
	Regular Season Review

Welcome

Welcome to Stats Perform’s US Major League Soccer season review for the 2020 regular 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 year 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:

Listed alphabetically across both conferences, this table outlines teams’ performances in front of goal, from both open play and set piece situations.
Despite only finishing seventh in the Western Conference, Los Angeles FC’s ability to generate high quality chances from open play was demonstrated by them amassing an xG output of 33.3 from 267 shots. New York City FC were the only other team to post more than 260 shots, however Ronny Delia’s side scored seven fewer open play goals and failed to match their xG output by nearly four.
Supporters’ Shield winners Philadelphia Union and MLS Is Back Tournament winners Portland Timbers were the joint-highest scorers from open play (34), with both teams exceeding their xG output by seven and ten goals respectively.
Three teams created over 30% of their total xG from set pieces: Real Salt Lake, Inter Miami and Nashville SC. Only Sporting Kansas City could match Nashville’s total of 12 set piece goals, which accounted for 50% of the MLS newcomer’s entire regular season output, helping them secure a playoff berth.
As well as Nashville, Sporting Kansas City, Colorado Rapids, Montreal Impact and DC United all exceeded their set piece xG by two clear goals.

Expected Goals For
	Set Play : Total	Open Play			Set Play
Team	xG Ratio	Shots	xG	Goals	SP Shots	SP xG	SP Goals
Atlanta United FC	0.20	144	15.0	17	71	4.6	3
Chicago Fire FC	0.23	238	28.6	24	77	9.4	7
Colorado Rapids	0.22	199	19.4	21	49	5.4	9
Columbus Crew SC	0.14	195	24.5	30	52	4.7	3
D.C. United	0.27	155	14.3	10	59	6.4	10
FC Cincinnati	0.29	160	13.7	8	72	6.3	2
FC Dallas	0.18	209	25.7	20	66	6.3	5
Houston Dynamo FC	0.17	257	27.0	22	68	6.2	6
Inter Miami CF	0.33	192	19.0	17	105	10.1	6
LA Galaxy	0.24	174	19.0	17	71	7.0	6
Los Angeles Football Club	0.21	267	33.3	32	89	9.2	11
Minnesota United FC	0.21	216	23.4	25	73	6.8	6
Montreal Impact	0.17	188	23.4	21	56	5.6	8
Nashville SC	0.31	182	17.0	12	90	8.1	12
New England Revolution	0.20	231	26.9	18	107	7.1	6
New York City FC	0.19	261	28.8	25	92	7.4	8
New York Red Bulls	0.24	188	21.6	20	56	7.0	8
Orlando City SC	0.17	198	26.8	31	67	5.8	7
Philadelphia Union	0.24	217	26.9	34	78	9.1	7
Portland Timbers	0.22	191	23.6	34	65	7.1	9
Real Salt Lake	0.34	185	17.0	14	89	10.1	7
San Jose Earthquakes	0.18	219	22.7	25	81	5.8	5
Seattle Sounders FC	0.26	191	27.5	28	84	10.8	9
Sporting Kansas City	0.29	229	23.9	24	83	10.0	12
Toronto FC	0.15	227	27.6	27	58	5.7	1
Vancouver Whitecaps FC	0.15	138	18.3	19	53	3.7	3

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:

After ranking in the bottom third for MLS open play xG conceded during 2019, Seattle demonstrated a huge turnaround in defensive fortunes in 2020, recording a league-low 15 xG against. The Sounders solidity was also evident at set piece situations, where they conceded chances worth just 4.7 xG.
New England Revolution ranked joint-top in MLS for the fewest open play goals conceded with Seattle, however based on the quality of chances the Revs conceded, they were perhaps fortune not to have conceded more goals.
Despite possessing a significant threat at attacking set plays, DC United were not as effective inside their own box, conceding the highest number of goals from set pieces (12). Portland were another team to concede a high volume of shots from set piece situations, which were collectively worth the highest xG in the competition (11.1).
Philadelphia Union conceded the fewest goals during the regular season, however their opposition failed to capitalise on the quality of chances they created. Based on Union’s xG conceded from both open play and set pieces, we would have perhaps expected the Supporters’ Shield winners to have conceded 40% more goals during the campaign.

