Season Review 2019/20

Welcome

Welcome to Stats Perform’s French Ligue 1 season review for the curtailed 2019/20 league season.

Interactive and showcasing a host of detailed performance metrics, this report provides insight into the league’s standout performers, applying innovative frameworks produced by our team of AI scientists.

Within this review, we share a comprehensive breakdown of performance at the key ends of the pitch, as well as sharing detailed insight on team style, both in and out of possession. Detailed player analysis also features, with key metrics ranked across different positions.

Notable additions to our reviews this summer include details on the teams most effective at generating opportunities from high turnovers, together with insights into how each team approached the changes to the goal kick rule. We also apply metrics to highlight the ball carrying players who were effective at generating goalscoring opportunities through running with the ball.

These new features reflect Stats Perform’s ongoing commitment to further explore how performance data can inform a club’s decision-making across performance analysis, recruitment and long-term strategic planning.

We hope you find some interesting insights from this review.

Team

Expected Goals (For)

Overview

Key Points:

• Ranked by league position, this table outlines teams’ performances in front of goal, from both open play and set piece situations.

• PSG’s ability to generate high quality chances from open play was demonstrated by them amassing an xG output of 59.5 from 346 shots. They exceeded the xG output of the next highest team, Monaco, by over 23, reinforcing their dominance over the competition.

• 88% of PSG’s xG came from open play, the highest ratio in the league. Montpellier and Nimes sat at the other end of the spectrum, having generated 30% of their xG output from set pieces. Montpellier also generated the highest overall xG from set piece situations in Ligue 1.

• Despite creating a substantially higher volume of high quality shots than their rivals, PSG underperformed on open play xG, scoring over three fewer goals than they would have perhaps expected. In contrast, seventh placed Lyon exceeded their xG by more than seven.

• Bordeaux scored the most set piece goals, exceeding their xG by three. They were the only Ligue 1 side to over perform on their set piece xG by two clear goals.

Expected Goals For

		Set Play : Total	Open Play			Set Play
	Team	xG Ratio	Shots	xG	Goals	SP Shots	SP xG	SP Goals
	Paris Saint-Germain	0.12	346	59.5	55	95	9.3	10
	Marseille	0.21	254	23.8	26	115	7.7	8
	Rennes	0.23	248	24.8	28	88	8.8	6
	Lille	0.20	249	26.3	24	93	7.9	6
	Nice	0.26	229	23.3	26	105	9.6	10
	Reims	0.25	245	19.3	15	88	7.7	6
	Lyon	0.17	279	26.7	34	75	6.5	3
	Montpellier	0.30	242	21.1	22	106	10.6	9
	Monaco	0.20	275	36.1	32	101	10.0	8
	Strasbourg	0.21	237	24.2	23	85	7.3	6
	Angers	0.26	261	21.3	21	102	7.6	5
	Bordeaux	0.29	215	20.0	26	93	9.0	12
	Nantes	0.21	233	22.7	19	99	6.5	2
	Brest	0.15	256	22.4	29	64	4.7	2
	Metz	0.25	226	17.3	19	80	6.9	6
	Dijon	0.28	232	21.3	17	104	8.7	8
	St Etienne	0.23	215	20.5	18	88	7.4	5
	Nîmes	0.30	250	20.1	19	105	9.3	8
	Amiens	0.20	170	18.0	21	59	5.6	6
	Toulouse	0.23	224	20.2	12	86	7.3	5

Graphic

Definitions

Metric	Definition
Set Play	Chances occuring as a result of a corner, direct free kick, indirect free kick or throw-in.
Set Play : Total xG Ratio	The proportion of a team’s total xG that resulted from set plays.
Expected Goals (xG)	Expected Goals (xG) measures the quality of a shot based on several variables such as assist type, shot angle and distance from goal, whether it was a headed shot and whether it was defined as a big chance. Adding up a player or team’s expected goals can give us an indication of how many goals a player or team should have scored on average, given the shots they have taken.

Expected Goals (Against)

Overview

Key Points:

• Reims’ sixth place league finish was underpinned by a resolute defence. While they ranked in Ligue 1’s bottom three for attacking xG, David Guion’s side conceded the fewest open play goals, the lowest number of shots and recorded the lowest xG conceded in the league, helping them secure a European spot for 2020/21.

• Brest’s xG conceded from set pieces was the highest in the league. They conceded almost double the number of shots as Angers, who recorded Ligue 1’s lowest output.

• Relegated Amiens were one of six teams to concede 100 shots from set pieces and their 17 goals conceded was by far the highest in the league – no other team conceded more than 10 set piece goals. Based on the quality of the chances their opponents created, we would have expected them to have conceded nearly 50% fewer goals from set play situations.

