Season Review 2019/20

Welcome

Welcome to Stats Perform’s German Bundesliga season review for the 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.

• FC Bayern München’s ability to generate high quality chances from open play was demonstrated by them amassing an xG output of 75 from 485 shots. RB Leipzig were the only other team to post more than 400 shots, whilst Borussia Mönchengladbach, who ranked second for open play xG, could only achieve 75% of Bayern’s total.

• Borussia Dortmund’s attacking players proved to be clinical in front of goal, scoring 20 more open play goals than they would have perhaps expected based on the quality of chances they created. Although fourth placed Gladbach created higher quality chances than Dortmund according to xG, they ended the season scoring 24 fewer open play goals than the league runners-up.

• 34% of 1.FC Union Berlin’s xG came from set pieces, the highest ratio in the league. The Bundesliga newcomers scored the lowest number of open play goals, but amassed 14.5 xG from set play situations, the highest of any team in the competition, from which they scored on 15 occasions.

• Eintracht Frankfurt scored the highest number of set piece goals (17), outperforming their xG by nearly four. Dortmund and 1.FC Köln also overperformed on their set piece xG by more than 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
	FC Bayern München	0.11	485	75.0	81	124	10.4	10
	Borussia Dortmund	0.13	343	52.0	72	99	8.0	10
	RB Leipzig	0.19	404	54.9	61	140	14.1	15
	Borussia Mönchengladbach	0.17	350	55.7	48	124	12.3	12
	Bayer 04 Leverkusen	0.19	372	46.9	45	120	11.5	10
	TSG Hoffenheim	0.25	305	39.6	38	142	14.1	11
	VfL Wolfsburg	0.24	316	37.9	30	155	12.9	14
	Sport-Club Freiburg	0.27	299	29.5	28	142	13.0	13
	Eintracht Frankfurt	0.22	354	45.2	38	146	13.2	17
	Hertha BSC	0.17	266	33.6	33	91	7.5	9
	1.FC Union Berlin	0.34	256	23.9	19	142	14.5	15
	FC Schalke 04	0.22	276	28.1	24	107	8.6	9
	1.FSV Mainz 05	0.22	316	35.7	31	134	10.8	8
	1.FC Köln	0.27	291	33.7	32	122	13.7	16
	FC Augsburg	0.18	247	31.5	33	111	8.0	9
	SV Werder Bremen	0.17	313	28.3	32	106	6.9	5
	Fortuna Düsseldorf	0.26	297	23.8	24	127	9.3	8
	SC Paderborn 07	0.25	292	26.8	26	135	9.7	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.

Expected Goals (Against)

Overview

Key Points:

• RB Leipzig conceded the lowest xG from open play during the season, just ahead of Bayern and VfL Wolfsburg. Although only one point separated Wolfsburg and Freiburg in the final league table, the latter conceded nearly 200 more open play shots over the course of the season.

• Despite possessing a significant threat at attacking set plays, Union Berlin were not as effective inside their own box, conceding the highest number of shots and xG from defensive set pieces. Based on the quality of the chances created by their opponents, they could have potentially conceded over 25% more set piece goals.

• Werder Bremen, who retained their top flight status following the relegation play-off, conceded 19 goals from set pieces, six more than we would have perhaps expected based on their opponent’s xG.

• At the other end of the scale, Augsburg conceded the joint-fewest set piece goals, however they also conceded more times from open play than anyone else. The 55 goals they conceded was seven more than expected based on their xG.