Expected Goals Against
	Set Play : Total	Open Play			Set Play
Team	xG Ratio	Shots	xG	Goals	SP Shots	SP xG	SP Goals
Atlanta United FC	0.30	195	19.6	21	70	8.5	9
Chicago Fire FC	0.26	208	25.6	29	68	9.7	8
Colorado Rapids	0.28	172	16.9	18	76	7.4	7
Columbus Crew SC	0.26	186	17.5	13	85	7.9	2
D.C. United	0.22	246	28.2	26	97	8.8	12
FC Cincinnati	0.19	209	25.1	25	77	6.3	8
FC Dallas	0.22	261	20.1	18	76	6.3	3
Houston Dynamo FC	0.18	251	29.1	29	86	7.0	7
Inter Miami CF	0.22	163	19.9	27	62	7.1	3
LA Galaxy	0.18	228	25.4	37	70	6.4	5
Los Angeles Football Club	0.26	153	19.9	24	78	8.1	8
Minnesota United FC	0.15	233	26.0	18	59	5.2	4
Montreal Impact	0.19	247	30.4	30	83	7.8	10
Nashville SC	0.19	209	21.7	18	55	5.1	4
New England Revolution	0.22	181	20.8	13	64	6.2	9
New York City FC	0.23	179	21.1	17	73	7.1	5
New York Red Bulls	0.20	186	24.8	23	71	6.7	4
Orlando City SC	0.25	204	20.5	15	98	7.3	8
Philadelphia Union	0.22	182	20.3	14	83	6.3	5
Portland Timbers	0.27	231	28.6	25	99	11.1	9
Real Salt Lake	0.19	174	22.6	24	54	5.3	8
San Jose Earthquakes	0.22	196	25.7	34	70	8.2	11
Seattle Sounders FC	0.22	152	15.0	13	56	4.7	7
Sporting Kansas City	0.19	143	19.2	19	54	5.0	4
Toronto FC	0.23	173	19.1	15	60	6.5	8
Vancouver Whitecaps FC	0.21	289	32.0	30	87	9.2	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.

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.
Toronto FC ranked first for the most passes per sequence and third for average sequence time. This patient possession-based approach saw Greg Vanney’s side being the only team to post more than 300 sequences comprising 10+ passes during the season.
Toronto recorded a similar sequence time and average number of sequence passes to Seattle and Atlanta, however the Canadian team looked to move the ball forward at a faster tempo. They also recorded nearly 40% more attacks derived from build-up sequences than any other MLS side.
At the other end of the scale, New York Red Bulls continued to adopt their more direct approach, recording the shortest sequence time and the lowest average number of sequence passes. They only completed 66 sequences comprising ten or more passes during the season, 73 fewer than the next lowest side, Inter Miami.

