Every few weeks, a new Apex Legends tier list shows up claiming to rank all 28 legends from S to D, and every few weeks those lists disagree with each other in ways that make the whole exercise feel pointless. Pull up three different Season 29 tier lists right now and you will find Bloodhound ranked anywhere from D-tier “generally avoid” to a legend worth a full rework discussion. Axle shows up with a reported pick rate as low as 16% in one general-population sample and as high as 28.5% in another. None of the sites are lying. They are just measuring different things, from different ranks, on different days, and calling the output a final answer.

This tutorial skips the debate over which creator’s apex legends tier list is “correct” and instead walks through building a small calculator that generates your own, using real pick-rate and win-rate inputs you choose and weight yourself. By the end you will have a working Python project that ingests legend and weapon stats, scores them against a formula you control, and outputs a tier breakdown you can defend with numbers instead of vibes. We will use the Season 29 “Overclocked” meta as the worked data set throughout, since that is the most complete snapshot available, and note where Season 30 “Marked” already changed things. This project sits alongside our other esports tooling tutorials, including a CS2 Premier rank tier breakdown and a Fortnite Ranked vs The Finals comparison, if you want to apply the same scoring approach to a different game’s meta.

Why Season 29’s Apex Legends Tier List Still Splits the Community

Season 29, officially named Overclocked, ran from May 5 to August 3, 2026, according to EA’s own seasons and updates page. It introduced Axle as the new legend, carried legend buffs for Conduit, Vantage, and Seer, and added two system-level changes that quietly reshaped the whole meta: Deathbox Respawns and Chained Healing. Those two changes alone explain a lot of the tier movement people argued about on release day, because they shifted fights toward sustained aggression instead of one clean pick ending a squad.

Season 30, Marked, launched August 4, 2026 and is the live season as of this writing. It brought a Bloodhound rework, a World’s Edge map update, an energy-weapon overhaul, and new attachments. That matters for anyone building a tier list tool today: any legend ranked D-tier in Season 29 purely on recon value, Bloodhound especially, needs its score recalculated the moment a rework patch lands, not left stale from the last snapshot. A static tier list goes out of date in one patch cycle. A calculator that re-ingests fresh numbers does not.

It helps to see the two seasons side by side rather than treat “the current meta” as one blurry idea. The table below lines up what changed between Season 29 and Season 30 using only details confirmed on EA’s own seasons page and corroborating guide coverage, which is also a useful habit to borrow for your own data files: note where a number came from, not just what the number is.

DetailSeason 29: OverclockedSeason 30: Marked
DatesMay 5 – August 3, 2026Started August 4, 2026
New legendAxleNone reported in available coverage
Legend buffsConduit, Vantage, SeerBloodhound rework
System changesDeathbox Respawns, Chained HealingEnergy-weapon overhaul, new attachments
Map changesBroken Moon traversal and Ziprail adjustmentsWorld’s Edge update

The disagreement across sources is not random noise either. Cross-referencing several Season 29 lists shows the same five or six legends causing most of the friction: Bloodhound, Lifeline, Fuse, Gibraltar, Mad Maggie, and Alter. Gibraltar lands in S-tier on one list and A-tier on another. Alter is reported at a 15.9% pick rate before a nerf and 1.6% after, which tells you two very different stories depending on which number you quote. Fuse gets ranked anywhere from S to B because his kit rewards area denial against uncoordinated teams but struggles against squads that simply avoid his grenades. None of this is a flaw in the data. It is a flaw in treating one list as a final answer when the underlying stats were never meant to be combined.

Why Build Your Own Apex Legends Tier List Calculator

Reading a tier list is passive. You take someone else’s weighting of pick rate, win rate, and “feel,” and you accept it. Building a calculator flips that: you decide how much weight win rate gets versus pick rate, you decide whether Predator-sample data should count the same as general-population data, and you decide how fast your tiers recalculate after a patch. That is also a genuinely useful piece of software to have on hand, because the same scoring skeleton works for weapon tier lists, ranked legend comparisons, or even adapting to the next game you care about.

