Season Review 2019/20

Welcome

Welcome to Stats Perform’s English Football League - League One 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.

• Neither of the teams that secured automatic promotion to the Championship ranked in the league’s top eight for open play xG. Fifth-placed Portsmouth generated the highest open play xG output, 39.2 from 350 shots, however they scored four fewer goals than their projection.

• Peterborough’s players proved to be clinical in front of goal, scoring 16 more open play goals than they would have perhaps expected based on the quality of chances they created. The Posh scored 52 open play goals, more than any other team.

• Set pieces played a key role in Rotherham’s promotion. 43% of the Millers’ xG came from set pieces, the highest ratio in the league, which resulted in 25 goals. In contrast, champions Coventry generated 81% of their xG from open play.

• Wycombe secured promotion to the second tier of English football for the first time via the play-offs, despite recording the lowest number of shots and xG output from open play in the league. They were another team to profit from set pieces, ranking second for both shots and goals scored from set plays.

• Over a third of the teams in League One underperformed 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
	Coventry City	0.19	325	31.7	37	116	8.4	7
	Rotherham United	0.43	301	28.7	29	190	24.5	25
	Wycombe Wanderers	0.33	247	22.0	18	184	14.8	15
	Oxford United	0.25	355	36.8	47	146	13.0	7
	Portsmouth	0.27	350	39.2	35	148	16.4	12
	Fleetwood Town	0.24	317	38.0	36	121	12.2	11
	Peterborough United	0.20	338	35.9	52	134	10.7	10
	Sunderland	0.27	301	29.4	30	132	12.8	11
	Doncaster Rovers	0.22	376	36.4	36	109	10.4	10
	Gillingham	0.38	265	25.8	23	173	19.1	12
	Ipswich Town	0.27	293	35.1	30	139	14.5	11
	Burton Albion	0.20	360	35.3	41	104	9.7	6
	Blackpool	0.26	260	26.6	27	119	11.1	8
	Bristol Rovers	0.25	289	25.0	22	113	10.1	9
	Shrewsbury Town	0.28	293	24.5	17	112	10.2	11
	Lincoln City	0.23	255	27.4	28	121	9.6	11
	Accrington Stanley	0.26	355	35.1	29	127	14.5	9
	Rochdale	0.18	282	27.7	30	72	6.3	7
	MK Dons	0.19	302	30.3	28	103	7.9	6
	AFC Wimbledon	0.23	256	25.9	29	111	8.9	5
	Tranmere Rovers	0.31	268	22.6	22	121	11.1	12
	Southend United	0.26	267	25.7	23	136	10.2	9
	Bolton Wanderers	0.23	270	23.2	19	90	7.0	8

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:

• Peterborough conceded the lowest xG from open play during the season. Only Doncaster conceded fewer open play goals than Darren Ferguson’s men, however based on the quality of the chances they conceded, Doncaster could have potentially conceded up to 30% more goals.

• AFC Wimbledon’s xG conceded from set pieces was the highest in the league. However the Dons conceded four fewer goals than Tranmere Rovers, who conceded the most of any team in League One during the season.