Open Play Sequences
	Sequence Summaries			Sequence Styles
Team	Sequence Time (secs)	Passes Per Sequence	Direct Speed (m/s)	10+ Pass OP Sequences	Build Up Attacks	Direct Attacks
Atlanta United FC	11.58	3.90	1.25	278	48	37
Chicago Fire FC	8.76	3.19	1.49	177	29	27
Colorado Rapids	10.99	3.82	1.43	206	34	25
Columbus Crew SC	10.33	3.64	1.38	233	51	31
D.C. United	8.54	3.28	1.52	202	22	28
FC Cincinnati	10.55	3.74	1.15	264	36	15
FC Dallas	9.80	3.47	1.49	204	42	33
Houston Dynamo	9.43	3.40	1.64	171	26	49
Inter Miami CF	8.24	2.94	1.74	139	30	33
LA Galaxy	7.79	2.98	1.82	155	30	39
Los Angeles Football Club	8.73	3.27	1.78	194	59	53
Minnesota United FC	8.22	3.14	1.78	149	25	40
Montreal Impact	9.81	3.40	1.45	190	31	52
Nashville SC	9.12	3.28	1.52	189	24	20
New England Revolution	7.99	3.01	1.64	151	28	48
New York City FC	9.32	3.46	1.60	227	45	34
New York Red Bulls	6.11	2.54	1.83	66	12	29
Orlando City SC	9.73	3.56	1.54	231	44	41
Philadelphia Union	8.62	3.04	1.47	157	27	37
Portland Timbers	8.70	3.21	1.72	173	42	34
Real Salt Lake	9.12	3.28	1.19	201	20	33
San Jose Earthquakes	10.24	3.57	1.40	232	47	26
Seattle Sounders FC	10.81	3.92	1.36	283	62	36
Sporting Kansas City	8.97	3.17	1.70	166	40	44
Toronto FC	10.84	4.00	1.44	325	86	40
Vancouver Whitecaps FC	9.58	3.31	1.56	182	24	25

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.
Both New York teams looked to win the ball back high up the field, ranking first and second in MLS for high turnovers and shots resulting from a high turnover. This approach paid off for New York City, who scored five goals, however NYRB only scored once.
Los Angeles FC were another team with a tendency to press, which is reflected in their PPDA score of 8.4. However Philadelphia were the team who scored the most goals resulting from a high turnover (6).
FC Cincinnati 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 MLS. However, unlike Montreal Impact, Jaap Stam’s side did manage to score once from a turnover within 40 metres of the opposition goal.
Selecting the ‘Graphic’ tab, we can see the high turnover pitch map of every MLS team.

Sequence Pressure Stats
		High Turnovers
Team	PPDA	Total	Shot Ending	Goal Ending
Atlanta United FC	12.5	85	9	3
Chicago Fire FC	9.9	132	24	2
Colorado Rapids	12.4	82	23	3
Columbus Crew SC	11.1	124	21	3
D.C. United	14.0	99	16	1
FC Cincinnati	13.3	67	15	1
FC Dallas	12.4	75	20	4
Houston Dynamo	13.2	116	24	3
Inter Miami CF	11.8	117	18	1
LA Galaxy	12.6	106	9	1
Los Angeles Football Club	8.4	156	29	4
Minnesota United FC	11.4	128	28	5
Montreal Impact	15.1	79	15	0
Nashville SC	14.7	92	18	1
New England Revolution	10.5	120	24	2
New York City FC	8.0	175	36	5
New York Red Bulls	12.0	169	30	1
Orlando City SC	12.2	94	22	4
Philadelphia Union	10.1	135	27	6
Portland Timbers	12.7	111	21	3
Real Salt Lake	10.2	99	19	3
San Jose Earthquakes	11.2	94	24	1
Seattle Sounders FC	13.4	92	12	1
Sporting Kansas City	12.4	125	16	1
Toronto FC	11.8	99	13	1
Vancouver Whitecaps FC	15.3	73	12	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.
Atlanta United recorded the highest proportion of goal kicks which ended inside their own box (43.3%), a tactic which resulted them gaining more territory compared to when they went long. Atlanta averaged over 48 metres ball progression upfield when they went short, compared to less than 45 metres when a goal kick cleared their own box.
Seattle were another team who regularly utilised short goal kicks. However, unlike Atlanta, there was very little difference in the Sounders ball progression upfield when they went short, compared to when they went long.
Six teams elected to clear their own box from goal kicks on over 80% of occasions, with Philadelphia Union 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)
Atlanta United FC	68	89	43.3	48.2	44.9
Chicago Fire FC	39	115	25.3	34.2	38.2
Colorado Rapids	44	90	32.8	48.3	42.3
Columbus Crew SC	43	137	23.9	43.9	38.6
D.C. United	38	147	20.5	50.2	40.5
FC Cincinnati	65	145	31.0	41.1	46.3
FC Dallas	80	124	39.2	43.5	45.2
Houston Dynamo	76	118	39.2	47.7	43.6
Inter Miami CF	39	100	28.1	47.8	39.0
LA Galaxy	21	147	12.5	52.3	43.2
Los Angeles Football Club	35	82	29.9	48.0	46.2
Minnesota United FC	14	154	8.3	56.5	40.0
Montreal Impact	74	108	40.7	34.8	42.2
Nashville SC	37	126	22.7	55.2	43.5
New England Revolution	23	133	14.7	56.5	45.8
New York City FC	49	108	31.2	46.0	43.1
New York Red Bulls	24	126	16.0	49.7	41.2
Orlando City SC	57	132	30.2	39.8	46.1
Philadelphia Union	13	169	7.1	57.5	46.1
Portland Timbers	45	176	20.4	45.6	43.2
Real Salt Lake	14	144	8.9	48.6	39.7
San Jose Earthquakes	54	103	34.4	44.7	48.0
Seattle Sounders FC	61	83	42.4	48.3	48.2
Sporting Kansas City	32	106	23.2	55.3	43.1
Toronto FC	50	119	29.6	47.4	46.1
Vancouver Whitecaps FC	54	145	27.1	42.2	40.5

