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PhantomStat
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PhantomStat is a sports analytics platform built for the gap between casual score sites and paywalled pro tools, covering five sports: football, MMA/UFC, MLB, NBA and tennis.
Football โ free preview pages for 1,230+ teams and 1,960+ players, with modules for Attack vs Defense (xG/shots/goals), Form Index & Rebound, Goal Geolocation Map, Cards Intelligence, Corner Mastery, Top Scorers Cross-Ref, Referee Tendency, Goalkeeper Weakness and Penalty Shot Tracker.
MMA/UFC โ 465+ fighters, 180+ analyzed fights. Free preview shows per-minute striking/grappling rates, KO Power and Cardio scores, a Performance Explorer (set any stat line, see how often the fighter clears it) and Opponent Level context. Pro unlocks the round-by-round Fatigue Curve and Strike Targets & Control breakdown.
MLB โ eight modules: Plate Discipline Lab, Statcast Quality Hub, Performance Spectrum, Situational Splits Matrix, Today's Matchup, Pitcher Arsenal, WAR Decomposition and a Similarity Engine.
NBA โ 30 teams, 500+ players, live matchups. Form Explorer across PTS/REB/AST/3PM/PRA, Opponent Defense, Rest/Back-to-Back splits, a Filter Engine and a multi-line threshold view.
Tennis โ career averages (win rate, holds/breaks serve, serve/return splits, aces, unforced errors, tiebreak% and deciding-set%) on every ATP player page.
Browsing is free with no signup. A free account adds saved watchlists and personalized dashboards; Pro unlocks the advanced modules sport-wide.
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PhantomStat's answer:
PhantomStat started from a simple gap: casual sports sites stop at the final score and a season average, while the analytics that go deeper are built for professionals and locked behind expensive tools. The goal was to put real per-entity detail โ 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 โ in front of any fan for free, with a paid Pro tier reserved for advanced filtering rather than for basic access to the numbers.
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.
PhantomStat's answer:
PhantomStat covers five sports in one platform โ football, MMA, MLB, NBA and tennis โ 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. Browsing is free with no signup required; a paid Pro tier ($29.99 / โฌ29.99 / ยฃ24.99 per month) unlocks advanced filtering and the full historical archive.
PhantomStat's answer:
Most single-sport score sites stop at the headline number, and most deep-analytics tools paywall everything before you can see anything. PhantomStat lets you browse every player, team, fighter and matchup page with no signup required, and covers five sports in one account (football, MMA, MLB, NBA, tennis) instead of forcing you to juggle a separate tool per sport. Pages are built for search too โ SEO-friendly URLs mean a specific player or matchup is usually one search away.
i like reddit very much
Based on our record, Reddit seems to be more popular. It has been mentiond 3301 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
From urllib.parse import urlparse Def normalize_gh(r): return { "title": r["name"], "url": r["url"], "source": "github", "score": r["stars_this_period"], "desc": r.get("description", ""), "date": r["trending_date"], "lang": r.get("language"), } Def normalize_hn(p): return { "title": p["title"].replace("Show HN: ", ""), "url":... - Source: dev.to / 4 months ago
@tool Def search_reddit(keywords: str, max_results: int = 20) -> list[dict]: """Fallback: search Reddit directly via PRAW.""" reddit = praw.Reddit( client_id=os.environ["REDDIT_CLIENT_ID"], client_secret=os.environ["REDDIT_CLIENT_SECRET"], user_agent="doug-agent/1.0", ) candidates = [] for submission in reddit.subreddit("all").search(keywords, sort="new",... - Source: dev.to / 3 months ago
Import requests Import time Def fetch_subreddit_posts(subreddit, sort="hot", limit=25): url = f"https://www.reddit.com/r/{subreddit}/{sort}.json" params = { "limit": limit, "raw_json": 1, # Prevents HTML encoding in responses } headers = { "User-Agent": "PythonScraper/1.0 (research project)" } response = requests.get(url, params=params, headers=headers) if... - Source: dev.to / 4 months ago
From sessionkeeper import SessionKeeper Async with SessionKeeper("reddit") as sk: page = await sk.get_authenticated_page("https://reddit.com") # You're logged in. Do your automation. await page.goto("https://reddit.com/r/blender/submit"). - Source: dev.to / 5 months ago
It's completely free, and takes just moments to set up - you just need to create an account, and set up keywords for the service to track. When your keywords are mentioned on Reddit, Hackernews, or Lobste.rs, you'll get a tidy little email in your inbox. - Source: dev.to / over 1 year ago
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