Expected Goals Against

		Set Play : Total	Open Play			Set Play
	Team	xG Ratio	Shots	xG	Goals	SP Shots	SP xG	SP Goals
	Paris Saint-Germain	0.29	195	16.6	20	71	6.7	3
	Marseille	0.20	192	21.7	21	81	6.5	2
	Rennes	0.24	258	19.3	14	96	7.5	6
	Lille	0.21	182	20.6	15	89	6.5	5
	Nice	0.22	272	28.8	23	112	10.0	8
	Reims	0.34	177	14.4	13	100	9.6	6
	Lyon	0.22	234	18.9	19	73	6.0	3
	Montpellier	0.24	271	22.7	23	96	8.6	6
	Monaco	0.20	265	29.7	27	97	8.8	10
	Strasbourg	0.20	196	20.5	22	72	6.4	4
	Angers	0.18	202	21.0	26	63	5.2	4
	Bordeaux	0.19	263	27.7	25	94	7.0	6
	Nantes	0.23	206	19.9	21	90	7.4	6
	Brest	0.25	281	30.0	27	125	11.4	6
	Metz	0.27	274	25.0	23	110	10.5	8
	Dijon	0.20	308	30.4	24	88	7.9	10
	St Etienne	0.16	248	28.4	35	72	6.3	5
	Nîmes	0.21	249	29.5	32	99	8.6	7
	Amiens	0.24	308	29.5	31	100	9.3	17
	Toulouse	0.18	305	34.3	45	103	8.3	9

Graphic

Definitions

Metric	Definition
Set Play	Chances occuring as a result of a corner, direct free kick, indirect free kick or throw-in.
Set Play : Total xG Ratio	The proportion of a team’s total xG that resulted from set plays.
Expected Goals (xG)	Expected Goals (xG) measures the quality of a shot based on several variables such as assist type, shot angle and distance from goal, whether it was a headed shot and whether it was defined as a big chance. Adding up a player or team’s expected goals can give us an indication of how many goals a player or team should have scored on average, given the shots they have taken.

Team Sequences (Style)

Overview

Key Points:

• In this section we explore team style using the Stats Perform sequence framework. Using sequence time and passes per sequence, we can assess a team’s approach in terms of how they move the ball. Using direct speed, we are able to identify who progresses the ball quickly.

• Unsurprisingly, PSG ranked first for the longest sequence time and the most passes per sequence. They also recorded a league low direct speed, moving the ball forward on average 1.41 metres per second. Fifth-placed Nice were the most stylistically similar to the champions, ranking second in each of these metrics.

• The threat posed by PSG’s patient, possession based approach, is reflected in the number of their attacks arising from build-up sequences, which was over 50% higher than the next best team.

• Of the teams who finished in the top ten, Montpellier are the stand-out for adopting a less possession-orientated approach. The most direct team in the competition were relegated Nimes, who were the only team who, on average, progressed the ball forward over two metres per second when in possession.

Open Play Sequences

		Sequence Summaries			Sequence Styles
	Team	Sequence Time	Passes Per Sequence	Direct Speed (m/s)	10+ Pass OP Sequences	Build Up Attacks	Direct Attacks
	Paris Saint-Germain	13.65	5.07	1.41	597	140	74
	Marseille	9.66	3.58	1.52	292	39	47
	Rennes	8.83	3.23	1.67	219	37	50
	Lille	8.98	3.39	1.60	278	42	54
	Nice	11.26	4.16	1.44	404	48	46
	Reims	9.00	3.18	1.72	206	35	41
	Lyon	10.71	3.98	1.57	387	66	50
	Montpellier	7.84	2.96	1.85	164	23	38
	Monaco	9.43	3.51	1.76	276	50	53
	Strasbourg	9.11	3.27	1.52	181	28	40
	Angers	8.14	3.18	1.79	235	41	53
	Bordeaux	9.93	3.74	1.45	329	42	27
	Nantes	7.96	3.14	1.55	208	34	51
	Brest	8.60	3.28	1.60	255	31	41
	Metz	8.29	2.98	1.53	208	27	57
	Dijon	8.42	3.08	1.72	193	24	50
	St Etienne	8.27	3.09	1.63	203	20	34
	Nîmes	6.61	2.58	2.04	113	27	40
	Amiens	7.43	2.93	1.72	191	21	30
	Toulouse	7.73	2.92	1.78	178	27	37

Graphic

Definitions

Metric	Definition
Sequences	Sequences are defined as passages of play which belong to one team and are ended by defensive actions, stoppages in play or a shot.
Possessions	Possessions are defined as one or more sequences in a row belonging to the same team. A series of passes leading to a shot which is saved and results in a corner kick would comprise one possession since the same team retains control, but more than one sequence, since the ball has gone out of play. A possession is ended by the opposition gaining control of the ball.
Sequence Time	The average time (in seconds) per sequence.
Passes per Sequence	The average number of passes per sequence.
Direct Speed	A measure of how quickly a team progresses the ball upfield (metres/second).
10+ Pass OP Sequences	The number of open play sequences that includes 10 or more passes.
Build Up Attacks	The number of open play sequences that contains 10 or more passes and either ends in a shot or has at least one touch in the box.
Direct Attacks	The number of open play sequences that starts just inside the team’s own half and has at least 50% of movement towards the opposition’s goal and ends in a shot or a touch in the opposition box.

Team Sequences (Pressure)

Overview

Key Points:

• The sequence framework can also be applied to assess a team’s approach out of possession. We can understand where on the pitch a team disrupts a sequence, and where they win the ball back.

• Three of Ligue 1’s top four also ranked in the top three for PPDA, reinforcing the leading teams’ approach to pressing. PSG forced more high turnovers than any other team, whilst no team scored more goals from high turnovers than Marseille.

• Rennes were the outlier in the league’s top four, committing the lowest volume of high turnovers and being one of only four teams who failed to score after winning the ball back high up the field.

• Angers were another team who failed to score from a high turnover and based on their PPDA, appeared more content to allow their opposition to have the ball.

• Interestingly, two of the relegated teams ranked in the league’s top ten for goals scored from high turnovers: Nimes and Amiens. The latter scored four times, despite only attempting ten shots from high turnovers, the lowest output in the league.

• Selecting the ‘Graphic’ tab, we can see the high turnover pitch map of every Ligue 1 team.

