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Tokyo Summer Olympics 2020: Scouting Team GB's wide forwards using data - scout report tactics

Tokyo Summer Olympics 2020: Scouting Team GB’s wide forwards using data – scout report

The Olympic Games is a quadrennial event and will be held this summer, 2020, in Tokyo, Japan. The creation of the Olympic games was inspired by the ancient Olympic Games which were held in Olympia, Greece from 8th century BC to the 4th century AD. The first modern-day Olympic games were held in Athens, Greece in 1896. Tokyo 2020 will feature 339 events in 33 different sports, of which women’s soccer (association football) will be one of those contested.

Whilst the participating men’s teams are restricted to U-23 players (with three over-age players), the women’s event has no age restrictions. 12 total women’s soccer teams will compete at the Olympic Games, including Great Britain, who qualified for the event via England’s final finishing position (4th) at the 2019 FIFA Women’s World Cup in France.

The Great Britain women’s soccer team roster at the Tokyo Olympics has the potential to include 18 total players from England, Wales, Scotland and Northern Ireland. This provides an interesting focus for a tactical analysis and scout report using data.

By utilising player assessment metrics, myself and Abdullah Abdullah will perform a data analysis utilising data of specific positional players. This will assist in the initial identification of a potential roster for Phil Neville’s Great Britain team. Each article will be split by position and eventually an analysis for the starting 11 players created. In this first article of the scout report series, I will be focusing on Team GB’s options at the wide forward position.

Head Coach Phil Neville and principles of play

Phil Neville was selected to lead Team GB on 30th June 2019 following England’s performance at the 2019 Women’s World Cup in France. To begin this scout report series, it will be beneficial to first analyse Phil Neville’s principles of play, system and how he utilises positional players.

Phil Neville implements two main system variations. These being 1-4-3-3 (1-4-1-4-1) and 1-4-4-2 (1-4-4-1-1), as you can see within the following two average position diagrams from England’s recent ‘She Believes’ competitive matches against the USA (left) and Japan (right).

Tokyo Summer Olympics 2020: Scouting Team GB's wide forwards using data - data analysis tactics
Average position map of England Vs. USA (left) and Japan (right): credit: Wyscout

When facing USA (left), England adopted a 1-4-4-2 system, however, evident is one central midfield (#4 Kiera Walsh) occupying a deeper role and one central midfielder (#8 Jill Scott) moving into more advanced positions. Also evident within the forward line is that one central forward remains higher (#18 Ellen White), whilst the other drops deeper to link play through the midfield (#16 Georgia Stanway). Lastly, two wide players can be seen occupying the wider and inside half-space channels (#20 Lauren Hemp and #7 Nikita Parris).

In comparison, when facing Japan (right), England adopted a 1-4-3-3 system, however, this became very similar to the system and average player positions as against the USA. This included the high forward (#9 Bethany England), two attacking central midfielders (#16 Georgia Stanway and #10 Jordan Nobbs), one deeper central midfielder (#4 Kiera Walsh) and two wide attacking players (#20 Lauren Hemp and #22 Chloe Kelly). Variations in the average positions of the wide forwards that seem to occupy a more central position can occur if players switch sides during games.

Utilising specific data points to assist with roster identification and selection

Abdullah Abdullah and I have applied initial specifications for players to be included in this data analysis and to be subsequently considered for selection. The stipulations are that the player must have been called into at least one national team selection roster during the last 24 months, the player currently plays in the top tier of their current competition and finally the player must have participated in a minimum of 450 minutes during the current/last season of play. The data utilised will be from the players current domestic league season play.

In order to provide a balanced roster and one that can face opponents implementing a variety of potentially different tactics and systems of play, the 18 players will occupy the following positions – two Goalkeepers, three wide defenders, three central defenders, two holding central midfielders, three attacking central midfielders, three wide forwards and two central forwards. This will provide cover at every position to the starting 11 (in a 1-4-3-3 or 1-4-4-1-1 system, discussed previously in the article).

