While you were hard at work watching the FIFA World Cup, we were busy compiling the latest trends in the application of Machine Learning and Computer Vision to sports technology. This is a topic of interest to us because of our work with KinaTrax, a pioneering sports analytics company that helps Major League Baseball teams improve their performance through computer vision technology.
From predictive analysis to the automation of sports journalism and the use of performance-enhancing computer vision technology, here are some ways in which machines are transforming how sports are played by athletes and enjoyed by fans.
Better than the referee?
Predictive analysis has come a long way since since the 2010 days of Paul the Octopus. Machine Learning models are now used routinely to predict the results of games.
This year, a model built by start-up Kickoff.ai used advanced mathematical techniques to ‘encode’ the current strength of teams and to project outcomes. Another statistical model was built by Goldman Sachs to simulate one million possible evolutions of the tournament. The model-based prediction did prove accurate to some extent, for example by projecting the high probability of France reaching the semi-finals.
Another way to leverage machine learning and large volumes of statistical data is to build an index of individual’s players performance potential. SciSports is a German start-up that builds a ‘SciSkill’ score for upcoming players and helps clubs assemble the best teams.
Connecting fans to their teams
A key challenge in sports broadcasting is the sheer volume of video content generated each day across sports, leagues and geographies. This used to translate into editors manually going through all the footage to produce clips of highlights each day. The solution has come in the form of AI-driven editing platforms that use ball- tracking technology and audio patterns to automate the recognition of key moments in each game. This lets journalists create clips in minutes, when it used to take hours to select shots and edit them manually. WSC Sports is one of the leaders in this field and works with a number of organisations, including the NBA. For fans, this new technology means faster and better access to video coverage of every sport and league, however niche.
The athlete and the machine
Cameras and sensors are cheap and plentiful. It is now easy to create datasets of images that record every single moment of a match from different angles. This year and for the first time in the history of the World Cup, VAR (Video Assistant Referee) systems by Hawk-Eye reviewed decisions made by the head referee with audio-visual footage. Cricket fans have known Hawk-Eye’s ball-tracking and trajectory-prediction technology for the past several years.
The combined capabilities of computer vision and motion capture technology give sports analysts powerful insight into what is invisible to the human eye. These statistics let clubs improve the training of their teams, and therefore their performance: the analysis of baseball pitchers provided by KinaTrax for example helped the Chicago Cubs win their first World Series in over a century.
Wearables are bringing another level of access to performance data. Sensors worn in athletes’ gear or equipment can provide in-game movement insights. The U.S. Soccer Federation has hired Irish wearables company STATSports to provide monitoring devices for its four million registered soccer players in the United States. Valued at over $1.5 billion, according to a statement released by the companies, the deal aims to create the world’s largest player data monitoring program and act as a first step towards outfitting youth and amateur soccer players with professional-grade performance technology.
The use of AI-powered technology has spread to every sport, beyond team sports where they first appeared. In race car driving, machines can detect small damages that predict the need for a tyre change or other mechanical problems. Biomechanical analysis of players can predict and prevent potential career-threatening injuries. In tennis, machine learning models can even detect movement patterns and learn to group similar data into different types of shots, which ultimately leads to finer analysis.
A recent report by leading data analytics company DataRobot mentioned some of the challenges involved in building these models, for example the talent and time required to process large quantities of data. By the time effective models are built, the season might be over. “Speed-to-insight” is therefore a key component of helping teams maintain their edge. This shows how a partner like iMerit, who is able to assemble large teams of data experts and produce actionable insights in time are also critical players in the sports tech ecosystem.