Sequence Pressure Stats

			High Turnovers
	Team	PPDA	Total	Shot Ending	Goal Ending
	Paris Saint-Germain	7.9	160	25	4
	Marseille	10.0	130	26	5
	Rennes	12.1	66	12	0
	Lille	10.8	131	23	4
	Nice	11.8	84	16	3
	Reims	13.2	98	18	1
	Lyon	11.4	121	28	4
	Montpellier	11.9	82	16	1
	Monaco	11.7	119	25	4
	Strasbourg	12.4	85	13	2
	Angers	16.7	123	27	0
	Bordeaux	11.2	114	26	2
	Nantes	13.5	116	27	3
	Brest	12.9	107	22	2
	Metz	14.8	92	19	2
	Dijon	13.5	86	13	1
	St Etienne	11.4	102	22	0
	Nîmes	11.9	121	29	3
	Amiens	12.6	108	10	4
	Toulouse	13.8	91	18	0

Graphic

Paris Saint-Germain

Marseille

Rennes

Lille

Nice

Reims

Lyon

Montpellier

Monaco

Strasbourg

Angers

Bordeaux

Nantes

Brest

Metz

Dijon

St Etienne

Nîmes

Amiens

Toulouse

Definitions

Metric	Definition
High Turnovers	The number of sequences that start in open play and begin 40m or less from the opponent’s goal.
Shot Ending High Turnovers	The number of shot-ending sequences that start in open play and begin 40m or less from the opponent’s goal.
Goal Ending High Turnovers	The number of goal-ending sequences that start in open play and begin 40m or less from the opponent’s goal.
PPDA	PPDA is the number of opposition passes allowed outside of the pressing team’s own defensive third, divided by the number of defensive actions by the pressing team outside of their own defensive third. A lower figure indicates a higher level of pressing, while a higher figure indicates a lower level of pressing.

Goal Kicks

Overview

Key Points:

• Following the introduction of the new goal kick rule, we can establish which teams have used the rule change to build-up attacks from the back.

• Nice recorded the highest proportion of goal kicks which ended inside their own box (44.3%), a tactic which resulted in them gaining more territory compared to when they went long. Nice averaged over 52 metres ball progression when they went short, compared to less than 48 metres when a goal kick cleared their own box.

• PSG were another team who regularly utilised short goal kicks. However they were marginally more successful in progressing the ball when it left the box. They exploited teams who sat off them by playing into central defenders outside the box, or on occasion passing into midfield outlets midway inside their own half.

• Seven teams elected to clear their own box from goal kicks on over 90% of occasions, with Metz recording the lowest number short goal kicks over the whole season.

• Selecting the ‘Graphic’ tab, we can see the end location of every goal kick taken by the goalkeepers for each club during the season.

New Goal Kick Rule: Who’s using it?

		Goal Kick End Location			Goal Kick Upfield Progression
	Team	In the box	Outside the box	% ending in the box	In the box (m)	Outside the box (m)
	Paris Saint-Germain	68	98	41.0	59.3	60.1
	Marseille	42	118	26.2	49.0	42.3
	Rennes	40	159	20.1	46.7	46.8
	Lille	13	187	6.5	63.5	50.2
	Nice	101	127	44.3	52.6	47.9
	Reims	46	164	21.9	58.8	43.7
	Lyon	57	137	29.4	53.4	50.0
	Montpellier	22	208	9.6	52.1	52.4
	Monaco	50	162	23.6	57.3	50.5
	Strasbourg	21	180	10.4	67.5	50.2
	Angers	12	210	5.4	62.6	43.8
	Bordeaux	55	191	22.4	47.6	42.5
	Nantes	23	175	11.6	45.7	42.9
	Brest	75	168	30.9	46.3	48.4
	Metz	5	214	2.3	62.4	40.8
	Dijon	36	213	14.5	50.0	47.4
	St Etienne	47	162	22.5	51.0	42.7
	Nîmes	15	199	7.0	55.6	47.1
	Amiens	19	236	7.5	51.8	44.5
	Toulouse	21	265	7.3	60.0	44.4

Graphic

Paris Saint-Germain

Marseille

Rennes

Lille

Nice

Reims

Lyon

Montpellier

Monaco

Strasbourg

Angers

Bordeaux

Nantes

Brest

Metz

Dijon

St Etienne

Nîmes

Amiens

Toulouse

Definitions

Metric	Definition
Total In Box Goal Kicks	The total number of a team’s goal kicks that ended inside the box
Total Out of Box Goal Kicks	The total number of a team’s goal kicks that ended outside the box
Average In Box Goal Kick Progression	Average distance in metres upfield a team reached while in control of possession following a team’s goal kick ending outside the box. Example (controlled possession): If Mustafi receives the ball from the goal kick inside his area and then attempts an unsuccessful long ball forwards that goes out for a throw in, then the progression was only controlled to the point of where he was in control of the ball in his own box.
Average Out of Box Goal Kick Progression	Average distance in metres upfield a team reached while in control of possession following a team’s goal kick ending outside the box.

Player

Expected Goals

Overview

Key Points:

• Ranked by xG per 90, this table illustrates who took the highest quality shooting opportunities throughout the season.

• Having generated substantially more xG than any other team, it is no surprise to see PSG players occupying the top three spots, with Neymar posting the highest per 90 output in Ligue 1. However unlike his two teammates, the Brazilian was unable to match his output in terms of actual goals.

