Season Review 2019/20

Welcome

Welcome to Stats Perform’s EFL Sky Bet Championship season review for the 2019/20 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.

• Champions Leeds United’s ability to generate high quality chances from open play was demonstrated by amassing an xG output of 67.3 from 574 shots.

• Leeds exceeded the xG output of Brentford by over 13, however the Bees ended the campaign as the league’s highest scorers from open play as Marcelo Bielsa’s men netted 14 fewer goals than we would have perhaps expected based on the quality of their shot locations.

• 39% of Millwall’s xG output came from set pieces, the highest ratio in the league. Brentford sit at the other end of the spectrum, having generated 83% of their xG output from open play.

• Despite creating most of their shots from open play, Brentford were one of three sides to overperform in front of goal from set pieces. Together with Cardiff, the league’s top scorers, and Blackburn Rovers, Brentford scored at least two more goals than expected from set play situations. Eight teams underperformed by at least two goals.

Expected Goals For

		Set Play : Total	Open Play			Set Play
	Team	xG Ratio	Shots	xG	Goals	SP Shots	SP xG	SP Goals
	Leeds United	0.19	574	67.3	53	175	16.4	12
	West Bromwich Albion	0.22	520	53.8	49	149	17.1	18
	Brentford	0.17	507	54.3	57	153	12.5	16
	Fulham	0.20	482	50.3	49	136	13.5	10
	Cardiff City	0.36	370	34.0	40	206	20.6	23
	Swansea City	0.24	428	46.2	39	142	16.3	15
	Nottingham Forest	0.30	420	35.9	37	165	17.2	16
	Millwall	0.39	337	31.9	32	188	23.2	20
	Preston North End	0.25	352	35.7	36	180	15.0	13
	Derby County	0.23	362	38.0	42	142	13.7	12
	Blackburn Rovers	0.26	365	36.5	40	159	13.9	18
	Bristol City	0.26	333	44.5	38	130	16.3	17
	Queens Park Rangers	0.23	376	46.7	49	172	14.8	13
	Reading	0.28	378	35.8	35	160	16.2	16
	Stoke City	0.30	398	41.1	36	149	18.8	20
	Sheffield Wednesday	0.25	431	44.1	33	168	17.0	17
	Middlesbrough	0.26	403	38.3	31	163	15.0	12
	Huddersfield Town	0.23	348	37.3	34	127	13.1	10
	Luton Town	0.27	270	30.7	28	150	13.5	15
	Birmingham City	0.34	394	39.4	31	185	21.1	19
	Barnsley	0.25	484	43.2	37	153	15.0	9
	Charlton Athletic	0.27	290	35.6	31	133	14.5	14
	Wigan Athletic	0.32	365	35.1	37	170	17.8	14
	Hull City	0.24	415	46.0	40	140	15.1	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.

Expected Goals (Against)

Overview

Key Points:

• None of the league’s top six clubs conceded more goals from open play than their xG total would suggest. At the other end of the table, Hull City conceded nearly twelve goals more than they would have perhaps expected based on the quality of the chances created by their opponents during the campaign.

• Wigan Athletic’s xG conceded from set pieces was the highest in the league. The Latics conceded 20 set piece goals, however three other teams surpassed that total during the campaign: Birmingham City, Huddersfield Town and QPR.

• Over half of all Championship teams conceded fewer set piece goals compared to the quality of the shots they gave away, with Swansea being one of the league’s standouts. Along with Blackburn Rovers, the Swans conceded a league-low eight set piece goals, however based on their xG conceded, they could have potentially conceded nearly 50% more goals from such situations.

