There’s a kind of magic that surrounds a soccer shot so powerful, it leaves spectators, players, and even commentators in a momentary state of awe. Think back to a moment when the sheer force of a strike left an entire Bundesliga stadium buzzing with energy. What exactly captures our imagination with such intensity? While there are many factors that contribute to an iconic goal, there’s a particular magnetism to shots that blaze through the air, especially those taken from a distance.
Over the years, the Bundesliga has witnessed players who’ve become legends, not just for their skill but for their uncanny ability to unleash thunderbolts. Bernd Nickel, a standout figure from Frankfurt’s illustrious squad in the 1970s and 1980s, earned the title “Dr. Hammer” from ardent fans. Over his illustrious career, he netted 141 times in 426 matches.
Beyond his shooting prowess, another feat of Nickel’s that stands out is his ability to directly score from corner kicks. In fact, he holds the unique distinction of having scored from all four corner positions at Frankfurt’s Waldstadion. An example was witnessed by Frankfurt’s fans in May 1971, during a high-stakes game against Kickers Offenbach when he unveiled a masterclass.
Nickel scored a stunning goal in the 17th minute, which eventually led Frankfurt to a 2:0 victory. What made this goal even more memorable was the manner in which it was executed—a spectacular sideways scissors-kick from the penalty spot, fitting perfectly into the top corner. This goal would later be recognized as the “Goal of the Month” for May 1971. Nickl’s impact on the field was undeniable, and during the time he represented Eintracht Frankfurt, the club won the DFB-Pokal three times (in 1974, 1975, and 1981) and the UEFA Cup once in 1980.
Similarly, Thomas “the Hammer” Hitzlsberger has etched his name into Bundesliga folklore with his stunning left-footed rockets. His 2009 strike against Leverkusen at a speed of 125 km/h is one that is vividly remembered because the sheer velocity of Hitzlsperger’s free-kick was enough to leave Germany’s number one goalkeeper, René Adler, seemingly petrified.
Struck during the fifty-first minute of the game from a distance of 18 meters, the ball soared past Adler, leaving him motionless, and bulged the net, making the score 2:0. This remarkable goal not only showcased Hitzlsperger’s striking ability but also demonstrated the awe-inspiring power that such high-velocity goals can have on a match.
Historical data has shown us a few instances where the ball’s velocity exceeded the 130 km/h mark in Bundesliga, with the all-time record being a jaw-dropping shot at 137 km/h by Bayern’s Roy Makaay.
With all this in mind, it becomes even clearer why the speed and technique behind every shot matters immensely. Although high shooting speed excites soccer fans, it has not been measured regularly in the Bundesliga until now. Recognizing this, we are excited to introduce the new Bundesliga Match Facts: Shot Speed. This new metric aims to shed light on the velocity behind these incredible goals, enhancing our understanding and appreciation of the game even further.
How it works
Have you ever wondered just how fast a shot from your favorite Bundesliga player travels? The newly introduced Bundesliga Match Facts (BMF) Shot Speed now allows fans to satisfy their curiosity by providing insights into the incredible power and speed behind shots. Shot speed is more than just a number; it’s a window into the awe-inspiring athleticism and skill of the Bundesliga players.
Shot speed has a captivating effect on fans, igniting debates about which player possesses the most potent shot in the league and who consistently delivers lightning-fast strikes. Shot speed data is the key to resolving these questions.
Besides that, the new BMF helps to highlight memorable moments. The fastest shots often result in spectacular goals that live long in the memory of fans. Shot speed helps immortalize these moments, allowing fans to relive the magic of those lightning-fast strikes.
But how does this work? Let’s delve into the details.
Data collection process
A foundation of shot speed calculation lies in an organized data collection process. This process comprises two key components: event data and optical tracking data.
Event data collection entails gathering the fundamental building blocks of the game. Shots, goals, assists, fouls, and substitutions provide vital context for understanding what happens on the pitch. In our specific case, we focus on shots, their variations, and the players responsible for them.
On the flip side, optical tracking data is collected using advanced camera systems. These systems record player movements and ball positions, offering a high level of precision. This data serves as the bedrock for comprehensive analysis of player performance, tactical intricacies, and team strategies. When it comes to calculating shot speed, this data is essential for tracking the velocity of the ball.
These two streams of data originate from distinct sources, and their synchronization in time is not guaranteed. For the precision needed in shot speed calculations, we must ensure that the ball’s position aligns precisely with the moment of the event. This eliminates any discrepancies that might arise from the manual collection of event data. To achieve this, our process uses a synchronization algorithm that is trained on a labeled dataset. This algorithm robustly associates each shot with its corresponding tracking data.
Shot speed calculation
The heart of determining shot speed lies in a precise timestamp given by our synchronization algorithm. Imagine a player getting ready to take a shot. Our event gatherers are ready to record the moment, and cameras closely track the ball’s movement. The magic happens exactly when the player decides to pull the trigger.
An accurate timestamp measurement helps us figure out how fast the shot was right from the start. We measure shot speed for shots that end up as goals, those that hit the post, or get saved. To make sure we’re consistent, we don’t include headers or shots that get blocked. These can get a bit tricky due to deflections.
Let’s break down how we transform the collected data into the shot speed you see:
- Extracting shot trajectory – After recording the event and tracking the ball’s movement, we extract the trajectory of the shot. This means we map out the path the ball takes from the moment it leaves the player’s foot.
