xG Explained

What is xG?

Very simply, xG (or expected goals) is the probability that a shot will result in a goal based on the characteristics of that shot and the events leading up to it. Some of these characteristics/variables include:

Location of shooter: How far was it from the goal and at what angle on the pitch?
Body part: Was it a header or off the shooter's foot?
Type of pass: Was it from a through ball, cross, set piece, etc?
Type of attack: Was it from an established possession? Was it off a rebound? Did the defense have time to get in position? Did it follow a dribble?

Every shot is compared to thousands of shots with similar characteristics to determine the probability that this shot will result in a goal. That probability is the expected goal total. An xG of 0 is a certain miss, while an xG of 1 is a certain goal. An xG of .5 would indicate that if identical shots were attempted 10 times, 5 would be expected to result in a goal.

There are a number of xG models that use similar techniques and variables, which attempt to reach the same conclusion. The model that FBref uses is provided by Opta. Opta's xG model includes a number of factors above just factors such as the location and angle. Their model also accounts for the clarity of the shooter's path to the goal, the amount of pressure the shooter is under from defensive players, the position of the goalkeeper, and more. That means that their xG model factors in the defense and goalkeeping when determining the odds of the shot reaching the goal.

Take this Diego Jota goal vs Southampton for example. The shot was taken directly in front of the goal from very close range. It's a very good chance. Using an older model that accounts for location, angle, pass type, and such, it would have a 0.68 xG. However, Opta's model also accounts for the fact that the goalkeeper is out of position and there's no defender in the way, which boosts the xG of this shot even higher, to 0.90.

xG does not take into account the quality of player(s) involved in a particular play. It is an estimate of how the average player or team would perform in a similar situation.

How xG is used

xG has many uses. Some examples are:

Comparing xG to actual goals scored can indicate a player's shooting ability or luck. A player who consistently scores more goals than their total xG probably has an above average shooting/finishing ability.
A team's xG difference (xG minus xG allowed) can indicate how a team should be performing. A negative goal difference but a positive xG difference might indicate a team has experienced poor luck or has below average finishing ability.
xG can be used to assess a team's abilities in various situations, such as open play, from a free kick, corner kick, etc. For example, a team that has allowed more goals from free kicks than their xGA from free kicks is probably below average at defending these set pieces.
A team's xGA (xG allowed) can indicate a team's ability to prevent scoring chances. A team that limits their opponent's shots and more importantly, limits their ability to take high probability shots will have a lower xGA.

Penalty Kicks

Each penalty kick is worth .79 xG since all penalty kicks share the same characteristics. Comparing a player's goals from penalty kicks to their penalty kick xG can indicate a player's penalty kicking ability. Likewise, we can do the same for goalkeepers in these situations.

FBref's xG totals include penalty kicks unless otherwise noted. For xG excluding PK, we recommend using npxG (non-penalty expected goals).

How we calculate xG totals for a single offensive possession

In some cases, a player or team's xG totals do not equal the sum of their shots. For instance, a team may attempt multiple shots in a single possession, but it is likely that these shots are contingent upon the outcome of the previous shot(s).

Take for example, this match between Schalke 04 and Nürnberg:

In the 78th minute, Nürnberg attempted three shots which ultimately led to a goal. Hanno Behrens attempts a shot that is saved, but he is able to take a second shot as the ball is deflected off the defender. The second shot goes off the woodwork, which allows Adam Zreľák to easily tap it in. According to Opta's expected goals model:

Behrens' first shot with the goalkeeper in his way = .41 xG
Behrens' second shot with the goalkeeper out of position but a defender in the way = .47 xG
Zreľák's shot with an open net = .79 xG

The sum of these three shots is 1.67 expected goals, even though it is impossible to score more than one goal in a single move. To solve this problem, we find the probability that the defending team does not allow a goal in this possession. In this case, the calculation is:

(1 - .41) x (1 - .47) x (1 - .79) = .0657 or a 6.57% probability that Schalke does not allow a goal.

To find Nürnberg's xG, we simply subtract that probability from 1:

1 - .0657 = .9343 xG

In other words, we estimate that an average team in a similar situation would be expected to score a goal 93.43% of the time.

We use a similar method when calculating xG for individual players. Adam Zreľák receives .79 xG from his single shot while Hanno Behrens receives:

1 - (1 - .41) x (1 - .47) = .6873 xG

This shows why a team or player's total xG may not equal the sum of the xG from their shots and why a team's total xG may not equal the sum of the xG from their players.