Expected Goals Against

		Set Play : Total	Open Play			Set Play
	Team	xG Ratio	Shots	xG	Goals	SP Shots	SP xG	SP Goals
	Leeds United	0.38	261	21.9	19	149	15.2	12
	West Bromwich Albion	0.33	312	31.1	21	165	17.3	16
	Brentford	0.21	301	27.5	22	110	7.8	12
	Fulham	0.24	371	41.4	34	145	14.1	10
	Cardiff City	0.22	453	43.0	35	152	13.3	17
	Swansea City	0.25	451	44.7	41	156	16.4	8
	Nottingham Forest	0.33	417	38.1	28	169	19.9	15
	Millwall	0.23	402	38.9	32	139	12.7	15
	Preston North End	0.32	347	32.7	33	176	17.5	14
	Derby County	0.22	453	46.3	37	150	15.5	16
	Blackburn Rovers	0.19	442	41.0	43	129	11.8	8
	Bristol City	0.23	511	52.8	42	172	16.4	16
	Queens Park Rangers	0.28	369	42.8	46	176	19.7	21
	Reading	0.24	488	48.3	39	189	16.0	15
	Stoke City	0.22	324	41.1	47	122	12.6	14
	Sheffield Wednesday	0.29	307	37.4	42	159	17.9	14
	Middlesbrough	0.24	414	47.5	41	166	16.0	13
	Huddersfield Town	0.31	413	41.0	43	159	19.1	24
	Luton Town	0.20	507	58.2	63	172	16.4	11
	Birmingham City	0.31	352	37.8	42	163	20.8	25
	Barnsley	0.29	345	45.9	48	181	19.8	15
	Charlton Athletic	0.19	548	57.7	44	158	15.1	15
	Wigan Athletic	0.35	375	38.0	34	186	20.9	20
	Hull City	0.23	439	46.5	58	152	15.6	18

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.

• In relation to longer sequences, Fulham recorded the longest sequence time and the most passes per sequence, as well as ranking second lowest for direct speed, moving the ball forward on average 1.3 m/s. Scott Parker’s team also created more attacks derived from build-up sequences than any other team (137).

• Another of the Championship’s play-off participants, Cardiff City, were successful by adopting a less possession-orientated approach, with the lowest average sequence time and passes per sequence in the competition.

• Leeds and Brentford led the Championship in relation to attacks resulting from direct sequences. Bottom side Hull City also scored highly in this metric, which perhaps would be expected given they recorded the highest direct speed in the competition (1.96 m/s).

Open Play Sequences

		Sequence Summaries			Sequence Styles
	Team	Sequence Time	Passes Per Sequence	Direct Speed	10+ Pass OP Sequences	Build Up Attacks	Direct Attacks
	Leeds United	8.90	3.34	1.65	395	83	112
	West Bromwich Albion	8.15	3.13	1.59	345	71	72
	Brentford	9.17	3.39	1.61	439	101	112
	Fulham	10.16	3.84	1.30	588	137	77
	Cardiff City	5.67	2.17	1.67	88	21	58
	Swansea City	8.25	3.20	1.60	360	65	75
	Nottingham Forest	7.51	2.72	1.73	252	43	77
	Millwall	6.05	2.36	1.77	123	17	42
	Preston North End	6.80	2.70	1.41	251	43	54
	Derby County	9.13	3.38	1.52	449	79	48
	Blackburn Rovers	8.03	3.00	1.27	285	49	67
	Bristol City	7.28	2.71	1.56	229	30	57
	Queens Park Rangers	8.56	3.13	1.57	330	61	76
	Reading	7.52	2.76	1.63	261	44	58
	Stoke City	7.03	2.66	1.33	207	32	49
	Sheffield Wednesday	6.63	2.49	1.63	170	32	83
	Middlesbrough	6.28	2.33	1.69	143	25	75
	Huddersfield Town	7.65	2.95	1.49	293	44	51
	Luton Town	7.13	2.59	1.43	207	29	57
	Birmingham City	6.20	2.38	1.92	144	24	64
	Barnsley	5.60	2.36	1.86	107	23	85
	Charlton Athletic	6.98	2.64	1.43	204	40	49
	Wigan Athletic	7.07	2.62	1.49	221	46	68
	Hull City	6.31	2.42	1.96	120	12	94

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.

• Whilst it will come as no surprise that Leeds ranked first in PPDA given their renowned high-intensity approach, it is noteworthy that Barnsley, who ranked second in this metric, completed more high turnovers and recorded the most shots as a result of high turnovers than any other Championship team.

• Millwall and Reading were two teams who based on PPDA, were quite content to allow their opposition to have the ball.

• Despite ranking in the bottom half of the competition for shots resulting from high turnovers, Bristol City scored more goals from these situations than any other team. Nottingham Forest were also a notable outlier, scoring four goals from high turnovers despite ranking 24th in both the number of high turnovers (116) and shots from high turnovers (19).

