LineupLab is a SaaS that ingests and normalizes match and player stats at scale and delivers optimal lineup generation and simulation for Daily Fantasy Sports (DFS) — all on a single screen. It tracks the latest stats for today's 7 matches and hundreds of players in real time.
Data ingestion, optimization, simulation, and recurring billing — the story of launching a new platform, built in-house from scratch in 9 months, that satisfies all four axes sports data analytics demands at once.
Sports Analytics Inc. is a company that provides stat visualization and decision support to users engaged in Daily Fantasy Sports (DFS). Its mission is to turn the vast, daily-updated stats into insights for winning.
The business model is a B2C subscription. On top of analytics such as rankings, box scores, and depth charts, premium features like lineup optimization and simulation are offered via recurring billing, with improving users' win rate at the core of the product's value.
None of these were questions of technology choice — they were business requirements tied directly to users' win rate, analytics experience, and recurring billing. Building new from scratch required a design that made them all hold at once.
Per game, hundreds of players × dozens of metrics. Rely on manual tallying and the analysis won't be ready in time, delaying decisions.
Under budget and position constraints, solving the optimum by hand takes over 30 min per lineup. You can't rack up iterations, so accuracy stalls.
Starters can shift up to kickoff. If the ingest/normalization base meant to keep pace with the latest stats is off, freshness across every output degrades.
With one-off use, churn stays high. Without designing value users want to return to every day, a subscription business can't hold.
From ingestion and normalization to ranking, optimization, simulation, and recurring billing, we built everything in-house from scratch. Large batches are distributed across a queue and optimization is solved in real time with linear programming — consolidating the core of sports data analytics into a single SaaS.
React
Ant Design
React Query
Express
TypeORM
MySQL
Redis · Bull Queue
AWS (S3)Linear-programming solverThis foundation continuously produces business outcomes — analytics efficiency, win rate, and recurring revenue — not technical metrics.
Automate ingestion and normalization so users can focus on decisions.
Finish optimization in seconds and maximize the number of trials.
Reflect pre-match changes in ingestion to keep analysis fresh.
Daily-use optimization and notifications lift retention.
Pick a sport, contest, and budget, then generate the optimal lineup in seconds with “Optimize.” Player locks are supported too.
Visualize the upside/downside distribution of generated lineups and the target-score hit rate with Monte Carlo.
Rank players by projected points, value, and ownership. Filter by position/team and export to CSV.