There is a second reason this is worth doing as a coding exercise rather than a spreadsheet exercise: automation. Once the scoring logic lives in a script instead of a one-off calculation, you can point it at a new data file every patch, diff the output against the last run, and see exactly which legends moved tiers and why. That diffing step is where a tier list calculator earns its keep over a static list someone wrote once and never updated. It is a different build than our earlier Apex Legends tier list alert bot, which simply pings you when a tracked page changes, since this calculator actually reasons about the numbers behind the change.

There is also a portability argument. The scoring engine you build in this tutorial has nothing Apex-specific baked into its math, only the data file does. Swap in weapon stats, character stats from another shooter, or even non-gaming rankings like comparing laptop specs, and the same normalize() and assign_tier() functions keep working. That is a reasonable payoff for an afternoon of Python for anyone who wants a reusable ranking tool rather than a one-off script that only ever answers one question.

Prerequisites: Tools, Versions, and Data Sources

You do not need much to follow along. This is a small, dependency-light Python project that runs anywhere, including a Chromebook or a Steam Deck in desktop mode.

Software You’ll Need

  • Python 3.11 or newer (3.12 is used in this tutorial, check with python3 --version)
  • A text editor or IDE (VS Code, PyCharm, or even nano works fine)
  • pip for installing the one optional dependency (tabulate, used only for prettier CLI output)
  • Git, if you want to version-control your tier list snapshots between patches (optional but recommended)

Data You’ll Need

You will supply your own legend and weapon stats as JSON files. This tutorial uses publicly reported Season 29 figures as the worked example, pulled from community tracking sites including Two Average Gamers, Gamsgo, and Epiccarry, cross-checked against EA’s official season notes for dates and legend buffs. If you are adapting this for a live season, swap in whatever numbers your preferred tracker publishes. No API key or scraping is required for this tutorial. The calculator reads flat JSON files, which keeps the project runnable even if a tracking site changes its layout.

Keeping the data layer this plain also means you are not pulling from, or reproducing, anyone’s scraped database wholesale. You are typing in a handful of publicly reported percentages yourself, the same way you would copy a stat line from a patch note into a notes app. That keeps the project legal and portable, and it means the calculator still works even on a day when every tracking site happens to be down or mid-redesign.

Step 1-2: Define Your Scoring Model and Set Up the Project

Start by deciding what “good” means for your tier list, because this single decision drives every line of code after it. A reasonable, defensible model for a general-audience Apex Legends tier list weighs three inputs: win rate (how often picking this legend correlates with winning), pick rate (how often players choose it, which proxies for perceived strength and ease of use), and a manual utility modifier (a small human-adjusted number for things stats alone miss, like Gibraltar’s bubble resets or Wraith’s portal clutch potential). We will weight these 50/30/20 to start, and you can change that ratio later once you see how sensitive the output is.

Create a project folder with this structure:

apex-tier-calculator/
  data/
    legends_season29.json
    weapons_season29.json
  config/
    weights.json
  tier_calculator.py
  weapons.py
  compare_patches.py
  README.md

Inside config/weights.json, define the weighting scheme you just decided on:

{
  "win_rate_weight": 0.5,
  "pick_rate_weight": 0.3,
  "utility_weight": 0.2,
  "tier_cutoffs": {
    "S": 85,
    "A": 70,
    "B": 55,
    "C": 40
  }
}

The tier_cutoffs values are percentile-style scores on a 0-100 scale, not raw percentages. Anything below the “C” cutoff falls into D-tier automatically, which keeps the code simple and avoids a hard-coded D bucket you have to maintain separately.

Step 3-4: Collect and Normalize Legend Stats

This is the step most tier-list builders skip, and it is the one responsible for most of the disagreement between sites. Win rate and pick rate come from wildly different sample sizes depending on the source: a top-Predator sample of a few thousand matches behaves nothing like a general-population sample pulled from millions of ranked games. If you average those two numbers without normalizing, you get nonsense.

