Tottenham’s home pressure should find openings against a fragile Palace defense, while Spurs’ own defensive vulnerability gives the visitors a route into the contest. Though neither attack is prolific, the matchup’s vulnerabilities point toward an open, goal-filled finish.
Match GoalsOver 2.5
Best Pick
BTTS
53%
Correct Score
Top
1-1
12.4%
9.00
Top
1-0
10.6%
10.00
2-1
9%
9.00
0-1
8.5%
21.00
2-0
7.7%
9.50
0-0
7.3%
17.00
1-2
7.2%
19.00
2-2
5.2%
17.00
144%
1.53TOP
X26%
4.20
230%
5.50
1X70%
X256%
1274%
Combined Bet Builder
3 legs
Tottenham’s home pressure should pin Palace back, with their frequent corners keeping the visitors under siege. Palace’s leaky defence and Spurs’ own vulnerability point to an open, entertaining contest with goals at both ends. Solanke looks primed to make the difference as Spurs edge it in a game that clears the goals line.
1Match WinnerTottenham Hotspur
2Match GoalsOver 2.5
3GoalscorersAnytime
Confirmed XI3-5-2
31
Kinský
6
Hecke
37
Ven
3
Robertson
14
Gray
18
Fernandes
16
Tonali
20
Kudus
8
Gallagher
11
Tel
22
Marmoush
Impact Player Hot Head
Tottenham Hotspur35 players
50.5/ 99Average
Top Rated
1Dejan Kulusevski MID70
2James Maddison MID70
3Marcos SenesiDEF68
Crystal Palace32 players
50.8/ 99Average
Top Rated
1Maxence LacroixDEF76
2Quinten TimberMID68
3Romain EsseMID68
Player Rankings
Snipers
Top goal threats
1
Omar MarmoushTottenham Hotspur
69
2
SavinhoTottenham Hotspur
67
3
RicharlisonTottenham Hotspur
58
4
Mathys TelTottenham Hotspur
56
5
Eddie Nketiah Crystal Palace
54
Wall
Top defenders
1
Tosin AdarabioyoTottenham Hotspur
85
2
Maxence LacroixCrystal Palace
74
3
Marcos SenesiTottenham Hotspur
73
4
Kevin DansoTottenham Hotspur
71
5
Chris RichardsCrystal Palace
68
Control
Top playmakers
1
James Maddison Tottenham Hotspur
79
2
Mateus FernandesTottenham Hotspur
72
3
Sandro TonaliTottenham Hotspur
72
4
Adam WhartonCrystal Palace
72
5
Jefferson LermaCrystal Palace
63
Bad Boys
Most likely to get booked
1
Pedro PorroTottenham Hotspur
1
Aggression 64
2
Rodrigo BentancurTottenham Hotspur
10
Aggression 70
3
Destiny UdogieTottenham Hotspur
12
Aggression 62
4
Yéremy PinoCrystal Palace
13
Aggression 53
5
Brennan JohnsonCrystal Palace
16
Aggression 51
Power Rankings
Strongest overall
1
Maxence LacroixCrystal Palace
76
2
Dejan Kulusevski Tottenham Hotspur
70
3
James Maddison Tottenham Hotspur
70
4
Marcos SenesiTottenham Hotspur
68
5
Quinten TimberCrystal Palace
68
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Tottenham Hotspur vs Crystal Palace based on each team's expected goals (xG). The top-3 most likely scorelines are shown above with their probability percentages. Even the highest-probability score rarely exceeds 12-15% chance, as football is a low-scoring sport.
The Bet Builder combines multiple selections from the Tottenham Hotspur vs Crystal Palace match into one bet. Our AI identifies outcomes that complement each other — for example, a home win paired with over 2.5 goals and both teams to score. All selections must win for the combined bet to pay out, which means higher combined odds but also higher risk.
xG (expected goals) measures the quality of scoring chances. If Tottenham Hotspur has an xG of 1.8, it means they are expected to score around 1-2 goals based on chance quality. The model uses pre-match xG to build a probability table for all possible scorelines. A team can overperform xG (score more than expected) or underperform xG (score fewer).
Yes, all predictions on ProSoccer are completely free. You can view predicted scorelines, over/under probabilities, and bet builder suggestions for Tottenham Hotspur vs Crystal Palace without paying or registering. Odds values are revealed after a tap to keep the page compliant with search engine guidelines.
A value bet occurs when the probability of an outcome is higher than what the bookmaker odds suggest. For example, if our model gives Tottenham Hotspur a 55% chance of winning but the odds imply only 40%, that is a value bet. Our AI highlights selections where the model probability exceeds the implied probability from the odds.
AI predictions use statistical modeling (Poisson distribution) based on historical data and pre-match metrics. They are more reliable over large samples than for any single match. No prediction is guaranteed — football has inherent unpredictability. Our model typically identifies the correct top-3 scorelines in 25-30% of matches, which is significantly better than random chance.