Expected Goals Against

		Set Play : Total	Open Play			Set Play
	Team	xG Ratio	Shots	xG	Goals	SP Shots	SP xG	SP Goals
	FC Bayern München	0.14	241	29.6	22	71	5.5	6
	Borussia Dortmund	0.18	260	31.6	28	89	7.4	7
	RB Leipzig	0.23	248	28.9	26	102	9.2	9
	Borussia Mönchengladbach	0.27	311	32.1	29	153	12.6	10
	Bayer 04 Leverkusen	0.19	272	40.2	33	103	10.1	9
	TSG Hoffenheim	0.15	367	44.8	40	124	8.9	5
	VfL Wolfsburg	0.23	261	29.6	31	115	9.8	10
	Sport-Club Freiburg	0.21	451	48.9	36	149	13.6	8
	Eintracht Frankfurt	0.23	315	37.0	42	122	11.9	13
	Hertha BSC	0.19	344	39.2	36	130	10.4	14
	1.FC Union Berlin	0.28	335	36.9	43	154	15.3	11
	FC Schalke 04	0.24	324	35.0	34	135	12.6	16
	1.FSV Mainz 05	0.23	353	43.0	45	147	13.7	16
	1.FC Köln	0.16	318	40.3	50	128	8.6	13
	FC Augsburg	0.14	363	48.0	55	111	8.0	5
	SV Werder Bremen	0.23	366	42.2	46	134	13.0	19
	Fortuna Düsseldorf	0.24	314	43.3	46	149	15.2	14
	SC Paderborn 07	0.19	339	51.4	53	151	12.7	15

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.

• Dortmund ranked first for the longest sequence time and the most passes per sequence. They also recorded a league low direct speed, moving the ball forwards on average 1.28 metres per second. This patient possession based approach also resulted in Dortmund having over 40% more 10+ passing sequences than two of the other teams to secure Champions League football next season, Leipzig and Gladbach.

• Bayern recorded a similar sequence time and average number of sequence passes to Dortmund, however they looked to move the ball forward at a faster tempo. The champions also recorded over 50 more attacks derived from build-up sequences than Lucien Favre’s side.

• At the other end of the table, Augsburg stand out for adopting a more direct approach, recording the shortest average sequence time and the lowest average number of sequence passes. They only completed 71 open play sequences comprising ten or more passes during the season, 43 fewer than the next lowest side, Union Berlin.

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
	FC Bayern München	11.78	4.50	1.51	656	166	75
	Borussia Dortmund	11.97	4.77	1.28	674	114	66
	RB Leipzig	9.12	3.67	1.67	397	76	66
	Borussia Mönchengladbach	9.14	3.49	1.74	362	63	78
	Bayer 04 Leverkusen	10.47	4.10	1.41	598	86	84
	TSG 1899 Hoffenheim	9.12	3.59	1.63	345	61	54
	VfL Wolfsburg	7.15	2.74	1.90	167	29	70
	Sport-Club Freiburg	8.57	3.15	1.55	234	43	37
	Eintracht Frankfurt	7.33	2.86	1.81	188	34	47
	Hertha BSC	8.16	2.95	1.69	196	33	63
	1.FC Union Berlin	6.12	2.50	1.92	114	16	47
	FC Schalke 04	8.06	2.89	1.52	215	22	52
	1.FSV Mainz 05	6.98	2.61	1.99	139	29	55
	1.FC Köln	7.39	2.81	1.76	158	21	63
	FC Augsburg	5.91	2.29	1.96	71	6	46
	SV Werder Bremen	7.90	3.16	1.76	275	41	46
	Fortuna Düsseldorf	7.62	2.95	1.84	221	28	56
	SC Paderborn 07	7.61	2.78	1.99	165	19	75

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.

• Following Hansi Flick’s appointment, Bayern adopted a more aggressive approach to pressing, which resulted in them completing the most high turnovers in the Bundesliga by a huge margin, as well as the most shots (47) and goals (8) resulting from a high turnover. In fact, Hansi Flick’s Bayern team recorded more high turnovers per 90 minutes (7.8) than any other manager in the top five European leagues.

• Of the teams who qualified for Europe, Hoffenheim completed the fewest high turnovers. Despite that, they managed to score more goals following a high turnover than Leverkusen, whose high-pressing style is reflected in their PPDA score of 9.5.

• Hertha and their city counterparts, Union Berlin, both recorded a low volume of shots from high turnovers. Neither team scored from a high turnover during the season.

• Despite finishing in the bottom three, Werder Bremen ranked joint second for shots resulting from a high turnover, scoring on five occasions.

