	40	042% 131% 216% 37% 43% 51% 6+0%
Cristian Gabriel RomeroDEF	Tottenham Hotspur	1.09	80provisional	0.97	25%	39	038% 136% 218% 36% 42% 5+0%
Fabian Lukas SchärDEF	Newcastle United	1.06	81provisional	0.94	25%	46	040% 135% 217% 36% 42% 5+0%

10 of 41 players shown, ranked by projection. Show all 41 →

Tackles41 playersHighest: Iyenoma Destiny Udogie, 2.30 expectedshow

What drives it — Possession share. A tackle count depends far more on how much of the match is played at your own end than on a player’s own form. See what has actually happened.

Player	Team	Model /90	Exp. mins	Projected	Over 1.5	n	Distribution
Iyenoma Destiny UdogieDEF	Tottenham Hotspur	2.85	73provisional	2.30	61%	39	016% 123% 222% 316% 411% 56% 6+6%
Lewis HallDEF	Newcastle United	2.53	77provisional	2.16	58%	49	019% 124% 221% 316% 410% 56% 6+5%
Pedro Antonio Porro SaucedaDEF	Tottenham Hotspur	2.37	81provisional	2.13	58%	61	017% 124% 223% 316% 410% 55% 6+4%
Cristian Gabriel RomeroDEF	Tottenham Hotspur	2.32	80provisional	2.07	58%	39	016% 126% 224% 317% 49% 55% 6+3%
Joelinton Cassio Apolinário de LiraMID	Newcastle United	2.34	78provisional	2.02	56%	52	018% 126% 223% 316% 49% 55% 6+3%
Yves BissoumaMID	Tottenham Hotspur	2.89	59provisional	1.88	51%	8	024% 126% 220% 314% 48% 54% 6+4%
Rodrigo Bentancur ColmánMID	Tottenham Hotspur	2.48	68provisional	1.88	52%	46	022% 126% 222% 315% 48% 54% 6+3%
Djed SpenceDEF	Tottenham Hotspur	2.25	70provisional	1.76	48%	44	026% 126% 220% 313% 48% 54% 6+3%
Jacob RamseyMID	Newcastle United	3.03	52provisional	1.74	45%	18	028% 127% 218% 312% 47% 54% 6+4%
Valentino LivramentoDEF	Newcastle United	1.97	77provisional	1.69	47%	49	024% 129% 222% 313% 47% 53% 6+2%

10 of 41 players shown, ranked by projection. Show all 41 →

Shots41 playersHighest: Dominic Solanke, 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
Dominic SolankeFWD	Tottenham Hotspur	2.50	76provisional	2.12	59%	36	017% 124% 223% 317% 410% 55% 6+4%
Richarlison de AndradeFWD	Tottenham Hotspur	3.11	53provisional	1.82	47%	29	027% 126% 218% 312% 48% 55% 6+4%
Mohammed KudusFWD	Tottenham Hotspur	1.84	81provisional	1.67	49%	18	020% 131% 225% 314% 46% 52% 6+1%
Dejan KulusevskiMID	Tottenham Hotspur	2.00	75provisional	1.66	47%	27	024% 129% 223% 314% 47% 53% 6+1%
Xavi SimonsMID	Tottenham Hotspur	2.21	63provisional	1.54	43%	23	027% 130% 221% 312% 46% 52% 6+1%
Mathys TelFWD	Tottenham Hotspur	2.62	51provisional	1.49	40%	28	035% 126% 217% 311% 46% 53% 6+2%
Harvey Lewis BarnesFWD	Newcastle United	2.50	53provisional	1.47	40%	44	031% 129% 219% 311% 46% 53% 6+2%
James MaddisonMID	Tottenham Hotspur	2.09	55provisional	1.26	35%	22	036% 129% 218% 310% 44% 52% 6+1%
Joelinton Cassio Apolinário de LiraMID	Newcastle United	1.31	78provisional	1.14	31%	52	034% 134% 220% 38% 43% 51% 6+0%
Jacob MurphyFWD	Newcastle United	1.74	59provisional	1.14	31%	54	037% 132% 219% 38% 43% 51% 6+0%