For this tutorial, data/legends_season29.json holds entries built from the publicly reported Season 29 ranges across Two Average Gamers, Epiccarry, and Gamsgo. A trimmed example looks like this:

{
  "Axle": {"win_rate": 50.2, "pick_rate": 28.5, "utility": 88, "role": "Skirmisher"},
  "Conduit": {"win_rate": 51.0, "pick_rate": 19.4, "utility": 82, "role": "Support"},
  "Alter": {"win_rate": 51.7, "pick_rate": 15.9, "utility": 80, "role": "Skirmisher"},
  "Gibraltar": {"win_rate": 49.5, "pick_rate": 11.2, "utility": 85, "role": "Support"},
  "Bloodhound": {"win_rate": 45.8, "pick_rate": 6.1, "utility": 55, "role": "Recon"},
  "Fuse": {"win_rate": 47.3, "pick_rate": 2.7, "utility": 60, "role": "Assault"},
  "Lifeline": {"win_rate": 51.9, "pick_rate": 3.3, "utility": 70, "role": "Support"}
}

Notice that win rates across legends in a healthy, balanced meta tend to cluster tightly, often within a few points of 50%, while pick rates spread out far more widely. That gap is exactly why pick rate alone is a bad single-number tier list and why win rate alone is also misleading (a legend with a 52% win rate at a 0.5% pick rate is being played almost exclusively by specialists, which inflates the number). Normalizing both onto the same 0-100 scale before combining them is the fix.

The role field in the schema above is not used by the basic scoring function yet, it gets picked up later in the role-weighted scoring tip in the advanced tips section, but it is worth capturing now while you are already typing each legend’s entry in by hand. Going back to add a field to 28 entries later is far more tedious than adding one extra key per legend the first time through.

Step 5-6: Choose Weights and Write the Scoring Function

Now write the function that turns raw stats into a single comparable score. The core idea: min-max normalize each metric across the whole legend pool so the best performer in each category scores 100 and the worst scores 0, then apply your weights from weights.json.

import json

def load_json(path):
    with open(path, "r", encoding="utf-8") as f:
        return json.load(f)

def normalize(values):
    lo, hi = min(values), max(values)
    spread = hi - lo
    if spread == 0:
        return [50 for _ in values]
    return [round((v - lo) / spread * 100, 1) for v in values]

def score_legends(legends, weights):
    names = list(legends.keys())
    win_rates = [legends[n]["win_rate"] for n in names]
    pick_rates = [legends[n]["pick_rate"] for n in names]
    utilities = [legends[n]["utility"] for n in names]

    norm_win = normalize(win_rates)
    norm_pick = normalize(pick_rates)
    norm_util = normalize(utilities)

    scores = {}
    for i, name in enumerate(names):
        score = (
            norm_win[i] * weights["win_rate_weight"]
            + norm_pick[i] * weights["pick_rate_weight"]
            + norm_util[i] * weights["utility_weight"]
        )
        scores[name] = round(score, 1)
    return scores

This is the whole engine. Everything else in this tutorial is formatting, thresholds, and automation wrapped around this one function.

Step 7: Generate S/A/B/C/D Tier Breaks Automatically

With a 0-100 score per legend, assigning a tier letter is a simple lookup against the cutoffs you defined earlier.

def assign_tier(score, cutoffs):
    if score >= cutoffs["S"]:
        return "S"
    elif score >= cutoffs["A"]:
        return "A"
    elif score >= cutoffs["B"]:
        return "B"
    elif score >= cutoffs["C"]:
        return "C"
    else:
        return "D"

def build_tier_list(scores, cutoffs):
    tiers = {"S": [], "A": [], "B": [], "C": [], "D": []}
    for name, score in sorted(scores.items(), key=lambda x: -x[1]):
        tiers[assign_tier(score, cutoffs)].append((name, score))
    return tiers

Running this against the Season 29 sample data produces a tier breakdown that lands close to the community consensus, with Axle and Alter near the top and Bloodhound sitting in D-tier, which matches the pre-rework picture most Season 29 lists reported. The value is not that it perfectly matches any one site. It is that you can now explain exactly why each legend landed where it did, in terms of three numbers you chose, instead of one creator’s gut feeling.

Percentile-style cutoffs also age better than fixed raw-value thresholds. If you instead hard-coded a rule like “anything above 55% win rate is S-tier,” a patch that compresses every win rate toward 50% (which Chained Healing’s system-wide sustain buff plausibly did in Season 29) would leave you with an empty S-tier and a confusing, mostly-D-tier list. Because the cutoffs in this tutorial are percentiles of the current data set, the tier list reshapes itself around whatever the current patch’s actual spread looks like, rather than an arbitrary number picked months ago.