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

Sequence Pressure Stats

			High Turnovers
	Team	PPDA	Total	Shot Ending	Goal Ending
	FC Bayern München	9.9	248	47	8
	Borussia Dortmund	12.8	152	28	5
	RB Leipzig	11.6	174	33	6
	Borussia Mönchengladbach	11.8	163	35	3
	Bayer 04 Leverkusen	9.5	161	34	3
	TSG 1899 Hoffenheim	13.2	111	24	4
	VfL Wolfsburg	11.2	151	30	2
	Sport-Club Freiburg	15.8	137	24	2
	Eintracht Frankfurt	11.2	154	27	2
	Hertha BSC	13.6	117	17	0
	1.FC Union Berlin	13.9	108	19	0
	FC Schalke 04	11.0	167	34	3
	1.FSV Mainz 05	13.4	149	35	3
	1.FC Köln	11.6	120	25	4
	FC Augsburg	16.8	119	26	2
	SV Werder Bremen	13.1	137	35	5
	Fortuna Düsseldorf	15.7	131	29	1
	SC Paderborn 07	13.5	108	14	2

Graphic

FC Bayern München

Borussia Dortmund

RB Leipzig

Borussia Mönchengladbach

Bayer 04 Leverkusen

TSG Hoffenheim

VfL Wolfsburg

Sport-Club Freiburg

Eintracht Frankfurt

Hertha BSC

1. FC Union Berlin

FC Schalke 04

1. FSV Mainz 05

1. FC Köln

FC Augsburg

SV Werder Bremen

Fortuna Düsseldorf

SC Paderborn 07

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.

• Bottom side Paderborn recorded the highest proportion of goal kicks which ended inside their own box (46.8%), however there was very little difference in their ball progression upfield when they went short, compared to when they went long.

• Leverkusen used short goal kicks less frequently than a number of their rivals, but still went short inside their own box at 27% of their restarts. This tactic resulted in them gaining more territory compared to when they went long - averaging over 53 metres ball progression when they went short, compared to less than 50 metres when a goal kick cleared their own box.

• While Bayern gained significantly more territory when their goal kicks ended outside of the box, you can see from their pitch map that they rarely went long with their goal kicks, instead passing just outside of their own penalty area.

• Five teams elected to clear their own box from goal kicks on over 80% of occasions, with Union Berlin 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 goalkeeper who featured in the most on-field minutes 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)
	FC Bayern München	73	117	38.4	49.6	60.3
	Borussia Dortmund	84	142	37.2	48.9	43.7
	RB Leipzig	86	151	36.3	48.9	50.5
	Borussia Mönchengladbach	112	170	39.7	44.0	44.4
	Bayer 04 Leverkusen	63	169	27.2	53.6	49.4
	TSG Hoffenheim	123	170	42.0	42.5	45.6
	VfL Wolfsburg	59	181	24.6	48.0	46.8
	Sport-Club Freiburg	64	301	17.5	49.3	46.5
	Eintracht Frankfurt	62	169	26.8	51.7	51.0
	Hertha BSC	35	238	12.8	43.1	45.5
	1.FC Union Berlin	17	259	6.2	50.2	49.0
	FC Schalke 04	60	207	22.5	46.6	40.6
	1.FSV Mainz 05	71	245	22.5	43.6	42.9
	1.FC Köln	93	204	31.3	48.0	44.2
	FC Augsburg	55	248	18.2	38.7	39.5
	SV Werder Bremen	70	240	22.6	44.1	44.9
	Fortuna Düsseldorf	38	218	14.8	49.0	45.6
	SC Paderborn 07	132	150	46.8	43.0	43.5

Graphic

FC Bayern München

Borussia Dortmund

RB Leipzig

Borussia Mönchengladbach

Bayer 04 Leverkusen

TSG Hoffenheim

VfL Wolfsburg

Sport-Club Freiburg

Eintracht Frankfurt

Hertha BSC

1. FC Union Berlin

FC Schalke 04

1. FSV Mainz 05

1. FC Köln

FC Augsburg

SV Werder Bremen

Fortuna Düsseldorf

SC Paderborn 07

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.

• The Bundesliga’s leading scorer, Robert Lewandowski, also posted the highest xG per 90 output in the competition, with nearly a quarter of his goals coming from headers.

• The impact of Erling Haaland following his January move to Dortmund is also evident. The quality of the Norwegian’s finishing is reflected by him exceeding his xG projection, in both xGOT and goals scored, by significant margins.