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 MLS leading scorer, Diego Rossi, also posted the highest xG per 90 output in the competition. The Uruguayan was ruthless from inside the box, scoring all his goals from within 18 yards, matching his xG output in the process.
New York City FC’s Valentín Castellanos was another player who managed to find high quality goalscoring locations, ranking second in the league for xG. Although he was unable to match his xG in terms of actual goals, his xGOT output, which marginally exceeded his xG, indicates he was somewhat unfortunate not to have scored more.
Of the players who exceeded their xG on this list, Robert Beric scored the highest proportion of goals with his head, with 25% of the Slovenian’s 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
	Diego Rossi	1698	0.73	0.89	0.74	0%	7%	93%	0%	100%
	Valentín Castellanos	1341	0.73	0.81	0.40	17%	0%	83%	0%	100%
	Ayo Akinola	1063	0.69	0.84	0.76	22%	22%	56%	0%	89%
	Raúl Ruidíaz	1427	0.60	0.58	0.76	8%	33%	58%	0%	83%
	Gyasi Zardes	1717	0.59	0.56	0.63	8%	50%	42%	0%	100%
	Bradley Wright-Phillips	1081	0.58	0.67	0.67	0%	13%	88%	0%	88%
	Lucas Cavallini	1457	0.53	0.49	0.37	33%	17%	50%	0%	100%
	Robert Beric	1931	0.51	0.53	0.56	25%	17%	58%	0%	100%
	Franco Jara	1388	0.51	0.41	0.45	29%	0%	71%	0%	100%
	Adam Buksa	1499	0.50	0.36	0.36	17%	50%	33%	0%	83%

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, Gyasi Zardes demonstrated an ability to get into good scoring locations inside the box. Nearly half of his attempts came from high clarity situations, with not a single attempt posted when his path to goal was blocked.
Although Adam Buksa was unable to match his xG for the season, we can establish that nearly 85% of his shots occurred when under high or medium pressure, meaning that opposition players were within tackling distance when he was looking to shoot.
No player listed here attempted more than 10% of their total shots from low clarity positions, indicating that the leading players for xG were reluctant to shoot if they did not have a clear sight of 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
	Diego Rossi	0.19	18.3	40.8	40.8	4.2	29.6	62.0	4.2
	Valentín Castellanos	0.14	42.7	34.7	22.7	0.0	22.7	68.0	9.3
	Ayo Akinola	0.23	33.3	47.2	19.4	5.6	30.6	61.1	2.8
	Raúl Ruidíaz	0.17	41.8	40.0	18.2	1.8	20.0	70.9	7.3
	Gyasi Zardes	0.28	51.2	12.2	36.6	0.0	48.8	51.2	0.0
	Bradley Wright-Phillips	0.21	36.4	51.5	12.1	3.0	18.2	69.7	9.1
	Lucas Cavallini	0.19	45.5	38.6	15.9	0.0	22.7	75.0	2.3
	Robert Beric	0.18	30.6	43.5	25.8	1.6	22.6	71.0	4.8
	Franco Jara	0.21	35.1	43.2	21.6	0.0	35.1	62.2	2.7
	Adam Buksa	0.14	49.2	33.9	16.9	0.0	16.9	79.7	3.4

