video1.0<iframe src="https://www.loom.com/embed/96fb25ef5fb143548bb4ee22ae148715" frameborder="0" width="1658" height="1243" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12431658Loomhttps://www.loom.com12431658https://cdn.loom.com/sessions/thumbnails/96fb25ef5fb143548bb4ee22ae148715-d7fc4c527887ffe4.gif537.237This Loom shares Jonathan’s introduction and his sports analytics project built to evaluate soccer player performance by game state. He describes using Python in Jupyter Notebook, Cloud Code for methodology support, and Stats API data, focusing on probabilities such as winning odds and how a goal’s impact changes with score, time, and situation. He provides examples including halftime down by one where scoring increases win probability from about 12% to 31.7%, and an 88th-minute tie where scoring raises the likelihood from about 12% to about 85%. He also explains why the retreat would support his growth, collaboration with diverse backgrounds, and connecting with his Asian heritage through an England trip and interest in reconnecting with Vietnam in the future.