<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/74643c0be893463b9f208af2a77b1c99&quot; frameborder=&quot;0&quot; width=&quot;1152&quot; height=&quot;864&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>864</height><width>1152</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>864</thumbnail_height><thumbnail_width>1152</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/74643c0be893463b9f208af2a77b1c99-00001.gif</thumbnail_url><duration>132.46666666666664</duration><title>1 - Fractalized Tennis Bets Version 1.0: Serving Tennis Enthusiasts and Data Scientists 🎾</title><description> In this video, I will be discussing the two flavors of the Fractalized Tennis Bets app. For tennis enthusiasts, there will be a version with two tabs: the backtesting tab simulator and the betting simulator. These tabs will help users estimate historic data and make informed decisions about capital allocation for upcoming tournaments. On the other hand, data scientists supporting tennis enthusiasts will have access to four additional apps. These apps will allow them to explore integrated data sources, evaluate different data generation hypotheses, compare and train models, and simulate pre-match paths for betting purposes. The video will provide a detailed overview of these features and their importance. Stay tuned for the next video where we will dive deeper into these topics.</description></oembed>