Frequently Asked Questions

What stats do you use? uses game data from Platinum to Challenger from NA, EUW, EUNE and Korea for the current patch, available through Riot's API

What advantage does have over any other statistic sites?

Most other LoL statistic websites are lacking in three areas. First, data doesn't necessarily reflect a champion's true performance if their play in certain roles isn't taken into account - this is something is very well equipped for; any champion can be selected in their played role to get tailored statistics.

Second, many statistical websites present their data over the past month/week/day. This gives an inaccurate view of the champion on the current patch. This is because the sample size can cover more than the current patch or be too small (in the case of a day). bases all data off the current patch apart from the player experience section - which uses the past 2 patches.

Third, many statistic websites don't show the win rates when a champion is (potentially) played at their best - provides win rates for the best builds, masteries, runes, summoners and skill order.

How is the Overall Placement/Performance Ranking determined?

Thanks for asking! The overall performance ranking takes more than win rate into account! Depending on the particular role, different attributes (such as Win Rate, Play Rate, Ban Rate, Kills, Deaths, CS, Damage Dealt/Taken etc.) are weighted at different levels to provide an overview of how the champion performs as a whole. For example, an ADC champion has a higher weighting for 'Damage Dealt' than that of a support champion.

How is the Counters Statistical Rating determined?

The statistical rating of counters is determined in a similar way as the overall performance ranking, but it also takes into account how well a champion normally performs, and the effect the particular matchup has on this performance. It also takes into account who has a stronger performance in the matchup.

How are the win rates/play rates of full builds calculated?

Because the number of games where a full build is successfully completed is very low, it is hard to accurately determine the overall win rate of a given build. This is because completed builds typically have extremely high win rates (players that are ahead have more items completed).

Therefore, places a similar weighting for partially completed builds that follow the same build path as the full build. This helps provide a better overview of the build as a whole as it takes into account all stages of the build. In order to further stabilize the win rate, it is normalized against the win rate of the champions role.

How do you determine what role a champion plays in? uses Riot's API which provides the lane and role of a particular champion. This is based on the areas a particular champion spends the majority of the early game/laning phase.

How frequently is data updated?

Data is updated as soon as possible whenever a new patch is released (usually within 3 days). Towards the end of a patch, data is updated less has over 300gb of champion data, so processing can take around 15 hours.

Why does x champion have such a high/low win rate?

Champions such as Urgot are seen very very rarely in Platinum+ games. Consequently, there isn't always a large enough sample size to determine whether the statistics are a true reflection of the champion's performance. If small sample size isn't an issue, chances are Riot is having balancing issues.

How can I support is and always will be free to use - it does, however, cost to process, download, and host gigabytes of data each day. There are a number of ways you can help out! You can share with your friends/groups, you can white list through your adblocker, or for those who don't like advertisements, you can consider donating.

Is open source?

I have the web development/design side of on GitHub - You can take a look for yourself! The data aggregation/processing side of things is in a private repo until I clean it up.

What programming language(s) did you use? is a full JavaScript endeavour, utilising Node.js/Express on the back end with MongoDB serving the data. The front end uses Angular.js as its primary framework. Data aggregation/analysis was performed with MongoDB's aggregation framework / Node.js.

How can I contact you?

Send me a message on Reddit at /u/joeldo, through the Facebook page or GitHub