Hey, thanks for the comment — I totally get where you’re coming from. Let me clarify a bit what Deepshot actually tries to do and why I built it. The project is

Hey, thanks for the comment — I totally get where you’re coming from. Let me clarify a bit what Deepshot actually tries to do and why I built it. The project isn’t meant to “beat Vegas” or make betting calls — it’s an analytical tool that explores whether a model can numerically describe which team is favored to win based purely on data. The EWMA part isn’t buzzword fluff: it’s a deliberate choice. Through a lot of testing, I found that using an exponentially weighted window of 25 games gave the most stable signal, minimizing error between predicted and actual outcomes. In practice, it captures a team’s momentum — how it’s been performing recently — better than simple averages or rolling means. Highlighting the key statistical differences (say, +5% in rebounding or turnover rate) isn’t “puffing up debug output”; it’s a way to help visualize why the model leans toward one side. The NBA is an extremely competitive environment, and even small statistical gaps can meaningfully shift game outcomes — that’s what I wanted to surface. As for the project itself — I’m not trying to sell it or claim it beats bookmakers. I’m sharing it because I’m 20, still learning, and I wanted to build something unique and interactive, not just another command-line model spitting numbers. Deepshot’s goal is to make basketball data exploration fun, transparent, and open to improvement by others who might want to contribute ideas or tweaks. In short — it’s not about betting or buzzwords, it’s about learning, experimenting, and hopefully getting feedback from people who care about sports analytics as much as I do.