Apex Legends Season 30, nicknamed “Marked,” launched on August 4, 2026, and it changed more than the loot pool. Respawn stretched Ranked Ladders from three days to five, kept the roster frozen at 28 legends, and left the Apex Predator cutoff exactly as murky as it’s always been: a moving target with no fixed RP floor. If you’ve ever alt-tabbed mid-game to check whether that last squad wipe pushed you into Diamond, or wondered why your RP total doesn’t match what the in-game leaderboard shows, you already know the pain point this guide solves.

This is a hands-on build. By the end you’ll have a Python script that polls your Apex Legends rank on a schedule, stores every snapshot in a local database, calculates your actual five-day ladder score the way Respawn does internally, and warns you when you’re close to the Predator threshold. No paid tools, no third-party dashboard subscription. Twelve steps, roughly 90 minutes if you’re copying code as you go, less if you’re just skimming for the API details.

The in-game ranked screen only shows you a single number and a progress bar. It doesn’t show which of your last games actually counted toward the five-day ladder, how much of your RP came from placement versus kills, or how many points separate you from the next division. Third-party lookup sites fill some of that gap, but they show everyone’s numbers, not a private history of yours over time, and most don’t calculate the ladder-window math at all. Building your own tracker fixes both problems and gives you a dataset you actually own, stored on your own machine instead of scattered across someone else’s dashboard.

What Changed in Apex Legends Ranked for Season 30 Marked

Marked is officially a rework season, not a new-legend season. EA’s own Marked patch notes, published August 3, 2026, confirm the roster holds at 28 legends while Bloodhound gets a full kit rework, Loba and Rampart pick up buffs, and Valkyrie, Seer, and Axle take nerfs. EA’s 2026 roadmap lays out two new legends total for the year, one tied to Season 29 and another slated for Season 32, so don’t expect a 29th legend to show up in your API responses mid-season.

Beyond the legend changes, Marked’s launch patch also introduced a regenerating energy ammo mechanic alongside a wider energy weapon overhaul, a new “Corrupted Attachments” variant that alters gun performance, and an update to the World’s Edge map rotation. None of that touches your RP directly, but it shifts which weapons and legends show up most in the matches your tracker is logging, which is worth keeping in mind if you ever try to correlate a rough patch of RP losses with a specific balance change rather than just bad luck in the lobby.

The ranked-specific change matters more for this build. Ranked Ladders now run five days instead of the older, shorter cycle, and the season’s Split 1 ladder calendar is fully published: Ladder 1 ran August 11 to 16, Ladder 2 ran August 18 to 23, Ladder 3 ran August 25 to 30, Ladder 4 ran September 1 to 6, and Ladder 5 ran September 8 to 13, 2026. A midseason update, patch 30.1, landed September 14 to 15 with meta tuning on top of the launch changes, according to EA’s Marked Midseason Designer’s Notes. Split 1 itself runs August 4 through September 15, 2026, before Split 2 opens and RP resets partway down the ladder.

Here’s the part most players miss: your ladder placement in Season 30 isn’t simply your accumulated RP. It’s the sum of your five best RP results from ranked matches played during that specific five-day window. Stack ten good games in Ladder 3 and only the top five count toward that ladder’s leaderboard score. That’s the exact logic this tracker replicates, because scraping the in-game leaderboard doesn’t tell you which five matches counted or how close you are to a better set.

The Apex Legends Rank Ladder, Tier by Tier

Before writing a line of code, it helps to know exactly what the numbers mean. The ladder runs Rookie, Bronze, Silver, Gold, Platinum, Diamond, Master, and Apex Predator. Bronze through Diamond each split into four divisions, IV down to I, while Master and Predator sit as single bands above everyone else. Entry cost, the RP you pay just to queue a ranked match, scales with tier: free at Rookie, then climbing from 10 RP at Bronze up to 90 RP per game at Master, based on EA’s published ranked rules. That cost resets to the lowest bracket right after a split tick, which is why the ladder always feels fastest to climb in the first few days of a new split.