Sequence Pressure Stats

			High Turnovers
	Team	PPDA	Total	Shot Ending	Goal Ending
	Leeds United	8.0	189	36	3
	West Bromwich Albion	12.0	187	48	3
	Brentford	11.3	160	31	0
	Fulham	10.1	188	39	5
	Cardiff City	13.4	166	30	5
	Swansea City	12.3	148	27	2
	Nottingham Forest	14.1	116	19	4
	Millwall	15.6	164	30	1
	Preston North End	11.1	175	27	2
	Derby County	12.1	138	33	4
	Blackburn Rovers	11.8	165	28	4
	Bristol City	12.5	139	29	6
	Queens Park Rangers	11.9	177	28	4
	Reading	15.5	132	32	2
	Stoke City	12.8	178	42	2
	Sheffield Wednesday	12.0	179	46	5
	Middlesbrough	11.7	142	30	3
	Huddersfield Town	11.0	166	34	3
	Luton Town	13.2	153	28	4
	Birmingham City	12.8	161	39	3
	Barnsley	9.4	205	49	3
	Charlton Athletic	12.8	134	31	2
	Wigan Athletic	11.2	146	25	1
	Hull City	13.3	174	38	1

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.

• Brentford recorded the highest proportion of goal kicks which ended inside their own box (50%), a tactic which resulted in them gaining more territory compared to when they went long. Brentford averaged over 47 metres ball progression when they went short, compared to 42.8 metres when a goal kick cleared their own box.

• Fulham were another team who utilised short goal kicks on more than one in three occasions and were notably more successful with this approach, averaging over 13 more metres in ball progression compared to when they went long.

• Although only one team passed into their own box from over 40% of their goal kicks, only two teams (excluding Cardiff, who didn’t attempt a single short goal kick during the season), gained more territory by going long: Millwall and Bristol City.

• 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)
	Leeds United	129	196	39.7	51.2	43.1
	West Bromwich Albion	94	248	27.5	51.7	48.7
	Brentford	167	167	50.0	47.2	42.8
	Fulham	130	230	36.1	55.0	41.8
	Cardiff City	0	398	0.0	0.0	42.7
	Swansea City	137	254	35.0	49.0	41.4
	Nottingham Forest	54	364	12.9	43.1	42.7
	Millwall	13	360	3.5	41.8	46.5
	Preston North End	49	340	12.6	47.0	43.8
	Derby County	134	271	33.1	47.6	42.6
	Blackburn Rovers	37	358	9.4	56.5	45.4
	Bristol City	42	370	10.2	45.0	47.0
	Queens Park Rangers	94	289	24.5	52.8	44.5
	Reading	111	335	24.9	44.0	38.4
	Stoke City	34	302	10.1	51.5	46.1
	Sheffield Wednesday	7	370	1.9	58.6	46.1
	Middlesbrough	20	359	5.3	55.0	41.1
	Huddersfield Town	51	341	13.0	46.9	41.0
	Luton Town	53	390	12.0	50.5	43.0
	Birmingham City	35	365	8.8	50.8	45.5
	Barnsley	95	268	26.2	44.4	38.0
	Charlton Athletic	92	375	19.7	50.0	44.3
	Wigan Athletic	36	366	9.0	50.7	46.8
	Hull City	73	341	17.6	47.8	44.9

Graphic

Leeds United

West Bromwich Albion

Brentford

Fulham

Cardiff City

Swansea City

Nottingham Forest

Millwall

Preston North End

Derby County

Blackburn Rovers

Bristol City

Queens Park Rangers

Reading

Stoke City

Sheffield Wednesday

Middlesbrough

Huddersfield Town

Luton Town

Birmingham City

Barnsley

Charlton Athletic

Wigan Athletic

Hull City

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.

• Charlie Austin only played a full 90 minutes on two occasions during the season, however the West Brom forward posted the highest xG per 90 output in the Championship. Although he was unable to match his xG in terms of actual goals, his xGOT output matched his xG which indicates he was somewhat unfortunate not to have scored more.

• Six of the league’s top ten for xG per 90 exceeded their output with actual goals. Of these players, Charlton’s Lyle Taylor exceeded his projection by the highest magnitude (0.07), and given the increase in his xGOT compared to xG, it indicates his ability to find high quality locations was also matched by the quality of his finishing.