• The quality of Kylian Mbappé’s finishing is reflected by the Frenchman exceeding his xG projection in both xGOT and goals scored. Lyon’s Moussa Dembele was another player to over perform on his xG, exceeding his output by a magnitude of 0.19, the highest differential amongst the players listed here.

• Saint Etienne’s Denis Bouanga’s performance is also noteworthy. Although he was unable to match his xG in terms of actual goals, his xGOT output, which exceeded his xG, indicates he was somewhat unfortunate not to have scored more.

• Bouanga is one of two players listed to have scored at least 30% of his goals with his head, together with Montpellier’s Andy Delort.

Top 10 Players Expected Goals Per 90 Minutes

			Per 90 Minutes			Goal Method %				Goal Loc. %
Team	Player	Minutes Played	xG	xGOT	Goals	Header	Left Foot	Right Foot	Other	Inside The Box
	Neymar	1320	1.02	0.96	0.89	0%	38%	62%	0%	85%
	Kylian Mbappé	1514	0.94	1.00	1.07	0%	22%	78%	0%	94%
	Mauro Icardi	1268	0.85	0.86	0.85	8%	17%	75%	0%	100%
	Islam Slimani	1286	0.65	0.58	0.63	22%	33%	44%	0%	100%
	Adrien Hunou	1208	0.63	0.54	0.60	25%	25%	50%	0%	100%
	Wissam Ben Yedder	2170	0.61	0.61	0.75	0%	33%	67%	0%	100%
	Victor Osimhen	2289	0.61	0.60	0.51	0%	8%	92%	0%	100%
	Moussa Dembele	2187	0.47	0.54	0.66	13%	6%	75%	6%	94%
	Denis Bouanga	2046	0.45	0.52	0.44	30%	0%	60%	10%	100%
	Andy Delort	2286	0.45	0.39	0.35	33%	0%	67%	0%	89%

Pressure & Clarity

Key Points:

• This section outlines these players’ shooting habits in terms of shot pressure and shot clarity.

• Recording the highest xG per shot output in this list, Mauro Icardi demonstrated an ability to get into good scoring locations inside the box. Approaching half of the Argentinian’s attempts came from high clarity situations, with over 40% of his shots also occurring when he was under low pressure.

• Rennes forward Adrien Hunou was another player who was able to find high quality locations and demonstrated good decision making in front of goal, which is reflected in his low clarity goal attempts. Only Mbappé posted a lower proportion of low clarity attempts.

• Although Islam Slimani marginally failed to match his xG for the season, we can see that nearly 50% of his shots occurred when under high pressure, meaning that opposition players were within tackling distance when he was looking to shoot.

Shot Pressure & Clarity Distribution

			Pressure Level %			Clarity Level %
Team	Player	xG per shot	High Pressure	Moderate Pressure	Low Pressure	Open Goal	High Clarity	Moderate Clarity	Low Clarity
	Neymar	0.21	19.7	32.4	47.9	1.4	22.5	57.7	18.3
	Kylian Mbappé	0.18	26.4	33.3	40.2	3.4	44.8	49.4	2.3
	Mauro Icardi	0.33	30.6	27.8	41.7	5.6	44.4	44.4	5.6
	Islam Slimani	0.17	47.2	18.9	34.0	11.3	17.0	64.2	7.5
	Adrien Hunou	0.30	28.6	42.9	28.6	7.1	32.1	57.1	3.6
	Wissam Ben Yedder	0.23	29.7	34.4	35.9	3.1	32.8	45.3	18.8
	Victor Osimhen	0.18	45.3	37.2	17.4	1.2	27.9	66.3	4.7
	Moussa Dembele	0.20	41.1	37.5	21.4	0.0	41.1	55.4	3.6
	Denis Bouanga	0.13	36.4	41.6	22.1	0.0	22.1	64.9	13.0
	Andy Delort	0.14	27.4	38.1	34.5	0.0	25.0	69.0	6.0

Graphic

Neymar

Kylian Mbappé

Mauro Icardi

Islam Slimani

Adrien Hunou

Wissam Ben Yedder

Victor Osimhen

Moussa Dembele

Denis Bouanga

Andy Delort

Definitions

Metric	Definition
Expected Goals (xG)	Expected Goals (xG) measures the quality of a shot based on several variables such as assist type, shot angle and distance from goal, whether it was a headed shot and whether it was defined as a big chance. Adding up a player or team’s expected goals can give us an indication of how many goals a player or team should have scored on average, given the shots they have taken.
Expected Goals On Target (xGOT)	Expected Goals on Target is a separate Expected Goals Model that includes the original xG of the shot, and the goalmouth location where the shot ended up. It gives more credit to shots that end up in the corners, vs shots that go straight down the middle, and is built on historical on-target shots.
Shot Pressure	Shot pressure is judged by the amount of pressure a player is under by opposition players when they shoot at goal. At the time of the shot, the opposition players must be moving towards the ball (even if only slightly) or trying to put the shooter off in order to add pressure on the shooter.
Shot Clarity	Shot clarity assesses the line of sight between a player and the goal as a shot is taken. It is determined by the number of players (opposition and own team included) obstructing the clarity of the ball’s path to goal.

Expected Assists

Overview

Key Points:

• Understanding that all assists cannot be measured in the same way (a through ball putting the striker one-on-one against a goalkeeper is not the same as a laying the ball off 30 yards from goal for a team mate), the expected assists metric can support in identifying the players who have made high quality passes throughout the season, and have contributed to their team’s chance creation.