Rank tierRP floor (Season 30)Divisions
Rookie0 (below Bronze floor)None
Bronze1,000 RPIV–I
Silver3,000 RPIV–I
Gold5,500 RPIV–I
Platinum8,500 RPIV–I
Diamond12,000 RPIV–I
Master16,000 RPSingle band
Apex PredatorNo fixed floor, top-ladder cutoff onlySingle band

Apex Predator is the one tier that breaks the pattern. It isn’t a number you cross, it’s a rank assigned to whoever sits in the top slice of players above the Master floor, which means the RP needed to hold Predator moves every time someone else climbs or falls. Community distribution data from LFCarry’s Season 30 rank distribution report, published in August 2026, shows Gold as the single largest tier at 36.39% of the ranked population, with Gold IV marking roughly the top 55% of the entire ladder. That single data point is useful context: if your tracker shows you sitting in Gold, you’re standing next to a third of the player base.

Entry cost deserves its own callout because it’s the detail most trackers built by hobbyists get wrong. A win doesn’t just add RP, it adds RP on top of whatever you already paid to queue. At Rookie the queue is free, so every point earned is pure gain. By Master you’re paying up to 90 RP just to load into a match, and a middling finish with a couple of kills can net out negative even though the match felt fine to play. According to the ranked rules relayed through bo3.gg’s breakdown of the Season 30 ranked system, that cost resets to the cheapest bracket right after a split tick, which is exactly why a tracker that logs entry cost separately from placement and kill RP is more useful than one that only logs the final delta.

Prerequisites: Tools and Versions You Need

Keep the stack simple. You need Python 3.12 or newer, pip at its latest version, the requests library installed via pip, and SQLite3, which ships with the Python standard library so there’s nothing extra to install there. A free API key from an Apex Legends stats provider is required, and matplotlib is optional if you want the advanced charting step later. Everything here runs on Linux, macOS, or Windows through WSL, and the cron scheduling step assumes a Unix-like shell.

  • Python 3.12+ (check with python3 --version)
  • pip, latest version (pip install --upgrade pip)
  • requests library, latest version via pip
  • SQLite3 (bundled with Python, no install needed)
  • A free Apex Legends Status API key
  • matplotlib, latest version, optional, for the advanced charting tip
  • cron or Task Scheduler access for automated polling

Python 3.12 is the floor here because the datetime handling in this tutorial leans on timezone-aware objects that behave more predictably on newer interpreter versions, and matching that baseline avoids a class of subtle off-by-one bugs in the ladder window math that older Python setups can introduce. None of the code below depends on anything exotic beyond that, so if you’re already running a recent Python install for other projects, you likely have everything you need except the API key.

Step 1: Get an Apex Legends Stats API Key

Respawn doesn’t publish a public stats API of its own, so the community has filled the gap. The most widely used option is the Apex Legends Status API, documented at apexlegendsapi.com. Registration happens through the site’s developer portal, and the documentation is explicit that only one API key is issued per project and person, so don’t spin up duplicate accounts hoping for a higher limit. Authentication works two ways: pass your key as a GET parameter named auth, or send it in an Authorization header. The default rate limit is 5 requests per second, which is generous enough for tracking your own account, and you can request an increase by linking a Discord account or filing a support ticket if you plan to track a full squad.

curl "https://api.apexlegendsstatus.com/bridge?auth=YOUR_API_KEY&player=YourOriginName&platform=PC"

Save that key somewhere your script can read without hardcoding it into a file you might commit to a public repo. A local .env file works fine for a personal project.

echo "APEX_API_KEY=your_key_here" > .env
echo "APEX_PLAYER_NAME=YourOriginName" >> .env
echo "APEX_PLATFORM=PC" >> .env

Step 2: Design the Local Rank History Database

The whole point of a tracker is history the game itself doesn’t show you. A single SQLite file, no server required, is enough to store every snapshot. Create one table for raw poll results and let the rest of the script derive ladder scores and deltas from it later.

import sqlite3

def init_db(path="apex_rank.db"):
    conn = sqlite3.connect(path)
    conn.execute("""
        CREATE TABLE IF NOT EXISTS rank_snapshots (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            player_name TEXT NOT NULL,
            platform TEXT NOT NULL,
            rank_tier TEXT,
            rank_division INTEGER,
            rank_score INTEGER,
            polled_at TEXT NOT NULL
        )
    """)
    conn.commit()
    return conn

Store every field the API gives you, even ones you don’t need immediately. Ranked tiers, divisions, and RP all get parsed from this table, and having the raw rank_score alongside the derived tier saves you from re-querying the API just to double-check a number.