Possessions that include a penalty kick

Similarly, we include shots taken from a rebound after a penalty kick with xG from penalty kicks. Take this Marco Reus penalty kick for example:

As mentioned above, the penalty kick attempt = .79 xG
The second shot after the rebound, from 2 yards and with the goalkeeper unrecovered from the save = .92 xG

Since the second shot is a result of the first, we use the same probabilistic method in the previous example. Rather than a total 1.71 xG (.79 + .92), the calculation is:

1 - (1 - .79) * (1 - .92) = .9832 expected goals

However, since the second shot is also considered to be a part of the penalty kick xG, Reus gets 0 npxG (non-penalty expected goals) on this play.

Note: We treat corner kicks and free kicks as a new possession, not a continuation of the previous possession, but are continuing to study the issue.

What is Post-Shot xG (PSxG)?

Regular xG, or what can be considered "Pre-Shot xG", is calculated considering all shots at the time of the shot without knowing the quality of the shot attempt. It not only includes shots that are on target, but also shots that are deflected or off target. Post-Shot xG is calculated after the shot has been taken, once it is known that the shot is on-target, taking into account the quality of the shot. As with xG, PSxG is provided by Opta and is further explained here.

All shots which are off target will have a PSxG of zero since there is a 0% chance that this trajectory will lead to a goal.

When evaluating a goalkeeper's shot stopping ability, we only want to include shots that are on target since these are the shots where the goalkeeper can have an impact. Therefore, we use PSxG to estimate the quality of shots in which they have faced.

What is xA (expected assists) and xAG (expected assisted goals)? How do they differ?

xA, or expected assists, is the likelihood that a given completed pass will become a goal assist. This statistic developed by Opta assigns a likelihood to all passes based on the type of the pass, the location on the pitch, the phase of play, and the distance covered. Players receive xA for every completed pass regardless of whether a shot occurred or not.

In order to just isolate the xG on passes that assist a shot, there's Expected Assisted Goals (xAG). This indicates a player's ability to set up scoring chances without having to rely on the actual result of the shot or the shooter's luck/ability. Players receive xAG only when a shot is taken after a completed pass.

We use xG+xAG for goal contributions since players' goal contributions are typically Goals + Assists and this better matches that standard.

Previous to October 2022, we used xA to mean expected assisted goals (now xAG). When we switched our data provider to Opta, they provided their version of xA described above. We made the name change to xAG. Opta: What are Expected Assists.

Where to find xG

Team xG, xG against, and xG difference can be found on league tables, such as this:

Regular season Table
Rk	Squad	MP	W	D	L	GF	GA	GD	Pts	xG	xGA	xGD
1	Manchester City	38	32	2	4	95	23	+72	98	84.3	24.7	+59.6
2	Liverpool	38	30	7	1	89	22	+67	97	73.7	28.8	+44.9
3	Chelsea	38	21	9	8	63	39	+24	72	58.6	36.4	+22.2
4	Tottenham	38	23	2	13	67	39	+28	71	54.9	47.1	+7.8
5	Arsenal	38	21	7	10	73	51	+22	70	60.1	54.2	+5.9
6	Manchester Utd	38	19	9	10	65	54	+11	66	61.4	50.6	+10.8
7	Wolves	38	16	9	13	47	46	+1	57	52.1	42.1	+10.1
8	Everton	38	15	9	14	54	46	+8	54	49.7	45.7	+4.0
9	Leicester City	38	15	7	16	51	48	+3	52	52.4	43.7	+8.7
10	West Ham	38	15	7	16	52	55	-3	52	47.6	61.9	-14.3
11	Watford	38	14	8	16	52	59	-7	50	48.2	59.2	-11.0
12	Crystal Palace	38	14	7	17	51	53	-2	49	47.6	50.1	-2.5
13	Newcastle Utd	38	12	9	17	42	48	-6	45	39.1	53.6	-14.5
14	Bournemouth	38	13	6	19	56	70	-14	45	53.3	57.2	-3.9
15	Burnley	38	11	7	20	45	68	-23	40	44.4	62.1	-17.7
16	Southampton	38	9	12	17	45	65	-20	39	46.9	55.1	-8.2
17	Brighton	38	9	9	20	35	60	-25	36	35.3	59.1	-23.8
18	Cardiff City	38	10	4	24	34	69	-35	34	42.4	61.5	-19.1
19	Fulham	38	7	5	26	34	81	-47	26	41.3	68.2	-26.8
20	Huddersfield	38	3	7	28	22	76	-54	16	28.8	60.9	-32.2

Player xG, npxG & xA can be found on team pages, such as this:

