Soccer Match Analysis Using KAFKA Streams
I’m a big fan of English football (“soccer” in the US). I’ve played a lot of recreational soccer and have been watching European leagues (especially English Premier League) for over twenty years. I’ve followed soccer experts, commentators, and critics for their live and post-match analysis with stats and analytics on how teams performed, what could have been better, which tactics worked (and which didn’t), and why.
Anexinet’s partnership with KAFKA will provide next-gen solutions for clients, including (but not limited to) live view of their system status, including output, errors, sales, returns and so on. Similar principles may hypothetically be applied to monitor a soccer match This concept may even be expanded to include a history of live statistics by match, team, player, venue etc. This, in turn, could be used by the clubs to analyze and change tactics, optimize teams based on player strengths, and so on.
Note: The purpose of this article is to demonstrate how KAFKA streams could be utilized in various applications and is based on several unverified assumptions. No study was done to determine if sensors exist that could withstand the rigorous on-field requirements.
Sensors: Each player would have sensors in their shoes, shin guards etc. to detect their location on the field and to detect contact with the soccer ball. The soccer ball would also have sensors to detect its location on the field and to detect player contact (e.g., NFC-compatible sensors). Devices placed on the field would collect data from the sensors.
Servers: On-field devices would connect to backend servers via the network (e.g., using MQTT) to generate raw data events as the match proceeds. Example events include a) the location of each player at every step, b) the location of the ball on every contact with the field, player, etc.
The servers running in the backend (using MQTT) would receive raw data from the sensors. These server processes would convert data into a readable format. For example, they would map sensors to players, compute distance-covered using sensor locations, mark player-to-ball contact, distance covered by ball, and so on. These attributes would be stored in KAFKA KTables and KAFKA KStreams as the events arrive.
The KSQL queries would be written to gather the required stream information. For example: distance covered by a player, passes completed, player location and movement, goals scored, etc.
With all this information available, the analysis visualization for the live and post-match analysis could be further improved by linking field data to the video feed. Additionally, all stats could be made available instantly. The team manager and coaches could use this data offline to analyze how the planned tactics worked and how they could be improved. The same data could be used to create AI models that help teams devise new tactics and play them on-screen, based on the AI model.
Naturally, this concept could also be applied to other sports and athletics. Lastly, if you have any questions about how Anexinet’s partnership with KAFKA could benefit your organization by providing next-gen solutions that include (but aren’t limited to) a live system-status view, including output, errors, sales, returns, and more, please don’t hesitate to reach out to us. We’d love to help you get started.