Using game totals and game script to make start/sit calls does anyone else build lineups around game environment?

Most start/sit advice focuses on the individual player, but I've found game environment often matters more a WR2 in a projected shootout can outscore a WR1 stuck in a low-total grind it out game.

The factors I lean on when I'm torn between two players:

  • Projected total higher-total games mean more scoring chances to go around. If two players are close, I'll usually take the one in the game with the higher total.
  • Game script / spread a team projected to trail throws more (good for pass-catchers), a team projected to blow the other out leans run-heavy in the second half (good for RBs, bad for garbage-time WRs). Blowout risk can quietly kill your stud's volume.
  • Pace faster-paced matchups mean more total plays, which lifts everyone's floor.

I built an app called SharpLines that projects these game-level numbers across the NFL slate (totals, spreads, game script), but you don't need any tool to use the idea Vegas totals and spreads are public, and just checking "which of my flex options is in the better game environment" has helped my start/sit calls more than chasing matchup rankings alone. SharpLines just puts those numbers in one place with the reasoning behind them.

Curious how others here weigh this: do you build around game environment, or stick mostly to individual player matchups and rankings?

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u/BowlRun — 3 days ago

Built a model projecting NFL game totals and blowout risk curious how you weight game environment in DFS lineups

NFL DFS is all about game environment you're hunting shootouts for stacks and dodging games where your RB1 sits the fourth quarter up 21. I've spent the past year solo building a prediction model, and the NFL side outputs the kind of game-level reads that actually matter for lineups. Wanted to share the approach and get input from people who grind NFL slates.

What the model produces per game:

  • Projected total the shootout finder. High-total games are where stacking a QB with his WR1/WR2 pays off
  • Spread / blowout risk a projected double-digit blowout is a red flag for your studs' second-half volume, especially RBs in negative game scripts
  • Win probability + pace game-script reads that tell you whether a team throws to catch up or runs to bleed clock

How it's built: a 10-model ensemble (Elo, Poisson, Monte Carlo, Bayesian, market signals) combined into one projection per game. Forward results only logged before kickoff, never backtested because a backtested NFL model that "would've crushed last season" tells you nothing about next Sunday.

What I'm curious about from this sub:

  • For NFL specifically, does projected total or spread drive more of your lineup decisions?
  • How much do you trust a model total over the Vegas total cross-check, or ignore?
  • Game script reads (pass heavy vs run-heavy) do you build around those, or is it too noisy week to week?

It runs in an app I built (SharpLines) if anyone wants to see the projection format, but I'm here for the NFL DFS input happy to go deeper on any part of it.

u/BowlRun — 3 days ago

Built a solo micro-SaaS in the sports analytics niche a year in, live on 3 platforms

Solo founder here. For the past year, I've been building a micro-SaaS in a niche most people overlook: AI sports analytics. Wanted to share the journey and the technical/business side with this community.

It predicts game outcomes (moneyline, spread, over/under) across 12 leagues and explains the reasoning behind each pick with an AI layer, instead of just handing users a number with no context. Live game tracking and accuracy reporting are built in.

It's called SharpLines (sharplines.ai) and here's the honest breakdown of how it runs as a one-person business:

The micro-SaaS mechanics:

  • Solo-built, subscription model, running on web + iOS + Android from a single React/Capacitor codebase, essential for one person maintaining three platforms
  • Niche with a moat: the sports-prediction space is flooded with products making fake "90% win rate" claims. I went the opposite direction: publish real forward accuracy, never inflate. In a low-trust niche, honesty is the differentiator competitors can't copy without rebuilding their whole brand
  • Lean stack: Node/Express, MongoDB, Stripe for subscriptions kept infrastructure cheap enough for one person to run sustainably

Current focus: improving model accuracy and the AI reasoning quality. Revenue is early but real, with paying subscribers across all three platforms.