• PSG players dominate the league’s top ten, but whilst Ángel Di María and Neymar posted a higher volume of assists per 90, it was Kylian Mbappé who generated the highest xA, demonstrating his all-round threat. Most of these threatening passes orginated from inside the box on the left-hand side.

• Romain Del Castillo and Moses Simon were the highest ranking players unattached to PSG amongst the league’s top ten. Playing in a team completing fewer passes per game than the champions, it is noteworthy that the Nantes winger ranked second for the most chances created per 100 passes amongst the players listed.

• The threat posed by Nantes’ wide players is reinforced by the fact that Abdoul Kader Bamba also appears in the league’s top 10. Despite contributing a respectable 0.12 assists per 90 during his first season in top flight football, the 26-year-old winger’s xA of 0.19 suggests the quality of his passing in attacking areas could have resulted in more goals.

Overall

Top 10 Overall Open Play Expected Assists Per 90 Minutes

			Per 90 Minutes		Chances Created
Team	Player	Minutes Played	xA	Assists	Per 90 Minutes	Per 100 Pass
	Kylian Mbappé	1514	0.52	0.30	2.02	5.91
	Ángel Di María	2005	0.41	0.45	2.02	4.29
	Neymar	1320	0.30	0.41	1.77	3.10
	Pablo Sarabia	1164	0.28	0.23	1.08	2.29
	Romain Del Castillo	1241	0.20	0.36	1.16	3.61
	Marco Verratti	1482	0.20	0.24	1.28	1.27
	Moses Simon	2179	0.20	0.21	1.20	4.98
	Thomas Meunier	1243	0.20	0.14	1.16	2.14
	Abdoul Kader Bamba	1466	0.19	0.12	1.72	4.31
	Adam Ounas	1131	0.18	0.32	1.59	4.94

Defenders

Top 10 Defenders Open Play Expected Assists Per 90 Minutes

			Per 90 Minutes		Chances Created
Team	Player	Minutes Played	xA	Assists	Per 90 Minutes	Per 100 Pass
	Thomas Meunier	1243	0.20	0.14	1.16	2.14
	Juan Bernat	1390	0.17	0.26	0.65	1.02
	Ruben Aguilar	1067	0.16	0.17	1.35	2.66
	Kenny Tete	1063	0.14	0.08	0.93	1.65
	Léo Dubois	1339	0.11	0.00	0.87	1.76
	Mehmet Zeki Çelik	2070	0.10	0.09	0.52	1.15
	Fode Ballo-Toure	1678	0.10	0.00	0.86	2.45
	Fouad Chafik	1531	0.09	0.06	1.18	2.82
	Romain Perraud	1669	0.09	0.05	0.70	1.58
	Hamari Traoré	2413	0.08	0.19	1.08	2.42

Midfielders

Top 10 Midfielders Open Play Expected Assists Per 90 Minutes

			Per 90 Minutes		Chances Created
Team	Player	Minutes Played	xA	Assists	Per 90 Minutes	Per 100 Pass
	Ángel Di María	2005	0.41	0.45	2.02	4.29
	Pablo Sarabia	1164	0.28	0.23	1.08	2.29
	Romain Del Castillo	1241	0.20	0.36	1.16	3.61
	Marco Verratti	1482	0.20	0.24	1.28	1.27
	Abdoul Kader Bamba	1466	0.19	0.12	1.72	4.31
	Matthieu Dossevi	1759	0.17	0.00	1.07	4.08
	Dimitri Payet	1936	0.16	0.09	1.81	4.56
	Gelson Martins	1681	0.16	0.05	1.39	3.99
	Jonathan Ikoné	2074	0.15	0.22	1.08	3.92
	Zinedine Ferhat	2248	0.15	0.16	1.24	4.28

Forwards

Top 10 Forwards Open Play Expected Assists Per 90 Minutes

			Per 90 Minutes		Chances Created
Team	Player	Minutes Played	xA	Assists	Per 90 Minutes	Per 100 Pass
	Kylian Mbappé	1514	0.52	0.30	2.02	5.91
	Neymar	1320	0.30	0.41	1.77	3.10
	Moses Simon	2179	0.20	0.21	1.20	4.98
	Adam Ounas	1131	0.18	0.32	1.59	4.94
	Wissam Ben Yedder	2170	0.17	0.17	1.70	5.51
	Raphinha	1695	0.16	0.16	1.43	4.92
	Islam Slimani	1286	0.15	0.49	1.75	5.14
	Andy Delort	2286	0.15	0.12	1.30	5.20
	Bertrand Traoré	1253	0.14	0.14	0.86	2.56
	Max-Alain Gradel	1641	0.14	0.11	0.66	2.52

Graphic

Kylian Mbappé

Ángel Di María

Neymar

Pablo Sarabia

Romain Del Castillo

Marco Verratti

Moses Simon

Thomas Meunier

Abdoul Kader Bamba

Adam Ounas

Definitions

Metric	Definition
Expected Assists (xA)	A measure of pass quality, showing the likelihood that a pass will be a primary assist. The model is based on the finishing location of the pass, what type of pass it was and a variety of other factors. This model is not reliant on whether a shot was taken from this pass, so credits all passes, regardless of whether they result in a shot.
Chances Created	A measure of the number of times a player assists a shot (including goals).

Carries

Overview

Key Points:

• Identifying ball carrying players who consistently create chances for themselves and their teammates can provide valuable insights into the league’s most dangerous dribblers. The list below highlights Ligue 1’s top 10 players for creating goal scoring opportunities as a result of a carry.

