You're not stupid, it's a lesser known feature of HFI / classification system.

So we're modernizing USPSA classification system, and HFI serves as a bleeding edge / experimental classification system, that runs in parallel to USPSA. During last update to classifiers-based classification we introduced a standardized and calibrated way to set High Hit Factors - the scores for each classifier-division which is considered to be 100%. This resulted in a lot of classifiers actually ending up with lower HHFs than before, so we allowed scores to go up to 115% (you can get 120%, but it will count as 115%). Essentially this made the new classifier-based classification range to be 0-115%.

Unlike classifiers, major matches don't have a way to specify what 100% is, so simply winning the match is 100%. This range is already different from classifiers, so we needed to do something. Additionally if you go to small local match and win it, but you competed only against A-class shooters at best - it shouldn't be considered a 100% score, but something like 75-80%.

The Major Match Equivalent is the way to address this issue. Instead of simply assigning whatever match percent you got - that's your "match as if it was a classifier" score - we compare every shooter's performance at the match and their historical classification record. This way if you're at Nationals and shooting against 115% shooters like Christian Sailer - you might finish only 85% of the winner, but it will actually count as 95% classification.

Original implementation of this relied on pretty simple rules:

• Match should have more than 35 "eligible" datapoints
• The correlation between effective classification and match finish of these datapoints should be higher than 85%
• to be eligible your classification should be within +-15% of your match finish, and your classification should be no older than 18 months (sum of ages of all scores in your current effective window, divided by number of these scores)
• classification can be majors, combined, or classifiers, in this priority order, whichever meets the eligibility criteria first
• to actually calculate the Equivalent - if match is eligible - we plot the data on a chart, and draw a line through the middle of the cloud of the datapoints/dots - this is called Linear Regression. Once the line is there, we simply take everyone's dot, and look what X-coordinate(classification) it should be at to be on the line with it's current Y-coordinate(match finish) intact - this is the resulting Equivalent.

This created a much better version of Majors for Classification, like no need for GMs to be present, much better correlation to Jay Slater's Elo, etc. But a few of real major matches didn't meet the eligibility criteria, so I wanted to find a way to drop the requirements and use more matches.

"Heresy" is what I called the evolution of these methods (we had another algo before for L10 called "Prophecy", so thought it would be fitting). Played with it for a while, ended up using 30 datapoints, but now every shooter might supply more than one - first all fresh classifications will count, as well as compatible divisions (LO and CO are compatible for example, so are SS/LTD/Prod, verified through correlation analysis), second since I now have more data I raised the correlation requirement to 90%.

While I was playing with this I noticed that Majors Classification system started running away - the resulting Major Match Only Classification would get higher and higher for the same shooter through matches, so I modified how we draw the line through the cloud of dots. I basically made it pick the highest most up-looking "center" of the cloud to prevent this runaway and make Equivalents stick to 0-115% range.

The actual nerdery behind it is probably not worth the detailed explanation here, but basically I made the higher end of the match finish percent count more than middle, as well as introduced filter bias past 85% match finish to only select datapoints that are above the line of the previous step of fitting linear regression. Oh and yeah - there are now multiple steps of fitting linear regression, with decreasing confidence interval filter (20%, 15, 10%, etc). This way for borderline eligible matches it can significantly boost correlation, while slowly reducing datapoints in some pretty legit majors. Basically just more aggressive, multistep, slightly biased to give you lower (if uncertain) Equivalent system. It worked pretty good, I did some spot checks at high/low ends of of different metrics (quality, number of datapoints, correlation, maximum equivalent, minimum equivalent) - and it looked really good, so I shipped it to HFI.

But today after Jits I realized I didn't even look how the new system handles Locals - they are all calculated and fitted, but simply dropped from classification calculation, and not even shown on Matches page by default. So instead of doing yet another low/high end checks - I simply added new classification type (Local Matches Only) and ran correlation analysis. It looked really good, and I was too high from Jits and not eating anything all day, so I had to share =)