Step 3: Write the Core Rank-Fetching Script

This is the function everything else builds on. It hits the /bridge endpoint, pulls the player’s current ranked data, and writes a timestamped row to the database. Use the UTC timestamp, not local time, because the next step buckets these snapshots into five-day ladder windows and mixing time zones will silently corrupt that logic.

import os
import requests
from datetime import datetime, timezone

API_URL = "https://api.apexlegendsstatus.com/bridge"

def fetch_rank(api_key, player_name, platform):
    params = {
        "auth": api_key,
        "player": player_name,
        "platform": platform,
    }
    response = requests.get(API_URL, params=params, timeout=10)
    response.raise_for_status()
    data = response.json()
    ranked = data.get("global", {}).get("rank", {})
    return {
        "rank_tier": ranked.get("rankName"),
        "rank_division": ranked.get("rankDiv"),
        "rank_score": ranked.get("rankScore"),
        "polled_at": datetime.now(timezone.utc).isoformat(),
    }

def save_snapshot(conn, player_name, platform, snapshot):
    conn.execute(
        "INSERT INTO rank_snapshots (player_name, platform, rank_tier, rank_division, rank_score, polled_at) VALUES (?, ?, ?, ?, ?, ?)",
        (player_name, platform, snapshot["rank_tier"], snapshot["rank_division"], snapshot["rank_score"], snapshot["polled_at"]),
    )
    conn.commit()

Run that once and check the output before automating anything.

{
  "rank_tier": "Platinum",
  "rank_division": 2,
  "rank_score": 9840,
  "polled_at": "2026-09-15T18:04:11.203119+00:00"
}

If rank_score comes back null or the whole rank object is missing, the account either hasn’t played a ranked match this season or the name-platform pair doesn’t match an existing profile. Both are covered in the troubleshooting section below.

Step 4: Track RP Changes Between Snapshots

Raw snapshots only tell you where you stand right now. The interesting signal is the delta between polls, which approximates your per-match RP gain or loss without needing Respawn’s exact placement and kill-point formula, something Respawn hasn’t published in full for Season 30. Pull the two most recent rows and subtract.

def get_rp_delta(conn, player_name, platform):
    rows = conn.execute(
        "SELECT rank_score, polled_at FROM rank_snapshots WHERE player_name=? AND platform=? ORDER BY polled_at DESC LIMIT 2",
        (player_name, platform),
    ).fetchall()
    if len(rows) < 2:
        return None
    latest, previous = rows
    return {
        "delta": latest[0] - previous[0],
        "since": previous[1],
        "now": latest[1],
    }

A positive delta means you gained RP net of entry cost since the last poll. A negative one means the loss outweighed placement and kill RP, which happens more often below Diamond where entry costs stay low but so do placement rewards for a mid-pack finish.

Step 5: Build the Five-Day Ladder Score Calculator

This is the step that actually mirrors what Respawn does internally, because the in-game leaderboard doesn't rank you by total RP, it ranks you by the sum of your five best RP results within the current ladder window. First, hardcode or fetch the current season's ladder boundaries, since EA doesn't expose them through the stats API.

from datetime import datetime, timezone

SEASON_30_LADDERS = [
    ("2026-08-11", "2026-08-16"),
    ("2026-08-18", "2026-08-23"),
    ("2026-08-25", "2026-08-30"),
    ("2026-09-01", "2026-09-06"),
    ("2026-09-08", "2026-09-13"),
]

def current_ladder_window(today=None):
    today = today or datetime.now(timezone.utc).date()
    for start, end in SEASON_30_LADDERS:
        s = datetime.strptime(start, "%Y-%m-%d").date()
        e = datetime.strptime(end, "%Y-%m-%d").date()
        if s <= today <= e:
            return s, e
    return None, None

Then pull every snapshot inside that window, compute the per-poll RP deltas as individual "match results," and sum the five highest. This is an approximation, since polling doesn't capture every single match the way a native game client hook would, but polling every 15 to 20 minutes during a play session gets close enough for personal tracking.