• Of the players in the top 10, Ollie Watkins featured for the highest number of minutes during the campaign. Operating in a more central role compared to the previous season, the Brentford man demonstrated versatility with his finishing, with a similar proportion of goals being scored with his head and either foot.

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
	Charlie Austin	1510	0.79	0.79	0.60	10%	0%	90%	0%	80%
	Patrick Bamford	3460	0.72	0.54	0.42	19%	81%	0%	0%	100%
	Aleksandar Mitrovic	3590	0.64	0.57	0.65	23%	0%	77%	0%	96%
	Matt Smith	1931	0.59	0.45	0.61	54%	8%	31%	8%	100%
	Steve Mounie	1414	0.57	0.50	0.51	38%	0%	50%	13%	88%
	Lyle Taylor	1603	0.55	0.61	0.62	9%	0%	91%	0%	91%
	Jarrod Bowen	2610	0.53	0.56	0.55	6%	44%	50%	0%	88%
	Ollie Watkins	4130	0.52	0.46	0.54	32%	28%	36%	4%	96%
	Scott Hogan	1709	0.52	0.43	0.53	10%	30%	50%	10%	100%
	Famara Diédhiou	2891	0.52	0.42	0.37	33%	33%	33%	0%	100%

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 as well as xG per 90, Charlie Austin demonstrated an ability to get into good scoring locations inside the box. One in three of the 31-year-old’s attempts came from high clarity and low pressure situations.

• Over half of Matt Smith’s goals came from headers and we can establish that over three-quarters of his shots occurred when under high pressure, meaning that opposition players were within tackling distance when he was looking to shoot.

• Leeds’ Patrick Bamford demonstrated a tendency not to shoot if the path to goal was blocked, demonstrated by his low proportion of attempts from low clarity situations. He also recorded the highest proportion of shots of the players listed where he had a clear path to goal.

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
	Charlie Austin	0.25	22.2	44.4	33.3	1.9	33.3	57.4	7.4
	Patrick Bamford	0.19	35.0	39.2	25.9	2.1	33.6	60.8	3.5
	Aleksandar Mitrovic	0.17	56.3	19.9	23.8	2.0	22.5	59.6	15.9
	Matt Smith	0.17	76.3	15.8	7.9	3.9	11.8	73.7	10.5
	Steve Mounie	0.21	47.6	31.0	21.4	2.4	23.8	57.1	16.7
	Lyle Taylor	0.21	26.1	43.5	30.4	0.0	28.3	65.2	6.5
	Jarrod Bowen	0.15	30.1	43.7	26.2	1.0	17.5	74.8	6.8
	Ollie Watkins	0.19	34.4	47.2	18.4	2.4	20.0	70.4	7.2
	Scott Hogan	0.23	50.0	25.0	25.0	2.3	27.3	63.6	6.8
	Famara Diédhiou	0.21	43.8	36.2	20.0	2.5	21.2	70.0	6.2

Graphic

Charlie Austin

Patrick Bamford

Aleksandar Mitrovic

Matt Smith

Steve Mounie

Lyle Taylor

Jarrod Bowen

Ollie Watkins

Scott Hogan

Famara Diédhiou

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.

• Saïd Benrahma’s all-round contribution to Brentford during the season is reflected in his xA, where he ranked top in the Championship as well as second in the chances created per 90 metric of the players listed here.

• Birmingham’s Daniel Crowley performance is also noteworthy. Despite only contributing 0.07 assists per 90, his xA of 0.25 suggests the quality of his passing in attacking areas could have resulted in more goals.

• Selected on the bench for the majority of the season, Bristol City’s Niclas Eliasson had a notable impact when coming on. The Swede created over eight chances per 100 passes, a number considerably higher than any other player on this list.