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 primary 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.
Houston Dynamo’s Darwin Quintero led MLS for assists during the regular season and also ended up ranking first for xA per 90 (0.31).
Diego Valeri contributed two open play primary assists for Portland during the campaign. However his xA output suggests the quality of the MLS veteran’s passing from central attacking areas could have resulted in more goals.
Playing in his first MLS season after moving up from USL, it is noteworthy that right-sided Real Salt Lake midfielder Maikel Chang created the most chances per 100 passes of all the players listed by a significant margin.

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
	Darwin Quintero	1689	0.31	0.21	2.45	5.37
	Maikel Chang	1107	0.30	0.24	2.52	8.40
	Alejandro Pozuelo	2015	0.29	0.40	2.23	3.84
	Nicolás Lodeiro	1782	0.28	0.25	1.36	2.08
	Diego Valeri	1628	0.28	0.11	1.60	3.90
	Fredy Montero	1059	0.27	0.17	1.10	3.06
	Alejandro Romero Gamarra	1089	0.26	0.17	1.82	3.96
	Djordje Mihailovic	1171	0.25	0.46	1.38	3.73
	Jordan Morris	1724	0.25	0.26	1.10	3.42
	Mauricio Pereyra	1107	0.24	0.41	1.79	3.48

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
	Brooks Lennon	1751	0.18	0.15	1.03	2.82
	Brandon Bye	1636	0.18	0.06	0.83	2.08
	Anton Tinnerholm	2001	0.17	0.18	1.57	3.33
	Bryan Reynolds	1267	0.16	0.21	0.78	2.07
	Ronald Matarrita	1649	0.16	0.00	0.98	2.10
	Ruan	1597	0.15	0.17	0.79	2.28
	DeJuan Jones	1134	0.15	0.00	1.43	3.03
	Ali Adnan	1769	0.13	0.20	0.41	1.07
	Kyle Duncan	1908	0.13	0.09	0.61	1.48
	Graham Zusi	1224	0.13	0.07	0.96	1.79

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
	Maikel Chang	1107	0.30	0.24	2.52	8.40
	Alejandro Pozuelo	2015	0.29	0.40	2.23	3.84
	Nicolás Lodeiro	1782	0.28	0.25	1.36	2.08
	Diego Valeri	1628	0.28	0.11	1.60	3.90
	Alejandro Romero Gamarra	1089	0.26	0.17	1.82	3.96
	Djordje Mihailovic	1171	0.25	0.46	1.38	3.73
	Mauricio Pereyra	1107	0.24	0.41	1.79	3.48
	Cristian Espinoza	1812	0.22	0.30	1.34	3.49
	Yimmi Chará	1233	0.22	0.22	1.02	2.45
	José Cifuentes	1150	0.21	0.16	1.80	3.38

