Season Review 2019/20 |
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.
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.
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 |
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. |
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.
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 |
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. |
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).
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 |
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. |
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).
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 |
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. |
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.
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 |
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. |
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.
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% |
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.
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 |
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. |
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.
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 |
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 |
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 |
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 |
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). |
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.
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 |
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. |
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.
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 |
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. |
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.
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 |
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 |
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 |
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 |
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. |
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.
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 |
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 |
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. |