Standard Stats 2018-2019 Manchester City: Premier League Table
				Playing Time				Performance								Expected				Progression			Per 90 Minutes
Player	Nation	Pos	Age	MP	Starts	Min	90s	Gls	Ast	G+A	G-PK	PK	PKatt	CrdY	CrdR	xG	npxG	xAG	npxG+xAG	PrgC	PrgP	PrgR	Gls	Ast	G+A	G-PK	G+A-PK	xG	xAG	xG+xAG	npxG	npxG+xAG
Ederson	br BRA	GK	24	38	38	3,420	38.0	0	1	1	0	0	0	2	0	0.0	0.0	0.1	0.1	0	3	0	0.00	0.03	0.03	0.00	0.03	0.00	0.00	0.00	0.00	0.00
Aymeric Laporte	es ESP	DF	24	35	34	3,057	34.0	3	3	6	3	0	0	3	0	3.0	3.0	0.8	3.8	94	294	9	0.09	0.09	0.18	0.09	0.18	0.09	0.02	0.11	0.09	0.11
Bernardo Silva	pt POR	MF,FW	23	36	31	2,854	31.7	7	7	14	7	0	0	3	0	7.4	7.4	7.8	15.2	152	156	277	0.22	0.22	0.44	0.22	0.44	0.23	0.25	0.48	0.23	0.48
Raheem Sterling	eng ENG	FW	23	34	31	2,771	30.8	17	9	26	17	0	0	3	0	13.7	13.7	9.6	23.3	155	87	436	0.55	0.29	0.84	0.55	0.84	0.44	0.31	0.76	0.44	0.76
Sergio Agüero	ar ARG	FW	30	33	31	2,459	27.3	21	8	29	19	2	2	4	0	18.1	16.5	5.0	21.5	81	76	253	0.77	0.29	1.06	0.70	0.99	0.66	0.18	0.85	0.60	0.79
Kyle Walker	eng ENG	DF	28	33	30	2,779	30.9	1	1	2	1	0	0	3	0	0.8	0.8	1.9	2.7	83	220	92	0.03	0.03	0.06	0.03	0.06	0.03	0.06	0.09	0.03	0.09
David Silva	es ESP	MF	32	33	28	2,401	26.7	6	8	14	6	0	0	2	0	7.8	7.8	8.5	16.3	118	270	222	0.22	0.30	0.52	0.22	0.52	0.29	0.32	0.61	0.29	0.61
Fernandinho	br BRA	MF	33	29	27	2,377	26.4	1	3	4	1	0	0	5	0	1.6	1.6	3.0	4.5	58	236	29	0.04	0.11	0.15	0.04	0.15	0.06	0.11	0.17	0.06	0.17
İlkay Gündoğan	de GER	MF	27	31	23	2,137	23.7	6	3	9	6	0	0	3	0	4.1	4.1	4.3	8.4	82	205	91	0.25	0.13	0.38	0.25	0.38	0.17	0.18	0.35	0.17	0.35
Leroy Sané	de GER	FW	22	31	21	1,867	20.7	10	10	20	10	0	0	1	0	6.7	6.7	7.4	14.1	84	67	341	0.48	0.48	0.96	0.48	0.96	0.32	0.36	0.68	0.32	0.68
John Stones	eng ENG	DF	24	24	20	1,764	19.6	0	0	0	0	0	0	1	0	0.3	0.3	0.2	0.6	44	118	5	0.00	0.00	0.00	0.00	0.00	0.02	0.01	0.03	0.02	0.03
Riyad Mahrez	dz ALG	FW,MF	27	27	14	1,343	14.9	7	4	11	7	0	1	0	0	5.5	4.7	4.6	9.3	87	73	191	0.47	0.27	0.74	0.47	0.74	0.37	0.31	0.68	0.32	0.62
Nicolás Otamendi	ar ARG	DF	30	18	14	1,236	13.7	0	0	0	0	0	0	1	0	1.3	1.3	0.2	1.5	27	92	3	0.00	0.00	0.00	0.00	0.00	0.10	0.01	0.11	0.10	0.11
Oleksandr Zinchenko	ua UKR	DF	21	14	14	1,151	12.8	0	3	3	0	0	0	1	0	0.2	0.2	1.5	1.7	47	95	94	0.00	0.23	0.23	0.00	0.23	0.01	0.12	0.13	0.01	0.13
Vincent Kompany	be BEL	DF	32	17	13	1,224	13.6	1	0	1	1	0	0	6	0	0.3	0.3	0.0	0.3	17	83	3	0.07	0.00	0.07	0.07	0.07	0.02	0.00	0.02	0.02	0.02
Kevin De Bruyne	be BEL	MF	27	19	11	975	10.8	2	2	4	2	0	0	2	0	1.4	1.4	5.7	7.0	50	109	88	0.18	0.18	0.37	0.18	0.37	0.13	0.52	0.65	0.13	0.65
Benjamin Mendy	fr FRA	DF	24	10	10	900	10.0	0	5	5	0	0	0	1	0	0.2	0.2	1.6	1.8	48	70	59	0.00	0.50	0.50	0.00	0.50	0.02	0.16	0.18	0.02	0.18
Danilo	br BRA	DF	27	11	9	807	9.0	1	0	1	1	0	0	1	0	0.4	0.4	0.2	0.6	20	77	33	0.11	0.00	0.11	0.11	0.11	0.05	0.02	0.07	0.05	0.07
Gabriel Jesus	br BRA	FW	21	29	8	1,036	11.5	7	3	10	6	1	1	1	0	11.2	10.5	2.3	12.7	35	21	128	0.61	0.26	0.87	0.52	0.78	0.97	0.20	1.17	0.91	1.11
Fabian Delph	eng ENG	DF	28	11	8	725	8.1	0	1	1	0	0	0	1	1	0.1	0.1	0.3	0.4	20	59	23	0.00	0.12	0.12	0.00	0.12	0.01	0.04	0.06	0.01	0.06
Phil Foden	eng ENG	MF	18	13	3	335	3.7	1	0	1	1	0	0	0	0	2.1	2.1	0.9	3.0	23	18	35	0.27	0.00	0.27	0.27	0.27	0.57	0.23	0.80	0.57	0.80
Philippe Sandler	nl NED	DF	21	0	0
Arijanet Muric	xk KVX	GK	19	0	0
Claudio Bravo	cl CHI	GK	35	0	0
Squad Total			26.7	38	418	3,420	38.0	91	71	162	88	3	4	44	1	84.3	81.3	65.5	146.7	1325	2429	2412	2.39	1.87	4.26	2.32	4.18	2.22	1.72	3.94	2.14	3.86
Squad Total			26.7	38	418	3,420	38.0	91	71	162	88	3	4	44	1	84.3	81.3	65.5	146.7	1325	2429	2412	2.39	1.87	4.26	2.32	4.18	2.22	1.72	3.94	2.14	3.86

