Sorry, off topic here. Why are the rankings in the app so much different than the rankings in gotsoccer? Doesn't the app get all the info from gotsoccer? Which one is considered "more accurate."
Thanks!
I had the exact same question for my wife quite awhile back when she shared info with me about YSR, which she had initially heard about from another parent, whose kid was starting to travel the country for MLS N. What could it be possibly doing with the game data that is heavily concentrated already on GotSoccer/GotSport, and calculating things differently to show completely different - and much more accurate - rankings and ratings. I was highly skeptical.
With some curiosity, I played with it a bit, and realized that it was in fact very accurate for all of the teams we are most familiar with, and predicted game results with uncanny accuracy. Over time I spent more free time playing with the team data itself, and merging/fixing teams in the area to improve the data quality over all. When it went down for good as a website - it did seem like something significant was lost, but Mark and team rebuilt it as an app and it has since far surpassed what it was in prior generations. If interested in exactly how it works, and how its accuracy can be measured and tracked over time -
here's a more detailed post from earlier in this thread.
But to jump to the end - SR is now expected to pick the right winner for a game between two rated teams, with 82% accuracy. GotSport rankings are at 55% - 60%, only slightly better than a coinflip. For the top 100 teams, GotSport does a little better, in the 70% range (probably due to the tournament bonus points system), but it's still miles behind SR. For context, if any of these systems were at 50%, they shouldn't call themselves rating/ranking systems, and should rebrand themselves as random number generators.
One limit is that, if you’re looking at the best team in a weak region, it will overestimate their strength.
Yes, some people certainly do believe that - but it is quite hard to prove it one way or another. On the one side, you have
this, where game data analysis on the system itself, limited only to games between teams in different states - shows that it is still essentially as accurate as games between teams in the same state. And on the other side, you now have the app providing data on the relative strength of the schedule teams have played over the past year, and there are examples of some very highly rated teams, who have played some relatively weaker schedules when arriving at their current rating. At some point - until the teams are on the same pitch on opposing sides, there are fundamental limits to how accurate future predictions can be. But these are pretty darned good. One thing to also keep in mind is that even the back-end algorithms aren't necessarily static and unchanging - they are tweaked over time to continue to optimize that predictability number. All of the weighting for how long game data should count for, how much does it decline over time, how much to weight goal differences, how to discount larger goal differences, and probably quite a few more parameters - those are the levers that can be tweaked to optimize the results.