The legendary Brazilian player Pele described soccer as \u201cthe beautiful game\u201d. The dynamic forward was fluid, unpredictable and lucky, just like the game itself. And while luck plays a significant role in the outcome of a soccer game, the action on the pitch is certainly quantifiable. So, can serious number-crunching turn the odds in one\u2019s favor? ASML and soccer club PSV joined forces off the pitch in order to improve performance on the pitch \u2013 sheer luck aside, that is.<\/p>\n\n\n\n
Most professional sports teams use a host of statistics to evaluate their games and players. Especially since the publication of Michael Lewis\u2019 2003 book (and later, movie) Moneyball, the role of data in sports has been of make-or-break importance to a team\u2019s aspirations. But Moneyball is about baseball, a game of discrete events: brief, well-defined and self-paced actions with a clear beginning and end. Soccer is different. It is a much more continuous game, where players have to constantly adapt to changing circumstances on the pitch \u2013 making it a less obvious candidate for data-driven analysis.<\/p>\n\n\n\n
\u201cIn fact, an estimated 31% of the outcome of a season is based on luck\u201d, says Ruud van Elk, head of sports science and analytics at PSV soccer club in an article on the website of Brainport Eindhoven<\/a>. \u201cOur job is to try and control the other 69%.\u201d<\/p>\n\n\n\n Control for performance starts with data. Professional soccer clubs in the Netherlands work with two key data sets: event data and tracking data. The event data is a manual log of sequential activity of the ball on the pitch, in simple terms, player A passes the ball to player B, which usually leads to around 2,000 rows of data per soccer match. The tracking data is an automated log of the position of each player and the ball on the pitch, measured 25 times per second, which means that it\u2019s much richer and contextual data than the event data. Specialized cameras in the stadium capture a staggering four million rows of data per match.<\/p>\n\n\n\n \u201cWe worked with both types of data separately,\u201d Ruud says. \u201cIt allowed us to pull up basic things like total distance traveled per player, passes per player, shots on goals. But to see the complete picture, you need to combine and visualize the data as a whole. Not many sports teams have the expertise to do this properly. That\u2019s why we looked to our Brainport partners for support.\u201d<\/p>\n\n\n\n Or more specifically, ASML\u2019s Big Data Analytics department. With over 300 people, it supports ASML\u2019s R&D and corporate functions with advanced analytics on lithography machine data, operational data and corporate data.<\/p>\n\n\n\n Rob Beeren, who heads ASML\u2019s corporate analytics group, recalls first learning about PSV\u2019s ambitions. \u201cASML is part of PSV\u2019s Brainport partnership. At a knowledge sharing event in 2020, PSV presented their future vision for the club. Boosting performance through data analytics was a key part of this vision. We realized that we could help and learn from each other by applying ASML\u2019s analytics expertise in PSV\u2019s high-performance sports environment.\u201d<\/p>\n\n\n\n Rob had a special team in mind for the job: the Innovation Lab, a specialized group of six data scientists within ASML\u2019s Big Data Analytics department that focuses on experimentation, fast learning and rapid prototyping. Led by Diederick Edel, the team was asked to sit down with PSV and see where opportunities for collaboration were.<\/p>\n\n\n\n Diederick: \u201cThe PSV staff was a little bit apprehensive at first, uncertain about what joint prototyping would be like with a high-tech company like ASML, and whether it would actually generate the results that they were looking for. But we put our \u2018less talking, more doing!\u2019 philosophy to work. The ASML-PSV team jointly sketched out the ambitions and the way to get there.\u201d<\/p>\n\n\n\n A first crucial step in the collaboration was to try and bring all available data sets together. That challenge landed with Maud Diepstraten, data scientist in Innovation Lab.<\/p>\n\n\n\n \u201cWith any data project, it\u2019s crucial to really understand the data and get familiar with what you have \u2013 or don\u2019t have. For example, capturing event data manually meant that it was prone to typos or missing values. It\u2019s something you have to somehow work with,\u201d Maud says.