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What is LoLalytics?

LoLalytics is a League of Legends stats and analytics site. It tracks champion performance across ranked play and packages that data into tier lists, builds, runes, counters, and patch-by-patch breakdowns. The version described here focuses on Patch 16.19 and Emerald+ Ranked Solo/Duo globally.

Its distinguishing claim is completeness: it says it analyses every champion from every ranked game within a selected tier bracket, rather than sampling only the most-played picks. The page cites 1,652,371 Emerald+ champions analysed for Patch 16.19 and an average Emerald+ win rate of 51.28%. That matters because win rates shift by rank, so a champion who looks strong in Emerald+ may behave differently in lower or higher brackets.

What you can use it for

• Checking the current meta: see which champions were buffed, nerfed, or adjusted in a patch and how their win, pick, and ban rates moved.
• Finding builds and runes: the site's core offering is champion-specific build, rune, and counter recommendations.
• Comparing roles and modes: beyond standard ranked, it lists ARAM and Arena tier lists, plus pro builds.
• Preparing for a matchup: counter data helps you decide what to pick or ban.

A practical example

Suppose you main jungle and want to know whether to keep playing a champion after a patch. You would open the Patch 16.19 champion performance section, find your champion, and compare the current win rate, pick rate, and ban rate against the previous patch. A champion like Fiora shows a win rate of 52.12% with a +1.03% change, while Nocturne shows 50.53% with a -2.09% change — the direction and size of those shifts tell you more than the raw number alone.

How it compares

Decision criterion

Use LoLalytics when you want to understand why a champion is strong or weak in the current patch, not just copy a build. If you mainly want a one-click build for your next game, a simpler build-focused site may be faster. If you want to review your own matches, a site with match history is more useful.

A good next step: pick your main champion, open its Patch 16.19 page, and check whether its win rate, pick rate, and ban rate are all moving in the same direction. When they are, the trend is more reliable than a single number.

How does LoLalytics calculate champion win rates and tier lists for the current patch?

LoLalytics builds its champion win rates from actual ranked games in a selected tier bracket, then uses those rates as the backbone of its tier lists. On the page for Patch 16.19, the site states it is analysing Emerald+ Ranked Solo/Duo globally and lists 1,652,371 champions analysed for that patch, with an average Emerald+ win rate of 51.28%.

The key methodological claim is inclusion: LoLalytics says it includes 100% of the champions played in the selected tier bracket and never includes champions from outside that bracket. That matters because it avoids the common problem where a site mixes games from different skill levels and produces a win rate that does not describe any real population. It also explains why the same champion can show different numbers on different sites.

The page presents champion performance in two main ways:

• Win rate: the share of games a champion won, shown alongside the change from the previous patch (for example, Fiora at 52.12%, up 1.03%).
• Pick and ban rates: how often the champion was chosen or banned, which helps separate genuine strength from low-sample noise.
• Sample size: the number in parentheses after each champion name, such as Aatrox (11,738), tells you how many games that champion appeared in.

The tier list is then derived from those bracket-specific win, pick and ban rates. A champion with a high win rate but a tiny sample, like Rumble at 2,591 champions, is less reliable than one with tens of thousands of games. LoLalytics also groups champions into Buffed, Nerfed and Adjusted sections based on the patch notes, so you can see whether a champion's current numbers reflect a recent change.

A practical next step: before trusting a tier placement, check the sample size and the tier bracket. If you play in Gold, an Emerald+ tier list is a starting point, not a direct answer. Pick a champion with a large sample and a stable win rate, then compare its pick and ban rates to judge how contested it is. For broader meta context, you can also consult U.GG and OP.GG, but expect their numbers to differ because their inclusion rules and brackets are not identical.

Which champions were buffed or nerfed in Patch 16.19 and how have their win rates changed?

Patch 16.19's buff and nerf lists are both visible on LoLalytics, with win-rate changes shown next to each champion. The pattern in the data is that buffs usually produced modest win-rate gains, while several nerfs cut win rates harder — Nocturne and Rumble stand out as the biggest losers.

Buffed champions (Emerald+)

Nerfed champions (Emerald+)

How to read this

A champion appearing under "Buffed" does not guarantee a win-rate rise, and the table proves it: Aphelios, Kha'Zix and Draven all sit in the buffed list with falling win rates. That is normal — pick and ban rates moved sharply for some of them (Kha'Zix pick +3.85, Aphelios pick +4.76), so a surge of newer players can drag the average down even when the kit got stronger. Conversely, Nasus kept a healthy 52.76% win rate despite a nerf, which suggests the change trimmed him rather than removed him.

Practical takeaway: treat the buff/nerf label as context, not as the verdict. Volibear, Fiora and Elise combine a buff with a genuine win-rate gain, making them the cleanest "rising" picks. Nocturne and Rumble are the clearest downgrades. If you main a champion in the buffed list whose win rate fell, check whether the pick rate jumped before concluding the buff failed.

Next step: open the patch page on LoLalytics and check your own rank bracket, since these figures are Emerald+ and lower or higher tiers can shift the picture. For pro-level build context, OP.GG and U.GG are common comparison points.

How can I use LoLalytics to find the best builds and runes for a specific champion?

Start on the champion's individual page, which you can reach through the tier list or the search box. The page bundles the recommended build, runes, skill order, starting items and counters into one view, so you don't have to assemble them from separate pages.

