In football, as in many sports, discussions about individual players have always been part of the fun. “Who is the best scorer?” or “Who is the king of defenders?” are questions perennially debated by fans, and social media amplifies this debate. Just consider that Erling Haaland, Robert Lewandowski, and Thomas Müller alone have a combined 50 million followers on Instagram. Many fans are aware of the incredible statistics star players like Lewandowski and Haaland create, but stories like this are just the tip of the iceberg.
Consider that almost 600 players are under contract in the Bundesliga, and each team has their own champions—players that get introduced to bring a specific skill to bear in a match. Look for example at Michael Gregoritsch of FC Augsburg. As of this writing (matchday 21), he has scored five goals in the 21/22 season, not something that would make anybody mention him in a conversation about the great goal scorers. But let’s look closer: if you accumulate the expected goal (xGoals) values of all scoring chances Gregoritsch had this season, the figure you get is 1.7. This means he over-performed on his shots on goal by +194%, scoring 3.2 more goals than expected. In comparison, Lewandowski over-performed by only 1.6 goals (+7%). What a feat! Clearly Gregoritsch brings a special skill to Augsburg.
So how do we shed light on all the hidden stories about individual Bundesliga players, their skills, and impact on match outcomes? Enter the new Bundesliga Match Fact powered by AWS called Skill. Skill has been developed through in-depth analysis by the DFL and AWS to identify players with skills in four specific categories: initiator, finisher, ball winner, and sprinter. This post provides a deep dive into these four skills and discusses how they are implemented on AWS infrastructure.
Another interesting point is that until now, Bundesliga Match Facts have been developed independent from one another. Skill is the first Bundesliga Match Fact that combines the output of multiple Bundesliga Match Facts in real time using a streaming architecture built on Amazon Managed Streaming Kafka (Amazon MSK).
An initiator is a player who performs a high number of valuable first and second assists. To identify and quantify the value of those assists, we introduced the new metric xAssist. It’s calculated by tracking the last and second-last pass before a shot at goal, and assigning the respective xGoals value to those actions. A good initiator creates opportunities under challenging circumstances by successfully completing passes with a rate of high difficulty. To evaluate how hard it is to complete a given pass, we use our existing xPass model. In this metric, we purposely exclude crosses and free kicks to focus on players who generate scoring chances with their precise assists from open play.
Let’s look at the current Rank 1 initiator, Thomas Müller, as an example. He has collected an xAssist value of 9.23 as of this writing (matchday 21), meaning that his passes for the next players who shot at the goal have generated a total xGoal value of 9.23. The xAssist per 90 minutes ratio is 0.46. This can be calculated from his total playing time of the current season, which is remarkable—over 1,804 minutes of playing time. As a second assist, he generated a total value of 3.80, which translates in 0.19 second assists per 90 minutes. In total, 38 of his 58 first assists were difficult passes. And as a second assist, 11 of his 28 passes were also difficult passes. With these statistics, Thomas Müller has catapulted himself into first place in the initiator ranking. For comparison, the following table presents the values of the current top three.
|Thomas Müller – Rank 1||9.23||0.46||3.80||0.18||38||11||0.948|
|Serge Gnabry – Rank 2||3.94||0.25||2.54||0.16||15||11||0.516|
|Florian Wirtz – Rank 3||6.41||0.37||2.45||0.14||21||1||0.510|
A finisher is a player who is exceptionally good at scoring goals. He has a high shot efficiency and accomplishes many goals respective to his playing time. The skill is based on actual goals scored and its difference to expected goals (xGoals). This allows us to evaluate whether chances are being well exploited. Let’s assume that two strikers have the same number of goals. Are they equally strong? Or does one of them score from easy circumstances while the other one finishes in challenging situations? With shot efficiency, this can be answered: if the goals scored exceed the number of xGoals, a player is over-performing and is a more efficient shooter than average. Through the magnitude of this difference, we can quantify the extent to which a shooter’s efficiency beats the average.