This specific data analysis article will focus on identifying the three wide forwards. Using those initial requirements discussed for consideration within the data analysis stage, 12 players have been selected for evaluation. These are, in alphabetical order, Beth Mead (Arsenal WFC, England), Chloe Kelly (Everton, England), Claire Emslie (Melbourne City FC/Orlando Pride, Scotland), Erin Cuthbert (Chelsea FC Women, Scotland), Kayleigh Green (Brighton and Hove Albion WFC, Wales), Kirsty Hanson (Manchester United WFC, Scotland), Lauren Hemp (Manchester City WFC, England), Lauren James (Manchester United WFC, England), Lisa Evans (Arsenal WFC, Scotland), Melissa Lawley (Liverpool FC Women, England), Nikita Parris (Olympique Lyonnais Feminin, England) and Toni Duggan (Atletico Madrid Femenino, England).

Attacking impact in the final third

For the first data comparison, I will start with a very simple player analysis on two specific metric combinations, these being goals per 90 minutes and assists per 90 minutes. This gives an indication of the players which, in their domestic leagues, have the most impact in terms for scoring goals or assisting goals.

The graph formed is split into four quadrants based on the average results. Players in the top left quadrant have above-average numbers of assists per 90 minutes but lower than the average number of goals per 90. The top right quadrant demonstrates players who have above average in both categories. Clearly shown is that only one player from the 12 listed has scored above average in both goals per 90 minutes and assists per 90 minutes. This player is Kirsty Hanson, however, she has scored only marginally better than the average in both.

Two specific groups have emerged from this comparison with Nikita Parris (0.84), Chloe Kelly (0.76), Toni Duggan (0.68) & Lauren James (0.68) scoring a high number of goals per 90 minutes (& low on the number of assists per 90 minutes) and Lauren Hemp (0.4), Lisa Evans (0.36) and Erin Cuthbert (0.34) the highest scoring in terms of assisting goals (& low on the number of goals per 90 minutes) along with Beth Mead (0.28).

Claire Emslie, Kayleigh Green and Melissa Lawley scored lower in both goals & assists per 90 minutes when compared to the average of all 12 players, shown by placement in the lower left quadrant.

When calculating the total of goals per 90 minutes PLUS the assists per 90 minutes, the highest-scoring player is Nikita Parris (0.96), followed in second position by Chloe Kelly (0.84) and third is Lauren Hemp (0.8). The bottom three scorers are Melissa Lawley (0.07), Kayleigh Green (0.28) and Lisa Evans (0.5).

Tokyo Summer Olympics 2020: Scouting Team GB's wide forwards using data - data analysis tactics
Goals per 90 & assists per 90

Responsibility: Goalscoring

One specific and major role of any forward, central or wide, is to score goals. To move deeper into the data analysis of each of the 12 players and to see clearer the goal-scoring potential of them, a comparison between the actual goals scored per 90 minutes and the xG (expected goals per 90 minutes) will be utilized during a data check. This comparison is important and can indicate a player’s level of shooting ability or ‘luck’. If a player is scoring more goals than their total xG they could be demonstrating an above-average shooting/finishing level.

As per the previous graph, Nikita Parris is the standout player (goals per 90 = 0.84, xG = 0;69), with Chloe Kelly (goals per 90 = 0.76, xG = 0.42) and Lauren James (goals per 90 = 0.68, xG = 0.38) both scoring highly. All three have also exceeded their xG with actual goals.

The second graph in the ‘goal-scoring’ wide forward analysis section is a comparison between touches in the box per 90 minutes and average shots on target per 90 minutes. Again, Nikita Parris is the standout wide forward, combining high scores for both touches in the box per 90 (6.84) and shots on target per 90 (2.14).

As with the previous graph, Lauren James also scores highly (6.34 touches in the box per 90, 1.97 shots on target per 90). Claire Emslie scores highly on both (5.28 touches in the box per 90, 1.3 shots on target per 90) whilst Chloe Kelly has a relatively high number of shots on target (1.34), but limited touches inside the box (3.04), ranking 9th of the 12th in this category.