• Joey Barton’s Fleetwood Town ranked second in both open play and set piece xG conceded, giving them the lowest collective xG in the league. Whilst they conceded roughly in line with what we would expect from open play, they did let in three more goals than projected 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
	Coventry City	0.25	218	23.6	21	113	9.3	3
	Rotherham United	0.27	221	24.8	25	106	10.8	8
	Wycombe Wanderers	0.31	290	27.3	25	129	14.1	9
	Oxford United	0.25	246	23.2	23	105	9.0	10
	Portsmouth	0.20	280	26.2	23	103	7.5	8
	Fleetwood Town	0.23	259	22.2	23	96	7.9	11
	Peterborough United	0.27	290	21.8	19	118	9.2	13
	Sunderland	0.26	232	26.1	20	123	10.0	10
	Doncaster Rovers	0.23	280	26.2	18	120	8.8	9
	Gillingham	0.21	265	27.6	27	104	8.2	5
	Ipswich Town	0.25	248	26.3	27	121	10.3	5
	Burton Albion	0.27	324	33.9	35	118	13.2	11
	Blackpool	0.24	313	29.3	30	126	10.4	8
	Bristol Rovers	0.19	343	35.9	40	123	9.0	5
	Shrewsbury Town	0.29	268	27.9	26	138	13.2	9
	Lincoln City	0.26	332	24.3	30	116	9.9	10
	Accrington Stanley	0.28	308	31.6	36	131	13.5	12
	Rochdale	0.27	395	37.7	38	142	14.9	12
	MK Dons	0.29	383	35.3	34	158	16.2	11
	AFC Wimbledon	0.29	343	41.8	34	169	17.6	14
	Tranmere Rovers	0.29	323	33.1	33	151	16.3	18
	Southend United	0.27	359	38.7	56	150	16.9	17
	Bolton Wanderers	0.26	405	43.6	45	161	17.1	14

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.

• It is noteworthy that two of the three promoted teams, Rotherham and Wycombe, were successful through adopting a more direct approach. Both teams progressed the ball forward over two metres per second on average during a possession sequence, as well as being two of four teams averaging fewer than two passes per sequence. Wycombe also recorded the shortest sequence time, 4.4 seconds.

• At the other end of the spectrum, two teams in the bottom half of the league, Rochdale and MK Dons, ranked first and second respectively for the longest sequence time and the most passes per sequence. Rochdale also recorded a league low direct speed, moving the ball forward on average 1.13 metres per second.

• Doncaster were another team who adopted a more possession-based approach, however they looked to move the ball forward at a faster tempo. Darren Moore’s side recorded more attacks derived from build-up sequences than any other side in the league.

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
	Coventry City	7.07	2.91	1.75	177	40	40
	Rotherham United	5.03	1.96	2.07	34	7	52
	Wycombe Wanderers	4.41	1.73	2.16	12	4	42
	Oxford United	7.60	2.98	1.78	258	51	73
	Portsmouth	5.36	2.27	1.85	91	15	64
	Fleetwood Town	6.80	2.70	1.43	202	34	52
	Peterborough United	6.54	2.48	1.51	158	27	61
	Sunderland	7.11	2.69	1.50	174	41	45
	Doncaster Rovers	7.25	2.96	1.73	197	64	58
	Gillingham	4.43	1.79	2.15	18	3	38
	Ipswich Town	5.91	2.50	1.30	176	30	65
	Burton Albion	6.21	2.52	1.97	115	24	58
	Blackpool	6.02	2.33	1.63	97	20	45
	Bristol Rovers	5.37	2.14	1.78	54	7	41
	Shrewsbury Town	5.77	2.24	1.60	88	15	57
	Lincoln City	6.70	2.59	1.68	128	29	46
	Accrington Stanley	5.49	2.18	1.95	52	9	40
	Rochdale	8.98	3.18	1.13	236	48	41
	MK Dons	8.43	3.03	1.62	212	41	55
	AFC Wimbledon	4.92	2.00	1.68	32	6	28
	Tranmere Rovers	4.95	1.98	1.64	37	13	54
	Southend United	5.70	2.33	1.40	92	12	32
	Bolton Wanderers	6.25	2.48	1.19	108	16	42

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.

• Rotherham’s aggressive approach to pressing is reflected in their PPDA score of 9.1, the best output in League One. The Millers committed more high turnovers than any other team, recording 158 over the shortened campaign.

• Coventry proved to be a team adept at generating goal scoring opportunities having won the ball back high up the pitch. The Sky Blues posted 35 shots following a high turnover, the joint highest in the league, scoring on six occasions. However the champions could not match the goal scoring output of Peterborough, who scored nine times.

• Rochdale were one team who did not adopt a pressing approach, allowing their opponents plenty of the ball and committing the lowest number of high turnovers in the league. However unlike Gillingham, Brian Barry-Murphy’s men did manage to score at least once from a turnover within 40 metres of the opposition goal.