Expected goals can also be found on a number of different pages such as league player stats, match reports, player pages and player match logs.

FBref Competitions with xG Data

FIFA Women's World Cup (2019 to 2023)
FIFA World Cup (2018 to 2022)
Copa America (2019 to 2024)
Copa Libertadores (2019 to 2024)
UEFA Champions League (2017-2018 to 2023-2024)
UEFA Europa Conference League (2021-2022 to 2023-2024)
UEFA Europa League (2017-2018 to 2023-2024)
UEFA European Football Championship (2021 to 2024)
UEFA Women's Champions League (2021-2022 to 2023-2024)
UEFA Women's Euro (2022)
American Major League Soccer (2018 to 2024)
American National Women's Soccer League (2019 to 2024)
Argentine Copa de la Liga Profesional (2021 to 2024)
Argentine Primera (2016-2017 to 2023)
Australian A-League Women (2018-2019 to 2023-2024)
Belgian Pro League (2017-2018 to 2023-2024)
Brazilian Série A (2019 to 2024)
Dutch Eredivisie (2018-2019 to 2023-2024)
English Championship (2018-2019 to 2023-2024)
English Premier League (2017-2018 to 2023-2024)
English Women's Super League (2018-2019 to 2023-2024)
French Division 1 Féminine (2021-2022 to 2023-2024)
French Ligue 1 (2017-2018 to 2023-2024)
French Ligue 2 (2017-2018 to 2023-2024)
German 2.Bundesliga (2017-2018 to 2023-2024)
German Bundesliga (2017-2018 to 2023-2024)
German Frauen-Bundesliga (2022-2023 to 2023-2024)
Italian Serie A (2020-2021 to 2023-2024)
Italian Serie A (2017-2018 to 2023-2024)
Italian Serie B (2018-2019 to 2023-2024)
Mexican Liga MX (2018-2019 to 2023-2024)
NWSL Challenge Cup (2020 to 2024)
NWSL Fall Series (2020)
Portuguese Primeira Liga (2018-2019 to 2023-2024)
Spanish La Liga (2017-2018 to 2023-2024)
Spanish Liga F (2022-2023 to 2023-2024)
Spanish Segunda (2017-2018 to 2023-2024)