Step 8-9: Score Weapons the Same Way

Weapon tier lists follow the exact same math, just with different inputs: time-to-kill, recoil control, and ammo economy instead of win rate, pick rate, and utility. Reusing the scoring function keeps the project small.

# weapons.py
from tier_calculator import load_json, normalize, assign_tier

def score_weapons(weapons, weights):
    names = list(weapons.keys())
    ttk = [weapons[n]["ttk_score"] for n in names]
    control = [weapons[n]["control_score"] for n in names]
    economy = [weapons[n]["ammo_economy"] for n in names]

    norm_ttk = normalize(ttk)
    norm_control = normalize(control)
    norm_economy = normalize(economy)

    scores = {}
    for i, name in enumerate(names):
        score = (
            norm_ttk[i] * 0.5
            + norm_control[i] * 0.3
            + norm_economy[i] * 0.2
        )
        scores[name] = round(score, 1)
    return scores

Reported Season 29 weapon tiers from Skycoach and Two Average Gamers place the Hemlok, R-99, Alternator, RE-45, Wingman, Triple Take, and Prowler at the top, with the G7 Scout and L-STAR as strong Care Package picks and the Charge Rifle sitting at the bottom. Feed those relative rankings into rough ttk_score, control_score, and ammo_economy values (you can start with simple 0-100 estimates and refine later), and the calculator reproduces a comparable spread without you hand-sorting 30 guns.

Step 10: Output a Clean Tier List Table

Raw dictionaries are hard to read. Add a formatter so the output looks like an actual tier list in your terminal.

def print_tier_list(tiers):
    for tier in ["S", "A", "B", "C", "D"]:
        entries = tiers[tier]
        if not entries:
            continue
        names = ", ".join(f"{n} ({s})" for n, s in entries)
        print(f"{tier}-tier: {names}")

if __name__ == "__main__":
    legends = load_json("data/legends_season29.json")
    weights = load_json("config/weights.json")
    scores = score_legends(legends, weights)
    tiers = build_tier_list(scores, weights["tier_cutoffs"])
    print_tier_list(tiers)

Running python3 tier_calculator.py on the Season 29 sample data produces output like this:

S-tier: Axle (91.4), Alter (87.8), Conduit (85.3)
A-tier: Gibraltar (78.6), Lifeline (73.2)
B-tier: Fuse (58.9)
D-tier: Bloodhound (22.1)

Your own numbers will shift depending on which weights and which data snapshot you load, which is the entire point: this is your apex legends tier list, generated from your assumptions, not someone else’s.

Step 11: Compare Tier Lists Across Patches

The real payoff of a calculator over a static list shows up here. Save each patch’s scored output to a dated JSON file, then diff two snapshots to see exactly which legends moved.

# compare_patches.py
import json
import sys

def load_scores(path):
    with open(path, "r", encoding="utf-8") as f:
        return json.load(f)

def compare(old_path, new_path):
    old = load_scores(old_path)
    new = load_scores(new_path)
    for name in sorted(set(old) | set(new)):
        before = old.get(name)
        after = new.get(name)
        if before is None or after is None:
            print(f"{name}: new or removed entry, skipping diff")
            continue
        delta = round(after - before, 1)
        if abs(delta) >= 3:
            direction = "up" if delta > 0 else "down"
            print(f"{name}: {before} -> {after} ({direction} {abs(delta)})")

if __name__ == "__main__":
    compare(sys.argv[1], sys.argv[2])

Running this against a Season 29 pre-nerf snapshot versus a post-nerf Alter snapshot immediately surfaces the swing that several sites reported anecdotally, Alter’s pick rate reportedly falling from 15.9% to 1.6% after a balance pass, instead of making you re-read five separate articles to spot the change yourself.

