xG expliqué

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 StatsBomb'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.

Quel est le xA (passes décisives attendues) et le xAG (buts assistés attendus) ? Comment diffèrent-ils ?

xA, ou le nombre de passes décisives attendues, est la probabilité qu’une passe réussie devienne un « assist ». Cette statistique, développée par Opta, attribue une probabilité à toutes les passes, en fonction du type de passe, de l’emplacement sur le terrain, de la phase de jeu et de la distance parcourue. Les joueurs reçoivent un xA pour chaque passe effectuée, qu’un tir ait lieu ou non.

Afin d’isoler le xG des passes assistant un tir, il existe les buts assistés attendus (xAG). Cela indique la capacité d’un joueur à créer des opportunités pour marquer un but sans avoir à compter sur le résultat actuel du tir ou sur la chance/la capacité du tireur. Le joueurs reçoivent un xAG uniquement quand un tir est effectué après une passe décisive.

Nous utilisons xG + xAG pour la contribution aux buts puisque la contribution aux buts des joueurs est généralement égale à la somme des buts et des passes décisives, ce qui correspond mieux à cette norme.

Jusqu’en octobre 2022, nous utilisions xA pour « buts assistés attendus » (aujourd’hui xAG). Lorsque Opta est devenu notre nouveau fournisseur de donnée, ils ont fourni leur version de xA décrite ci-dessus. Nous avons donc changé le nom en xAG. Opta : que sont les « passes décisives attendues ».

Où trouver le xG

Les xG d'équipe, xG de l'adversaire et différentiel xG peuvent être trouvés dans les tableaux des championnats, comme ceci :

Regular season Table
Clt	Équipe	MJ	V	N	D	BM	BE	DB	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

Les xG du joueur, npxG & xA peuvent être trouvés sur les pages des équipes, comme ceci :

Statistiques basiques 2018-2019 Manchester City: Premier League Table
				Temps de jeu				Performance								Attendu				Progression			Par 90 minutes
Joueur	Nation	Pos	Âge	MJ	Titulaire	Min	90	Buts	PD	B+PD	B-PénM	PénM	PénT	CJ	CR	xG	npxG	xAG	npxG+xAG	PrgC	PrgP	PrgR	Buts	PD	B+PD	B-PénM	B+PD-PénM	xG	xAG	xG+xAG	npxG	npxG+xAG
Ederson	br BRA	GB	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	MT,AT	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	AT	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	AT	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	MT	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	MT	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	MT	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	AT	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	AT,MT	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	MT	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	AT	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	MT	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	GB	19	0	0
Claudio Bravo	cl CHI	GB	35	0	0
Total de l'équipe			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 de l'équipe			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