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
	Saïd Benrahma	3547	0.28	0.20	1.83	4.68
	Grady Diangana	1867	0.27	0.24	1.69	5.07
	Pablo Hernández	2502	0.26	0.25	2.30	3.49
	Daniel Crowley	2508	0.25	0.07	1.61	4.14
	Niclas Eliasson	1994	0.24	0.27	1.40	8.56
	Ivan Cavaleiro	3039	0.24	0.18	1.54	4.53
	Emiliano Marcondes	1251	0.22	0.43	0.79	2.03
	Kamil Grosicki	2871	0.22	0.19	1.25	4.75
	Alfie Doughty	1968	0.20	0.14	0.96	3.31
	Anthony Knockaert	2845	0.20	0.09	1.17	3.02

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
	Jack Hunt	2500	0.15	0.18	0.65	1.67
	Tommy Smith	2511	0.15	0.14	0.86	2.01
	Joe Bryan	3415	0.13	0.13	0.79	1.62
	Darnell Fisher	2286	0.13	0.12	0.47	1.37
	Luke Ayling	3176	0.12	0.11	0.65	1.10
	Cyrus Christie	1431	0.12	0.06	0.94	2.24
	Harry Toffolo	1710	0.12	0.05	0.95	2.32
	Darnell Furlong	2239	0.12	0.04	0.92	2.63
	Pedro Pereira	1253	0.12	0.00	0.86	2.57
	Angel Rangel	1663	0.12	0.00	0.38	0.75

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
	Grady Diangana	1867	0.27	0.24	1.69	5.07
	Pablo Hernández	2502	0.26	0.25	2.30	3.49
	Daniel Crowley	2508	0.25	0.07	1.61	4.14
	Niclas Eliasson	1994	0.24	0.27	1.40	8.56
	Emiliano Marcondes	1251	0.22	0.43	0.79	2.03
	Kamil Grosicki	2871	0.22	0.19	1.25	4.75
	Alfie Doughty	1968	0.20	0.14	0.96	3.31
	Anthony Knockaert	2845	0.20	0.09	1.17	3.02
	Marvin Johnson	2618	0.19	0.07	1.27	4.75
	Jack Harrison	3805	0.18	0.17	1.73	5.48

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
	Saïd Benrahma	3547	0.28	0.20	1.83	4.68
	Ivan Cavaleiro	3039	0.24	0.18	1.54	4.53
	Jérémie Bela	1973	0.19	0.27	0.64	2.75
	Hélder Costa	2954	0.14	0.12	1.31	4.57
	Joe Lolley	3228	0.14	0.11	1.17	3.82
	Andreas Weimann	3607	0.13	0.15	0.75	3.34
	Jarrod Bowen	2610	0.12	0.14	0.86	4.08
	Tyrese Campbell	1616	0.12	0.11	0.67	3.35
	Lee Tomlin	2168	0.11	0.37	1.12	4.07
	Ashley Fletcher	3239	0.11	0.19	0.92	5.13

Graphic

Saïd Benrahma

Grady Diangana

Pablo Hernández

Daniel Crowley

Niclas Eliasson

Ivan Cavaleiro

Emiliano Marcondes

Kamil Grosicki

Alfie Doughty

Anthony Knockaert

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

• Saïd Benrahma again tops the list, seeing 20% more chances being created following one of his carries than any other player listed here. The Brentford winger contributed the most assists following a carry (6), a number matched by West Brom’s Matheus Pereira.

• Blackburn’s Adam Armstrong was the most prolific player in terms of scoring, netting on six occasions following a carry.

• Clicking on the ‘Graphics’ 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
	Saïd Benrahma	3547	21	11.49	56	34	6	3	90
	Joe Lolley	3228	17	12.48	46	29	3	5	75
	Kadeem Harris	3389	15	14.67	41	33	3	2	74
	Kamil Grosicki	2871	14	13.98	40	22	1	2	62
	Matheus Pereira	3374	15	10.44	28	32	6	2	60
	Adam Armstrong	3481	10	12.55	45	13	0	6	58
	Bersant Celina	2477	16	12.74	35	21	1	2	56
	Anthony Knockaert	2845	15	11.15	27	26	2	0	53
	Jack Harrison	3805	14	11.84	20	33	4	0	53
	André Ayew	3908	11	12.04	36	17	4	1	53

Graphic

Saïd Benrahma

Joe Lolley

Kadeem Harris

Kamil Grosicki

Matheus Pereira

Adam Armstrong

Bersant Celina

Anthony Knockaert

Jack Harrison

André Ayew

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, Charlton’s Dillion Phillips enjoyed a strong season as the goalkeeper who conceded the most shots and the highest xGOT. The average Championship goalkeeper would have been expected to concede nearly 7 more goals based on the quality of shots faced by the Addicks stopper, who was an ever-present between the sticks.