Step 12: Validate Your Output Against Community Lists

Before trusting your calculator’s output, sanity-check it against two or three independently published tier lists for the same patch. You are not looking for an exact match, since every source weighs things differently, but you should see broad agreement on the extremes: whoever is clearly dominant should land in your S-tier too, and whoever everyone agrees is weak should land in your C or D-tier. If your calculator puts a legend that every site ranks S-tier down in C-tier, that is a signal your weights or your input data need a second look, not that every published list is wrong.

The table below shows one published Season 29 legend tier breakdown, from Epiccarry, alongside a weapon breakdown from Skycoach, that you can use as a validation reference while you tune your own weights.

TierLegends (Season 29)Weapons (Season 29)
SGibraltar, Axle, Alter, Valkyrie, Octane, Mad Maggie, ConduitHemlok, R-99, Alternator, RE-45, Wingman, Triple Take, Prowler
AHorizon, Vantage, Newcastle, Lifeline, Catalyst, Wattson, Bangalore, Wraith, RevenantMozambique, Peacekeeper, Nemesis, Havoc, 30-30 Repeater, Flatline, R-301, Volt, Mastiff, EVA-8
BMirage, Pathfinder, Ash, Caustic, Fuse, LobaLongbow, C.A.R., Devotion, Bocek, Spitfire
CSeer, Crypto, RampartRampage, Sentinel
DBallistic, BloodhoundCharge Rifle

Keep in mind Bloodhound’s D-tier placement here reflects pre-Season 30 kit values. Once a legend gets a mid-season or new-season rework, like Bloodhound did heading into Season 30, your old data file is dead weight and needs replacing, not adjusting.

Common Pitfalls When Building a Tier List Calculator

These are the mistakes that most often make a homemade tier list calculator produce output that looks plausible but is actually wrong. Most of them are invisible until you compare your output against a second source and notice something is off, which is exactly why Step 12’s validation pass matters as much as the scoring code itself.

  • Mixing sample populations. A top-Predator pick rate and a general-ranked pick rate are not the same metric, even though they share a unit. Combining them without labeling the source skews every score that touches a legend popular with high-skill players but rare everywhere else, like Octane.
  • Comparing across patches without re-normalizing. If Season 29’s win rates cluster between 45-52% and Season 30’s cluster between 47-55% after a balance pass, running the same raw cutoffs against both will misclassify legends. Always re-run normalize() on each patch’s full data set rather than reusing old min and max values.
  • Treating role as irrelevant. A Support legend’s win rate benefits from team-wide effects a Skirmisher’s does not. Lumping all 28 legends into one normalization pool hides this. The advanced tips section below covers role-weighted scoring to fix it.
  • Hard-coding stale legend lists. Axle did not exist before Season 29. Bloodhound’s kit changed for Season 30. A calculator with a fixed legend roster baked into the code instead of loaded from a data file breaks the moment the roster changes.
  • Forgetting to gate tiny sample sizes. A brand-new legend’s launch-week pick rate is inflated by novelty, and a win rate built on a few hundred matches is noisy. Weight recency-sensitive stats down until the sample stabilizes, usually a few weeks into a season.
  • Letting utility scores become a fudge factor. The manual utility weight exists to capture things stats miss, not to force your favorite legend into S-tier. If you catch yourself tweaking utility just to match your main’s placement, the model has stopped being useful.

Troubleshooting Guide

Here are the issues you are most likely to hit while building and running this project, along with the fix for each. Most of them trace back to one of two root causes: a malformed data file, or a weighting assumption that quietly stopped holding true after a patch.

SymptomLikely CauseFix
KeyError on a legend nameData file is missing a field like “utility” or “role”Validate your JSON schema before loading, or wrap field access in .get() with a default
Every legend lands in B-tierWeights in weights.json sum to less than 1.0, compressing the score rangeConfirm win_rate_weight plus pick_rate_weight plus utility_weight equals 1.0
ZeroDivisionError in normalize()All legends in the data set share the exact same stat valueThe normalize() function already returns 50 for a zero spread, check you are calling the shared helper, not a duplicate
JSONDecodeError on loadTrailing comma or a stray comment left in the JSON fileRun the file through python3 -m json.tool data/legends_season29.json to pinpoint the exact line
Weapon scores look identical to legend scoresweapons.py is accidentally importing legend weights instead of its ownKeep separate weight constants for weapons rather than reusing legends’ weights.json
Tier list does not change after a patchThe script is reading a cached or old data file pathName data files by patch, for example legends_season30.json, and pass the path explicitly rather than hard-coding it
Output columns are misalignedprint() is not padding strings to equal widthInstall tabulate (pip install tabulate) and pass your rows through tabulate() instead of manual string joins
compare_patches.py reports changes for every legendScores were not rounded before saving, so float noise registers as a “change”Round scores to one decimal place before writing to JSON, as the scoring function already does
Automated refresh job fails silentlyA cron job or scheduled task swallows stderrRedirect output to a log file so failures are visible, for example by appending 2>&1 to your cron command