Sequence Pressure Stats

			High Turnovers
	Team	PPDA	Total	Shot Ending	Goal Ending
	Coventry City	12.1	124	35	6
	Rotherham United	9.1	158	18	3
	Wycombe Wanderers	12.2	116	19	2
	Oxford United	9.6	157	32	6
	Portsmouth	10.1	146	35	2
	Fleetwood Town	15.0	124	20	2
	Peterborough United	11.6	138	31	9
	Sunderland	11.7	105	22	2
	Doncaster Rovers	12.1	127	35	2
	Gillingham	11.4	146	22	0
	Ipswich Town	9.3	151	19	2
	Burton Albion	12.5	157	22	2
	Blackpool	12.3	130	24	1
	Bristol Rovers	12.0	116	12	1
	Shrewsbury Town	10.6	122	28	4
	Lincoln City	12.4	119	23	1
	Accrington Stanley	11.5	112	25	1
	Rochdale	14.5	94	15	2
	MK Dons	12.2	123	29	1
	AFC Wimbledon	11.8	137	23	3
	Tranmere Rovers	12.3	111	18	1
	Southend United	13.0	132	29	3
	Bolton Wanderers	12.3	124	25	3

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.

• MK Dons recorded the highest proportion of goal kicks which ended inside their own box (31.4%). This tactic resulted in them gaining more territory compared to when they went long, averaging over 45 metres ball progression when they went short, compared to less than 41 metres when a goal kick cleared their own box.

• Over half of the teams in League One elected to clear their own box from goal kicks on over 90% of occasions, with three teams, including Rotherham and Wycombe, not taking a single goal kick ending inside their own box 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)
	Coventry City	67	231	22.5	48.9	44.8
	Rotherham United	0	286	0.0	0.0	46.4
	Wycombe Wanderers	0	348	0.0	0.0	46.4
	Oxford United	29	250	10.4	59.4	42.1
	Portsmouth	7	268	2.5	62.2	43.6
	Fleetwood Town	72	251	22.3	50.8	38.8
	Peterborough United	78	250	23.8	47.6	40.6
	Sunderland	13	244	5.1	48.8	45.8
	Doncaster Rovers	84	223	27.4	42.4	42.1
	Gillingham	2	298	0.7	58.5	41.2
	Ipswich Town	17	278	5.8	49.1	44.4
	Burton Albion	56	254	18.1	48.8	45.5
	Blackpool	16	347	4.4	52.8	42.1
	Bristol Rovers	6	314	1.9	42.0	42.7
	Shrewsbury Town	4	302	1.3	27.2	38.4
	Lincoln City	101	242	29.4	49.3	43.1
	Accrington Stanley	85	217	28.1	50.1	41.6
	Rochdale	101	264	27.7	45.3	44.6
	MK Dons	104	227	31.4	45.8	40.4
	AFC Wimbledon	0	369	0.0	0.0	40.8
	Tranmere Rovers	11	311	3.4	43.8	42.0
	Southend United	28	336	7.7	37.1	41.7
	Bolton Wanderers	3	380	0.8	23.3	40.2

Graphic

Coventry City

Rotherham United

Wycombe Wanderers

Oxford United

Portsmouth

Fleetwood Town

Peterborough United

Sunderland

Doncaster Rovers

Gillingham

Ipswich Town

Burton Albion

Blackpool

Bristol Rovers

Shrewsbury Town

Lincoln City

Accrington Stanley

Rochdale

MK Dons

AFC Wimbledon

Tranmere Rovers

Southend United

Bolton Wanderers

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.

• Freddie Ladapo posted the highest xG per 90 output in the competition, however the 27-year-old was one of five players in the league’s top ten who were unable to match their xG in terms of goals scored.

• The quality of finishing by the league’s top scorer, Ivan Toney, is reflected by his xGOT exceeding his xG projection by a magnitude of 0.11, the highest differential of any player listed here.

