A concept which has greatly interested me recently in CS:GO is the idea of consistency. Being able to consistently perform at a high level is one of the key aspects which separates players with the talent to be elite from the actual world class players. Despite the importance of consistency and its relative ease to measure, there has not been a huge amount of thorough analysis done to analyse it, which is why I’ve written up this.
Data taken is from 250k+ Lan events throughout 2016, and to be included a player had to have played a minimum of 40 maps. Consistency is measured as the coefficient of variation (Standard Deviation/Mean). The higher the coefficient of variation (cv), the more inconsistent the player is. As I was not able to embed the chart, here is an image of it instead. Amount of maps played is represented in size of the player’s point on the graph, the larger the point, the more maps played. To view a much better, interactive version of the chart please click here. The full data, with a few other statistics is included in a table here.
High Rating and Consistent
Dominating this category are the three highest rated players of 2016: Magiskb0y, device and coldzera. The three were a distance ahead of the competition in terms of the least poor maps (< 1 HLTV rating) in 2016, with 23%, 27% and 26% respectively, compared to the 4th place of 36%. Even more impressive is Magisk’s minuscule 5% of maps below a .8 rating while the average was 26% (device 10% and coldzera 11%). Magisk’s consistency score was only lowered by his huge number of excellent performance, with a whopping 28% of his maps above a 1.5 rating, a whole 8% ahead of second place. Cloud 9’s autimatic and Mousesports’ NiKo were also impressive in this category, with low consistency scores of 0.2650 and 0.2708 respectively. A surprising name up here is Happy, who had the 3rd most consistent year with a 0.2574 consistency score. Despite much public outrage of his playstyle, Happy had very few poor maps with only 16% below a .8 rating.
High Rating and Inconsistent
Na’Vi’s Edward had the second highest consistency score at 0.3802, which was partially due to his big variation in performances at different tournaments. While the Ukrainian rifler dominated events such as MLG Columbus with a 1.25 rating and ESL One Cologne with a 1.20 rating, Edward often had poor tournaments such as his 0.89 rating at ESL One New York. OpTic star RUSH came in at 3rd most inconsistent, with an impressive 14% of his maps above a 1.5 rating but was hindered by a shocking 34% of maps below a .8 rating, the lowest of any player with a positive HLTV rating. Another North American star in Stewie2k had an inconsistent year, being ranked as the 5th most inconsistent player, possibly due to his high risk high reward playstyle. FaZe’s Aizy saw a resurgence of form with new IGL Karrigan joining the team in late 2016, though his previous form in the year led him to be ranked as the 4th most inconsistent of the year. Interestingly, Olofmeister had the second highest percentage (20%) of maps above a 1.5 rating despite his struggles with injury and form throughout 2016, showing just how good he was at his peak form early in the year.
Low Rating and Inconsistent
The obvious outlier of the entire data is RUBINO with a staggeringly high consistency score of 0.4384, a whole 15% above second place. However, this is mostly due to one performance, an insane rating of 3.01 against CLG at ESL One Cologne. If you remove this one match, his consistency score drops to a high yet more reasonable 0.3445, though his rating also drops to a poor 0.92. His teammate, MSL had the lowest average rating throughout 2016 but was very also inconsistent. The Danish IGL had by far the lowest maps above a 1.2 rating with 5.26% compared to the second lowest of 10.20%, though when the MSL played well, he certainly did, with 2 out of his 3 maps above a 1.2 rating also being above a 1.5 rating.
Low Rating and Consistent
Surprisingly, the most consistent player of 2016 was Skadoodle with a score of 0.2529, who actually received a lot of criticism in the latter half of the year for his lackluster performances. Despite his slightly lower than average rating, the American AWPer had 23% of his maps below a .8 rating compared to the average of 26%, though also had fewer good and excellent maps than the average. Denis from Mouzsports posted stats that were so low rated and consistent that there was literally nothing interesting to infer from them. Teammate Spiidi and ex-FaZe/NiP stand-in Maikelele were the only players in 2016 who played more than 40 maps in 2016 and didn’t record a single map above a 1.5 rating.
Something that is important to note is that a high consistency rating is not necessarily a good thing. Coefficient of variation effectively measures how close a player usually is to their average rating. Skadoodle ended up as the most consistent player on this dataset and that was purely because he didn’t have very many poor games but didn’t have many highly rated games; he was consistently close to his relatively mediocre rating. Another example of how this consistency rating could be potentially misleading is NiKo vs Magiskb0y. NiKo ended with a consistency score of 0.2708 compared to Magisk’s 0.2930. NiKo actually had a significantly higher proportion of poor games than Magiskb0y, with 38.5% of his maps played falling below a 1 HLTV rating as opposed to Magisk’s 23.26%. However, Magisk had a much higher proportion of excellent games than NiKo, with 27.91% of his maps being above a 1.5 HLTV rating compared to NiKo’s 11.54%. These excellent maps lower Magisk’s consistency rating as they are significantly higher than his usual rating. This is why it is of extreme importance to consider rating when taking into account this measure of consistency.
To access the full data in a spreadsheet format click here. If you want to sort the data, click on the tab at the top labeled ‘Data’ → ‘Filter Views’ → ‘Create new temporary filter view’. Please raise any queries about the data to my twitter (PMA) or on the reddit thread on /r/globaloffensive, though due to Australian timezones I will likely be too asleep to answer immediately.
Vertical Axis was flipped to be more intuitive. Amount of maps played now is represented in size of the player’s point on the graph.