Scaling StatsX: Moving NFL Projections to the Backend and Preparing for AI

Posted on April 25, 2025 · Tags: AI, Backend Development, Scalability, Sports Analytics

When I first launched StatsX, the focus was on getting real-time NFL player analysis working smoothly. Early on, I handled calculations like hot/cold player detection, matchup advantages, and projections directly in React components. While this approach worked at small scale, it wasn't sustainable as the app grew.

After stepping away to work on other projects like my AIcademy, I returned to StatsX with fresh eyes and a deeper understanding of scalable architecture. I realized that for long-term growth — and especially for AI integration — I needed to move heavy calculations to the backend.

Today, I'm proud to say that StatsX now fully supports backend-driven player calculations: - Players to Watch (hot/cold players based on last 3 games) - Top Picks (top 2 players per position based on performance gap) - Weekly Stat Leaders (best players across NFL stat categories)

Here's a glimpse of the code that now powers smarter backend aggregation, fetching only the most important players per position:

const { data } = await supabase.from("players_to_watch").select("*");

const enrichedData = data.map((player) => ({
  ...player,
  performance_gap: Math.abs((player.last_3_avg || 0) - (player.season_avg || 0)),
}));

const groupedByPosition: Record<string, any[]> = {};
enrichedData.forEach((player) => {
  if (!groupedByPosition[player.position]) {
    groupedByPosition[player.position] = [];
  }
  groupedByPosition[player.position].push(player);
});

const topPlayers = ["QB", "RB", "WR", "TE"].flatMap((position) =>
  (groupedByPosition[position] || [])
    .sort((a, b) => b.performance_gap - a.performance_gap)
    .slice(0, 2)
);

Moving this logic server-side made the app dramatically faster, cleaner, and more scalable — exactly the kind of structure that hiring managers and senior engineers value when evaluating backend or AI engineering projects.

Thanks to these changes, StatsX is now ready to take the next step: AI integration. With a clean, normalized dataset and a scalable backend architecture, we can confidently introduce predictive models, confidence percentages, and natural language stat querying.

With around 65% baseline accuracy already achieved through calculated performance trends, I'm excited to see how much smarter the system can become once true machine learning models are in place.

Reflecting on this journey, I realize how much I've grown as a developer — not just writing code, but designing systems that scale, adapt, and set the foundation for AI to thrive.