Tokyo Summer Olympics 2020: Scouting Team GB's wide forwards using data - data analysis tactics
Goals per 90 and xG per 90
Tokyo Summer Olympics 2020: Scouting Team GB's wide forwards using data - data analysis tactics
Touches in the box per 90 and shots on target per 90

Responsibility: Creating opportunities for others

A second very important role of a wide forward is creating opportunities for team-mates. xA measures the likelihood that any given pass will become an assist. Adding up the xA gives an indication of how many assists a player should have based on their attacking play. It does not rely on the result/ability of the shooter or luck.

The first graph compares the assists per 90 minutes and xA per 90 minutes, Erin Cuthbert is a clear standout player (assists per 90 = 0.34, xA = 0.37) with Lisa Evans (assists per 90 = 0.36, xA = 0.23), Lauren Hemp (assists per 90 = 0.4, xA = 0.19), Beth Mead (assists per 90 = 0.28, xA = 0.21) and Kirsty Hanson (assist per 90 = 0.18, xA = 0.22) also scoring highly and placing in the top right quadrant.

Claire Emslie, Kayleigh Green, Chloe Kelly, Nikita Parris, Melissa Lawley and Lauren James have scored low compared to the average. Toni Duggan has scored higher on the xA per 90 (0.26) than actual assists per 90 (0).

The second data comparison is a data check between the average number of crosses per 90 minutes and average number of through passes per 90 minutes. Wyscout has defined a ‘cross’ as “a cross from the side of the pitch into the penalty area, when the player is kicking the ball inside it, trying to find some of his teammates or to create some dangerous situation” (credit: Wyscout). Wyscout has also defined a ‘through pass’ as a “direct pass in the empty space behind the defensive line, leading the attacking player against the goalkeeper” (credit: Wyscout).

Beth Mead, Lisa Evans, Chloe Kelly and Erin Cuthbert, using this data check scored highly (above average) on both measurements which can be seen through placement in the top right quadrant. Claire Emslie, Kayleigh Green, Melissa Lawley and Lauren James scored below average on both and are located in the bottom left quadrant.

Tokyo Summer Olympics 2020: Scouting Team GB's wide forwards using data - data analysis tactics
Assists per 90 and xA per 90
Tokyo Summer Olympics 2020: Scouting Team GB's wide forwards using data - data analysis tactics
Crosses per 90 and through passes per 90

Conclusion

In conclusion, in order to provide a balanced team GB Olympic women’s soccer roster, data has been utilised to help initially identify 18 potential players. Through this article, the data has assisted in identifying standout players for inclusion as a position-specific wide forward in terms of two specific areas. These two areas were goal-scoring ability and ability to create chances for others.

The standout player in terms of goal-scoring ability is Nikita Parris scoring above average in all categories – actual goals per 90, xG per 90, touches in the box per 90 and shots on target per 90. If Phil Neville wanted to include a second goal-scoring wide forward, Lauren James also scored above average in all of the above categories.

Chelsea‘s Erin Cuthbert is the standout player in terms of creating chances for others, scoring above average in all categories – actual assists per 90, xA per 90, crosses per 90 and through passes per 90. If Phil Neville wanted to include a second forward with the potential to create chances for others, both Arsenal‘s Beth Mead and Lisa Evans also scored above average in all four of the above categories.

Data analysis is only the first step, however, and it must also be noted that the data analysis compares players across multiple leagues (FAWSL, NWSL, W-League, D1 Feminine and Primera Division) which all have different strengths of schedules. The average data collected does also not differentiate or account for any variation in role/positioning within the players’ domestic team.

The next step must be to utilise the data and combine with video scouting to provide more detail and complete analysis. This can be achieved alongside Coach Neville’s specific positional requirements and specific game model and team tactical principles that will be implemented by the GB women’s soccer team.