Greetings, my name is StatsCop and today my post is an attempt to clarify on who are the best clutchers of 2019.
If we look into the most simple stats about number of clutches won in 2019, we can find and gather information like this from HLTV (top 20 list, stats regarding Big Events, +45 maps):
|Position||Player||Team||HLTV Rating||Number of clucthes won|
The question is: can we establish any conclusions solely based on this information? Are Xyp9x, Twistzz, nitr0, Ethan and CeRq the best clutchers of 2019?
Since the metric “number of Clutches won” is just an absolute frequency of a certain event, there is no point for instance in comparing for example Twistzz’s number of clutches (3,957 rounds played more precisely) with other players because he naturally played a different number of rounds throughout the year comparing to players of other teams. If you compare those numbers with ZywOo’s or s1mple’s numbers for example (3085 and 2632 respectively), the difference on number of rounds played is quite abysmal.
To solve this problem, it must be taken into account the amount of rounds played by each player to calculate the metric “number of rounds needed on average to perform one clutch” (or the other way around, “number of clutches performed on average in 1000 rounds”, for example).
To make this analysis, it was considered all players that played at least 45 maps in big events throughout the year of 2019. By making the calculations needed, we can come with this table for the top 20 clutchers of 2019 (from a total of 78 players):
|Position||Player||Team||HLTV Rating||Number of clucthes won||Total rounds played||Number of rounds needed on average to perform one clutch (less is better)|
Starting from the top 5 listed players, names like Xyp9x, shox and s1mple come as no surprise. After all, most casual fans of the game recognize them as clutch stars for several years. The big surprise probably comes when observing that KSCERATO and daps are 1st and 3rd in this list. This can be manly explained with the fact that they have a considerably smaller sample size of maps/rounds played when comparing to the average player in a top 10 team that played consistently throughout the year (1396 and 1681 rounds played respectively). For that reason their results can be a bit inaccurate and inflated comparing to other players that played much more rounds. When analysing KSCERATO and his team path and success throughout the year, it is evident that FURIA did not go deep on big tournaments as other teams like Astralis or Liquid, so this argument can equally be presented to explain how high is KSCERATO on this list of top clutchers. Nevertheless, both these players appearing in a top 5 position represent an accomplishment in itself. One intriguing thing about daps is that he is the lowest rated player from all 78 players considered in this list with a 0.84 HLTV rating, still he has a surprising success on clutching rounds. Taking the data from the first table shown and combining with the data calculated from the second table, a table like this can be made:
|Position||Player||Team||HLTV Rating||Number of clucthes won||Number of rounds needed on average to perform one clutch|
In this table it can be concluded that these players are not indeed statistically the top clutchers of 2019. Naturally there is a case to be made about how usually some of these players reached playoffs which naturally made them play more difficult games on average comparing to other teams that often get eliminated in early stages of the game. However, there is no denial that jumping into conclusions solely considering metrics like “total number of clutches won” can be something misleading and inaccurate to do.
Despite all these efforts to try to find the list of best clutchers of 2019, there are some flaws in this analysis that must be taken into account. For instance, it was not considered the total number of attempts of clutching a certain round. With that information, metrics like “clutch success rate” could be calculated but since all the data utilized for this study was taken from the HLTV website, there was not enough information at disposal to determine these types of metrics (it was only available for 1v1 situations).
I would greatly appreciate it if you kindly give me some feedback on this small research. Thank you.
Note: Data used on this analysis available in this google sheets link.
Edit: If you are on mobile, do not forget to scroll right tables 2 and 3 for full information.