For the finisher, we focus more on goals. The following table gives a closer look at the current top three.
|Robert Lewandowski – Rank 1||24||1.14||1.55||0.813|
|Erling Haaland – Rank 2||16||1.18||5.32||0.811|
|Patrik Schick – Rank 3||18||1.10||4.27||0.802|
Robert Lewandowski has scored 24 goals this season, which puts him in first place. Although Haaland has a higher shot efficiency, it’s still not enough for Haaland to be ranked first, because we give higher weighting to goals scored. This indicates that Lewandowski profits highly from both the quality and quantity of received assists, even though he scores exceptionally well. Patrick Schick has scored two more goals than Haaland, but has a lower goal per 90 minutes rate and a lower shot efficiency.
The sprinter has the physical ability to reach high top speeds, and do so more often than others. For this purpose, we evaluate average top speeds across all games of a player’s current season and include the frequency of sprints per 90 minutes, among other metrics. A sprint is counted if a player runs at a minimum pace of 4.0 m/s for more than two seconds, and reaches a peak velocity of at least 6.3 m/s during this time. The duration of the sprint is characterized by the time between the first and last time the 6.3 m/s threshold is reached, and needs to be at least 1 second long to be acknowledged. A new sprint can only be considered to have occurred after the pace had fallen below the 4.0 m/s threshold again.
The formula allows us to evaluate the many ways we can look at sprints by players, and go further than just looking at the top speeds these players produce. For example, Jeremiah St. Juste has the current season record of 36.65 km/h. However, if we look at the frequency of his sprints, we find he only sprints nine times on average per match! Alphonso Davies on the other hand might not be as fast as St. Juste (top speed 36.08 km/h), but performs a staggering 31 sprints per match! He sprints much more frequently at with a much higher average speed, opening up space for his team on the pitch.
A player with this ability causes ball losses to the opposing team, both in total and respective to his playing time. He wins a high number of ground and aerial duels, and he steals or intercepts the ball often, creating a safe ball control himself, and a possibility for his team to counterattack.
As of this writing, the first place ball winner is Danilo Soares. He has a total of 235 defensive duels. Of the 235 defensive duels, he has won 75, defeating opponents in a face-off. He has intercepted 51 balls this season in his playing position as a defensive back, giving him a win rate of about 32%. On average, he intercepted 2.4 balls per 90 minutes.
The Skill Bundesliga Match Fact enables us to unveil abilities and strengths of Bundesliga players. The Skill rankings put players in the spotlight that might have gone unnoticed before in rankings of conventional statistics like goals. For example, take a player like Michael Gregoritsch. Gregoritsch is a striker for FC Augsburg who placed sixth in the finisher ranking as of matchday 21. He has scored five goals so far, which wouldn’t put him at the top of any goal scoring ranking. However, he managed to do this in only 663 minutes played! One of these goals was the late equalizer in the 97th minute that helped Augsburg to avoid the away loss in Berlin.
Through the Skill Bundesliga Match Fact, we can also recognize various qualities of each player. One example of this is the Dortmund star Erling Haaland, who has also earned the badge of sprinter and finisher, and is currently placed sixth amongst Bundesliga sprinters.
All of these metrics are based on player movement data, goal-related data, ball action-related data, and pass-related data. We process this information in data pipelines and extract the necessary relevant statistics per skill, allowing us to calculate the development of all metrics in real time. Many of the aforementioned statistics are normalized by time on the pitch, allowing for the consideration of players who have little playing time but perform amazingly well when they play. The combinations and weights of the metrics are combined into a single score. The result is a ranking for all players on the four player skills. Players ranking in the top 10 receive a skill badge to help fans quickly identify the exceptional qualities they bring to their squads.
Implementation and architecture
Bundesliga Match Facts that have been developed up to this point are independent from one another and rely only on the ingestion of positional and event data, as well as their own calculations. However, this changes for the new Bundesliga Match Fact Skill, which calculates skill rankings based on data produced by existing Match Facts, as for example xGoals or xPass. The outcome of one event, possibly an incredible goal with low chances of going in, can have a significant impact on the finisher skill ranking. Therefore, we built an architecture that always provides the most up-to-date skill rankings whenever there is an update to the underlying data. To achieve real-time updates to the skills, we use Amazon MSK, a managed AWS service for Apache Kafka, as a data streaming and messaging solution. This way, different Bundesliga Match Facts can communicate the latest events and updates in real time.