Set your filters first. LoLalytics defaults to a ranked bracket and region; the excerpt shows an Emerald+ Ranked Solo/Duo global view with an average win rate of 51.28%. Switching to your own bracket (Gold, Platinum, Diamond+) and region matters, because the same champion can post different win rates and item choices in each. The site states it includes 100% of champions played within the selected bracket, which is why its numbers can differ from other stat sites — useful to know if you cross-check.

Read the numbers in context.

Work through a concrete case. Suppose you want to play Fiora. Her page shows a 52.12% win rate with a +1.03% change and roughly 8,035 games in the sample — a healthy sample and a rising trend, so the recommended build is worth following rather than improvising. Compare that with Rumble, sitting near 49% with a sharp negative change; if you main him, treat the suggested runes as a starting point and check whether the drop is patch-related before committing.

Use the counters and matchup sections, not just the core build. The build page tells you what to buy; the matchup data tells you when to deviate. If a specific opponent shows a lopsided win rate against your champion, adjust your early items or rune page accordingly.

Next step: open your champion's page, set bracket and region to match your own games, then copy the highest-sample rune page and starting items. After a few games, revisit and compare your results against the listed win rate — if you're far below it, the issue is usually execution or matchup knowledge rather than the build.

For a second opinion on pro-level builds, OP.GG and U.GG publish similar data with different sample handling; League of Legends has the official patch notes if you want to confirm what actually changed.

What is the difference between Emerald+ and other tier brackets on LoLalytics?

The main difference is scope and sample size: Emerald+ is the site's default ranked view, aggregating data only from games in that tier and above, while other brackets (Platinum, Gold, Diamond, Master, etc.) restrict the same analysis to their own player pools. Because LoLalytics counts every champion played in the selected bracket and excludes champions from outside it, the average win rate changes from bracket to bracket — the page itself notes this variation and gives an Emerald+ average win rate of 51.28% across 1,652,371 champions analysed for Patch 16.19.

What that means in practice:

• Emerald+ is a broad "high-ish elo" filter. It mixes Emerald, Diamond, Master, Grandmaster and Challenger games, so it's a reasonable default if you want stats that reflect competent play without being skewed by the very top of the ladder.
• Lower brackets (Gold, Silver, Bronze) reward different picks. Champions that need coordination, precise execution or early-game team play tend to underperform there, while straightforward, self-sufficient champions often overperform.
• Higher brackets (Master+, Challenger) shrink the sample fast. Win rates and pick rates get noisier, and a single strong player or a small pool of one-tricks can move the numbers more than in Emerald+.
• Patch-to-patch movement is easier to read in Emerald+. With over a million champions analysed, buff and nerf effects (the page lists buffed, nerfed and adjusted champions) show up more reliably than in a narrow bracket.

A quick decision rule: use Emerald+ when you want a stable, general read on a champion or build; switch to your own bracket when your games consistently look different — for example, if you're in Gold and a champion's Emerald+ win rate looks strong but its Gold win rate is flat, the champion probably depends on teammates or mechanics you can't count on yet.

Next step: open the tier list for your actual bracket, then compare the same champion's Emerald+ row against it. If the two disagree sharply, trust your bracket for pick/ban decisions and use Emerald+ for build and rune choices, where larger samples tend to be more reliable. For a second opinion on pro-level play, OP.GG and U.GG publish their own bracket filters you can cross-check.

How does LoLalytics compare to other League of Legends stats sites like U.GG or OP.GG?

LoLalytics positions itself around one methodological choice: it says it analyses every champion from every ranked game in the selected tier bracket, rather than sampling. That matters most when you're comparing champions with low play rates, where a smaller sample can swing win rates by a percentage point or more.

The practical differences between the major stats sites usually come down to three things: which ranks they default to, how they handle low-sample champions, and how much they show you versus how much they decide for you.

H3 Where LoLalytics fits

From the page evidence, LoLalytics presents patch-by-patch champion performance with win rate, pick rate and ban rate, plus the change from the previous patch. Its default view is Emerald+ Ranked Solo/Duo, global, and it states the average Emerald+ win rate (51.28% on this patch) and the number of champions analysed (1,652,371 on 16.19, versus 24,623,375 on 16.18). It also breaks champions into Buffed, Nerfed and Adjusted groups with per-champion deltas.

That structure suits a specific kind of reader:

• Patch-transition players. If you want to know whether a buff actually moved a champion, the displayed delta is the fastest read.
• Off-meta and low-pick-rate picks. The claim of full-bracket coverage is most useful for champions with thin samples, where other sites may show noisier numbers.
• Players who want numbers, not a verdict. LoLalytics leans toward tables and deltas. If you'd rather be handed a single recommended build and rune page with minimal reading, a site built around one-click builds will feel faster.

H3 Quick comparison

H3 A concrete way to choose

Suppose you main a champion with a 1.5% pick rate and you want to know if this patch's buff is real. Start on LoLalytics for the win-rate delta and the sample size behind it, then cross-check the recommended build on U.GG so you're not theorycrafting items from scratch. If you're instead checking how a friend or a pro has been playing, a profile-oriented site like OP.GG is the more direct tool.

The trade-off is consistent across all of them: sites that aggregate everything give you more context but require you to interpret it, while sites that hand you a build save time but hide the reasoning. Pick based on whether you want to decide or just execute.