• Although neither Wout Weghorst or Philippe Coutinho were able to match their xG in terms of actual goals, their xGOT outputs, which either matched or exceeded their respective xG totals, indicate they were both somewhat unfortunate not to have scored more.

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
	Robert Lewandowski	2762	1.00	1.00	1.11	24%	9%	68%	0%	97%
	Erling Haaland	1062	0.74	0.95	1.10	8%	69%	23%	0%	100%
	Timo Werner	2807	0.68	0.68	0.90	0%	18%	82%	0%	89%
	Andrej Kramaric	1423	0.63	0.62	0.76	17%	25%	58%	0%	92%
	Breel Embolo	1550	0.62	0.44	0.46	0%	25%	63%	13%	100%
	André Silva	1580	0.61	0.49	0.68	33%	17%	50%	0%	100%
	Wout Weghorst	2740	0.61	0.61	0.53	19%	6%	75%	0%	100%
	Gonçalo Paciência	1277	0.57	0.54	0.49	14%	29%	57%	0%	100%
	Lucas Alario	1164	0.56	0.56	0.54	14%	14%	71%	0%	86%
	Philippe Coutinho	1412	0.55	0.62	0.51	0%	13%	88%	0%	88%

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, Erling Haaland demonstrated an ability to get into good scoring locations inside the box. Over 35% of his attempts came from high clarity situations, with just under a third occurring under low pressure.

• Compared to teammate Lewandowski, Philippe Coutinho attempted more shots from low clarity situations. This is likely to be influenced by a number of efforts from long range, which are documented on his shot map.

• Although Leverkusen’s Lucas Alario narrowly failed to match his xG for the season, we can establish that over half of his shots occurred when under high pressure, meaning that opposition players were within tackling distance when he was looking to shoot.

• Two players listed, Erling Haaland and André Silva, did not attempt a single shot from a low clarity position, indicating they were reluctant to shoot if the path to goal was blocked.

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
	Robert Lewandowski	0.22	23.9	42.0	34.1	1.4	36.2	55.8	6.5
	Erling Haaland	0.26	32.4	35.3	32.4	5.9	35.3	58.8	0.0
	Timo Werner	0.17	20.3	46.3	33.3	4.1	27.6	61.8	6.5
	Andrej Kramaric	0.23	20.9	27.9	51.2	2.3	37.2	53.5	7.0
	Breel Embolo	0.23	39.1	30.4	30.4	2.2	26.1	67.4	4.3
	André Silva	0.20	34.5	40.0	25.5	1.8	30.9	67.3	0.0
	Wout Weghorst	0.22	49.4	28.2	22.4	0.0	32.9	57.6	9.4
	Gonçalo Paciência	0.16	43.1	29.4	27.5	0.0	17.6	66.7	15.7
	Lucas Alario	0.15	52.0	32.0	16.0	2.0	14.0	74.0	10.0
	Philippe Coutinho	0.15	17.2	37.9	44.8	5.2	12.1	67.2	15.5

Graphic

Robert Lewandowski

Erling Haaland

Timo Werner

Andrej Kramaric

Breel Embolo

André Silva

Wout Weghorst

Gonçalo Paciência

Lucas Alario

Philippe Coutinho

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.

• Unsurprisingly Bayern players occupy the league’s top three, with Thomas Müller, who set a new record for assists in a single Bundesliga campaign (21), also ranking top in the competition for xA.

• Müller’s teammate, Kingsley Coman, contributed three assists during the campaign. However his xA output suggests the quality of his passing from wide positions into attacking areas could have resulted in more goals.

• Playing in a team completing fewer passes per game than Bayern, Dortmund and Leverkusen, it is noteworthy that the Leipzig midfielder Christopher Nkunku created the most chances per 100 passes of all the players listed.

