Saudi Pro League Player xG Stats 2026/2027

Ivan Toney of Al Ahli leads the Saudi Pro League expected goals charts in 2026/2027, with 9.2 xG from 7 appearances. Iker Kortajarena has scored furthest above their chances, 4 goals from 1.6 xG. Houssem Aouar sits furthest below, 0 goals from chances worth 2.3 xG. The boards here cover 402 players and sort on every column; expected goals for each club, with expected points, are on the Saudi Pro League xG table.

Saudi Pro League player expected goals 2026/2027

#	Player	Team	Apps	Goals	xG	Goals minus xG
1	Ivan Toney	Al Ahli	7	10	9.2	+0.9
2	Moussa Dembélé	Al Ettifaq	7	3	4.2	-1.2
3	Julián Quiñones	Al-Qadsiah	7	3	3.7	-0.7
4	Julien Domingues	Al Kholood	6	3	3.7	-0.7
5	Steven Bergwijn	Al Ittihad	7	4	3.6	+0.4
6	Crysencio Summerville	Al Hilal	7	2	3.5	-1.5
7	Youssef En-Nesyri	Al Ittihad	7	4	3.3	+0.7
8	Alexandre Mendy	Al Faisaly	7	1	2.8	-1.8
9	João Félix	Al Nassr	6	2	2.8	-0.8
10	Rúben Neves	Al Hilal	7	3	2.7	+0.3
11	Mateo Retegui	Al-Qadsiah	7	4	2.6	+1.4
12	Ollie Watkins	Al Hilal	4	3	2.5	+0.5
13	Cristiano Ronaldo	Al Nassr	6	3	2.5	+0.5
14	Afimico Pululu	Al Hazm	6	1	2.4	-1.4
15	Darwin Núñez	Al Draih	4	3	2.3	+0.7
16	Houssem Aouar	Al Ittihad	6	0	2.3	-2.3
17	Michy Batshuayi	Abha	5	1	2.1	-1.1
18	George Ilenikhena	Al Ittihad	6	4	2.1	+1.9
19	Gabriel Martinelli	Al Hilal	3	3	2.1	+0.9
20	Abderrazak Hamdallah	Al Taawoun	5	2	2.0	0.0
21	Nasser Al-Dawsari	Al Hilal	7	1	2.0	-1.0
22	Lázaro	Al-Fayha	7	2	2.0	0.0
23	Shaquille Pinas	Al Kholood	6	1	2.0	-1.0
24	Sergej Milinkovic-Savic	Al Hilal	7	4	1.8	+2.2
25	Roger Martínez	Al Shabab	6	2	1.8	+0.2
26	Yannick Carrasco	Al Shabab	7	2	1.8	+0.2
27	Teddy Okou	Al Riyadh	6	3	1.7	+1.3
28	Alexandre Lacazette	NEOM SC	6	3	1.6	+1.4
29	Iker Kortajarena	Al Kholood	6	4	1.6	+2.4
30	Francisco Trincão	Al Ahli	7	3	1.6	+1.4
31	Coba da Costa	Al Kholood	6	0	1.6	-1.6
32	Lukas Haraslin	Al Fateh	6	1	1.5	-0.5
33	Sultan Mandash	Al Hilal	7	3	1.5	+1.5
34	Haroune Camara	NEOM SC	6	0	1.5	-1.5
35	Faisal Al-Abdulwahed	Al Fateh	5	1	1.5	-0.5
36	Tijjani Reijnders	Al-Qadsiah	5	3	1.5	+1.5
37	Younes El Bahraoui	Al Taawoun	6	0	1.4	-1.4
38	Mohammed Abu Al-Shamat	Al-Qadsiah	7	3	1.4	+1.6
39	Musa Barrow	Al Khaleej	6	0	1.4	-1.4
40	Musab Al-Juwayr	Al-Qadsiah	7	0	1.4	-1.4
41	Amadou Koné	NEOM SC	6	3	1.4	+1.6
42	Sadio Mané	Al Nassr	7	2	1.4	+0.6
43	Mohamed Al-Thani	Al Shabab	7	0	1.3	-1.3
44	Fashion Sakala	Al-Fayha	7	1	1.3	-0.3
45	Kalidou Koulibaly	Al Hilal	7	0	1.2	-1.2
46	Wilfried Kanga	Al Fateh	6	0	1.2	-1.2
47	Adil Boulbina	Al Draih	3	2	1.2	+0.9
48	Josh Brownhill	Al Shabab	7	1	1.1	-0.1
49	Karim Benzema	Al Hilal	2	1	1.1	-0.1
50	Joshua King	Al Khaleej	3	1	1.1	-0.1

How these player xG numbers are calculated

Expected goals measure the quality of the chances a player gets, with every shot scored between 0 and 1 by how often shots like it are converted. A player's xG is those values added up, so the gap between the goals they scored and their xG shows whether they finished better or worse than their chances deserved. Underlying data is supplied by Sportmonks.

Frequently Asked Questions

What is player xG?

Expected goals measures the quality of the chances a player gets. Every shot is scored between 0 and 1 by how often shots like it are converted, and a player's xG is those values added up. A player on 8 xG has had chances a typical finisher would score about eight times from.

What does the +/- column mean?

Goals minus xG. A positive number means the player has scored more than the quality of their chances suggested, a negative number means fewer. It is the quickest way to see who is finishing well and who is not.

Will a player below their xG start scoring again?

Usually. Finishing skill is real but small and hard to sustain, so across a full season most players end up close to their expected goals. A large gap either way is more likely to close than to carry on, which makes it a guide rather than a guarantee.

Where does the Saudi Pro League player xG data come from?

Shot-level data is supplied by Sportmonks and updates after every finished match, usually within a few hours of full time. Each shot is credited to the player who took it. Providers train their models on different data, so our numbers will not match another site's exactly.