Blue side and Red side in League of Legends are not the same seat at the table. Jungle pathing, dragon pit proximity and vision setup all differ depending on which side of the map a team spawns on, and that asymmetry shows up in the numbers. If you're building a stats dashboard, a fantasy scoring model, or a betting model that wants to account for side bias, the PandaScore API lets you pull team and player statistics filtered specifically by side. Here's how.

Why Side Matters in League of Legends

Blue side and Red side spawn in different corners of the map with different jungle camp layouts relative to the two dragon pits and Baron pit. That structural difference has historically translated into a measurable win rate skew between sides, one that shifts with each patch as jungle pathing and objective priority evolve. For anyone building analysis on top of LoL data, treating a team's overall win rate as a single number hides a genuinely important split: a team can be significantly stronger on one side than the other, and that gap is exactly the kind of signal that gets lost if you're only pulling aggregate stats.

The Endpoints

PandaScore exposes side filtering on both the team stats and player stats endpoints for League of Legends.

Team stats by side

GET https://api.pandascore.co/lol/teams/{team_id_or_slug}/stats?side=blue
GET https://api.pandascore.co/lol/teams/{team_id_or_slug}/stats?side=red

This returns detailed statistics for a given team, filtered to only the games where they played on the specified side. Available to customers on a historical or real-time data plan.

Player stats by side

GET https://api.pandascore.co/lol/players/{player_id_or_slug}/stats?side=blue
GET https://api.pandascore.co/lol/players/{player_id_or_slug}/stats?side=red

Same filtering logic, applied at the individual player level. Useful for anything from fantasy scoring to scouting a specific player's tendencies when their team has first pick versus when they don't.

Other Useful Query Parameters

Both endpoints support a handful of parameters you'll likely want to combine with the side filter:

• games_count: limits the stats calculation to a specific number of the team or player's most recent games, useful for a current-form view rather than a full-season average
• videogame_version: accepts latest, all, or a specific patch number, so you can isolate stats to the current meta rather than blending in data from a different patch
• from and to: date filters for restricting the stats window to a specific period, such as a single split or tournament

Combining side with games_count and videogame_version gives you a genuinely current, side-specific read on a team or player, rather than a blended figure that could be several patches or several months out of date.

Example Request

Pulling a team's Blue side statistics over their last ten games on the current patch:

GET https://api.pandascore.co/lol/teams/128379/stats?side=blue&games_count=10&videogame_version=latest
Authorization: Bearer YOUR_TOKEN

The response includes the team's win rate, average game length, objective control rates and per-player breakdowns for the filtered set of games, all restricted to the Blue side sample. Swap the side parameter to red to pull the same shape of response for their Red side games, and compare the two directly.

Common Use Cases

Stats dashboards: Surface a team's Blue versus Red side win rate as a dedicated split rather than burying it inside a single aggregate number. It's a genuinely sticky feature for viewers who want to understand a matchup before a game starts.

Fantasy and DFS products: Player performance can shift meaningfully by side depending on role and playstyle. A jungler's clear speed and gank timing, or a support's vision setup, can look different on Blue versus Red. Side-filtered player stats let a fantasy scoring model account for that rather than treating every game as identical.

Pre-match analysis and odds models: Knowing a team's side-specific win rate ahead of a series where the side is known (or ahead of a coin flip, factoring in both scenarios) is a straightforward, well-supported input into a win probability model.

Frequently Asked Questions

Do I need a specific plan to access side-filtered LoL stats?

Yes, the team and player stats endpoints, including the side filter, are available to PandaScore customers on a historical or real-time data plan.

What values does the side parameter accept?

The side query parameter accepts blue or red on both the team stats and player stats endpoints.

Can I combine the side filter with a specific patch or date range?

Yes. The videogame_version, from, to and games_count parameters can all be combined with side in the same request, letting you isolate a very specific slice of a team or player's history.

Get Building

The full API reference, including the team stats and player stats endpoints, is available at developers.pandascore.co. If you're using an AI coding assistant, the LLM-optimised documentation index is at developers.pandascore.co/llms.txt.

Ready to start building? Join our customer Slack community where developers ask questions and share what they're building with PandaScore data.