The underlying architecture for Skill consists of four main parts:
- An Amazon Aurora Serverless cluster stores all outputs of existing match facts. This includes, for example, data for each pass (such as xPass, player, intended receiver) or shot (xGoal, player, goal) that has happened since the introduction of Bundesliga Match Facts.
- A central AWS Lambda function writes the Bundesliga Match Fact outputs into the Aurora database and notifies other components that there has been an update.
- A Lambda function for each individual skill computes the skill ranking. These functions run whenever new data is available for the calculation of the specific skill.
- An Amazon MSK Kafka cluster serves as a central point of communication between all these components.
The following diagram illustrates this workflow. Each Bundesliga Match Fact immediately sends an event message to Kafka whenever there is an update to an event (such as an updated xGoals value for a shot event). The central dispatcher Lambda function is automatically triggered whenever a Bundesliga Match Fact sends such a message and writes this data to the database. Then it sends another message via Kafka containing the new data back to Kafka, which serves as a trigger for the individual skill calculation functions. These functions use data from this trigger event, as well as the underlying Aurora cluster, to calculate and publish the newest skill rankings. For a more in-depth look into the use of Amazon MSK within this project, refer to the Set Piece Threat blogpost.
In this post, we demonstrated how the new Bundesliga Match Fact Skill makes it possible to objectively compare Bundesliga players on four core player dimensions, building on and combining former independent Bundesliga Match Facts in real time. This allows commentators and fans alike to uncover previously unnoticed player abilities and shed light on the roles that various Bundesliga players fulfill.
The new Bundesliga Match Fact is the result of an in-depth analysis by the Bundesliga’s football experts and AWS data scientists to distill and categorize football player qualities based on objective performance data. Player skill badges are shown in the lineup and on player detail pages in the Bundesliga app. In the broadcast, player skills are provided to commentators through the data story finder and visually shown to fans at player substitution and when a player moves up into the respective top 10 ranking.
We hope that you enjoy this brand-new Bundesliga Match Fact and that it provides you with new insights into the game. To learn more about the partnership between AWS and Bundesliga, visit Bundesliga on AWS!
About the Authors
Simon Rolfes played 288 Bundesliga games as a central midfielder, scored 41 goals and won 26 caps for Germany. Currently Rolfes serves as Sporting Director at Bayer 04 Leverkusen where he oversees and develops the pro player roster, the scouting department and the club’s youth development. Simon also writes weekly columns on Bundesliga.com about the latest Bundesliga Match Facts powered by AWS
Luuk Figdor is a Senior Sports Technology Specialist in the AWS Professional Services team. He works with players, clubs, leagues, and media companies such as the Bundesliga and Formula 1 to help them tell stories with data using machine learning. In his spare time, he likes to learn all about the mind and the intersection between psychology, economics, and AI.
Pascal Kühner is a Cloud Application Developer in the AWS Professional Services Team. He works with customers across industries to help them achieve their business outcomes via application development, DevOps, and infrastructure. He is very passionate about sports and enjoys playing basketball and football in his spare time.
Tareq Haschemi is a consultant within AWS Professional Services. His skills and areas of expertise include application development, data science, machine learning, and big data. Based in Hamburg, he supports customers in developing data-driven applications within the cloud. Prior to joining AWS, he was also a consultant in various industries such as aviation and telecommunications. He is passionate about enabling customers on their data/AI journey to the cloud.
Jakub Michalczyk is a Data Scientist at Sportec Solutions AG. Several years ago, he chose math studies over playing football, as he came to the conclusion, he was not good enough at the latter. Now he combines both these passions in his professional career by applying machine learning methods to gain a better insight into this beautiful game. In his spare time, he still enjoys playing seven-a-side football, watching crime movies, and listening to film music.
Javier Poveda-Panter is a Data Scientist for EMEA sports customers within the AWS Professional Services team. He enables customers in the area of spectator sports to innovate and capitalize on their data, delivering high-quality user and fan experiences through machine learning and data science. He follows his passion for a broad range of sports, music, and AI in his spare time.