Snapshot Challenge 15:33

The challenge is simple: look at a screenshot that shows one team’s perspective on the game, and use the available information to recommend their next move. It’s a technique for testing and training analysts, and it’s a good discipline for improving game knowledge.

Let’s give it a try.


Click for larger image

I posted this example to Twitter and got a lot of responses, with a variety of rationales. Responses to the tweet included different ideas in various levels of detail, but I also received many DMs, including some experienced professionals. Some of their answers are below, as well as the video and a breakdown of what actually happened. Continue reading Snapshot Challenge 15:33

Better Meta Analysis: Using Wilson Score intervals to evaluate win rates at the 2017 World Championship

With Game 5 of Team WE vs. Cloud9 complete, the Quarterfinals stage of the 2017 World Championship is over. The tournament clocks in at 107 games so far, and it’s clear which champion is strongest: Kalista is the only champion picked or banned in every single game thus far, a feat achieved by only one champion in each of the World Championships since 2013. But there are twenty champions in each game – ten picked and ten banned – and correctly choosing the other nineteen goes a long way towards winning a game.

While many analysts, broadcasters, and statistics websites use statistics like presence (pick+ban%), games played, and win rate to rank champions, none of these measurements truly capture the power level of a champion. As an alternative, we can use a Binomial Proportion Wilson Score Interval to attempt to evaluate win rates and adjust them, in order to find the “best” champions at Worlds 2017. Continue reading Better Meta Analysis: Using Wilson Score intervals to evaluate win rates at the 2017 World Championship

The premier source for League of Legends esports data, analytics, and insights