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
	Darwin Quintero	1689	0.31	0.21	2.45	5.37
	Fredy Montero	1059	0.27	0.17	1.10	3.06
	Jordan Morris	1724	0.25	0.26	1.10	3.42
	Michael Barrios	1681	0.19	0.16	1.66	6.86
	Diego Rossi	1698	0.18	0.16	1.27	4.56
	Diego Rubio	1040	0.17	0.26	1.30	4.07
	Justin Meram	1146	0.17	0.24	0.94	2.64
	Kacper Przybylko	1975	0.17	0.18	1.23	4.76
	Cristian Pavón	1980	0.17	0.09	1.50	4.45
	Johnny Russell	1529	0.17	0.06	1.18	3.98

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 MLS’ top 10 players for creating goal scoring opportunities as a result of a carry.
LA Galaxy loanee Cristian Pavón tops the list, having created 44 chances following a carry. No player scored more than Pavón’s four goals following a carry.
Fellow Argentinean Cristian Espinoza was the only other player to create over 40 chances following a carry, offering both his own goal threat and the ability to find a teammate. He scored or assisted eight times for San Jose Earthquakes after completing a carry, a combined total substantially higher than any other player.
Operating in wide areas on both flanks, Romell Quioto demonstrated a tendency shoot following a carry, which resulted in the midfielder scoring three times during the season for Montreal.
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
	Cristian Pavón	1980	18	12.21	26	18	0	4	44
	Cristian Espinoza	1812	18	12.01	21	21	5	3	42
	Michael Barrios	1681	10	13.26	16	20	1	0	36
	Romell Quioto	1483	11	11.23	24	8	1	3	32
	Diego Rossi	1698	12	11.56	19	13	1	2	32
	Darwin Quintero	1689	16	11.00	16	15	0	2	31
	Lewis Morgan	1952	16	12.99	17	14	2	1	31
	Gustavo Bou	1329	11	10.69	18	11	0	0	29
	Alejandro Pozuelo	2015	20	10.27	14	15	1	0	29
	Brian Rodríguez	1446	16	11.79	14	12	2	0	26

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, New York City’s Sean Johnson stands out. The average MLS goalkeeper would have been expected to concede over eight more goals based on the quality of shots faced by the 31-year-old, a level of over performance which could have been pivotal in securing his team a place in next year’s Leagues Cup.
Revs’ stopper Matt Turner also enjoyed a strong campaign for the second year in succession. Excluding penalties and own goals, he conceded 21 times, over eight 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. Andre Blake and Ryan Meara both have the same goals prevented rate (1.25), despite the Philadelphia custodian ‘preventing’ more goals. Normalising for the volume of shots allows us to see that both goalkeepers were expected to concede 1.25 goals for every goal that they actually conceded.

Most Featured Goalkeepers Per Team
Player	% Of Team Mins	Goals Prevented Rate	Goals Prevented	xGOT Conceded	Goals Conceded	Shots Faced
Matt Turner	96%	1.40	8.4	29.4	21	89
Sean Johnson	100%	1.38	8.4	30.4	22	102
Jimmy Maurer	70%	1.28	3.1	14.1	11	65
Andre Blake	91%	1.25	4.3	21.3	17	80
Ryan Meara	55%	1.25	3.3	16.3	13	52
Dayne St. Clair	62%	1.17	2.1	14.1	12	59
Eloy Room	73%	1.17	1.9	12.9	11	52
Joe Willis	100%	1.13	2.9	24.9	22	91
Tim Melia	94%	1.13	2.4	21.4	19	75
Marko Maric	100%	1.12	4.1	40.1	36	115
Stefan Frei	100%	1.11	2.2	22.2	20	80
Pedro Gallese	83%	1.11	1.9	19.9	18	70
Clément Diop	87%	1.10	3.2	36.2	33	105
Bill Hamid	74%	1.05	1.4	30.4	29	84
Bobby Shuttleworth	74%	1.05	1.3	27.3	26	79
Quentin Westberg	87%	1.05	1.2	24.2	23	71
Steve Clark	87%	1.03	0.8	28.8	28	91
Thomas Hasal	36%	1.01	0.1	13.1	13	48
William Yarbrough	78%	1.00	0.1	17.1	17	65
Andrew Putna	68%	0.94	-1.4	21.6	23	61
Brad Guzan	100%	0.94	-1.7	28.3	30	89
Luis Robles	65%	0.88	-2.7	19.3	22	55
Daniel Vega	52%	0.84	-5.1	26.9	32	73
Pablo Sisniega	64%	0.80	-3.5	13.5	17	52
David Bingham	82%	0.80	-6.5	25.5	32	82
Przemyslaw Tyton	52%	0.77	-3.9	13.1	17	47