• Mbappé again tops the list, having contributed 45 chance creating carries during the campaign. No other player scored more goals following a carry (7) in Ligue 1.

• Neymar averaged 29 carries per 90, 12 more than Mbappé, however he was unable to match the combined goal and assist count of either his French teammate or Ángel Di María, who provided more assists following a carry than any other player.

• Dimitri Payet also created a large volume of chances following a carry, providing the joint highest number of key passes amongst the players listed. However he is one of two players who didn’t contribute an assist from these situations during the season.

• Denis Bouanga demonstrated a tendency to shoot following a carry, instead of looking to pass to a teammate. Although he attempted more shots than any other player, he only scored once for Saint Etienne after carrying the ball at least five metres.

• Clicking on the ‘Graphic’ tab brings up the pitch map for each player, plotting the chances, assists, shots and goals resulting from their carries during the season.

Top 10 Players: Shot Ending Carries

			Carry Frequency		Carry End Product
Team	Player	Minutes Played	Carries per 90	Average Carry Distance (m)	Shot Ending	Key Pass Ending	Assist Ending	Goal Ending	Total Chance Creating Carries
	Kylian Mbappé	1514	17	12.68	27	18	2	7	45
	Dimitri Payet	1936	19	11.92	22	19	0	4	41
	Moses Simon	2179	14	13.19	19	19	2	1	38
	Denis Bouanga	2046	13	12.17	29	6	0	1	35
	Ángel Di María	2005	19	11.19	16	18	4	4	34
	Jonathan Bamba	1941	15	12.43	22	11	2	0	33
	Neymar	1320	29	11.99	19	13	2	1	32
	Moussa Doumbia	1732	17	12.43	20	12	1	1	32
	Ludovic Blas	1856	14	12.46	20	11	1	1	31
	Houssem Aouar	1962	18	11.81	22	9	1	3	31

Graphic

Kylian Mbappé

Dimitri Payet

Moses Simon

Denis Bouanga

Ángel Di María

Jonathan Bamba

Neymar

Moussa Doumbia

Ludovic Blas

Houssem Aouar

Definitions

Metric	Definition
Carries	The total number of carries where a carry is defined as the player moving the ball five metres or more.
Average Carry Distance	The average distance (in metres) that a player moves the ball per carry.
Shot Ending Carry	The number of carries that were followed by a shot (including goals).
Chance Created Ending Carry	The number of carries that were followed by a key pass/chance created.
Assist Ending Carry	The number of carries that were followed by an assist.
Goal Ending Carry	The number of carries that were followed by a goal.

Goalkeepers

Overview

Key Points:

• In this section we apply shot-stopping metrics that consider the quality of the shot that the goalkeeper faces. While shots faced and save percentage can often be misleading and favour those facing a high volume of shots, these metrics, particularly assessing by ‘goals prevented rate’, can account for that. All metrics here are non penalty and exclude own goals.

• Taking each goalkeeper that appeared most frequently for their team this season, Marseille’s Steve Mandanda stands out. The average Ligue 1 goalkeeper would have been expected to concede over six more goals based on the quality of shots faced by the 35-year-old.

• Nice’s Walter Benítez also enjoyed a strong campaign. Despite his side ranking 18th in Ligue 1 for xG conceded, Benítez’s performances between the posts helped the club secure European football next season. Excluding penalties and own goals, he conceded 30 times, seven fewer goals compared to what we would expect from the xGOT his team conceded.

• The ‘goals prevented rate’ metric can account for different keepers facing a different number of shots throughout the season. Keylor Navas, Matz Sels and Mike Maignan all share the same goals prevented rate (1.04), despite the latter two custodians ‘preventing’ more goals. Normalising for the volume of shots allows us to see that all three goalkeepers were expected to concede 1.04 goals for every goal that they actually conceded.

Most Featured Goalkeepers Per Team

Team	Player	% Of Team Mins	Goals Prevented Rate	Goals Prevented	xGOT Conceded	Goals Conceded	Shots Faced
	Steve Mandanda	94%	1.31	6.4	27.4	21	88
	Walter Benítez	93%	1.23	7.0	37.0	30	125
	Predrag Rajkovic	96%	1.21	3.5	20.5	17	81
	Gautier Larsonneur	95%	1.20	6.0	37.0	31	127
	Edouard Mendy	86%	1.14	2.1	17.1	15	75
	Anthony Lopes	93%	1.05	0.9	20.9	20	84
	Mike Maignan	100%	1.04	0.9	20.9	20	78
	Matz Sels	100%	1.04	0.9	26.9	26	97
	Keylor Navas	78%	1.04	0.6	17.6	17	66
	Gerónimo Rulli	89%	1.03	0.7	23.7	23	114
	Benjamin Lecomte	100%	1.02	0.8	37.8	37	128
	Alexandre Oukidja	95%	1.00	-0.1	27.9	28	126
	Benoit Costil	100%	0.98	-0.5	30.5	31	105
	Alfred Gomis	64%	0.96	-0.7	17.3	18	80
	Alban Lafont	96%	0.88	-2.9	22.1	25	90
	Paul Bernardoni	87%	0.86	-4.6	28.4	33	104
	Régis Gurtner	100%	0.84	-7.6	40.4	48	140
	Ludovic Butelle	93%	0.82	-5.3	23.7	29	79
	Stéphane Ruffier	79%	0.82	-6.6	30.4	37	97
	Baptiste Reynet	81%	0.72	-12.6	32.4	45	113