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
	Thomas Müller	2259	0.54	0.80	2.43	5.22
	Serge Gnabry	2202	0.47	0.41	1.76	4.21
	Kingsley Coman	1507	0.35	0.18	1.79	4.26
	Moussa Diaby	1769	0.31	0.25	1.37	4.58
	Jadon Sancho	2291	0.28	0.55	1.61	2.77
	Christopher Nkunku	1926	0.27	0.37	1.68	5.37
	Timo Werner	2807	0.27	0.26	1.44	4.90
	Alassane Pléa	2149	0.26	0.38	1.21	4.53
	Ivan Perisic	1202	0.26	0.22	1.12	2.86
	Karim Bellarabi	1423	0.25	0.38	1.14	3.33

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
	Danny da Costa	1389	0.25	0.26	1.17	4.10
	Pavel Kaderábek	2476	0.19	0.25	1.09	2.83
	Angeliño	1095	0.16	0.16	0.99	2.13
	William	1365	0.16	0.07	0.73	1.75
	Daniel Brosinski	1705	0.16	0.00	1.32	3.94
	Alphonso Davies	2234	0.15	0.20	1.29	2.16
	Benjamin Pavard	2790	0.15	0.13	0.48	0.68
	Stefan Lainer	2712	0.15	0.10	1.03	2.72
	Christian Günter	3032	0.15	0.09	1.25	4.16
	Almamy Touré	1494	0.15	0.06	1.14	3.43

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
	Jadon Sancho	2291	0.28	0.55	1.61	2.77
	Christopher Nkunku	1926	0.27	0.37	1.68	5.37
	Ivan Perisic	1202	0.26	0.22	1.12	2.86
	Dominick Drexler	1584	0.25	0.23	1.25	3.83
	Thorgan Hazard	2388	0.24	0.41	0.94	2.33
	Filip Kostic	2959	0.22	0.15	1.13	3.62
	Joshua Kimmich	2822	0.22	0.06	1.02	1.30
	Josip Brekalo	1674	0.21	0.22	1.94	5.22
	Florian Kainz	1563	0.21	0.12	1.44	4.80
	Philippe Coutinho	1412	0.20	0.38	1.21	2.45

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
	Thomas Müller	2259	0.54	0.80	2.43	5.22
	Serge Gnabry	2202	0.47	0.41	1.76	4.21
	Kingsley Coman	1507	0.35	0.18	1.79	4.26
	Moussa Diaby	1769	0.31	0.25	1.37	4.58
	Timo Werner	2807	0.27	0.26	1.44	4.90
	Alassane Pléa	2149	0.26	0.38	1.21	4.53
	Karim Bellarabi	1423	0.25	0.38	1.14	3.33
	Robert Lewandowski	2762	0.23	0.13	1.04	4.31
	Marcus Thuram	2216	0.22	0.32	0.89	3.55
	Gonçalo Paciência	1277	0.21	0.21	1.34	7.36

Graphic

Thomas Müller

Serge Gnabry

Kingsley Coman

Moussa Diaby

Jadon Sancho

Christopher Nkunku

Timo Werner

Alassane Pléa

Ivan Perisic

Karim Bellarabi

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 the Bundesliga’s top 10 players for creating goal scoring opportunities as a result of a carry.

• Chelsea-bound Timo Werner tops the list, having created 65 chances following a carry, 18 more than any other Bundesliga player. No player in the top five European leagues scored more than Werner’s eight goals following a carry.

• Dortmund’s Jadon Sancho completed more carries per 90 than any other player listed and contributed eight assists following a carry, more than any player. Despite being more likely to pass to a teammate than shoot, he also scored four goals during the campaign after completing a carry.

• Operating mainly on the left for Fortuna Düsseldorf, Erik Thommy demonstrated a tendency to cut inside and shoot with his preferred right foot, which resulted in the on-loan winger scoring four times during the season.