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 midfield position some familiar names appear, reinforcing this approach to identifying players who perform well in this role. Despite starting fewer possessions, Los Angeles FC’s Eduard Atuesta ranks extremely highly in regards to starting sequences, as well as being the midfielder recording the highest volume of ball recoveries per 1000 opposition touches.

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
	Francisco Calvo	1911	10.68	19.56	2.02	5.75	10.01
	Gastón Giménez	1557	10.59	23.10	2.74	4.02	11.59
	Eddie Segura	1886	10.42	24.09	1.90	3.55	12.24
	Ilie Sánchez	1259	9.72	21.86	1.69	2.64	13.73
	James Sands	1409	9.55	19.54	1.95	3.26	9.01
	Alexander Ring	2016	8.91	19.89	2.08	3.27	11.65
	Judson	1701	8.68	18.43	4.15	2.07	10.60
	Yeimar Gómez	1626	8.37	18.38	1.39	4.27	9.02
	Henry Kessler	1823	8.31	17.03	1.48	2.39	8.66
	Ronald Matarrita	1649	8.13	18.32	3.12	2.96	10.76

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
	Francisco Calvo	1911	10.68	19.56	2.02	5.75	10.01
	Eddie Segura	1886	10.42	24.09	1.90	3.55	12.24
	James Sands	1409	9.55	19.54	1.95	3.26	9.01
	Yeimar Gómez	1626	8.37	18.38	1.39	4.27	9.02
	Henry Kessler	1823	8.31	17.03	1.48	2.39	8.66
	Ronald Matarrita	1649	8.13	18.32	3.12	2.96	10.76
	Mark McKenzie	1980	7.78	16.03	1.62	2.77	7.03
	Maxime Chanot	1712	7.63	17.88	2.05	3.28	8.78
	Maynor Figueroa	1717	7.54	16.70	2.05	3.00	8.64
	Donny Toia	1674	7.06	17.11	1.78	3.49	8.68

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
	Gastón Giménez	1557	10.59	23.10	2.74	4.02	11.59
	Ilie Sánchez	1259	9.72	21.86	1.69	2.64	13.73
	Alexander Ring	2016	8.91	19.89	2.08	3.27	11.65
	Judson	1701	8.68	18.43	4.15	2.07	10.60
	João Paulo	1501	7.34	18.01	3.15	2.62	11.98
	Eduard Atuesta	1326	7.20	21.07	2.47	3.19	15.21
	Jan Gregus	1599	7.13	15.83	1.33	2.82	10.03
	José Martínez	1148	7.06	20.67	2.02	2.32	13.31
	Michael Bradley	1059	7.04	19.86	1.83	1.41	13.24
	Thiago Santos	1623	7.03	18.18	2.52	1.68	11.08

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
	Cristian Cásseres Jr	1310	7.48	17.08	2.27	2.42	9.22
	Latif Blessing	1720	5.65	20.35	2.57	2.82	14.04
	Gianluca Busio	1275	5.30	14.48	2.40	1.57	7.95
	Russell Teibert	1585	4.13	8.96	0.42	1.82	6.02
	Fabian Herbers	1365	3.99	10.34	1.62	1.11	6.42
	Yuya Kubo	1284	3.39	8.48	1.37	1.05	5.33
	Justin Meram	1146	3.08	8.92	1.22	0.24	4.38
	Jordan Morris	1724	3.07	8.39	0.60	0.60	5.62
	Darwin Quintero	1689	2.81	11.15	0.89	0.62	6.43
	Cristian Dájome	1603	2.76	7.31	1.56	0.84	4.73

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. Brazilian defensive midfielder Artur is one major stand-out, having been involved in 52 shot-ending sequences for Columbus Crew during the regular season.

