Frequently Asked Questions
What exactly is new?
We have made it a web-based application, allowing users to do the following from a mobile device:
- Create their Clique & Club group
- Input Players
- Input match results
- Submit batches for ratings updates
What isn’t new?
Deep Green, our ratings engine, is the same ratings engine as Clique & Club version 1.0, as well as the ratings engine that is processing the official tournament ratings in the U.S.
What devices do and don’t work with the new version?
This works on both mobile devices (phones and tablets) as well as desktop computers. Regarding browsers, it works on Chrome, Safari, and Firefox Mozilla. Internet Explorer version 9 or newer is also compatible.
Does my C&C rating have anything to do with a tournament rating (like the UTPR)?
No. There will be no shared match result data between one “data set” (tournament) and the other (C&C). The C&C ratings tool was designed to process match results from club play (leagues, ladders, etc.), whereas tournament ratings are input through tournament management software programs and processed independently of Clique & Club ratings.
How else are C&C player ratings different than tournament player ratings?
There are five tournament rating categories – two gender and one mixed doubles, and two gender singles. For multiple reasons (such as pickleball play at the club level tends to be almost entirely doubles), there is only a blended four digit rating for C&C that reflects the vast majority of club play.
Can someone have more than one C&C rating?
Absolutely. For example, if you play in two different clubs, a league, and at the local YMCA, you could be in four different C&C groups. An administrator from each of these four entities may create a C&C account and populate it with a player roster and match results, meaning that you could have a C&C rating in four different C&C pages.
Are the ratings calculated in an identical fashion to how the UTPR ratings are calculated?
Identical, no. There are no weightings; it is strictly based on the Elo algorithm, which is essentially a predictor of outcomes based on the disparity of the ratings between two opponents.
Additionally, we have programmed the K factor (the variable in the algorithm that determines how much a rating "swings" with each win or loss) to swing more in a player's first 25 matches, then considerably less after that player's 25th match. This essentially creates a scenario in which, per our testing, the group or club's overall player ratings table becomes an accurate snapshot at any given time of the true ranking of individuals within the ratings table. The admin will see in the body of the email received after updating their group's ratings "MATURE: Doe, Jane", for example. This means that because Jane Doe has more than 25 matches, the K factor for her rating has been adjusted and her player rating has become more "stable," in effect.
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