Graphic

Steve Mandanda

Walter Benítez

Predrag Rajkovic

Gautier Larsonneur

Edouard Mendy

Anthony Lopes

Mike Maignan

Matz Sels

Keylor Navas

Gerónimo Rulli

Benjamin Lecomte

Alexandre Oukidja

Benoit Costil

Alfred Gomis

Alban Lafont

Paul Bernardoni

Régis Gurtner

Ludovic Butelle

Stéphane Ruffier

Baptiste Reynet

Definitions

Metric	Definition
Expected Goals On Target (xGOT)	Expected Goals on Target is a separate Expected Goals Model that includes the original xG of the shot, and the goalmouth location where the shot ended up. It gives more credit to shots that end up in the corners, vs shots that go straight down the middle, and is built on historical on-target shots.
xGOT Conceded	The number of goals that a keeper was expected to concede, given the quality of the on-target shots he faced.
Goals Prevented	The number of goals that a goalkeeper was expected to concede compared to the number that they actually conceded, according to xGOT. Calculated as xGOT conceded from shots on target faced, minus goals conceded.
Goals Prevented Rate	The Goals Prevented metric adjusted to reflect the number of shots a keeper faced. It is the number of goals that a goalkeeper was expected to concede as a proportion of the number of goals they actually conceded. Calculated as: xGOT conceded divided by goals conceded.

Player Sequences (Defending)

Overview

Key Points:

• Ranked by players who initiate their team’s possessions in open play most frequently, we are able to identify players who win the ball back from the opposition and initiate a sequence for their team.

• Unsurprisingly, central defenders and defensive midfielders dominate this list, which is why positions can be filtered, allowing us to better understand which players are more involved from a defensive perspective.

• Out of the league’s forwards some familiar names appear, reinforcing this approach to identifying players who perform well in this role. Despite starting fewer possessions, Dijon’s 21-year old winger Mounir Chouiar ranks well with regards to starting sequences, as well as making the most tackles and ranking second for ball recoveries of the players listed here.

Overall

Top 10 Overall Open Play Sequence Starts

			Per 1000 Opposition Touches
Team	Player	Minutes Played	Open Play Possession Start	Open Play Sequence Start	Tackles Won	Interceptions	Recoveries
	Andrei Girotto	2179	10.15	19.54	3.11	2.00	7.69
	Nicolas Pallois	1890	9.70	22.57	2.59	2.88	12.58
	Fabien Centonze	2520	8.97	17.79	2.53	3.04	7.73
	Benjamin André	2055	8.85	20.57	2.68	3.68	11.09
	Idrissa Gueye	1654	8.59	21.38	3.04	2.95	11.54
	Otávio	2213	8.38	18.25	2.92	2.66	12.15
	Aurélien Chedjou	1972	8.07	18.19	1.18	2.42	9.62
	Baptiste Santamaría	2506	7.93	19.80	2.58	2.09	9.53
	Marco Verratti	1482	7.84	19.38	2.88	1.71	11.09
	Eduardo Camavinga	2112	7.81	15.93	3.91	2.08	8.97

Defender

Top 10 Defender Open Play Sequence Starts

			Per 1000 Opposition Touches
Team	Player	Minutes Played	Open Play Possession Start	Open Play Sequence Start	Tackles Won	Interceptions	Recoveries
	Nicolas Pallois	1890	9.70	22.57	2.59	2.88	12.58
	Fabien Centonze	2520	8.97	17.79	2.53	3.04	7.73
	Aurélien Chedjou	1972	8.07	18.19	1.18	2.42	9.62
	Boubacar Kamara	2140	7.68	19.68	2.29	2.29	11.32
	Pedro Mendes	1377	7.66	16.63	1.59	2.62	9.34
	Axel Disasi	2430	7.52	15.21	1.21	1.50	7.52
	Marquinhos	1474	7.48	18.91	1.97	2.46	12.61
	Pablo Martinez	2339	7.31	16.66	1.23	3.39	8.53
	Arturo Calabresi	1879	7.30	15.65	2.04	1.79	7.73
	Alexander Djiku	2062	7.10	15.85	1.96	3.37	7.83

Midfielders

Top 10 Midfielders Open Play Sequence Starts

			Per 1000 Opposition Touches
Team	Player	Minutes Played	Open Play Possession Start	Open Play Sequence Start	Tackles Won	Interceptions	Recoveries
	Andrei Girotto	2179	10.15	19.54	3.11	2.00	7.69
	Benjamin André	2055	8.85	20.57	2.68	3.68	11.09
	Idrissa Gueye	1654	8.59	21.38	3.04	2.95	11.54
	Otávio	2213	8.38	18.25	2.92	2.66	12.15
	Baptiste Santamaría	2506	7.93	19.80	2.58	2.09	9.53
	Marco Verratti	1482	7.84	19.38	2.88	1.71	11.09
	Eduardo Camavinga	2112	7.81	15.93	3.91	2.08	8.97
	Valentin Rongier	2061	7.55	19.03	3.38	2.33	10.93
	Thiago Mendes	1817	7.32	17.75	2.19	3.47	10.19
	Xavier Chavalerin	2148	7.18	16.76	3.43	2.33	8.54