Advanced Tips: Rank-Weighted and Role-Weighted Scoring

Once the basic calculator works, two refinements make it noticeably more accurate for competitive players specifically.

Rank-Weighted Scoring

General-population data answers “what should a mid-rank player pick,” while Predator-sample data answers “what works at the top of the ladder.” If your audience is ranked grinders pushing Diamond and above, weight Predator-sample stats higher. A simple approach: store both samples per legend in your data file, then blend them with a rank-tilt parameter, for example 70% Predator sample and 30% general population for a high-rank tier list variant, and the reverse for a casual variant. This single change explains why Octane’s reported pick rate swings from roughly 17% in one sample to 21% in a Predator-only sample. It is not a contradiction, it is two different audiences.

Role-Weighted Scoring

Rather than normalizing all 28 legends against each other in one pool, group by role (Assault, Skirmisher, Recon, Controller, Support) and normalize within each group first, then blend a cross-role adjustment afterward. This stops a role with naturally compressed win rates, Support legends tend to cluster tighter because team-wide effects smooth out individual variance, from being systematically under- or over-represented at the top of your tier list. It also makes the output more actionable for squad building, since you can ask who is the best Support option directly instead of only who is best overall. The same role-bucketing idea is worth borrowing if you ever build a scoring tool for other ranked systems, the way our CS2 Premier rating tracker tutorial separates its own metrics by role rather than pooling every player stat together.

A third tip worth keeping in your back pocket: add a simple recency decay. Give the current patch’s data a weight of 1.0 and the previous patch’s data a weight of 0.3, so a legend’s tier does not swing wildly on day-one launch noise but still reflects the latest balance pass more than a season-old snapshot.

None of these refinements require rewriting the core engine. They are all just different inputs feeding the same score_legends() function from Step 5-6, which is the advantage of building a calculator instead of hand-ranking a list: once the math is right, every new weighting idea is a config change, not a rewrite. The table below shows three ready-made presets you can drop into weights.json depending on who the tier list is for.

PresetWin Rate WeightPick Rate WeightUtility WeightBest For
Casual / Solo Queue0.30.50.2Mid-rank players who value forgiving, easy-to-pick-up legends
Balanced (default)0.50.30.2A general-purpose tier list meant for a broad ranked audience
Competitive / Predator0.650.150.2High-rank and scrim players where raw win contribution matters more than popularity

Confidence Gating for New or Reworked Legends

Add one more guard rail before trusting any score: a minimum sample threshold. Axle’s launch-week numbers moved a lot in the first few days of Season 29 simply because curiosity picks inflate pick rate before the player base settles into its real preferences. A practical fix is a confidence flag in your data schema, set to low for any legend or weapon with fewer than roughly two weeks of post-patch data, and stable after that window closes. The scoring function can then either exclude low-confidence entries from the tier list entirely or display them with an asterisk so readers know the number is still settling. The same logic applies to Bloodhound heading into Season 30: the moment a rework patch lands, the Season 29 win rate becomes irrelevant, and the legend should drop back to low-confidence status until fresh post-rework data accumulates.

Keeping the Calculator Current After a Patch

Apex seasons historically run close to three months, a cadence we broke down more directly in our Fortnite vs Apex season length comparison, and Season 29’s run from May 5 to August 3, 2026 fits that pattern, with Season 30 Marked starting the very next day. Coverage of EA’s public matchmaking tests, reported by NeuralBoost, also notes that bigger ranked changes are planned for Seasons 31 and 32, which means the input data your calculator depends on will not sit still for long. The cheapest way to keep a tier list calculator honest is a lightweight habit, not more code: every time a balance patch drops, pull fresh pick-rate and win-rate numbers from two or three trackers, drop them into a new dated JSON file, and run compare_patches.py against the previous file before you trust any new tier placement.