Happy to talk shop about running a solo SaaS, pricing in a skeptical niche, or the technical side. What's worked for others here selling subscriptions in a niche where trust is the main barrier?

reddit.com
u/BowlRun — 7 days ago

I spent months building a sports-analytics platform that shows its real accuracy instead of faking 90% win rates SharpLines

Solo founder here. Sharing my project and genuinely want feedback.

The backstory: I got tired of every sports-prediction site screaming "90% WINNERS 🔥" while hiding any real, verifiable numbers. It's almost always backtested or invented. So I built SharpLines around one principle radical honesty about accuracy. It publishes real forward prediction accuracy, tracks calibration live, and never presents mock or backtested data as real. Right now that's [X]% [screenshot], and my goal is to push it higher honestly rather than fake a bigger number.

What it does:

  • Predictions for moneyline, spread, and over/under across 12 leagues (NBA, NFL, MLB, NHL, NCAAB, NCAAF, MLS + the five big European soccer leagues)
  • A live gamecard with real-time game state (inning,quarter, clock, venue), current odds, plus team stats, injuries, and standings
  • An AI reasoning layer that explains why the model landed on each prediction for that specific game, instead of just handing you a pick

Tech stack (since this sub likes the details):

  • Node Express backend on Railway, React + Capacitor for iOS + Android, Vercel for the frontend
  • MongoDB Atlas, Supabase for auth, Stripe for subscriptions
  • Live data from The Odds API + API-Sports, ESPN for game state
  • A 10-model ensemble under the hood (Elo/win-probability, Poisson, Bayesian, Monte Carlo, no vig fair odds, line movement, Kelly sizing, etc.)

The two things I'd most like feedback on:

  1. Is "we show our real accuracy" actually a trust builder for you, or does the market just want confident-sounding picks? I'm betting people are tired of touts but I might be wrong.
  2. The gamecard shows a lot of data. Useful, or overload?
u/BowlRun — 8 days ago
▲ 3 r/TestMyApp+1 crossposts

I spent a year solo-building an AI sports analytics platform — 10-model ML ensemble, 12 leagues, live on iOS/Android

Hey everyone, solo founder,dev here. I built SharpLines sharplines.ai an AI-powered sports analytics and predictions platform covering NBA, NFL, MLB, NHL, NCAA basketball,football, and 6 major soccer leagues.

What it does:
Generates predictions for moneyline, spread, and over or under on every game, backed by a 10-model ML ensemble Elo ratings, Poisson distribution, Monte Carlo simulation, Bayesian inference, no vig fair odds, line movement analysis, and more. Each prediction comes with AI-generated reasoning explaining why the model likes a side not just a pick with no context. Live game cards show real time scores, quarter inning, and venue.

Why I built it:
Most sports prediction sites either hide their methodology or claim absurd win rates with zero receipts. I wanted to build the opposite a platform that shows its work, tracks its own accuracy honestly, and never fabricates results.

Honest maturity check (per the rules):
This is a functioning product, not a polished enterprise app. It's live on web, the App Store, and Google Play. The models currently run around 50% to 55% accuracy on forward predictions. I track this transparently with a built in calibration system, and improving it is my main focus. There are rough edges I'm actively fixing. If you try it and something breaks, tell me and I'll fix it.

Stack: Node Express on Railway, React + Capacitor single codebase for web,iOS,Android, MongoDB Atlas, Supabase auth, Stripe, DeepSeek for the reasoning generation.

https://preview.redd.it/92u3p3w860jh1.png?width=1584&format=png&auto=webp&s=4fd34b2418ebcbdc2cb30e193515b34801fd7dec

https://preview.redd.it/cup7lfn960jh1.png?width=1591&format=png&auto=webp&s=5bfa8c3faad78ceb290f5511341482de240a837d

https://preview.redd.it/ph58xtga60jh1.png?width=1566&format=png&auto=webp&s=7b8eed1fba8dc13710215dc37fbd9e682cbbcf20

reddit.com
u/BowlRun — 8 days ago