def ladder_score(conn, player_name, platform):
    start, end = current_ladder_window()
    if not start:
        return None
    rows = conn.execute(
        "SELECT rank_score, polled_at FROM rank_snapshots WHERE player_name=? AND platform=? AND date(polled_at) BETWEEN ? AND ? ORDER BY polled_at ASC",
        (player_name, platform, start.isoformat(), end.isoformat()),
    ).fetchall()
    deltas = [rows[i][0] - rows[i - 1][0] for i in range(1, len(rows))]
    gains = sorted([d for d in deltas if d > 0], reverse=True)
    return sum(gains[:5])

Step 6: Add an Apex Predator Threshold Checker

Since Predator has no fixed RP floor, the only reliable way to know how far off you are is to ask the API directly. The Apex Legends Status API documentation describes a dedicated /predator endpoint that returns the RP or AP needed to reach Predator, broken out by platform.

def predator_cutoff(api_key, platform):
    response = requests.get(
        "https://api.apexlegendsstatus.com/predator",
        params={"auth": api_key, "platform": platform},
        timeout=10,
    )
    response.raise_for_status()
    return response.json()

def distance_to_predator(api_key, platform, current_rp):
    cutoff_data = predator_cutoff(api_key, platform)
    threshold = cutoff_data.get("RPPredator") or cutoff_data.get("rank_score")
    if threshold is None:
        return None
    return threshold - current_rp

Field names on community APIs shift between updates more often than official ones, so wrap this in error handling and log the raw JSON the first time you run it, then adjust the key names to match what you actually get back.

Handling Your API Key and Player Data Responsibly

A personal rank tracker only ever needs to query your own account, so treat the API key like any other credential: keep it out of version control, load it from .env or an environment variable, and never paste it into a public gist or a Discord channel while asking for debugging help. If you extend the tracker to a full squad, as covered in the advanced tips below, get explicit consent from teammates before logging their player names and match history, even informally. Community stats APIs aggregate publicly queryable data, but a local database that quietly accumulates months of a friend's ranked performance is still their data, and it's good practice to let them know it exists and ask before sharing exported CSVs or charts outside the group.

Step 7: Schedule Automatic Polling With Cron

A tracker that only runs when you remember to run it isn't much better than checking the game manually. Wrap the fetch-and-save logic in a small script and let cron call it every 15 minutes during hours you actually play.

*/15 18-23 * * * cd /home/you/apex-tracker && /usr/bin/python3 poll.py >> poll.log 2>&1

Running every 15 minutes for a five-hour play window gives you roughly 20 snapshots a day, enough resolution to catch most individual match swings without tripping the 5 requests per second rate limit even if you're tracking two or three accounts from the same cron job.

Step 8: Export a CSV Report and Terminal Chart

Raw database rows aren't useful mid-session. A quick CSV export and an ASCII sparkline give you a readable snapshot without opening a spreadsheet app.

import csv

def export_csv(conn, player_name, platform, out_path="rank_history.csv"):
    rows = conn.execute(
        "SELECT rank_tier, rank_division, rank_score, polled_at FROM rank_snapshots WHERE player_name=? AND platform=? ORDER BY polled_at ASC",
        (player_name, platform),
    ).fetchall()
    with open(out_path, "w", newline="") as f:
        writer = csv.writer(f)
        writer.writerow(["tier", "division", "rp", "polled_at"])
        writer.writerows(rows)
    return len(rows)

def sparkline(values):
    blocks = "▁▂▃▄▅▆▇█"
    lo, hi = min(values), max(values)
    span = max(hi - lo, 1)
    return "".join(blocks[int((v - lo) / span * (len(blocks) - 1))] for v in values)

A sample run against a few hours of Diamond-tier grinding looks like this in the terminal.