Top 10 Goal Ending Open Play Sequence Involvements
			Per 100 Open Play Sequence Involvements			Open Play Sequence Involvements
Team	Player	Minutes Played	Goal Ending p100	Total xG p100	Shot Ending p100	Goal Ending	Total xG	Shot Ending
	Daryl Dike	1222	4.78	3.20	19.12	13	8.70	52
	Bradley Wright-Phillips	1081	4.76	3.47	19.05	12	8.74	48
	Gyasi Zardes	1717	4.39	3.34	17.57	17	12.93	68
	Raúl Ruidíaz	1427	4.37	3.16	20.31	17	12.30	79
	Ayo Akinola	1063	3.51	4.14	21.49	8	9.43	49
	Jeremy Ebobisse	1062	3.38	2.13	15.69	11	6.93	51
	Felipe Mora	1204	3.02	2.98	17.58	11	10.86	64
	Jordan Morris	1724	2.79	2.79	13.00	18	18.00	84
	Kevin Molino	1264	2.69	1.69	12.64	17	10.72	80
	Diego Rossi	1698	2.63	2.84	17.49	17	18.35	113

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
	Darlington Nagbe	1199	1.74	1.07	5.62	9	5.52	29
	Xavier Arreaga	1147	1.71	1.41	5.76	8	6.59	27
	Daryl Dike	1222	1.69	1.73	7.17	4	4.11	17
	Jhegson Méndez	1087	1.50	1.17	4.68	8	6.25	25
	Josh Williams	1021	1.40	1.04	7.71	6	4.43	33
	Artur	1831	1.30	0.76	6.15	11	6.40	52
	Yimmi Chará	1233	1.21	1.01	6.87	6	5.01	34
	Kevin Molino	1264	1.20	0.71	4.98	7	4.14	29
	Mauricio Pereyra	1107	1.18	1.19	6.69	6	6.02	34
	Eloy Room	1511	1.17	0.44	2.04	4	1.52	7

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.

Welcome

Team

Expected Goals (For)

Overview

Graphic

Definitions

Expected Goals (Against)

Overview

Graphic

Definitions

Team Sequences (Style)

Overview

Graphic

Definitions

Team Sequences (Pressure)

Overview

Graphic

Atlanta United FC

Chicago Fire FC

Colorado Rapids

Columbus Crew SC

D.C. United

FC Cincinnati

FC Dallas

Houston Dynamo FC

Inter Miami CF

LA Galaxy

Los Angeles Football Club

Minnesota United FC

Montreal Impact

Nashville SC

New England Revolution

New York City FC

New York Red Bulls

Orlando City SC

Philadelphia Union

Portland Timbers

Real Salt Lake

San Jose Earthquakes

Seattle Sounders FC

Sporting Kansas City

Toronto FC

Vancouver Whitecaps FC

Definitions

Goal Kicks

Overview

Graphic

Atlanta United FC

Chicago Fire FC

Colorado Rapids

Columbus Crew SC

D.C. United

FC Cincinnati

FC Dallas

Houston Dynamo FC

Inter Miami CF

LA Galaxy

Los Angeles Football Club

Minnesota United FC

Montreal Impact

Nashville SC

New England Revolution

New York City FC

New York Red Bulls

Orlando City SC

Philadelphia Union

Portland Timbers

Real Salt Lake

San Jose Earthquakes

Seattle Sounders FC

Sporting Kansas City

Toronto FC

Vancouver Whitecaps FC

Definitions

Player

Expected Goals

Overview

Pressure & Clarity

Graphic

Diego Rossi