As a matter of curiosity, we did some data analysis on some of the top teams in Indiana for the Starstruck season. All the teams had their ups and downs, but i was surprised to see that at the end of the season they converged towards the same general area.
Can this be used as a measure of the strength of a region?
Can this be used as a measure of the inherent randomness of the VEX competition setup?
Is there any meaningful conclusion to draw from this?
No idea. Right now it is just a pretty graph.
To be clear, this data was taken manually from VexDB, and shows the Year To Date Win Percentage of each team, including both Qualifying and Elimination Matches. This includes elimination matches from that team’s alliance, even if that team may not have necessarily participated, and it does NOT include any ties.
Hm looks like it’s time for my team to invest in a bunch more stickers to stick all over our stuff so it is more obvious what team we are I personally do not think that this would be a good indicator of the strength of a region. A region could have bad teams, but one that is average, and that team would end up having a high win percentage. This could however, be used to determine the strengths of teams in the same region.
It’s possible that you’re onto something: Assuming that for every win an alliance has, another alliance must have lost, then we can assume the average of all teams in any area is 50%. Now if you take the top teams and compare their winning rate to the average of 50% you might be able to draw some conclusions as to the region strength. For example if the average of the top teams in an area have a win rate of 60% but they are top teams, one could infer that they likely live in a more competitive region than a a region where the average win rate of the top robots is 90%. Just a thought.
Edit: this only works if you assume that the top few teams from every region are roughly equal. Obviously this won’t always be the case but in starstruck the division between the best and the middle of the pack seem closer than in games like NBN or sky rise where the top teams were far superior than the other elite teams.
I like this idea. I think it could be a start. I also considered taking the derivative of the data so that you could gauge a teams progress. If they were advancing faster than average, at the same pace, or falling behind the pack. I still think it will have to be limited to within a region, but that is a step in the right directions. The more flags we can calculate to say “this team has potential, check them out” the better.
Sorry… Totally my mistake. For some reason, all season i have been seeing CCHS and thinking that the H meant Heritage. I actually had no idea that was the organization name for 6210X. I always thought of them as Quad Core.
Partially my fault, saw this mistake in his early notes and didn’t anything… I didn’t know it was destined for public consumption.
I would be interested in seeing the YTD W/L of teams at each region’s regional. if anyone is able to pull that data in mass and wiling to deposit it here would make two nerds very happy.
We need each teams Win/loss at each event they attended by date
One or two regions is all we need at first, we are obviously most interested in Indiana, but we aren’t picky.
It would be best if everything were taken using the same method, just to ensure comparable data. I used VexDB, searched each team i had an interest in, then went through their Events tab. I made a note of the event name and the date. Then i went to the Results tab and manually counted the checks and X’s. I did not count ties at all (this will have a minor effect on the data, but it is rare enough that i am not concerned). Due to historical reporting errors, i counted all elimination matches that the team was a part of, even if they were listed as the sitting team (many tournaments fail to accurately report what team was sitting, which has lead me to discount this from all of my data analysis). I then used the sum of wins and losses as the total, put them into my spreadsheet chronologically, and then calculated their year to date numbers. Finally, plotted. I have attached my workbook for anyone interested. WinLossData Starstruck Indiana.xlsx (19.8 KB)
I personally do not think that this would be a good indicator of the strength of a region. A region could have bad teams, but one that is average, and that team would end up having a high win percentage. This could however, be used to determine the strengths of teams in the same region.