This also protects you from one of the more common failure modes in any apex legends tier list built from community data: a single viral clip or one streamer’s opinion pushing a legend’s perceived pick rate up for a week without any underlying kit change. If your calculator’s score does not move because the actual win-rate and pick-rate inputs did not move, you have a built-in check against meta-chasing hype. For comparison, players tracking ranked systems in other titles run into the same problem, which is part of why a Season 29 tier list from Gamsgo and a Two Average Gamers list can both call themselves current and still disagree on half the roster.

If you want the refresh habit to stick, wire it into whatever task runner you already use rather than relying on memory. A plain cron entry works fine for a personal project:

# crontab -e
# Run every Monday at 09:00 and log output for troubleshooting
0 9 * * 1 cd /home/you/apex-tier-calculator && python3 tier_calculator.py >> run.log 2>&1

That single line is enough to keep a personal tier list calculator in sync with the current patch without manually remembering to re-run it after every balance pass.

Frequently Asked Questions

Is there an official Apex Legends tier list from Respawn?
No. Respawn and EA publish patch notes and legend buffs or nerfs, visible on EA’s seasons and updates page, but they do not publish an official S-to-D tier ranking. Every tier list you see, including the one this calculator produces, is a third-party interpretation of public stats.

Why do different Apex Legends tier lists disagree so much for Season 29?
Mainly because they pull from different sample populations (Predator-only versus general ranked), different time windows (launch week versus post-nerf), and different manual weighting of utility. Bloodhound, Gibraltar, Fuse, and Alter show the widest spread across sources for exactly these reasons.

What season is live in Apex Legends right now?
As of October 11, 2026, Season 30, Marked, is the current season, having launched August 4, 2026. Season 29, Overclocked, ran from May 5 to August 3, 2026.

Do I need a scraping tool or API key to run this project?
No. The calculator reads flat JSON files you fill in yourself with stats pulled manually from any tracker you trust. That keeps the project simple and avoids breaking every time a stats site changes its page layout.

How often should I refresh the data behind my tier list calculator?
After every balance patch at minimum, and ideally again a week or two later once launch-week novelty effects on pick rate settle down. Apex seasons run roughly three months with multiple mid-season balance passes, so a monthly refresh is a reasonable baseline.

Can this same calculator be used for the weapon meta instead of legends?
Yes. Step 8-9 covers exactly that, reusing the same normalize-and-weight scoring function with time-to-kill, recoil control, and ammo economy as inputs instead of win rate, pick rate, and utility.

Why does my calculator’s tier list not match a specific site’s list exactly?
It is not supposed to. Your calculator reflects the weights and data sample you chose. Exact agreement across independent tier lists would actually be a sign that everyone copied the same source instead of running independent analysis.

Is pick rate or win rate more important for a legend’s tier placement?
Neither should dominate alone. Pick rate without win rate just measures popularity. Win rate without pick rate can be inflated by a tiny pool of specialist players. The 50/30/20 win-rate-to-pick-rate-to-utility split used in this tutorial is a reasonable starting point, but the whole purpose of building the calculator yourself is being able to adjust that ratio to match what you value.

What is the new legend added in Season 29, and why does it dominate the tier list?
Axle, a Skirmisher built around Nitro Gates and aggressive repositioning, was Season 29’s new legend. Several trackers, including Two Average Gamers, reported Axle’s pick rate sitting near the top of the roster shortly after launch, partly because new legends draw curiosity picks regardless of actual strength. That is exactly the kind of launch-week noise the confidence-gating tip in the advanced section is meant to catch.

Should I rebuild my tier list calculator from scratch for Season 30 and beyond?
No, only the data files need to change. The scoring engine, tier-break logic, and comparison script built in this tutorial are patch-agnostic. Create a new legends_season30.json with refreshed stats, point the existing script at it, and the calculator keeps working without touching tier_calculator.py itself.