Many sites like this one track these imbalances. We report on things like champion and item order win rates. With each patch, we track new surfacing metas that introduce us to a new lineup of favored champions and builds, as well as leper champions–choices that have fallen out of the meta so much that your teammates will audibly complain should you dare lock them in.
One of the most static variables in a League of Legends game is the map. But consider that each map half does not mirror the other and the side of the map that you spawn on will have its own advantage and disadvantages; they may not be inherently balanced. In fact, several entities have previously purported an imbalance on map side in professional games. But is there credence to the accusations?
If you look at the raw statistics for blue vs red, they seem fair. For the purpose of writing this article, I ran the numbers. I took data from nine different regions over the last three patches, only including games from Platinum players and higher. In a sample of over 1,022,667 full games recorded, the blue team had a win rate of 50.63%, while the red team had a win rate of 49.37%. This is an absolutely fantastic in terms of balance.
In over 1,022,667 full games recorded during patch 5.22, 5.23, and 5.24, the blue team had a win rate of 50.63% while the red team had a win rate of 49.37%
But as I’ve already stated, there have been instances in which map balance has been called into question for professional games. In 2014, the Daily Dot examined several professional games and found that the blue side held a statistically measurable advantage–professional teams playing the blue side in their sample won 57.5% of the time, leaving an underwhelming 42.5% of the games to the red team. That’s weird; that’s not balanced.
Although the Daily Dot’s sample data is admittedly minuscule, we would expect to see some of the imbalance translate into ranked solo queue, but we don’t–at least not by first glance.
To investigate this phenomenon further, I used a sample of over a million ranked solo queue games. I grouped each match by length into buckets of six minute intervals. For example, if a match ended after fifteen minutes, it was grouped into the “12-18 MIN” bucket.
The blue side has a statistically significant advantage in matches ending before the twenty five minute mark. It seems that the red team has a much more difficult time closing out a game early. This doesn’t influence the total win rates in a obvious manner because short matches make up a small fraction of all games. In my sample, only 23.25% of all games ended at or before the 25 minute mark. The difference is even more significant in games ending before the 18 minute mark. Blue win 55.58% of those games, although a game will only finish that quickly 1.66% of the time.
On average, the blue side wins 52.47% of games ending before the 25 minute mark with the red team picking up the remaining 47.53% of matches. Blue enjoys a 55.7% win rate in any game ending before the 18 minute mark.The red team has a much more difficult time closing out a game early.
These are interesting data points, but are even more interesting when paired up with champion data. As most players already know, certain champions excel in the early game while others need time to build strength–but consider that map side will also affect their performance.
We can make numerous observations from the raw aggregate data. For example, Blitzcrank has incredible shut out potential with one of the undeniably strongest early games in League of Legends. In games ending on or before the 20 minute mark with blue side winning, Blitzcrank has a 71.2% win rate. This drops 8.26% when Blitzcrank is on the red team.
In short games, many champions have a significantly higher winrate when they are on the blue side; for example, Amumu has almost 16% higher win rate when he is on blue (but only when the game ends before 20 minutes). Could this be because of his extremely deadly level six bottom lane gank potential? Many early gank champions had very large win rate differences depending on the side of the map they were playing, although only if the game ended early. By mid game, it all balances out. It seems to me that blue enjoys easier ganks.
The above values represent 1000-2000 games in a huge vat of over a million matches. I still believe these trends to be of some statistical significance. Because Riot gives developers access to kill information such as timestamps and coordinates, we could certainly examine this in greater focus. I’ll revisit this topic in the future if there is enough interest.
I did not include an analysis of which lanes were the ones feeding in those sub-25 minute games. I expected to see jungle, top, and bottom lane being affected the most, while middle mostly unaffected, but I found that middle KDA on the losing team was the lowest on the losing team of a short game. This analysis is incomplete and raises many questions.
Why do you think the blue side has a greater potential to dominate the early game? What analysis would you like to see in the future?