Forwards

Top 10 Forwards Open Play Sequence Starts

			Per 1000 Opposition Touches
Team	Player	Minutes Played	Open Play Possession Start	Open Play Sequence Start	Tackles Won	Interceptions	Recoveries
	Raphinha	1695	3.22	11.41	1.32	1.24	7.17
	Nicolas de Preville	2001	3.22	10.14	1.41	0.40	6.58
	Maxwel Cornet	1328	3.12	9.57	1.32	1.46	6.11
	Mounir Chouiar	1512	2.93	10.50	1.64	0.93	7.86
	Samuel Kalu	1357	2.86	7.92	1.21	1.71	5.43
	Max-Alain Gradel	1641	2.83	9.02	1.35	0.74	6.06
	Neymar	1320	2.59	13.80	0.86	0.49	7.02
	Renaud Ripart	2031	2.49	8.41	1.43	0.75	5.73
	Jonathan Bamba	1941	2.22	10.73	0.37	0.99	7.96
	Fousseni Diabaté	1199	2.12	7.07	0.64	0.51	5.14

Definitions

Metric	Definition
Sequences	Sequences are defined as passages of play which belong to one team and are ended by defensive actions, stoppages in play or a shot.
Possessions	Possessions are defined as one or more sequences in a row belonging to the same team. A series of passes leading to a shot which is saved and results in a corner kick would comprise one possession since the same team retains control, but more than one sequence, since the ball has gone out of play. A possession is ended by the opposition gaining control of the ball.
Possession Start	The number of times that a player initiates the first open play sequence in a possession. A player initiating the first sequence in a possession (open play sequence start) is regaining control of the ball from the opposition. A player initiating a sequence that isn’t the first in a possession will be recovering the ball in open play following the end of their own team’s sequence (such as a shot or an opposition defensive action).
Sequence Start	The number of times that a player initiates an open play sequence.
Interception	A defending player intercepts a pass between opposition players.
Recovery	When a player takes possession of a loose ball.

Player Sequences (Attacking)

Overview

Key Points:

• Ranking by players who are involved in the most amount of goals (per 100 open play sequence involvements), we can see that centre forwards dominate this list.

• Moving across to the ‘Build Up’ tab, the list shows players involved, but removes both the shot creator and the player who took the shot, providing a list that rewards players involved in earlier phases of the sequence.

• Haris Belkebla’s inclusion is noteworthy, given the lower volume of open play shots created by Brest compared to PSG. The Algerian international was involved in 35 shot ending, and seven goal ending sequences, from a deeper lying midfield position during the season.

Top 10 Goal Ending Open Play Sequence Involvements

			Per 100 Open Play Sequence Involvements			Open Play Sequence Involvements
Team	Player	Minutes Played	Goal Ending p100	Total xG p100	Shot Ending p100	Goal Ending	Total xG	Shot Ending
	Mauro Icardi	1268	7.54	8.94	31.66	15	17.79	63
	Kylian Mbappé	1514	3.66	4.04	20.28	26	28.69	144
	Kasper Dolberg	1894	3.65	2.59	15.36	14	9.96	59
	Victor Osimhen	2289	2.92	2.87	17.90	15	14.75	92
	Islam Slimani	1286	2.57	2.70	16.51	14	14.70	90
	Lebo Mothiba	1046	2.54	2.77	17.03	7	7.65	47
	Adrien Hunou	1208	2.54	2.92	13.71	10	11.49	54
	Wissam Ben Yedder	2170	2.39	2.47	14.80	20	20.69	124
	Mama Baldé	1681	2.20	1.89	15.43	11	9.43	77
	Moussa Dembele	2187	2.19	1.57	15.01	13	9.29	89

Build Up

Top 10 Goal Ending Open Play Build Up Sequence Involvements

			Per 100 Open Play Build Up Sequence Involvements			Open Play Build Up Sequence Involvements
Team	Player	Minutes Played	Goal Ending p100	Total xG p100	Shot Ending p100	Goal Ending	Total xG	Shot Ending
	Thomas Meunier	1243	1.35	1.06	5.98	9	7.09	40
	Presnel Kimpembe	1224	1.33	1.60	7.37	9	10.88	50
	Thiago Silva	1573	1.31	1.34	7.65	12	12.30	70
	Marco Verratti	1482	1.29	1.38	7.02	14	14.90	76
	Mauro Icardi	1268	1.29	2.99	12.26	2	4.63	19
	Joris Gnagnon	1275	1.09	0.77	5.45	5	3.52	25
	Keylor Navas	1890	1.08	1.16	6.51	5	5.36	30
	Marquinhos	1474	1.07	1.40	8.41	9	11.83	71
	Haris Belkebla	1816	1.01	0.47	5.04	7	3.27	35
	Idrissa Gueye	1654	0.96	1.38	7.30	10	14.33	76

Definitions

Metric	Definition
Open Play Sequence Involvement	The number of unique open play sequences that a player was involved in. (Note: these are unique because multiple involvements in a single sequence will only count as one involvement)
Goal Ending Sequence Involvement	The number of unique goal-ending sequences in open play that a player is involved in.
Shot Ending Sequence Involvement	The number of unique shot-ending sequences in open play that a player is involved in.
xG Sequence Involvement	The total xG value of unique open play shot or goal ending sequences that a player was involved in.
Build Up Sequence Involvement	The number of unique build up sequences (shot and key pass removed) in open play that a player was involved in.
Goal Ending Build Up Sequence Involvement	The number of unique goal-ending build up sequences (shot and key pass removed) in open play that a player is involved in.
Shot Ending Build Up Sequence Involvement	The number of unique shot-ending build up sequences (shot and key pass removed) in open play that a player is involved in.
xG Build Up Sequence Involvement	The total xG value of unique build up sequences that a player was involved in that resulted in a shot or a goal.