Major League Soccer Player xG Stats 2026

Lionel Messi of Inter Miami leads the Major League Soccer expected goals charts in 2026, with 14.1 xG from 21 appearances. Guilherme has scored furthest above their chances, 13 goals from 5.8 xG. Gennadiy Synchuk sits furthest below, 1 goal from chances worth 5.1 xG. The boards here cover 818 players and sort on every column; expected goals for each club, with expected points, are on the Major League Soccer xG table.

Major League Soccer player expected goals 2026

#	Player	Team	Apps	Goals	xG	Goals minus xG
1	Lionel Messi	Inter Miami	22	19	14.1	+4.9
2	Rafael Navarro	Colorado Rapids	25	14	13.5	+0.5
3	Brian White	Vancouver Whitecaps	21	14	13.5	+0.5
4	Kelvin Yeboah	Minnesota United	24	13	13.5	-0.5
5	Petar Musa	Dallas	22	18	13.3	+4.7
6	Milan Iloski	Philadelphia Union	24	12	11.5	+0.5
7	Prince Owusu	CF Montréal	23	13	11.4	+1.6
8	Kévin Denkey	Cincinnati	23	13	10.4	+2.7
9	Preston Judd	SJ Earthquakes	23	12	10.4	+1.7
10	Sam Surridge	Nashville SC	19	15	10.2	+4.8
11	Hugo Cuypers	Chicago Fire	11	13	10.2	+2.9
12	Tai Baribo	DC United	19	14	9.9	+4.1
13	Julian Hall	New York RB	25	9	9.6	-0.6
14	Anders Dreyer	San Diego	23	9	9.2	-0.2
15	Evander	Cincinnati	22	13	8.9	+4.1
16	Carles Gil	New England	24	10	8.7	+1.3
17	Denis Bouanga	Los Angeles FC	25	13	8.7	+4.3
18	Germán Berterame	Inter Miami	22	7	8.6	-1.6
19	Simon Becher	St. Louis City	25	7	8.6	-1.6
20	Luis Suárez	Inter Miami	21	12	8.5	+3.5
21	Nicolás Fernández Mercau	New York City	24	15	8.5	+6.6
22	Kevin Kelsy	Portland Timbers	20	9	8.5	+0.6
23	Heung-min Son	Los Angeles FC	24	5	8.1	-3.1
24	Marcus Ingvartsen	San Diego	21	11	8.1	+2.9
25	Bruno Damiani	Philadelphia Union	24	6	8.0	-2.0
26	Idan Toklomati	Charlotte	25	9	8.0	+1.0
27	Dor Turgeman	New England	24	6	8.0	-2.0
28	Sergi Solans	Real Salt Lake	22	9	8.0	+1.0
29	Hany Mukhtar	Nashville SC	25	10	7.9	+2.2
30	Philip Zinckernagel	Chicago Fire	22	5	7.7	-2.7
31	Martín Ojeda	Orlando City	19	11	7.4	+3.7
32	Pep Biel	Charlotte	25	12	6.6	+5.4
33	Josh Sargent	Toronto	18	7	6.6	+0.5
34	Dejan Joveljić	Sporting KC	23	10	6.5	+3.5
35	Miguel Almirón	Atlanta United	19	4	6.2	-2.2
36	Mateusz Bogusz	Houston Dynamo	24	5	6.2	-1.2
37	Joseph Paintsil	LA Galaxy	20	5	6.2	-1.2
38	Guilherme	Houston Dynamo	24	13	5.8	+7.2
39	Myrto Uzuni	Austin	18	6	5.8	+0.2
40	Emil Forsberg	New York RB	23	3	5.7	-2.7
41	Louis Munteanu	DC United	20	6	5.7	+0.3
42	Gabriel Pec	LA Galaxy	14	5	5.6	-0.6
43	Marco Reus	LA Galaxy	24	6	5.6	+0.4
44	Anthony Markanich Jr.	Minnesota United	25	7	5.5	+1.5
45	Dániel Sallói	Toronto	25	7	5.4	+1.6
46	Jorge Ruvalcaba	New York RB	24	7	5.4	+1.6
47	Marcel Hartel	St. Louis City	16	6	5.3	+0.7
48	Thomas Müller	Vancouver Whitecaps	20	7	5.3	+1.7
49	Logan Farrington	Dallas	24	9	5.3	+3.7
50	Ousseni Bouda	SJ Earthquakes	25	6	5.2	+0.8

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 Major League Soccer 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.