• Rhys Healy is another player whose goals total exceeded his xG by more than 0.1, with two of his goals coming from outside of the box.

• From this list, Armand Gnanduillet scored the highest proportion of goals with his head, with 47% of the Frenchman’s 15 goals coming from headers inside the box.

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
	Freddie Ladapo	1858	0.76	0.67	0.68	21%	21%	57%	0%	100%
	Matty Taylor	1783	0.71	0.73	0.66	31%	38%	31%	0%	100%
	James Norwood	1918	0.71	0.60	0.52	27%	9%	64%	0%	91%
	Ivan Toney	2842	0.63	0.74	0.76	25%	13%	63%	0%	100%
	Matt Godden	1967	0.54	0.53	0.64	21%	7%	71%	0%	93%
	Colby Bishop	2063	0.53	0.52	0.44	30%	10%	60%	0%	100%
	Rhys Healey	1409	0.52	0.51	0.70	9%	36%	55%	0%	82%
	Armand Gnanduillet	2245	0.52	0.54	0.60	47%	7%	47%	0%	100%
	Paddy Madden	2390	0.52	0.51	0.56	13%	0%	87%	0%	100%
	Ched Evans	1969	0.51	0.45	0.41	22%	11%	67%	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, Matty Taylor demonstrated an ability to get into good scoring locations inside the box. Over 31% of his attempts came from high clarity situations, with a similar proportion occurring under low pressure.

• Armand Gnanduillet’s impressive performance in front of goal is further reinforced by the fact 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.

• Only 5% of Paddy Madden’s goal attempts came from a low clarity position, indicating the Irishman was reluctant to shoot if his path to goal was blocked. In contrast, more than one in five of James Norwood’s attempts occurred from such situations.

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
	Freddie Ladapo	0.18	36.5	47.1	16.5	2.4	16.5	74.1	7.1
	Matty Taylor	0.26	37.0	33.3	29.6	1.9	31.5	57.4	9.3
	James Norwood	0.18	31.4	37.2	31.4	0.0	30.2	48.8	20.9
	Ivan Toney	0.15	36.1	31.6	32.3	1.5	30.8	48.9	18.8
	Matt Godden	0.18	31.2	39.1	29.7	1.6	34.4	57.8	6.2
	Colby Bishop	0.21	54.2	20.3	25.4	5.1	23.7	62.7	8.5
	Rhys Healey	0.13	27.4	38.7	33.9	3.2	25.8	59.7	11.3
	Armand Gnanduillet	0.16	50.6	30.4	19.0	1.3	17.7	72.2	8.9
	Paddy Madden	0.18	38.2	42.1	19.7	2.6	14.5	77.6	5.3
	Ched Evans	0.15	41.1	32.9	26.0	0.0	20.5	63.0	16.4

Graphic

Freddie Ladapo

Matty Taylor

James Norwood

Ivan Toney

Matt Godden

Colby Bishop

Rhys Healey

Armand Gnanduillet

Paddy Madden

Ched Evans

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.

• The competition’s leading assist provider, Liam Feeney, also ranked top in the competition for xA. Fellow veteran James Coppinger was also a standout performer, creating more chances per 90 minutes than any other player listed here.

• Playing in a team adopting a less possession-orientated approach, it is noteworthy that Rotherham’s Chiedozie Ogbene created the most chances per 100 passes of all the players listed.

• Ipswich’s Alan Judge contributed four assists during the campaign, however his xA output suggests the quality of his passing into 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
	Liam Feeney	2869	0.25	0.38	1.13	4.20
	Marcus Harness	1499	0.23	0.30	1.14	3.35
	James Coppinger	2051	0.21	0.22	2.33	4.74
	Scott Fraser	2294	0.19	0.35	1.92	4.78
	James Henry	2593	0.18	0.28	1.04	2.68
	Jordan Clark	3060	0.17	0.15	1.68	4.93
	Callum O’Hare	1837	0.17	0.15	1.67	5.01
	Chiedozie Ogbene	1907	0.16	0.14	1.04	5.41
	Denver Hume	2560	0.16	0.14	0.95	2.73
	Alan Judge	1942	0.16	0.09	1.44	3.47

