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Home Analysis

Finding data trends to identify high scoring strikers – data analysis

Abdullah Abdullah by Abdullah Abdullah
April 21, 2020
in Analysis, Data Analysis
0
Finding data trends to identify high scoring striker's - data analysis statistics

Over the past two seasons, Division 1 Feminine has been dominated by Olympique Lyon with the champions scoring 156 goals. The goal-scoring charts have been led by the talismanic Norwegian striker Ada Hegerberg who scores goals for fun in both the Champions League and Division 1. However, right behind her has been another player who has also scored a number of goals in both competitions and has kept pace with the Norwegian. Marie Antoinette-Katoto is a striker playing for Paris Saint-Germain Feminine and at 21 years old, remains one of the top young strikers in women’s football today.

My objective with this data analysis is to see if I can find common data metrics that contribute to a striker’s goal-scoring exploits and find out whether we can use the results to begin searches for other elite-level strikers for recruitment. I will initially be conducting a comparison of metrics that contribute towards goal scoring and discuss the results based on Hegerberg and Katoto. I will then compare both players to other top-level strikers and see if the results are in line with their season’s data.

What metrics are we using for comparison?

For our initial search, I identified four sets of data metrics that can be typically used to find out about a striker’s effectiveness. For this analysis, I have used Average Expected Goals (xG) vs Average goals, Average xG vs average Touches in the box, Average Shots vs average Shots on target, and Average Dribbles vs Average Shots. The reason for using these four comparison metrics is because it should act as the best indicator of a strikers effectiveness in the final third through the actions they perform most often. Starting with shots, it is a good indicator because of the number of times a striker will look to shoot in a season. If there’s any aspect of a striker that will give you an indication of whether they’re getting opportunities, it is the number of shots they’re taking per game and we need to also look at the goal scoring opportunities they are getting by testing the goalkeeper by using shots on target. 

Expected Goals (xG) is one of the most sought after metric in data analysis today, every club and recruitment scout report will have xG figures as a basis of goal-scorers effectiveness. Measuring it against their goals average this season will tell us whether they are able to outscore the number of chances presented to them proving their scoring efficiency and effectiveness. Using xG to compare the number of average touches in the box will also give us an indication of how much time the strikers spend in the penalty area and see their quality of chances.

Next, we’ll observe the number of shots, against the number of shots on target to show their accuracy in front of goal and see how often they test the goalkeepers. Lastly, by looking at the average number of dribbles against the average number of shots, it will give us a suggestion if these strikers are effective as players who are good on the counter-attack plus dribble themselves into shooting positions.

Assumptions

For the purpose of this analysis, I have used the 2019/20 season’s statistics as the information available was most accurate with a differential of 1-2 goals across for both Hegerberg and Katoto. The data will only comprise of statistics from the league and Champions League.

Before we look at the results, it’s important to note that Katoto has scored 19 goals this season including one scored penalty and Hegerberg has 22 goals with three penalties. It’s better to understand how many of their goals came from penalties as to not skew the results. In this case, because the numbers are low, we can include them in our data set. Out of the 41 goals scored between them, 37 have come from open play which still gives us a healthy sample size to base our results on.

Average xG vs Average Goals

Finding data trends to identify high scoring striker's - data analysis statistics
[Data Source: Wyscout]
For the first data comparison, I am looking at Average xG and Average goals scored. This is a very common comparison where the results will give us a high-level indicator of whether these strikers are outscoring the number of chances they are presented across the season. The higher the xG, the better the quality of chances to score and if their average goals exceed this number, it means they are scoring more than expected, be it an easy or a hard chance.

The results are quite interesting. We can see that Hegerberg is scoring at a consistent rate of 1.29 goals and equally matching her average xG of 1.29. This means the Norwegian is keeping pace with the chances being presented to her. Naturally, this makes some sense because of the number of chances and possession Lyon have in the majority of their games. While one would have thought that Hegerberg should have outscored her xG simply due to the number of chances Lyon create per game, it’s worth noting that she did sustain a season-ending ACL injury that kept her out of three games before the season was suspended. 

Katoto, however, has exhibited some interesting results with her performances. With a goal average of 1.06, she has been outscoring her xG of 0.71 which suggests that the Parisian striker is putting away more difficult chances and is clinical in front of goal. It would have been interesting to see if she could have continued her scoring rate with the latter stages of the Champions League yet to be played with high-quality opposition such as Arsenal to come but nonetheless, it is remarkable.

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