Player: YourOriginName (PC)
Current: Diamond III, 12,940 RP
Ladder 4 score (Sep 1-6 window): 1,180 RP (best 5 of 9 sessions)
RP trend: ▂▃▃▅▄▆▇█
Distance to Predator cutoff: 4,260 RP

Step 9: Handle Rate Limits and API Errors

The default 5 requests per second limit is easy to hit if you're tracking multiple accounts or debugging with a tight loop. The API exposes an X-Current-Rate response header so you can back off before getting throttled instead of reacting after a 429.

import time

def fetch_with_backoff(api_key, player_name, platform, max_retries=3):
    for attempt in range(max_retries):
        response = requests.get(
            API_URL,
            params={"auth": api_key, "player": player_name, "platform": platform},
            timeout=10,
        )
        if response.status_code == 429:
            wait = 2 ** attempt
            time.sleep(wait)
            continue
        response.raise_for_status()
        return response.json()
    raise RuntimeError("Rate limit exceeded after retries")

Step 10: Assemble the Complete Tracker

Here's every piece wired into one runnable file. Save it as tracker.py, drop your credentials in .env, and run it directly or from the cron entry above.

import os
import sqlite3
import requests
from datetime import datetime, timezone

API_URL = "https://api.apexlegendsstatus.com/bridge"
PREDATOR_URL = "https://api.apexlegendsstatus.com/predator"

def load_env(path=".env"):
    env = {}
    with open(path) as f:
        for line in f:
            if "=" in line:
                k, v = line.strip().split("=", 1)
                env[k] = v
    return env

def init_db(path="apex_rank.db"):
    conn = sqlite3.connect(path)
    conn.execute("""
        CREATE TABLE IF NOT EXISTS rank_snapshots (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            player_name TEXT NOT NULL,
            platform TEXT NOT NULL,
            rank_tier TEXT,
            rank_division INTEGER,
            rank_score INTEGER,
            polled_at TEXT NOT NULL
        )
    """)
    conn.commit()
    return conn

def fetch_rank(api_key, player_name, platform):
    resp = requests.get(API_URL, params={"auth": api_key, "player": player_name, "platform": platform}, timeout=10)
    resp.raise_for_status()
    ranked = resp.json().get("global", {}).get("rank", {})
    return {
        "rank_tier": ranked.get("rankName"),
        "rank_division": ranked.get("rankDiv"),
        "rank_score": ranked.get("rankScore"),
        "polled_at": datetime.now(timezone.utc).isoformat(),
    }

def main():
    env = load_env()
    conn = init_db()
    snap = fetch_rank(env["APEX_API_KEY"], env["APEX_PLAYER_NAME"], env["APEX_PLATFORM"])
    conn.execute(
        "INSERT INTO rank_snapshots (player_name, platform, rank_tier, rank_division, rank_score, polled_at) VALUES (?, ?, ?, ?, ?, ?)",
        (env["APEX_PLAYER_NAME"], env["APEX_PLATFORM"], snap["rank_tier"], snap["rank_division"], snap["rank_score"], snap["polled_at"]),
    )
    conn.commit()
    print(f"{snap['rank_tier']} {snap['rank_division']}, {snap['rank_score']} RP at {snap['polled_at']}")

if __name__ == "__main__":
    main()

Step 11: Run It and Read Your First Report

Run python3 tracker.py once manually before trusting cron with it. Confirm the printed line matches what you see in your in-game ranked screen, tier, division, and RP. Let it run through one full five-day ladder window before you judge the ladder score calculator, since that function needs at least two snapshots inside the current window to produce a meaningful number, and it needs the window to actually close to compare against the official leaderboard.

A realistic first week looks messier than the clean sparkline example earlier. Expect gaps where you forgot the laptop was asleep during a play session, a handful of duplicate-looking rows a minute apart if you ran the script manually while also testing cron, and at least one session where RP goes down twice in a row before climbing again. None of that is a bug. It's what a genuine ranked grind looks like once you're measuring it in 15-minute increments instead of glancing at a single end-of-session number.

Step 12: Verify Your Data Against the Official Ladder

Cross-check your calculated ladder score against the in-game ranked leaderboard at the end of a ladder window. Small mismatches are expected since polling every 15 minutes can miss a match that both starts and ends inside that gap. A mismatch larger than one full match's worth of RP usually means your ladder window dates are off, often because of a timezone bug, or your snapshot table has a gap from the script crashing silently. Check poll.log first.

