Last week, we have discussed the transfer pattern of Fulham for the past three years in part one of this series. This gives us a filter to find the replacement candidates. Now, in this part two analysis, we will try to come up with a list, in the end, utilising the standard we’ve discovered last week.
This is the second part of the recruitment analysis. In this second data analysis, we’ll be utilising data and statistics to find a shortlist of three players as candidates, which will be of use in the third part of the analysis.
Aleksandar Mitrović’s profile
Before conducting the data analysis to find his replacement, we first need to know Aleksandar Mitrović’s playing style, role and how his attributes affect team performance. Firstly, we want to mention some of his unquantifiable attributes. Then we will get to his statistical profile.
Mitrović is 188 cm in height. Since he is tall and strong, when Fulham finds playing out from the back is unavailable, he can serve as the target man for long balls. He has 8.56 aerial duels per 90, with the success rate of 41.88%. When he succeeds in combating these long balls, he will lay off to his teammates facing the opponent’s goal. This helps the team progress the play.
With his strong physique and well-time runs, he can also drop to the midfield to help open the play. He can hold up his matchup and time his run into the midfield, receiving passes from the defenders and one-touch pass back to the pivot. You can see from the above heat map that he will drop to the midfield to help progress the play:
In Fulham’s 4-3-3 playing system, this is a common pattern to find the single pivot. With his well-timed run, strong physique along with his mobility, the pivot doesn’t need to run a lot to receive the ball in a useful position. The below image is an example:
In the above scenario, Fulham was trying to instigate Leeds United to press and free the pivot. As the pivot’s matchup left his zone to press the centre-back, this was the trigger for Mitrović to drop. Then Mitrović timed his run, holding up his matchup and one-touch passed to the unmarked pivot. The pivot could now face the opponent’s goal and dictate the play.
On top of his contribution to the build-up stage, in the finishing stage, he also contributes a lot. In Fulham’s 4-3-3 playing system, flanks are resorts to create finishing opportunities. With 1v1 specialists like Ivan Cavaleiro in the flank, Mitrović will stay in the central defenders’ area to keep them from helping full-backs in the flank. This creates more space and time for 1v1 specialist to operate in the flank. And he will also serve as a target man in crosses. Below is an example:
You can see from the above example that Fulham was attacking the right fank. Mitrović was in the central area to hold the centre-back from helping and covering the full-back. This helped create more space for the winger in 1v1 situation, and the winger could his technique to beat the full-back and deliver a fine cross to find Mitrović in the box.
After talking about some basic attributes in attacking, his curving run in defending also helps Fulham in high press. You can see from the below example that his curving eliminated pass-back option and successfully made the play predictable:
Ben White passed to the goalkeeper. That was when Mitrović curved his run, eliminating the pass-back option to White. With the good positioning of his teammates, now Leeds United’s goalkeeper didn’t have many choices in the back. Hence he was forced to play long and Fulham gained possession in the back in this scenario.
The above attributes are unquantifiable, which will be of use when we come up with the final list and do the scouting. Now we will take a look at the data side, using a radar graph to show his key statistical attributes:
Mitrović now has 23 goals in 36 games in the EFL Championship. His xG per shot ranks second in the league (0.18). Also, his goal per 90 (0.7) exceeds xG per 90 (0.63), which means that he scores more than expected and can score in a difficult situation. He has 3.93 touches in the box, ranking the 26th in the league. On top of this, he takes many offensive duels per 90 (14.88) with a poor success rate (26%). His dribbles per 90 are 3.34, with the success rate of only 39.23%. He is not a dribbler that can bring the ball forwards for a long distance. Furthermore, he has 3.23 forward passes per 90, with a success rate of 67%, and also 1.64 progressive passes per 90 (40%). These forward passes are usually executed when he drops to help open the play.
Now we have known his statistical profile. He is quite effective in finishing while other stats are not that standing out. Thus, we want to find a striker that can do the same thing as Mitrović, and even better in terms of passing or other facets.
