Datascouting: finding the best forwards in the Women’s Championship | OneFootball

One of the most exciting things about football is to discover new players. Whether that is a new talent coming through the academy or just unknown players with a great season. It can give great satisfaction to do so. One of those methods to get there is to scout players using data. In this article we will look at the Women’s Championship in England. We will try to find the best forwards of the 2023-2024 season.

Data

The data used in this analysis comes from Wyscout. In the dataset for the attackers, I’ve selected each player who primarily plays in either the winger or striker position. Other players have played in these positions. But, I’ve only selected the players that have played as an attacker as the dominant position in the current season. This leaves me with 63 players qualifying for the English Women’s Championship.

Because I’m looking at the current season, which isn’t a full season, I want to make a selection of players that played a decent amount of games for me to assess them. They must have played at least 500 minutes in the current season. After looking at that I’m left with 36 players in my dataset and they will go through my analysis process. The data was retrieved on January 14th, 2024.

I will look at the following categories and metrics to assess their abilities through data:

Looking at shot quality can be measured in different things. In the scatterplots below I will look at the volume of the shots. And, the expected goals that are generated through the shots.

Shots

In the shot volume, we can see that the following players do well. Goodwin (2,98 shots per 90), Green (3,05 shots per 90), and Boye-Hlorkah (7,24 shots per 90) stand out in terms of the number of shots in the league.

The best performers in terms of expected goals per 90 are the following: Boye-Hlorkah with 7,24 xG, Andrews with 0,58 xG, and Green with 0,67 xG.

Dribbling – Women’s Championship

Dribbling often is linked to wide midfielders of wingers, but it can be a valuable aspect of an attacker’s game as well. The ability to control the ball, progress on the pitch, and deal positively with a 1v1 situation with an opponent defender, is not to be underestimated. Especially when you are not playing a typical central forward role, but playing with two strikers.

If we look at the number of dribbles per 90 in the Women’s Championship, the following players come out on top of their respect metric: Satchell with 6,71 dribbles per 90, Rayner with 8,53 dribbles per 90, and Boye-Hlorkah with 10,02 dribbles per 90.

When we look closer at the success rate of the dribbles, we can see that a different set of players scores high – but attempt fewer dribbles per 90: Troelsgaard with 70,36% successful dribbles, Thomas with 70,83% successful dribbles, and Boye-Hlorkah with 94,74% successful dribbles.

Assists

Expected metrics seem simple but can become incredibly complicated when combining things. In the scatterplot above I’ve taken a look at the probability of the pass becoming an assist per 90 minutes and looking at the actual assists of a player per 90 minutes.

If we look at the expected assists per 90, we can see that a few players stand out from the crowd with a significantly higher xA per 90 than the rest. Hornby has 0,26 expected assists per 90, Ivana Fuso has 0,37 expected assists per 90, and Lucy Quinn has 0,44 expected assists per 90.

Looking more closely, we can see that the actual assists per 90 does correspond with the three players with the highest expected assists per 90. Hornby has 0,46 assists per 90, Dennis has 0,46 assists per 90, and Lucy Quinn has 0,65 assists per 90.

Goals – Women’s Championship

In the end, the most important thing for an attacker is her output: goals. I’m looking at the probability of scoring a goal with a certain short and looking at the actual goals scored by a particular player per 90 minutes.

The best performers in terms of expected goals per 90 are the following: Boye-Hlorkah with 7,24 xG, Andrews with 0,58 xG, and Green with 0,67 xG.

When we look more closely at the actual goals scored per 90 we see that Goodwin 0,63 goals per 90, followed by Hughes with 0,94 goals per 90,  and Boye-Hlorkah with 1,08 goals per 90.

Final thoughts

In the data analysis above we have seen a few key elements of a striker’s attacking play. In every aspect of those key elements, we have seen which players have been the best and which players we would do well to track.

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