Push Notification
scouting Bayern Munich's academy - data analysis statistics

Scouting Bayern Munich’s academy – data analysis

Many of the European league champions are renowned for buying world-class players to build their star-studded squads. Bayern Munich are no exception to this, buying a plethora of young and experienced players to win the Bundesliga and Champions League. Philippe Coutinho, Lucas Hernandez, and Benjamin Pavard are three examples of high profile signings made by the Bavarians in recent transfer windows. However, they have made shrewd signings with the biggest success being Alphonso Davies. The Canadian left-winger cum left-back has lit up the world and is considered one of the best talents in world football. However, Bayern’s academy has a number of talented players that could make the step up to the first team and make a real impact. 

In this data analysis, we will analyse Bayern Munich’s academy through data and statistics to find the most talented youngsters that could make the next step up to the first team.

Parameters

Before we delve into the analysis,  we will need to establish the parameters used to be able to arrive at the results. We’ve decided to take a look at all the Bayern Munich II players using the data available on Wyscout for the 2019/20 season.

Since the academy mainly consists of very young players, it is imperative we set a parameter to measure players who have had some meaningful game time to establish a base. So it’s important to note that we’ll only highlight players who have played 500 minutes or more this season and only the ones whose data set allows us to take a more in-depth look.

The analysis will cater towards looking at the most attack-minded players in the squad, analysing their goal contribution, creativity, and ball progression to give us a clear picture of some of the most influential players the Bayern academy squad has on offer.

Goal contribution

scouting Bayern Munich's academy - data analysis statistics

The first graph we’ll be looking at is the players’ goal contribution – goals and assists per 90 – and expected goal contribution.

We can immediately see there are quite a few names that stand out in this metric. Kwasi Okyere Wriedt is the highest performer with a goal contribution of 0.78 and xGoal contribution of 0.69. Wriedt is by far the best performer for Bayern Munich II in this category and can be considered a major goal threat. Oliver Batista Meier averages a goal contribution of 0.48 and xGoal contribution of 0.53. Jannik Rochelt averages 0.39 goal contribution and an xGoal contribution of 0.32, albeit he has played 916 minutes. Leon Dajaku is another name that sits in the top performer’s quadrant with a goal contribution of 0.32 and xGoal contribution of 0.25 but he too has less than 1,000 minutes. Sarpreet Singh has performed quite well by outdoing his xGoal contribution of 0.30 by averaging 0.67 goal contribution. Lastly, Marcel Zylla has a goal contribution of 0.32 with an xGoal contribution of 0.25.

Creativity

scouting Bayern Munich's academy - data analysis statistics

The next graph we’ll analyse is Bayern Munich II players’ creativity. For this, we will use final third passes per 90 and passes to penalty area per 90.

Four players stand out from this graph who have performed above average in this regard this season. Adrian Fein has averaged 9.1 final third passes per 90 and 3.2 passes to the penalty area. Timo Kern has 8.8 final third passes and 2.5 passes to the penalty area per 90. Singh averages slightly less final third passes in 7.3 per 90 but has 3.1 passes to the penalty area per 90. Lastly, Derrick Köhn averages 5.8 final third passes per 90 and 3.1 passes to the penalty area per 90.   

The numbers registered by these forward players is quite high and bodes well for such an attacking setup that Bayern have set in the first team.

Ball progression

Next, we will be analysing and looking at how the squad fares in terms of ball progression and who are the most competent players at it. We will be using progressive actions per 90 (progressive runs combined with progressive passes per 90) and dribbles per 90 minutes to find the most effective players.

scouting Bayern Munich's academy - data analysis statistics

There are two players that once again a standout in this metric comparison. Köhn has progressive actions of 10.72 per 90 with 5.4 dribbles per 90. Singh has 11.30 progressive actions and 4.1 dribbles per 90. Wreidt has the second-highest dribbles per 90 but is quite below the average line when it comes to progressive actions. Kern seems to be situated just under the average line for dribbles per 90 (3.0) but has a higher average for progressive actions per 90 (10.16).

Shortlist

After analysing the academy across the three metrics, we have come up with a shortlist of the players that statistically have the best numbers who could figure in the first team. WriedtKöhn, and Singh are the two players that the data has repeated and would be worth closer inspection.

scouting Bayern Munich's academy - data analysis statistics

The first name we see repeated in two of the three metrics is Wriedt. The striker has an incredible goal contribution outperforming his xGoal contribution. In both accounts, he registers the highest average figure. The forward also stands out in ball progression with a high number of dribbles. Wriedt seems to be a very competent goal scorer with 17 goals in Bundesliga .3 this season and would be worth a detailed scout report on. Singh, however, seems to be a better a link player and creative forward with his higher number of assist, progressive actions and passing metrics. This can also be seen by his lower number of touches in the box per 90, making him a player that could be hugely beneficial playing alongside a central striker or even possibly as a number 10.

scouting Bayern Munich's academy - data analysis statistics

Köhn has high numbers when it comes to offensive duels and ball progression highlighting his ability to progress and shield the ball playing in an attacking system. The only defender in the shortlist, Köhn seems to be a promising attacking left-back who is able to successfully deliver the ball into the box. His 5.20 dribbles per 90 and 3.17 passes to the penalty area is the second-highest of the squad making him a very dangerous player in the final third for Bayern Munich II.  

Final remarks

This data analysis can only go so far in giving us information about these players. Without context, the results are meaningless and we will need more to be able to truly understand what it is that makes these players score so highly on these metrics. The next step would be to write detailed scout reports on them to further analyse their skillset.