<\/p>\n\n\n\n It was also a challenge to perfectly match the manually logged events on the pitch to the automated tracking data. Maud: \u201cFor example, the timestamps for passes just didn\u2019t line up correctly. We solved it by matching the timestamp when the ball is closest to a player in a 1.5 second timeframe.\u201d<\/p>\n\n\n\n As an Applied Mathematics graduate with a Data Science Management degree in the works, Maud enjoys making sense out of huge data sets. But this particular project offered a special personal perspective, because Maud is also a midfielder at local soccer club Beerse Boys.<\/p>\n\n\n\n \u201cWorking with this data was a bit of a break from the norm for me. I usually work with the very abstract data that comes out of ASML\u2019s machines. But with soccer data, I can actually imagine what\u2019s going on with 22 players on the pitch.\u201d<\/p>\n\n\n\n Combining multiple files with various formats, Maud laid the foundation for visual analytics in PSV\u2019s preferred analytics platform Tableau. And the team managed to get quick results.<\/p>\n\n\n\n \u201cWe decided to test our new data set by looking at a specific aspect of the game: sprinting,\u201d says Ruud van Elk. \u201cBefore, we already had data on all sprints of a player over the course of the game, but just the start and the endpoints. A sprint is so much more. There\u2019s direction, acceleration, speed, turns.\u201d<\/p>\n\n\n\n The team iterated in fast scrum cycles to deliver a Tableau visualization based on the aggregated data set, getting Ruud\u2019s feedback, and tweaking the product on-the-go. Within two weeks, a sprint visualization was in the hands of PSV\u2019s performance trainers.<\/p>\n\n\n\n Ruud: \u201cBeing able to visualize all of this, it\u2019s just massively important to understand how we should actually train sprinting for individual players.\u201d<\/p>\n\n\n\n The ASML-PSV team proved the value of their collaboration with the sprint analytics and got the go-ahead to up the ante.<\/p>\n\n\n\n Maud: \u201cThe real fun starts when you add complexity and granularity to the data. Together, we decided to create an advanced algorithmic model that could help PSV understand how the team controls the pitch throughout the game.\u201d<\/p>\n\n\n\n Using the raw tracking data as the foundation for the model, the team calculated pitch control by understanding where every player is at a specific timestamp, where they\u2019re going and how fast they\u2019re moving. Maud: \u201cWe defined pitch control as which player controls which part of the pitch at any one time. So by projecting a grid on the pitch, we can calculate which player will reach a particular cell the fastest or the first,\u201d Maud explains. \u201cCalculating that for each grid cell, we can visualize the overall pitch control of a team.\u201d<\/p>\n\n\n\n Calculation pitch control for all grid cells at 25 frames per second requires some heavy-duty computing power. The ASML team worked with PSV on the back-end infrastructure, a powerful combination of Azure, Microsoft\u2019s cloud computing platform used by ASML, and Tableau, a data visualization tool already in use by PSV\u2019s performance trainers.<\/p>\n\n\n\n Diederick: \u201cWe rebuilt PSV\u2019s analytics environment within a day to do the calculations on Azure. We offloaded the pitch control calculations to the massive computing power of Azure Databricks.\u201d<\/p>\n\n\n\n To feedback that computing power to PSV\u2019s visualization platform Tableau, the team connected the environments through Azure Synapse. This enabled the PSV performance trainers to analyze tens of millions of rows of data in the platform that they were already familiar with.<\/p>\n\n\n\n \u201cWorking with this pitch control model is pretty unique for Dutch soccer,\u201d Ruud van Elk says. \u201cIt\u2019s something that we\u2019re looking to leverage in our training, but we also see possibilities to add even more value. For example, we tried to link pitch control to certain events to understand how a pass influences pitch control.