Reading the Output: A Sample Week of Tracked Data

Here's what a Diamond-tier grind looks like once exported to CSV and grouped by day. Numbers below are an illustrative example built from the tracker's own output format, not a real player's data, so you can see how a session with a losing streak still nets out differently from one with a hot run late in the night.

DayMatches loggedNet RP changeNotes
Mon6+140Steady session, no promotions
Tue4-60Two early exits, entry cost outweighed placement RP
Wed00No games played, cron kept polling with no change
Thu9+310Best session of the week, promoted a division
Fri5+45Mixed results, roughly break-even

Thursday's session is the one that would count most toward that ladder window's score, since the calculator only sums the five best individual gains, not every session's total. A quiet Wednesday with zero matches costs nothing in this system, which is worth remembering if you're deciding whether a rest day will hurt your ladder standing. It won't, as long as your best five results elsewhere in the window hold up.

Common Pitfalls When Building an Apex Rank Tracker

  • Hardcoding a made-up RP formula. Respawn hasn't published exact placement and kill-point tables for Season 30. Read match RP from the API's delta, don't guess at a formula and present it as fact.
  • Treating Apex Predator as a fixed number. It's a top-ladder cutoff, not a floor. Query the /predator endpoint each time rather than caching last week's threshold.
  • Storing timestamps in local time. Ladder windows are date-based. A local-time timestamp near midnight can land a snapshot in the wrong five-day bucket depending on your timezone offset from UTC.
  • Assuming ladder score equals raw RP. It's the sum of your five best results in the current window, not your total RP gain for the season.
  • Polling too aggressively across multiple accounts. The default limit is 5 requests per second total, not per player. Stagger multi-account polling or request a rate increase first.
  • Ignoring split and season rollovers. RP floors, division counts, and even which legends are eligible can change at a season boundary. Don't assume Season 30's numbers carry forward unchanged.

Apex Legends Rank Tracker Troubleshooting Guide

  • 401 or 403 response: your API key is missing, expired, or malformed. Re-copy it from the developer portal, don't retype it by hand.
  • 429 Too Many Requests: you've exceeded 5 requests per second. Add the backoff function from Step 9 and check the X-Current-Rate header.
  • Player not found: the name-platform pair doesn't match. Origin display names are case-sensitive on some platforms, and cross-play accounts sometimes need the platform you actually queued ranked from, not the one you're currently logged into.
  • Null rank data on a valid account: the player hasn't completed placement or played a ranked match yet this season. Rookie-tier accounts with zero games often return an empty rank object rather than a Rookie tier string.
  • "Database is locked" errors: a cron run and a manual run overlapped. Add a simple file lock or stagger the schedule so two processes never write to the same SQLite file at once.
  • RP shows as negative or drops sharply overnight: a split reset likely happened. Check the split dates, not your gameplay, before assuming a bug in your delta math.
  • Predator endpoint returns null: some platforms or regions don't populate this field outside of peak population hours. Retry later rather than treating a null as zero.
  • Stale-looking data despite a successful request: the upstream API caches responses briefly. Wait a minute or two between manual test calls instead of hammering it in a loop while debugging.

Advanced Tips: Alerts, Multi-Player Tracking, and Discord Webhooks

Once the base tracker runs reliably, a Discord webhook turns it into a passive alert system. Fire a POST request whenever a snapshot crosses a division boundary, and you get a ping the moment you promote without tabbing out mid-match.

def notify_discord(webhook_url, message):
    requests.post(webhook_url, json={"content": message}, timeout=10)

# Call this after saving a snapshot if rank_tier or rank_division changed
notify_discord(webhook_url, "Promoted to Diamond II, 12,600 RP")

For a squad, extend the player_name and platform columns into a config list and loop the fetch function across teammates, respecting the shared rate limit by adding a short delay between calls rather than firing them concurrently. If you want a proper visual instead of a terminal sparkline, matplotlib can plot the rank_score column against polled_at straight out of the SQLite table with a handful of lines, which is worth adding once you have a few weeks of history to actually chart.

The database also holds up fine across season boundaries if you leave it running. Add a season column alongside polled_at and populate it from a small config value you update manually each time a new season launches, since the stats API doesn't reliably expose season numbers on every response. That one extra column lets you filter out Season 29 data when you're only interested in your Season 30 Marked climb, and it means the same script keeps working without a rewrite once Season 31 replaces Marked later this year.