Data analysis – scoring goals
After doing our search on Fulham’s transfer pattern and Mitrović’s attributes, we can finally conduct the data analysis. With the standard we found in the last piece, we will try to conclude with a list of 3 players, whose age will be 23 to 27 years old. They will be from the English Premier League, Championship, La Liga, Ligue 1, Liga NOS and Bundesliga, who plays more than 900 minutes this season. Their market value is below 30 million. What’s more, we also want to find strikers that are effective as Mitrović. Hence we add one more filtration in the first place: xG per 90 should exceed 0.3.
Using these filtrations, we now can start our data analysis. The first we want to know about is the goal-scoring performance of these strikers since finishing and scoring are natural responsibility for them. Therefore, we’ll be looking at the first metrics here: xG per 90 and goals per 90.
From the above scatter plot we can observe that Vinícius from Benfica stands out, with his goals per 90 (0.9) higher than xG per 90 (0.62). Danny Ings from Southampton and Patrik Schick from RB Leipzig also excel in terms of these metrics. Their goals per 90 (0.67 and 0.66 respectively) both exceed xG per 90 (both are 0.42), which means that they score more than expected and they could catch some difficult chances to score. On top of this, André Silva is also excellent in these aspects (xG per 90: 0.62, goals per 90: 0.5). Mitrović stands out in these aspects as well, with the figures we’ve mentioned in the above section. Players like Steve Mounié, Paulinho, Wout Weghorst, Dominic Calvert-Lewin, Ollie Watkins also possess some impressive figures in these metrics.
The second metrics we’ll delve into are goal conversion and shots per 90. We intend to see how effective the striker is using these metrics.
In this aspect, Vinícius stands out again, with his impressive goal conversion of 0.3269, with means that on average one shot of his will produce 0.3269 goals. His shots per 90 also exceed the average, which is 2.75. And we keep looking down the second quadrant, familiar names as Ings, Schick, Silva, Paulinho and Calvert-Lewin once again catch our attention. Ings has 0.2535 in goal conversion, 2.64 in shots per 90; Schick has 0.2381 in goal conversion, 2.76 in shots per 90. André Silva also does well with goal conversion of 0.2308 and shots per 90 of 2.71. Mitrović also stays in the second quadrant, which means that he is above the average in both metrics. Then we also take a look at the first metrics, meaning the goal conversion is above the average while shots per 90 are not. Adrien Hunou and Fábio Abreu have some great numbers in goal conversion (0.2963 and 0.2927 respectively). We can also see some names which we’ve seen in the first metrics: Mounié, Watkins are in this first quadrant (0.2667 and 0.2347 in goal conversion).
Data analysis – aerial duels
After looking into the goal-scoring performance of these strikers, we now come to the aerial duels, since Mitrović is a target man in front for long ball and crosses. We intend to find a striker with the same ability, or even better in this facet. So we use the metrics of aerial duels and the success rate here to find out who stands out in this aspect:
From the above scatter plot we can see that Steve Mounié and Kieffer Moore excel in the aerial duels and the success rate. They have 18.15 and 15.75 aerial duels per 90, and the success rate of 54.29% and 53.10%. Weghorst in the second quadrant possesses a fabulous success rate of 54.34%, the best amongst all these strikers, with 6.78 in aerial duels per 90. Schick to his right has a decent success rate as well (53.57%), with aerial duels per 90 of 7.36. These are the four players whose success rate is higher than 50%. Gonçalo Paciência in the average-axis has 7.30 aerial duels per 90, with 49.55% in the success rate. Calvert-Lewin also possesses some decent figures in the first quadrant, with 13.31 aerial duels per 90 and 43.84% success rate. Oliver McBurnie from Sheffield United has 14.81 aerial duels per 90 and 41.72% in the success rate. Mitrović is in the second quadrant in this graph, which means that his aerial duels per 90 and the success rate are both above the average figures.
Data analysis – offensive duels
Mitrović takes many challenges offensively; however, his success rate in this aspect is poor with only 26%. Thus, we want to find players that could contribute to offensive duels as many as Mitrović whereas with a decent success rate.
In terms of the success rate, Rachid Alioui stands out with a figure of 46.4% (6.76 offensive duels per 90). In the second quadrant of the graph, Breel Embolo excels in the offensive duels per 90 (19.07) amongst all the strikers, with a decent success rate of 35.65%, above the average. Another player that catches our attention is Léo Bonatini. His offensive duels per 90 are 4.68, with the success rate of 42.68%. The rest are clustering near the average point, the origin of axes. Mehdi Taremi has 9.21 offensive duels per 90 and 42.58% in success rate. Toni Martínez from Famalicão takes 7.16 offensive duels per 90, with the success rate of 41.77%. Mitrović stays in the third quadrant of the graph, which means that his offensive duels per 90 are above the average whereas the success rate is below the average.