• The ‘goals prevented rate’ metric can account for different keepers facing a different number of shots throughout the season. Starting the season as Fulham’s second choice, Marek Rodák claimed the keeper’s jersey in October and went on to prevent more than six goals compared to what we would expect from the xGOT his team conceded.

Most Featured Goalkeepers Per Team

Team	Player	% Of Team Mins	Goals Prevented Rate	Goals Prevented	xGOT Conceded	Goals Conceded	Shots Faced
	Marek Rodák	66%	1.21	6.2	36.2	30	127
	Rafael Cabral	96%	1.12	6.1	55.1	49	195
	Sam Johnstone	100%	1.12	4.3	41.3	37	147
	Dillon Phillips	100%	1.12	6.8	65.8	59	213
	Bartosz Bialkowski	99%	1.11	4.9	51.9	47	185
	Brice Samba	87%	1.11	3.7	38.7	35	143
	Alex Smithies	63%	1.03	0.9	27.9	27	93
	Kiko Casilla	78%	1.01	0.3	27.3	27	98
	Daniel Bentley	93%	1.01	0.6	54.6	54	184
	Christian Walton	100%	0.99	-0.6	50.4	51	173
	Aynsley Pears	52%	0.99	-0.1	27.9	28	91
	Simon Sluga	72%	0.98	-1.1	44.9	46	131
	Ben Hamer	54%	0.95	-1.4	26.6	28	91
	David Raya Martin	94%	0.95	-1.5	32.5	34	133
	Declan Rudd	100%	0.94	-2.8	44.2	47	156
	Freddie Woodman	90%	0.92	-3.6	43.4	47	154
	Cameron Dawson	50%	0.89	-3.3	26.7	30	80
	David Marshall	85%	0.88	-5.7	40.3	46	132
	Lee Camp	78%	0.88	-5.6	41.4	47	124
	Bradley Collins	41%	0.88	-3.2	23.8	27	82
	Jack Butland	76%	0.84	-8.1	41.9	50	124
	Joe Lumley	59%	0.84	-6.7	35.3	42	109
	George Long	98%	0.77	-17.3	57.7	75	191
	Kamil Grabara	61%	0.77	-9.8	32.2	42	103

Graphic

Marek Rodák

Rafael Cabral

Sam Johnstone

Dillon Phillips

Bartosz Bialkowski

Brice Samba

Alex Smithies

Kiko Casilla

Daniel Bentley

Christian Walton

Aynsley Pears

Simon Sluga

Ben Hamer

David Raya Martin

Declan Rudd

Freddie Woodman

Cameron Dawson

David Marshall

Lee Camp

Bradley Collins

Jack Butland

Joe Lumley

George Long

Kamil Grabara

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 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 midfield position some familiar names appear, but what is notable is the significant influence of Kalvin Phillips in Leeds’ central midfield. The 24-year-old initiated substantially more sequences than any other midfielder listed, as well as demonstrating defensive tenacity by topping each of the tackles, interceptions and recovery metrics.

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
	Liam Cooper	3127	12.65	29.19	1.49	3.68	13.08
	Yoann Barbet	2422	12.00	23.17	1.14	3.94	9.59
	Mads Andersen	3270	11.70	25.07	1.03	4.19	10.34
	Kalvin Phillips	3254	11.59	31.44	3.39	2.94	15.89
	Darragh Lenihan	3307	11.51	23.93	0.90	2.00	8.80
	Michael Sollbauer	1530	11.49	25.12	1.83	3.46	9.86
	Ben White	4140	11.30	25.86	2.08	5.33	13.61
	Christian Nørgaard	3468	11.26	26.41	3.01	3.14	11.13
	Michael Hector	1800	10.95	26.44	1.98	1.23	11.52
	Jordy de Wijs	3040	9.60	19.07	1.30	2.82	7.21

