Tag Archives: stats theory

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

Snowballing: Tracking How Gold Leads Grow (and Shrink)

Snowballing is an important part of League of Legends: if you can’t convert a large gold lead into a nexus lead, you will never win the game, and if you can’t convert a small gold lead into a large gold lead, the enemy team will take your lead away. Every team should be constantly trying to increase their gold lead, or dig themselves out of a gold deficit.

However, not every team is equally good at this. Some teams know how to recover from a disastrous early game to a miraculous comeback victory, as TSM did vs Immortals in Game 4 of the NA LCS Summer finals. Some teams seem hopeless, as they either squander a big lead, or dig themselves into a deeper hole every passing minute, such as when Dignitas struggled against CLG in the NA LCS 3rd-place match.

Today we’re introducing a new statistic called Snowball that measures this aspect of League of Legends.

The Formula

We define Snowball as the following:

Note that whenever a team gains Snowball, the opposite team loses Snowball of equal value. If the gold distribution changes from 52%/48% to 51%/49%, blue team lost 1 Snowball while red team gained 1 Snowball. This rewards teams for increasing their % gold lead, while punishing teams for losing it. Continue reading Snowballing: Tracking How Gold Leads Grow (and Shrink)