• 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
	Timo Werner	2807	11	12.89	43	22	7	8	65
	Serge Gnabry	2202	16	11.44	33	14	1	3	47
	Milot Rashica	2278	12	12.47	28	17	4	4	45
	Alassane Pléa	2149	11	10.39	31	13	4	0	44
	Filip Kostic	2959	13	13.75	22	22	2	0	44
	Christopher Antwi-Adjei	2473	11	14.36	23	20	1	0	43
	Christopher Nkunku	1926	14	11.42	16	26	6	2	42
	Moussa Diaby	1769	16	13.58	23	16	5	3	39
	Jadon Sancho	2291	19	11.93	16	23	8	4	39
	Erik Thommy	2265	12	12.66	28	10	0	4	38

Graphic

Timo Werner

Serge Gnabry

Milot Rashica

Alassane Pléa

Filip Kostic

Christopher Antwi-Adjei

Christopher Nkunku

Moussa Diaby

Jadon Sancho

Erik Thommy

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, Borussia Mönchengladbach’s Yann Sommer stands out. The average Bundesliga goalkeeper would have been expected to concede nearly seven more goals based on the quality of shots faced by the Swiss international, a level of overperformance which could have been pivotal in a tight race for the final Champions League spots.

• Manuel Neuer also enjoyed a return to form during 2019/20. Excluding penalties and own goals, he conceded 27 times, over four 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. Roman Bürki and Alexander Nübel both have the same goals prevented rate (0.97), despite the Dortmund custodian ‘preventing’ fewer goals. Normalising for the volume of shots allows us to see that both goalkeepers were expected to concede 0.97 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
	Yann Sommer	100%	1.17	6.7	45.7	39	155
	Manuel Neuer	97%	1.16	4.3	31.3	27	106
	Lukás Hrádecky	100%	1.12	4.9	46.9	42	141
	Péter Gulácsi	94%	1.11	3.7	35.7	32	115
	Alexander Schwolow	68%	1.01	0.2	29.2	29	115
	Oliver Baumann	88%	1.00	0.2	42.2	42	139
	Rune Jarstein	83%	0.98	-0.8	36.2	37	135
	Alexander Nübel	76%	0.97	-1.0	33.0	34	114
	Roman Bürki	91%	0.97	-1.1	32.9	34	92
	Robin Zentner	63%	0.93	-2.8	35.2	38	113
	Rafal Gikiewicz	97%	0.93	-3.5	46.5	50	155
	Zack Steffen	50%	0.92	-2.7	31.3	34	97
	Florian Kastenmeier	50%	0.91	-2.3	23.7	26	75
	Koen Casteels	76%	0.90	-3.3	30.7	34	100
	Kevin Trapp	65%	0.90	-3.5	31.5	35	104
	Leopold Zingerle	82%	0.89	-5.9	46.1	52	148
	Tomás Koubek	71%	0.86	-6.9	42.1	49	121
	Timo Horn	100%	0.78	-13.9	49.1	63	150
	Jirí Pavlenka	97%	0.78	-13.9	49.1	63	168

Graphic

Yann Sommer

Manuel Neuer

Lukás Hrádecky

Péter Gulácsi

Alexander Schwolow

Oliver Baumann

Rune Jarstein

Alexander Nübel

Roman Bürki

Robin Zentner

Rafal Gikiewicz

Zack Steffen

Florian Kastenmeier

Koen Casteels

Kevin Trapp

Leopold Zingerle

Tomás Koubek

Timo Horn

Jirí Pavlenka

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.

• In the forward position some familiar names appear, reinforcing this approach to identifying players who perform well in this role. Despite starting fewer possessions, Bayern’s Kingsley Coman ranks highly in regards to starting sequences, as well as being the forward recording the second highest volume of ball recoveries.

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
	Sven Bender	2729	11.11	22.54	1.82	3.58	9.35
	Mats Hummels	2583	10.55	23.02	1.59	3.13	12.25
	Wendell	1906	9.05	20.28	2.62	2.25	9.86
	Niklas Moisander	1979	8.97	20.51	1.04	3.41	11.40
	Thiago	1771	8.52	23.56	2.00	3.45	14.97
	Omar Mascarell	2029	8.46	21.18	1.58	4.26	11.42
	Edmond Tapsoba	1057	8.42	20.74	1.94	1.17	12.44
	Joshua Kimmich	2822	8.22	22.86	1.63	1.63	12.39
	Sebastian Schonlau	2070	7.69	16.66	2.62	2.44	8.97
	Rafael Czichos	2210	7.68	16.27	1.92	1.98	8.81