10 of 41 players shown, ranked by projection. Show all 41 →

Fouls won41 playersHighest: James Maddison, 1.65 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
James MaddisonMID	Tottenham Hotspur	2.73	55provisional	1.65	45%	22	028% 127% 219% 313% 47% 53% 6+2%
Xavi SimonsMID	Tottenham Hotspur	1.83	63provisional	1.27	35%	23	033% 132% 220% 310% 44% 51% 6+1%
Conor GallagherMID	Tottenham Hotspur	1.48	74provisional	1.22	34%	15	032% 133% 220% 39% 43% 51% 6+0%
Iyenoma Destiny UdogieDEF	Tottenham Hotspur	1.41	73provisional	1.14	31%	39	035% 133% 219% 38% 43% 51% 6+0%
Joelinton Cassio Apolinário de LiraMID	Newcastle United	1.31	78provisional	1.13	31%	52	034% 134% 220% 38% 43% 51% 6+0%
Lewis HallDEF	Newcastle United	1.27	77provisional	1.08	30%	49	037% 133% 218% 38% 43% 51% 6+0%
Dejan KulusevskiMID	Tottenham Hotspur	1.23	75provisional	1.02	27%	27	039% 133% 218% 37% 42% 51% 6+0%
Dominic SolankeFWD	Tottenham Hotspur	1.17	76provisional	1.00	27%	36	040% 133% 217% 37% 42% 51% 6+0%
Richarlison de AndradeFWD	Tottenham Hotspur	1.55	53provisional	0.90	23%	29	047% 130% 214% 36% 42% 51% 6+0%
Mathys TelFWD	Tottenham Hotspur	1.55	51provisional	0.88	23%	28	049% 128% 214% 36% 42% 51% 6+0%

10 of 41 players shown, ranked by projection. Show all 41 →

Cards41 playersHighest: Joelinton Cassio Apolinário de Lira, 0.32 expectedshow

Known defect — Prices ANY card — yellow, second yellow or straight red — matching how books settle "to be shown a card". The trends column counts the same thing.

What drives it — Fouls first, then how readily the referee reaches for a card. Two officials averaging four cards a match can do it for opposite reasons. See what has actually happened.

Player	Team	Model /90	Exp. mins	Projected	Over 0.5	n	Distribution
Joelinton Cassio Apolinário de LiraMID	Newcastle United	0.37	78provisional	0.32	27%	52	073% 123% 24% 3+0%
Dan BurnDEF	Newcastle United	0.31	84provisional	0.29	25%	62	075% 121% 23% 3+0%
Cristian Gabriel RomeroDEF	Tottenham Hotspur	0.30	80provisional	0.27	23%	39	077% 120% 23% 3+0%
Rodrigo Bentancur ColmánMID	Tottenham Hotspur	0.32	68provisional	0.25	21%	46	079% 119% 23% 3+0%
Fabian Lukas SchärDEF	Newcastle United	0.26	81provisional	0.23	20%	46	080% 118% 22% 3+0%
Ben DaviesDEF	Tottenham Hotspur	0.28	73provisional	0.23	20%	17	080% 118% 22% 3+0%
Mickey van de VenDEF	Tottenham Hotspur	0.24	85provisional	0.23	20%	47	080% 118% 22% 3+0%
Kevin DansoDEF	Tottenham Hotspur	0.28	69provisional	0.21	19%	27	081% 116% 22% 3+0%
Conor GallagherMID	Tottenham Hotspur	0.24	74provisional	0.20	18%	15	082% 116% 22% 3+0%
Pedro Antonio Porro SaucedaDEF	Tottenham Hotspur	0.21	81provisional	0.19	17%	61	083% 115% 22% 3+0%

10 of 41 players shown, ranked by projection. Show all 41 →

Saves3 playersHighest: Nick Pope, 3.36 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
Nick PopeGK	Newcastle United	3.37	90provisional	3.36	62%	55	05% 113% 219% 320% 416% 511% 6+15%
Antonín KinskýGK	Tottenham Hotspur	2.55	90provisional	2.55	46%	13	010% 121% 224% 320% 413% 57% 6+6%
Guglielmo VicarioGK	Tottenham Hotspur	2.53	90provisional	2.53	45%	55	010% 121% 224% 319% 413% 57% 6+6%

1 of 7 markets have no projection for this fixture.