PandaScore Launches New Player Rating Tool for League of Legends

PandaScore has introduced a new player evaluation tool for League of Legends that focuses on individual performance rather than match results. The company calls it PandaSkill, and it is designed to improve esports analysis, player comparisons, and betting accuracy.

Good to know

• PandaScore has launched PandaSkill, a new open-source player rating tool for League of Legends.
• It uses machine learning and Bayesian modeling to measure individual performance fairly by role.
• The system supports betting products like Player Props and BetBuilder by increasing odds accuracy.

Built by PandaScore’s data science team, PandaSkill uses machine learning to assess player skill across different in-game roles. Each role is analyzed separately to account for unique responsibilities. The model then updates skill ratings using OpenSkill, a Bayesian framework that focuses on individual contributions rather than just win-loss outcomes.

Unlike traditional metrics, PandaSkill evaluates players by comparing performance relative to the global player base. This approach gives fans, bettors, and analysts a new way to understand what separates top-tier play from the rest.

The tool is open-source, making it accessible for use on websites, mobile apps, or even esports coverage platforms. Anyone can use it to compare players and teams over time, helping guide wagers and enrich discussions around player stats and rankings.

For PandaScore, the tool also supports its broader product offerings. PandaSkill helps sharpen odds in its Player Props and BetBuilder features, which are key parts of its esports betting services. By linking data models more closely to true probabilities, PandaScore aims to improve both transparency and betting precision.

Oliver Niner, Head of B2B at PandaScore, explained the thinking behind the product. “The idea for PandaSkill was borne out of a culture of understanding games at an intrinsic, data-driven level, what drives wins and what constitutes good or bad performance,” he said.

He also pointed out the overlap between esports fans and bettors who rely on stats to make decisions. “PandaSkill takes this to the next level, helping inform public discussion, culture building and broadcast commentary, as well as helping players make smarter bets while also allowing us to improve the products and services we offer to operators.”

According to Niner, the tool is an important part of PandaScore’s overall offering and reflects its focus on staying at the edge of esports data and betting innovation.