Using Artificial Intelligence to Run your Best Marathon

I've been writing marathon-related blog posts for about 2 years now, describing a range of studies on different aspects of marathon running, such as the influence of age, gender, and experience on performance and pacing, and focusing on race-records from a wide range of big-city marathons around the world. To date these studies have focused on analysing marathon data with a view to gaining insights into what has happened in the past; something that is often referred to as descriptive analytics in the world of data science. Recently I have turned my attention to the future, to use this marathon data to gain insights into what might happen in the future -- predictive analytics -- and, in particular to make predictions about the potential of runners to achieve new personal best (PB) finish-times. In fact, what began as a bit of data-fun in my spare-time, has now started to leak into my day-job, and this week I will present a scientific paper based on this prediction work. This is not so unusual. As a Professor in the area of artificial intelligence, machine learning, and recommender systems, a major part of my job involves publishing and presenting research ideas.