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
	Sven Bender	2729	11.11	22.54	1.82	3.58	9.35
	Mats Hummels	2583	10.55	23.02	1.59	3.13	12.25
	Wendell	1906	9.05	20.28	2.62	2.25	9.86
	Niklas Moisander	1979	8.97	20.51	1.04	3.41	11.40
	Edmond Tapsoba	1057	8.42	20.74	1.94	1.17	12.44
	Sebastian Schonlau	2070	7.69	16.66	2.62	2.44	8.97
	Rafael Czichos	2210	7.68	16.27	1.92	1.98	8.81
	Alphonso Davies	2234	7.62	20.82	2.43	1.75	11.96
	Dayot Upamecano	2359	7.60	21.51	1.69	2.42	13.80
	Jonathan Tah	2090	7.45	17.91	1.54	1.54	8.99

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
	Thiago	1771	8.52	23.56	2.00	3.45	14.97
	Omar Mascarell	2029	8.46	21.18	1.58	4.26	11.42
	Joshua Kimmich	2822	8.22	22.86	1.63	1.63	12.39
	Nicolas Höfler	2784	7.60	19.33	1.95	3.37	9.64
	Konrad Laimer	2122	7.45	17.10	2.25	1.68	9.70
	Ellyes Skhiri	2674	7.16	17.57	2.28	2.94	10.10
	Charles Aránguiz	2125	7.14	20.54	2.47	1.47	13.61
	Josuha Guilavogui	2063	7.02	17.98	1.59	2.65	10.67
	Denis Zakaria	1947	7.02	16.59	2.07	2.48	11.91
	Robert Andrich	2579	6.81	18.24	1.26	2.19	8.82

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
	Thomas Müller	2259	4.21	10.93	1.54	0.68	6.26
	Marco Reus	1501	3.15	9.17	1.44	0.81	4.67
	Serge Gnabry	2202	2.76	8.80	0.90	0.90	5.02
	Mark Uth	1519	2.39	7.28	0.84	0.48	5.13
	Karim Bellarabi	1423	2.33	6.66	0.69	0.63	4.21
	Lucas Höler	2489	2.15	7.20	0.67	0.54	4.97
	Ihlas Bebou	2094	2.11	6.93	0.70	0.52	4.64
	Kingsley Coman	1507	2.10	9.58	0.77	0.28	6.09
	Marcus Thuram	2216	2.08	7.15	0.91	0.61	4.97
	Marius Bülter	2330	1.96	7.93	0.90	0.45	5.48

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. David Alaba is one major stand-out, having been involved in 114 shot-ending sequences from central defence.

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
	Erling Haaland	1062	7.04	4.35	19.63	19	11.74	53
	Robert Lewandowski	2762	4.57	3.78	20.10	45	37.24	198
	Marco Reus	1501	3.27	2.44	14.73	20	14.90	90
	Kevin Volland	2050	3.20	3.02	18.75	22	20.78	129
	Jadon Sancho	2291	3.16	2.13	12.25	41	27.62	159
	Vedad Ibisevic	1256	3.11	2.74	16.96	9	7.93	49
	Thomas Müller	2259	3.08	2.80	15.73	39	35.43	199
	Timo Werner	2807	2.99	2.81	18.29	35	32.86	214
	Ivan Perisic	1202	2.96	2.21	13.49	18	13.45	82
	Thorgan Hazard	2388	2.90	1.91	12.51	32	21.04	138

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
	Ivan Perisic	1202	1.99	1.25	5.05	11	6.90	28
	Robert Lewandowski	2762	1.80	0.86	5.87	15	7.22	49
	Erling Haaland	1062	1.75	0.81	4.82	4	1.84	11
	Julian Brandt	2208	1.61	1.00	5.22	20	12.46	65
	Per Ciljan Skjelbred	1636	1.32	0.86	5.59	8	5.22	34
	Christoph Kramer	1464	1.32	0.96	5.57	9	6.55	38
	Jonas Hofmann	1632	1.31	1.04	5.82	9	7.15	40
	Thorgan Hazard	2388	1.28	0.91	4.93	13	9.27	50
	Manuel Akanji	2309	1.24	0.78	5.28	15	9.46	64
	David Alaba	2498	1.14	1.09	6.83	19	18.23	114

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.