We present SoccerGuard, a novel framework for predicting injuries in women's soccer using Machine Learning (ML). This framework can ingest data from multiple sources, including subjective wellness and training load reports from players, objective GPS sensor measurements, third-party player statistics, and injury reports verified by medical personnel. We experiment with a number of different settings related to synthetic data generation, input and output window sizes, and ML models for prediction. Our results show that, given the right configurations and feature combinations, injury event prediction can be undertaken with considerable accuracy. The optimal results are achieved when input windows are reduced and larger combined output windows are defined, in combination with an ideally balanced data set. The framework also includes a dashboard with a user-friendly Graphical User Interface (GUI) to support interactive analysis and visualization.

The year 2017 saw a sudden emergence of interest and support for Artificial Intelligence (AI) programming by children. Stephen Wolfram wrote a chapter in his An Elementary Introduction to the Wolfram Language textbook about machine learning. He wrote a blog post Machine Learning for Middle Schoolers, where he describes the rationale behind the different examples and exercises. Dale Lane launched Machine Learning for Kids, which he recently described in his excellent article in Hello World 4. Google launched the first of two AIY projects for voice and vision kits for Raspberry Pi projects. Google also launched the Teachable Machine and a collection of browser-based machine learning experiments.