AI-Based Performance Analysis for Track and Field Athletes

We propose a performance analysis and ranking methodology for track and field athletes, based on an AI model trained using historical data collected from actual races over several years. Track coaches will then have a data-driven tool to (i) quantify their athletes' performances and progress during the training period and (ii) predict their level of consistency and competitiveness for upcoming competitions. To achieve this, we have utilized the public domain data of races recorded in TFRRS database, curated data, extracted key statistical features, and trained a neural network clustering (ART2) technique. The records of an athlete (e.g. during the training phase) can then be mapped to a few clusters to rank and explain their overall performance.