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
	Denver Hume	2560	0.16	0.14	0.95	2.73
	Lewie Coyle	3020	0.13	0.15	1.01	2.16
	Reece James	2248	0.12	0.08	0.88	2.02
	Joe Mattock	1974	0.12	0.00	1.00	2.96
	Lee Brown	1346	0.12	0.00	0.94	2.61
	James Bolton	1786	0.11	0.15	0.71	1.86
	Colin Daniel	1715	0.11	0.10	0.73	1.69
	Rhys Norrington-Davies	2334	0.11	0.04	0.85	1.85
	Luke Matheson	1689	0.10	0.11	0.96	3.12
	Scott Golbourne	1197	0.10	0.08	1.13	3.14

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
	Liam Feeney	2869	0.25	0.38	1.13	4.20
	Marcus Harness	1499	0.23	0.30	1.14	3.35
	James Coppinger	2051	0.21	0.22	2.33	4.74
	Scott Fraser	2294	0.19	0.35	1.92	4.78
	Jordan Clark	3060	0.17	0.15	1.68	4.93
	Callum O’Hare	1837	0.17	0.15	1.67	5.01
	Alan Judge	1942	0.16	0.09	1.44	3.47
	Chris Cadden	1843	0.15	0.20	1.07	2.57
	Madger Gomes	1190	0.15	0.15	0.68	1.79
	Andrew Cannon	1082	0.15	0.08	1.41	4.42

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
	James Henry	2593	0.18	0.28	1.04	2.68
	Chiedozie Ogbene	1907	0.16	0.14	1.04	5.41
	Kayden Jackson	2519	0.15	0.25	0.93	6.00
	Liam Boyce	1699	0.14	0.16	1.54	5.77
	Joseph Dodoo	1619	0.13	0.22	0.72	3.86
	Kyle Vassell	1079	0.13	0.17	0.75	3.81
	Ivan Toney	2842	0.13	0.16	1.49	6.46
	Paddy Madden	2390	0.13	0.11	1.13	5.47
	John Akinde	1388	0.12	0.13	1.36	5.40
	Dion Charles	2342	0.12	0.12	1.23	9.20

Graphic

Liam Feeney

Marcus Harness

James Coppinger

Scott Fraser

James Henry

Jordan Clark

Callum O’Hare

Chiedozie Ogbene

Denver Hume

Alan Judge

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

• Portsmouth’s Ronan Curtis tops the list, having created 53 chances following a carry. The Irishman demonstrated a tendency to shoot when cutting inside from the left hand side, which resulted in two goals.

• Of the players listed, Accrington’s Jordan Clark and Alex Gilbey of MK Dons completed the most carries per 90. Clark made more key passes than any player following a carry and contributed three assists, whilst Gilbey preferred to shoot, netted three goals in the process.

• 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
	Ronan Curtis	2784	13	11.60	37	16	2	2	53
	Jordan Clark	3060	14	11.77	28	23	3	2	51
	Marcus Maddison	1609	13	13.03	30	9	1	2	39
	Kieran Sadlier	2512	12	12.41	28	11	3	2	39
	Jon Taylor	1957	12	13.99	25	13	1	1	38
	Lucas Akins	3142	13	11.88	20	17	0	2	37
	Alex Gilbey	2323	14	13.26	27	7	1	3	34
	Dion Charles	2342	9	12.19	21	13	2	2	34
	Kieron Morris	2917	11	11.93	17	14	2	0	31
	Dennis Politic	1698	13	11.47	23	7	0	0	30

Graphic

Ronan Curtis

Jordan Clark

Marcus Maddison

Kieran Sadlier

Jon Taylor

Lucas Akins

Alex Gilbey

Dion Charles

Kieron Morris

Dennis Politic

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, Nathan Trott stands out. The average League One goalkeeper would have been expected to concede over seven more goals based on the quality of shots faced by the on-loan stopper, a level of overperformance which could have been pivotal in AFC Wimbledon’s survival from relegation.