- Smoothing velocity curve – The data we get is detailed but can sometimes have tiny variations due to factors like camera sensitivity. To ensure accuracy, we smooth out the velocity curve. This means we remove any minor bumps or irregularities in the data to get a more reliable speed measurement.
- Calculating maximum speed – With a clean velocity curve in hand, we then calculate the maximum speed the ball reaches during its flight. This is the key number that represents the shot’s speed and power.
We analyzed around 215 matches from the Bundesliga 2022–2023 season. The following plot shows the number of fast shots (>100 km/h) by player. The 263 players with at least one fast shot (>100 km/h) have, on average, 3.47 fast shots. As the graph shows, some players have a frequency way above average, with around 20 fast shots.
Let’s look at some examples from the current season (2023–2024)
The following videos show examples of measured shots that achieved top-speed values.
Measured with top shot speed 118.43 km/h with a distance to goal of 20.61 m
Measured with top shot speed 123.32 km/h with a distance to goal of 21.19 m
Measured with top shot speed 121.22 km/h with a distance to goal of 25.44 m
Measured with top shot speed 113.14 km/h with a distance to goal of 24.46 m
How it’s implemented
In our quest to accurately determine shot speed during live matches, we’ve implemented a cutting-edge solution using Amazon Managed Streaming for Apache Kafka (Amazon MSK). This robust platform serves as the backbone for seamlessly streaming positional data at a rapid 25 Hz sampling rate, enabling real-time updates of shot speed. Through Amazon MSK, we’ve established a centralized hub for data streaming and messaging, facilitating seamless communication between containers for sharing a wealth of Bundesliga Match Facts.
The following diagram outlines the entire workflow for measuring shot speed from start to finish.
Match-related data is gathered and brought into the system via DFL’s DataHub. To process match metadata, we use an AWS Lambda function called
MetaDataIngestion, while positional data is brought in using an AWS Fargate container known as
MatchLink. These Lambda functions and Fargate containers then make this data available for further use in the appropriate MSK topics.
At the heart of the BMF Shot Speed lies a dedicated Fargate container named
BMF ShotSpeed. This container is active throughout the duration of the match, continuously pulling in all the necessary data from Amazon MSK. Its algorithm responds instantly to every shot taken during the game, calculating the shot speed in real time. Moreover, we have the capability to recompute shot speed should any underlying data undergo updates.
Once the shot speeds have undergone their calculations, the next phase in our data journey is the distribution. The shot speed metrics are transmitted back to DataHub, where they are made available to various consumers of Bundesliga Match Facts.
Simultaneously, the shot speed data finds its way to a designated topic within our MSK cluster. This allows other components of Bundesliga Match Facts to access and take advantage of this metric. We’ve implemented an AWS Lambda function with the specific task of retrieving the calculated shot speed from the relevant Kafka topic. Once the Lambda function is triggered, it stores the data in an Amazon Aurora Serverless database. This database houses the shot speed data, which we then use to create interactive, near real-time visualizations using Amazon QuickSight.
Beyond this, we have a dedicated component specifically designed to calculate a seasonal ranking of shot speeds. This allows us to keep track of the fastest shots throughout the season, ensuring that we always have up-to-date information about the fastest shots and their respective rankings after each shot is taken.
In this blog post, we’re excited to introduce the all-new Bundesliga Match Facts: Shot Speed, a metric that allows us to quantify and objectively compare the velocity of shots taken by different Bundesliga players. This statistic will provide commentators and fans with valuable insights into the power and speed of shots on goal.
The development of the Bundesliga Match Facts is the result of extensive analysis conducted by a collaborative team of soccer experts and data scientists from the Bundesliga and AWS. Notable shot speeds will be displayed in real time on the live ticker during matches, accessible through the official Bundesliga app and website. Additionally, this data will be made readily available to commentators via the Data Story Finder and visually presented to fans at key moments during broadcasts.
We’re confident that the introduction of this brand-new Bundesliga Match Fact will enhance your understanding of the game and add a new dimension to your viewing experience. To delve deeper into the partnership between AWS and Bundesliga, please visit Bundesliga on AWS!
We’re eagerly looking forward to the insights you uncover with this new Shot Speed metric. Share your findings with us on X: @AWScloud, using the hashtag #BundesligaMatchFacts.
About the Authors
Tareq Haschemi is a consultant within AWS Professional Services. His skills and areas of expertise include application development, data science, and machine learning (ML). He supports customers in developing data-driven applications within the AWS 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 and artificial intelligence (AI) journey to the cloud.
Jean-Michel Lourier is a Senior Data Scientist within AWS Professional Services. He leads teams implementing data-driven applications side-by-side with AWS customers to generate business value out of their data. He’s passionate about diving into tech and learning about AI, ML, and their business applications. He is also an enthusiastic cyclist, taking long bike-packing trips.
Luc Eluère is a Data Scientist within Sportec Solutions AG. His mission is to develop and provide valuable KPIs to the soccer industry. At university, he learned the statistical theory with a goal: to apply its concepts to the beautiful game. Even though he was promised a nice career in table soccer, his passion for data science took over, and he chose computers as a career path.
Javier Poveda-Panter is a Senior Data and Machine Learning Engineer 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 ML, data science, and analytics. He follows his passion for a broad range of sports, music, and AI in his spare time.