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
	Liam Cooper	3127	12.65	29.19	1.49	3.68	13.08
	Yoann Barbet	2422	12.00	23.17	1.14	3.94	9.59
	Mads Andersen	3270	11.70	25.07	1.03	4.19	10.34
	Darragh Lenihan	3307	11.51	23.93	0.90	2.00	8.80
	Michael Sollbauer	1530	11.49	25.12	1.83	3.46	9.86
	Ben White	4140	11.30	25.86	2.08	5.33	13.61
	Michael Hector	1800	10.95	26.44	1.98	1.23	11.52
	Jordy de Wijs	3040	9.60	19.07	1.30	2.82	7.21
	Patrick Bauer	3617	8.99	17.30	1.53	1.92	4.94
	Shaun Hutchinson	3240	8.98	18.23	1.22	2.77	5.70

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
	Kalvin Phillips	3254	11.59	31.44	3.39	2.94	15.89
	Christian Nørgaard	3468	11.26	26.41	3.01	3.14	11.13
	Matt Grimes	4140	8.94	23.75	2.74	2.52	13.26
	Harrison Reed	1824	8.86	23.01	1.67	2.61	9.73
	Sam Hutchinson	1796	8.61	18.11	2.60	2.46	7.24
	Ben Pearson	3329	8.44	25.93	2.17	2.45	14.29
	Alex Mowatt	3916	8.12	24.09	2.25	1.63	13.44
	Jonathan Hogg	3022	8.05	20.67	2.61	1.74	11.07
	Ben Watson	4000	7.93	19.55	2.39	2.67	10.00
	Jake Livermore	3745	7.80	21.46	2.57	1.25	11.82

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
	Tom Lawrence	3223	4.03	10.95	1.45	1.27	6.65
	Bobby De Cordova-Reid	2936	3.39	10.25	1.44	0.65	7.01
	Jacob Brown	3344	3.38	9.64	1.35	0.80	6.09
	Ivan Cavaleiro	3039	3.35	11.30	1.28	1.24	7.63
	Joe Lolley	3228	3.23	10.41	1.58	0.86	5.92
	Jamie Paterson	1858	3.15	8.74	1.02	1.02	5.63
	Wayne Rooney	1739	3.14	14.92	0.85	0.76	10.76
	André Ayew	3908	3.13	10.51	1.69	1.02	7.12
	Cauley Woodrow	3289	3.09	11.00	0.63	0.47	7.79
	Jamal Lowe	3465	3.06	10.07	1.84	0.81	7.08

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. Wigan’s Sam Morsy is one stand-out, having been involved in 14 goal ending sequences from a deeper lying position.

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
	Scott Hogan	1709	4.18	3.57	19.51	12	10.25	56
	Lucas João	1199	3.05	1.42	14.25	12	5.59	56
	Ollie Watkins	4130	2.81	2.66	18.59	39	36.93	258
	Nahki Wells	2808	2.81	2.50	14.06	25	22.21	125
	Rhian Brewster	1671	2.72	2.23	15.48	13	10.64	74
	Chris Martin	2093	2.68	1.66	11.55	19	11.79	82
	Macauley Bonne	2352	2.58	2.57	14.36	14	13.94	78
	Britt Assombalonga	2510	2.54	2.59	17.81	13	13.22	91
	Lewis Grabban	3912	2.37	2.54	19.87	18	19.29	151
	Sam Baldock	1103	2.33	2.65	17.51	6	6.80	45

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	Nineties	Goal Ending p100	Total xG p100	Shot Ending p100	Goal Ending	Total xG	Shot Ending
	Marcus Bettinelli	13.8	1.04	0.59	5.99	4	2.25	23
	Max Bird	21.2	1.04	0.43	4.52	9	3.72	39
	Luke Amos	23.5	0.97	0.71	4.25	8	5.83	35
	Ben Cabango	19.7	0.93	0.25	4.32	6	1.59	28
	Cheyenne Dunkley	22.2	0.93	0.47	3.26	4	2.00	14
	Ollie Watkins	45.9	0.92	0.60	5.29	11	7.10	63
	Sam Morsy	43.0	0.88	0.41	4.64	14	6.56	74
	Ben Wilmot	16.5	0.86	0.63	3.08	5	3.70	18
	Fran Villalba	13.5	0.84	0.66	4.18	4	3.16	20
	Wayne Rooney	19.3	0.84	0.41	4.44	7	3.42	37

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.