• Sunderland’s Jon McLaughlin also enjoyed a strong campaign. Excluding penalties and own goals, he conceded 25 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. Anssi Jaakkola, Marko Marosi and Seny Timothy Dieng all have the same goals prevented rate (1.09), despite the latter ‘preventing’ fewer goals. Normalising for the volume of shots allows us to see that both goalkeepers were expected to concede 1.09 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
	Nathan Trott	66%	1.25	7.4	36.4	29	124
	Jon McLaughlin	88%	1.17	4.2	29.2	25	94
	Anssi Jaakkola	58%	1.09	2.0	24.0	22	87
	Marko Marosi	100%	1.09	2.0	26.0	24	95
	Seny Timothy Dieng	79%	1.09	1.7	21.7	20	89
	Daniel Iversen	97%	1.07	2.2	34.2	32	114
	Lee Nicholls	100%	1.04	1.7	46.7	45	166
	Ryan Allsop	86%	1.04	1.2	32.2	31	118
	Jack Bonham	100%	1.03	0.8	32.8	32	117
	Kieran O’Hara	93%	1.00	0.1	39.1	39	134
	Max O’Leary	88%	1.00	0.0	28.0	28	110
	Jak Alnwick	61%	0.95	-1.0	18.0	19	64
	Simon Eastwood	76%	0.95	-1.6	28.4	30	99
	Scott Davies	80%	0.95	-2.1	39.9	42	126
	Robert Sánchez	76%	0.93	-2.7	37.3	40	127
	Tomas Holy	58%	0.90	-1.5	14.5	16	56
	Alex Cairns	68%	0.86	-2.6	15.4	18	53
	Christy Pym	100%	0.86	-4.4	27.6	32	114
	Mark Oxley	54%	0.86	-5.2	32.8	38	101
	Josh Vickers	100%	0.84	-6.5	33.5	40	129
	Remi Matthews	97%	0.80	-11.8	47.2	59	169
	Craig MacGillivray	54%	0.78	-4.6	16.4	21	67
	Dimitar Evtimov	54%	0.76	-6.1	18.9	25	71

Graphic

Nathan Trott

Jon McLaughlin

Anssi Jaakkola

Marko Marosi

Seny Timothy Dieng

Daniel Iversen

Lee Nicholls

Ryan Allsop

Jack Bonham

Kieran O’Hara

Max O’Leary

Jak Alnwick

Simon Eastwood

Scott Davies

Robert Sánchez

Tomas Holy

Alex Cairns

Christy Pym

Mark Oxley

Josh Vickers

Remi Matthews

Craig MacGillivray

Dimitar Evtimov

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, Chiedozie Ogbene ranks extremely highly with regards to starting sequences, despite starting fewer possessions. He also recorded the highest volume of ball recoveries of all the forward players listed.

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
	Michael Ihiekwe	2970	14.48	29.59	2.28	2.68	9.81
	Alex Rodriguez	2443	13.81	25.31	3.31	4.53	7.60
	Tom Naylor	2970	13.34	31.38	2.43	3.23	13.49
	Ross Sykes	2762	13.32	23.35	1.30	3.57	7.75
	Robert Dickie	3015	13.29	28.07	1.27	4.74	11.31
	Mark Hughes	2762	12.40	25.24	1.63	2.56	8.43
	Jack Tucker	2321	11.58	21.40	1.10	1.99	6.23
	Christian Burgess	2796	11.25	25.15	1.35	2.16	7.90
	Tom Davies	1636	11.25	21.49	1.95	3.05	7.53
	Anthony Stewart	3060	11.22	22.95	1.20	1.45	8.27

