Explicación de xG

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:

View Match Highlights on YouTube

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.

¿Qué es Post-Shot xG? (PSxG)?

El xG regular, o lo que se puede considerar "Pre-Shot xG", se calcula considerando todos los tiros en el momento del tiro sin conocer la calidad del intento de tiro. No solo incluye tiros que dan en el blanco, sino también tiros detenidos o desviados. El Post-Shot xG se calcula después de que se ha realizado el tiro, una vez que se sabe que el tiro está en el objetivo, teniendo en cuenta la calidad del tiro. Al igual que con xG, Opta proporciona PSxG y se explica con más detalle here.

Todos los tiros desviados tendrán un PSxG de cero ya que hay un 0 % de probabilidad de que esta trayectoria conduzca a gol.

Al evaluar la capacidad de parada de tiro de un portero, solo queremos incluir los tiros a puerta, ya que estos son los tiros en los que el portero puede tener un impacto. Por ello, utilizamos el PSxG para estimar la calidad de tiros en los que se han enfrentado.

¿Qué es xA (Asistencias esperadas) y xAG (Goles esperados con asistencia)? ¿En qué se diferencian?

xA, o asistencias esperadas, es la probabilidad de que un determinado pase completado se convierta en una asistencia a gol. Esta estadística desarrollada por Opta asigna una probabilidad a todos los pases basados en el tipo de pase, la ubicación en el campo, la fase de juego y la distancia recorrida. Los jugadores reciben xA por cada pase completado, independientemente de si se ha producido un tiro o no.

Para aislar los xG en los pases que asisten a un tiro, hay Goles esperados con asistencia (xAG). Esto indica la capacidad de un jugador para crear ocasiones de gol sin tener que depender del resultado real del tiro o de la suerte/habilidad del tirador. Los jugadores reciben xAG únicamente cuando se realiza un tiro tras un pase completado.

Utilizamos xG+xAG para indicar las participaciones en los goles, ya que estas suelen ser goles + asistencias y se ajustan mejor a la norma.

Antes de octubre de 2022, utilizábamos xA para referirnos a los Goles esperados con asistencia (ahora xAG). Cuando sustituimos nuestro proveedor de datos por Opta, nos proporcionaron su versión de xA descrita anteriormente. Hicimos el cambio de nombre a xAG. Opta: Qué son las asistencias esperadas.

Dónde encontrar los xG

Los xG del equipo, los xG en contra y los xG diferenciales se pueden encontrar en las tablas de ligas, como esta:

Liga Premier Table
RL	Equipo	PJ	PG	PE	PP	GF	GC	DG	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	+5.8
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

Los xG, npxG y xA del jugador se pueden encontrar en las páginas de los equipos, como esta:

Estadísticas estándar 2018-2019 Manchester City: Premier League Table
				Tiempo Jugado				Rendimiento								Expectativa				Progresión			Por 90 Minutos
Jugador	País	Posc	Edad	PJ	Titular	Mín	90 s	Gls.	Ass	G+A	G-TP	TP	TPint	TA	TR	xG	npxG	xAG	npxG+xAG	PrgC	PrgP	PrgR	Gls.	Ast	G+A	G-TP	G+A-TP	xG	xAG	xG+xAG	npxG	npxG+xAG
Ederson	br BRA	PO	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	CC,DL	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	DL	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	DL	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	CC	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	CC	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	CC	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	DL	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	DL,CC	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	CC	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	DL	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	CC	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	PO	19	0	0
Claudio Bravo	cl CHI	PO	35	0	0
Total del equipo			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
Total del equipo			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