\u201d<\/p>\n\n\n\n Looking back on the collaboration, the ASML-PSV team is proud. Group leader Rob Beeren commends the agility and speed of the cross-organizational project. \u201cWe pushed ourselves and PSV to really commit to something unique \u2013 and to make fast progress on it. The team on our end was also really excited to work with PSV, many colleagues are actually fans. That meant high energy all around!\u201d<\/p>\n\n\n\n PSV was also impressed by the speedy progress, as Ruud van Elk confirms. \u201cThe whole project and all deliverables took just three months. To get such a result in such a short time contributes to the dream for the future of PSV.\u201d<\/p>\n\n\n\n The team is ready for more and wants to industrialize what\u2019s already there, so that PSV\u2019s performance trainers will receive automated reports after each match. The team is also actively investigating new use cases that match PSV\u2019s ambitions.<\/p>\n\n\n\n Head of ASML\u2019s Innovation Lab, Diederick Edel, is also looking ahead with high hopes: \u201cAt ASML, we know that the field of data science is evolving rapidly. The potential is pretty much endless and we\u2019ll have to make smart choices for the best results and highest impact. If you ask me, we\u2019re just getting started.\u201d<\/p>\n","protected":false},"author":1658,"featured_media":296552,"template":"","meta":{"_acf_changed":false,"advgb_blocks_editor_width":"","advgb_blocks_columns_visual_guide":""},"categories":[8553],"tags":[5205,29783,28927,124676,49980,36200],"location":[],"internal_archives":[],"class_list":["post-296551","selected","type-selected","status-publish","has-post-thumbnail","hentry","category-digital","tag-asml-en","tag-brainport-eindhoven-en","tag-data-analytics-en","tag-moneyball","tag-psv-en","tag-soccer"],"blocksy_meta":[],"acf":[],"featured_img":"https:\/\/ioplus.nl\/archive\/wp-content\/uploads\/2022\/04\/Screenshot-2021-06-25-11.44.52-1.png","coauthors":[],"author_meta":{"author_link":"https:\/\/ioplus.nl\/archive\/author\/arjan-paans\/","display_name":"Arjan Paans"},"relative_dates":{"created":"Posted 5 years ago","modified":"Updated 5 years ago"},"absolute_dates":{"created":"Posted on June 25, 2021","modified":"Updated on June 25, 2021"},"absolute_dates_time":{"created":"Posted on June 25, 2021 11:56 am","modified":"Updated on June 25, 2021 11:56 am"},"featured_img_caption":"\u00a9ASML","tax_additional":{"category":{"linked":["Digital<\/a>"],"unlinked":["Digital<\/span>"],"slug":"category","name":"Categories"},"post_tag":{"linked":["asml<\/a>","Brainport Eindhoven<\/a>","data analytics<\/a>","moneyball<\/a>","PSV<\/a>","soccer<\/a>"],"unlinked":["asml<\/span>","Brainport Eindhoven<\/span>","data analytics<\/span>","moneyball<\/span>","PSV<\/span>","soccer<\/span>"],"slug":"post_tag","name":"Tags"},"language":{"linked":["EN<\/a>"],"unlinked":["EN<\/span>"],"slug":"language","name":"Tags"},"post_translations":{"linked":["pll_60d5accf97e0d<\/a>"],"unlinked":["pll_60d5accf97e0d<\/span>"],"slug":"post_translations","name":""},"location":{"linked":[],"unlinked":[],"slug":"location","name":"Locations"},"internal_archives":{"linked":[],"unlinked":[],"slug":"internal_archives","name":"Internal Archives"}},"series_order":"","_links":{"self":[{"href":"https:\/\/ioplus.nl\/archive\/wp-json\/wp\/v2\/selected\/296551","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ioplus.nl\/archive\/wp-json\/wp\/v2\/selected"}],"about":[{"href":"https:\/\/ioplus.nl\/archive\/wp-json\/wp\/v2\/types\/selected"}],"author":[{"embeddable":true,"href":"https:\/\/ioplus.nl\/archive\/wp-json\/wp\/v2\/users\/1658"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ioplus.nl\/archive\/wp-json\/wp\/v2\/media\/296552"}],"wp:attachment":[{"href":"https:\/\/ioplus.nl\/archive\/wp-json\/wp\/v2\/media?parent=296551"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ioplus.nl\/archive\/wp-json\/wp\/v2\/categories?post=296551"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ioplus.nl\/archive\/wp-json\/wp\/v2\/tags?post=296551"},{"taxonomy":"location","embeddable":true,"href":"https:\/\/ioplus.nl\/archive\/wp-json\/wp\/v2\/location?post=296551"},{"taxonomy":"internal_archives","embeddable":true,"href":"https:\/\/ioplus.nl\/archive\/wp-json\/wp\/v2\/internal_archives?post=296551"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Measure, analyze, improve<\/h2>\n\n\n\n
Applying analytics expertise in sport<\/h2>\n\n\n\n
Matching the timestamp<\/h2>\n\n\n\n
Direction, acceleration speed, turns<\/h2>\n\n\n\n
Modeling pitch control<\/h2>\n\n\n\n
Ready for more<\/h2>\n\n\n\n