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
	Michael Ihiekwe	2970	14.48	29.59	2.28	2.68	9.81
	Ross Sykes	2762	13.32	23.35	1.30	3.57	7.75
	Robert Dickie	3015	13.29	28.07	1.27	4.74	11.31
	Mark Hughes	2762	12.40	25.24	1.63	2.56	8.43
	Jack Tucker	2321	11.58	21.40	1.10	1.99	6.23
	Christian Burgess	2796	11.25	25.15	1.35	2.16	7.90
	Tom Davies	1636	11.25	21.49	1.95	3.05	7.53
	Anthony Stewart	3060	11.22	22.95	1.20	1.45	8.27
	Harry Souttar	2979	11.13	22.66	1.29	1.73	7.22
	Kyle McFadzean	2532	10.72	20.83	1.39	1.83	7.89

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
	Alex Rodriguez	2443	13.81	25.31	3.31	4.53	7.60
	Tom Naylor	2970	13.34	31.38	2.43	3.23	13.49
	Stephen Quinn	2333	10.28	26.56	2.26	4.63	14.69
	John-Joe O’Toole	2119	10.16	18.72	1.85	2.46	6.83
	Liam Bridcutt	1301	10.10	24.64	3.50	2.58	14.23
	Luke O’Nien	3131	9.72	20.98	1.89	2.51	8.09
	Michael Bostwick	1364	9.58	16.22	0.76	2.85	5.60
	Flynn Downes	2501	9.57	25.64	2.60	2.36	12.23
	Joe Morrell	2550	9.33	19.97	2.40	3.00	9.38
	Mark Milligan	2682	9.24	22.67	2.90	1.56	9.78

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
	James Henry	2593	5.12	15.90	0.91	1.28	8.96
	Chiedozie Ogbene	1907	4.25	17.86	0.54	1.31	9.51
	Lucas Akins	3142	4.21	12.88	1.24	1.68	8.86
	Simon Cox	1430	3.73	9.96	0.72	1.00	4.44
	Wes Burns	2719	3.40	13.26	0.95	1.30	8.20
	Alex Rodman	2031	3.37	11.16	0.79	0.99	6.75
	Ivan Toney	2842	3.28	10.16	1.00	0.78	5.55
	Connor Jennings	1988	3.12	10.05	1.15	0.68	6.25
	Oliver Sarkic	1855	2.95	9.02	0.45	0.91	5.56
	Matty Taylor	1783	2.44	7.59	0.21	0.42	3.55

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. Alex Rodriguez is one major stand-out, having been involved in 71 shot-ending sequences from central midfield.

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
	Mohamed Eisa	2177	3.80	2.63	20.25	15	10.39	80
	Marcus Forss	1346	3.72	2.52	16.89	11	7.46	50
	Matt Godden	1967	3.36	2.83	20.13	15	12.63	90
	Matty Taylor	2003	2.98	3.32	17.02	17	18.92	97
	Freddie Ladapo	1858	2.73	2.98	19.50	13	14.20	93
	Paddy Madden	2409	2.65	2.90	18.09	18	19.73	123
	Ivan Toney	2842	2.57	2.26	16.42	26	22.88	166
	Tyler Walker	2273	2.49	1.87	14.15	16	12.02	91
	Ian Henderson	2618	2.42	1.74	15.19	18	12.91	113
	Kayden Jackson	2519	2.40	2.41	14.37	16	16.08	96

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
	Ellis Harrison	1723	1.54	0.42	3.74	7	1.92	17
	Alex Rodriguez	2697	1.08	0.80	6.41	12	8.91	71
	Joe Wright	1542	1.07	0.73	5.57	5	3.39	26
	Josh Knight	1601	1.05	0.61	4.00	5	2.92	19
	Kyle Dempsey	1699	0.89	1.04	6.22	6	7.01	42
	Tom Anderson	2839	0.84	0.54	4.50	8	5.21	43
	Madger Gomes	1190	0.84	0.61	5.66	4	2.91	27
	Kane Wilson	1104	0.82	0.34	4.70	4	1.64	23
	Nathan Broadhead	1032	0.81	0.80	5.69	3	2.97	21
	Zain Westbrooke	1708	0.81	0.44	5.43	6	3.23	40

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.