Apex Legends Stats API Cheat Sheet

Keep this table next to your code editor while you build. It covers the endpoints this tutorial actually uses, sourced from the Apex Legends Status API documentation.

EndpointPurposeAuthDefault rate limit
/bridgeCurrent player stats, including ranked tier, division, and RPauth param or Authorization header5 requests/second
/predatorRP or AP needed to reach Apex Predator, by platformauth param or Authorization header5 requests/second
/bridge (skipRank=true)Player stats without ranked data, faster for non-ranked lookupsauth param or Authorization header5 requests/second

Where Season 30 Players Actually Rank

Once your tracker has logged a few sessions, it helps to know where that number sits among everyone else grinding the same ladder. Community rank distribution data compiled in August 2026 puts Gold as the single densest tier, holding 36.39% of the ranked population, with the Gold IV division marking roughly the 55th percentile of all ranked players. That means anyone reading a Platinum or Diamond result out of this tracker is already ahead of well over half the ladder, and anyone still working through Bronze or Silver is in the same range as a much larger share of the population than the leaderboard screen makes it feel like. Respawn hasn't published a full breakdown across every tier for Season 30, so treat any number outside the Gold data point as an estimate until you pull it yourself from a distribution-focused API call.

If you're building trackers for other titles too, the same snapshot-and-delta pattern applies almost unchanged. The Rocket League Rank Tracker and Overwatch 2 Rank Tracker guides use the same database structure, and the Marvel Rivals Rank Tracker Setup walks through squad-based polling in more depth. For a side-by-side look at how Apex's ladder compares to another battle royale's system, see Fortnite vs Apex Ranks. And if legend power rankings matter more to your ranked climb than your RP number does, the Apex Legends Tier List covers the Season 30 Marked meta separately from anything in this guide.

More broadly, this build sits inside our wider esports coverage, where we track ranked systems, patch notes, and competitive meta shifts across most major titles as they happen.

Frequently Asked Questions

How many rank tiers are in Apex Legends Season 30?

Eight: Rookie, Bronze, Silver, Gold, Platinum, Diamond, Master, and Apex Predator. Bronze through Diamond each break into four divisions, while Master and Predator are single bands.

What RP do I need to reach Master in Season 30?

16,000 RP, based on EA's published Season 30 rank floors. That's a fixed threshold, unlike Apex Predator, which has no fixed RP number.

Is there an official Apex Legends API?

Respawn and EA don't publish a public stats API for third-party developers. This tutorial uses the community-run Apex Legends Status API instead, which requires its own free key and has its own rate limits separate from anything EA operates.

How is Apex Predator rank determined if there's no RP floor?

Predator is assigned to the top slice of the ladder above the Master floor, so the RP needed to hold it shifts constantly as other players climb or fall. Query the /predator endpoint for a current estimate rather than relying on a cached number.

How often should I poll the API for rank data?

Every 15 to 20 minutes during active play sessions is enough resolution for personal tracking without approaching the 5 requests per second rate limit, even across a few tracked accounts.

Can I track my rank without coding, using a website instead?

Yes, sites built on the same Apex Legends Status API show current rank and RP without any setup. The advantage of building your own tracker is the historical ladder-score math and custom alerts this guide walks through, which most lookup sites don't offer.

What happens to my RP when a new split starts?

Splits carry a partial RP reset that pulls players back down the ladder without erasing all progress. Entry costs also reset to the cheapest bracket immediately after the tick, making the first few days of a new split the fastest window to climb.

Does entry cost RP even if I don't get any kills?

Yes. Entry cost is deducted from your total regardless of match performance, ranging from free at Rookie up to 90 RP per game at Master in Season 30. Placement and kill or assist RP are added on top, so a bad match can produce a net loss even with a modest placement.

Why does my tracker's ladder score differ slightly from the in-game leaderboard?

The most common cause is polling gaps. If a match starts and finishes entirely between two 15-minute polls, your database never captures that RP change as its own delta, so it either gets folded into the next reading or missed if you switched accounts in between. Tightening the polling interval to every 5 to 10 minutes during a session closes most of that gap, at the cost of more API calls against your rate limit.