PhantomStat's answer

PhantomStat started from a simple gap: casual sports sites stop at the final score and a season average, while the tools that go deeper are built for professionals and priced for them. The goal was to put real per-entity detail — xG and PSxGA for soccer, round-by-round fatigue curves for MMA, matchup splits for MLB, opponent-defense splits for NBA, serve/return breakdowns for tennis — one page away for any fan, on a free account rather than an enterprise contract. The paid Pro tier at $29.99 per month adds the full dashboards, the advanced filters and the historical archive.

TranscriptFetch's answer:

It started with discovering there is no good way to get the text of a video. YouTube’s official Data API will confirm a caption track exists and then refuse to hand it over, because captions.download requires the video owner’s OAuth token. The popular open-source libraries work until you deploy them, at which point platforms start refusing datacenter IPs. And YouTube is the easy case: TikTok and Instagram publish no caption file at all. Every workaround solved one platform, worked locally, and broke in production. TranscriptFetch is the version that handles the failure cases as first-class behaviour rather than edge cases.

PhantomStat's answer

Sports fans who want more than a final score, and fantasy/simulation players who need the underlying splits rather than a season average. That covers casual fans checking a player's recent form, fantasy managers comparing matchup histograms before setting a lineup, and anyone who follows football, MMA, MLB, NBA or tennis closely enough to want per-round, per-game or per-matchup detail instead of a single headline stat.

TranscriptFetch's answer:

Developers and technical teams building on video and web content. The common cases are RAG and retrieval pipelines that need video as text, AI agents that need to read a link mid-conversation, content teams repurposing short-form video at scale, and media monitoring and research tools. It is an API first, so the buyer is usually the person writing the integration rather than an end user. The free browser tools exist for one-off transcripts and for evaluating output quality before writing any code.

PhantomStat's answer

PhantomStat covers five sports in one platform — football, MMA, MLB, NBA and tennis — with dedicated pages per player, team, fighter and matchup. Instead of just headline numbers, each page surfaces the underlying breakdown: xG and PSxGA for football, per-round fatigue curves for MMA, a Prop Line Explorer with histograms for MLB, form and opponent-defense splits for NBA, and serve/return breakdowns for tennis. A free account unlocks a quota of pages across every sport; a paid Pro tier ($29.99 / €29.99 / £24.99 per month) unlocks advanced filtering and the full historical archive.

TranscriptFetch's answer:

Most short-form video has no caption track to download. TikTok’s auto-captions are opt-in per upload, Instagram never publishes a downloadable track, and many captions on both are burned into the video frames where no parser can read them. TranscriptFetch transcribes the audio when no caption track exists, on the same endpoint, with the same response shape. Your code never branches on which method produced the text. It also covers YouTube, TikTok, Instagram, X and Facebook plus any web page as clean Markdown, so a pipeline spanning several sources is one integration rather than five.

PhantomStat's answer

Most single-sport score sites stop at the headline number, and most deep-analytics tools are priced for professionals. PhantomStat covers five sports in one place (football, MMA, MLB, NBA, tennis) instead of forcing you to juggle a separate tool per sport, and every player, team, fighter and matchup gets its own page rather than a row in a table. A free account unlocks a quota of those pages across every sport; the full dashboards, filters and comparison tools are $29.99 per month. Pages are built for search too — SEO-friendly URLs mean a specific player or matchup is usually one search away.

TranscriptFetch's answer:

Three reasons. Coverage: one API key and one response shape across five video platforms and the open web, instead of stitching together a library per platform. Reliability: requests run through rotating infrastructure, so code that works locally keeps working from a server, which is where most open-source approaches break. Billing that matches reality: one credit per successful response, with failed, blocked and empty results never charged. That last point matters on short-form video, where a meaningful share of any batch is music with no speech in it. There is also an MCP server, so AI agents can fetch transcripts as a tool without a custom integration.

TranscriptFetch's answer:

Next.js with TypeScript and Tailwind on the front end and API layer, Clerk for auth with SHA-256 hashed API keys, Neon Postgres with Drizzle ORM, Redis for caching, and Stripe for billing. The extraction layer is a Python and FastAPI service. Speech-to-text uses Whisper-class models. The MCP server is published in the official Model Context Protocol registry with a DNS-verified namespace.