As many of you know, recently there has been a couple of discussions about the distribution of global points. Below you can find the threads that reference this topic.
Updating my feeling
How about 3D
..
To keep a long story short, the core of the issue is that due to XTU attracting a lot of active members, it yields the highest amount of global points. Along with XTU, the 2D benchmarks are also more popular than the 3D benchmarks in contemporary overclocking. The technical side of the problem is that the point algorithm is based on the popularity of a certain ranking to determine how much points are given. This has as effect:
Due to extreme high activity, XTU is maxed out in points
Due to diminishing popularity, the legacy and 3D benchmarks yield less points than 2D benchmarks In other threads there's been plenty of discussion on how to address this problem. The main stream of thought is to add a "difficulty" parameter to the point algorithm. In this thread I want to explore a different path and propose a more technical solution to the current situation. To keep things fairly simple: the HWBOT point function or equation has 6 parameters which determine how much points a given result yields. By adjusting these parameters we can change things like the maximum of points for a #1 position, the difference between the #1 and the rest of the top-10, the maximum amount of points in a given ranking, and so on. I've worked on adjusting these parameters to achieve the following goals: More benchmarks will hit the maximum participation and thus maximum points
the maximum points for a #1 score increases to 200 (now 167)
less steep point slope for top scores in global ranking
The adjustment is currently being tested on the UAT server (recalculation in progress) but is not final in design. The main discussion points are:
How many global rankings should be at maximum point capacity?
How steep should the point slope be for the global rankings?
I will update the thread when the recalculation on our UAT test server has completed. Below a bit more information about the various aspects of the adjustment.
1. Maximum Participation and Maximum Points
The point algorithm is a natural logarithm. The equation expresses the points of a given ranking as function of participation and position. Participation is measure as amount of unique users who submitted in a ranking in the past 365 days. There is a participation threshold set at 2000 participants. The threshold prevents the points from growing infinitely. It is this threshold that caps the XTU 4xCPU global ranking with over 17,000 participants from being excessively more valued than HWBOT Prime 4xCPU with about 1,800 participants this year. The most active 3D benchmark is 3DMark Fire Strike 1xGPU with close to 1,000 participants.
Note: take a moment to consider the algorithm is able to deal quite well with the XTU popularity.
The parameter adjustment is to lower the threshold to 1,000 participants. This will increase the amount of global rankings that hit the maximum points from 2 to 5 and the amount of ranking that are over half capacity from 1.5% to 8.3%. The five rankings are: XTU 4xCPU, XTU 2xCPU, XTU 6xCPU, HWBOT Prime 4xCPU and 3DMark Fire Strike 1xGPU.
If we would also enlarge the participation window from 1 year to 2 year, there would be 12 maxed out rankings and 14% of the rankings would be over half capacity.
2. Maximum Points for First Place
As you read in the previous section, the current maximum global points is 167pts at maximum participation. In the adjustment we will increase this to 200pts. This is mainly to avoid people losing points because of the adjustment.
Note that this will affect the balance between global and hardware points in the Overclockers League!
3. Point Slope in Global Ranking
The global points of any submission is derived from the points of first place in the ranking using a natural logarithm, except for second place and third place which are a fixed fraction. The current implementation has a very steep slope to reward the first place in a global ranking. This is to reward being the best in the ranking in times when binning was not such a widespread practice yet.
The adjument changes the slope quite drastically, increasing the points for second place from 75% to 95% of #1 and the points for third place from 56.25% to 92.5% of #1. From position 4 the points increase via updated parameters.
In a maxed out ranking, this means that the the point distribution is:
Pos. Points
#1 199.8 pts, currently 166.4 pts
#2 189.8 pts, currently 124.8 pts
#3 184.9 pts, currently 93.6 pts
#4 179.9 pts, currently 64.8 pts
#5 171.9 pts, currently 61.8 pts
#10 147.0 pts, currently 52.6 pts
#50 89.1 pts, currently 31.1 pts
#100 64.2 pts, currently 21.9 pts
#250 31.2 pts, currently 9.7 pts
#500 6.3 pts, currently 0.1 pts
#1000 0.1 pts, currently 0.1 pts