Data analysis – progressive passes
With Mitrović’s dropping back into the midfield, the play of Fulham can be linked and opened effectively, and he can also release some progressive passes in this scenario. Thus, we also want to find a player that can play progressive passes.
From the above scatter plot we can see that Lebo Mothiba is better than the rest with the accuracy of 100%, 0.97 progressive passes per 90. Then we shift to the right of the graph, Alassane Pléa from Borussia M’gladbach has the most progressive passes per 90 amongst all these strikers, with an accuracy of 77%. Cauley Woodrow also does well in progressive passes per 90 (3.81), and the accuracy is 76.52%. Names like Gonçalo Paciência and Embolo also appear in the first quadrant, whose progressive passes per 90 (2.63 and 2.32 respectively) and the accuracy (82.5%, 73.81%) are both above the average. Also, Watkins and Weghorst spot in the second quadrant. Watkins possesses 1.91 progressive passes per 90, with 80.49% accuracy, while Weghorst has more progressive passes per 90 (2.54) with a slightly lower accuracy (70.73%). Mitrović stays in the first quadrant of this scatter plot, which means that his accuracy in progressive passes is above the average level of all these strikers. His progressive passes per 90 are lower than the average level though.
After delving into some many metrics, we now can conclude with a final shortlist of three players. Since we want to find players that have the same function in the team, and even better than Mitrović, we first consider the aerial ability to determine if the player could fit in the target-man tactics from a statistical point of view. Then we will compare the goal-scoring stats amongst these strikers. Offensive duels and progressive passes are considered later. Then we will also look up the rumours about these players. For example, the potential transfer fee of Vinícius from Benfica is quite high (100 million), whereas his market value does not exceed 30 million. Therefore, he is excluded from the list even though his stats are outstanding.
Thus, we come to the final shortlist of three players. These three players will be scouted further in the next piece:
Wout Weghorst: The 27-year-old Dutch from Wolfsburg is now the top scorer in the team. He has scored 16 goals in 30 matches. His goal per 90 is 0.5, slightly less than his xG per 90 (0.51). The goal conversion rate of his is 0.2025, better than Mitrović’s. He has 6.78 aerial duels per 90, with 54.34% in success rate, the highest amongst all these strikers above. He also has 2.54 progressive passes per 90, which are more than Mitrović’s, with a decent accuracy of 70.73%. A weakness of his seems to be his offensive duels success rate (25.08%). The market value of his is 21 million and there are rumours that Arsenal has the intention to sign this player with 35 million. He is a good choice for Fulham if they can reach the Premier League next season.
Paulinho: The 27-year-old Portuguese in Sporting Braga. He has scored 12 goals in 24 matches. His goals per 90 (0.54) exceed his xG per 90 (0.51), which means that he can turn some difficult chances into goals. His goal conversion is also the same as Mitrović’s (0.1791). On top of this, his aerial duels’ success rate is 43.69%, better than Mitrović’s, whereas his aerial duels per 90 are 4.65, less than Mitrović’s. His offensive duels’ success rate (30.57%) is better than Mitrović’s. He can release 2.48 progressive passes per 90 with an accuracy of 67.27%. AC Milan is interested in this Portuguese striker. He is at a suitable price for no matter if Fulham can make it to the Premier League next season.
Gonçalo Paciência: The 25-year-old Portuguese striker from Eintracht Frankfurt has scored seven goals in 1368 minutes. He has 0.46 goals per 90, exceeding 0.45 xG per 90. His aerial duels’ success rate is 49.55%, 7.3 per 90. Compared to other candidates, he excels in progressive passes, with 82.5% accuracy and 2.63 per 90. He also takes a lot of offensive duels per 90 (17.3) but with a poor success rate (27.38%).
Hence we have already come up with a shortlist of three players, using data and statistics. In the next part, the final part of this analysis, we will delve into their playing styles and attributes using a scout report.