With all the chatter about how insane foul points are this year, here’s a tool that shows all the matches where foul points decide the match
http://public.tableausoftware.com/views/Fouls/TeamMatchesDecidedByFouls?:embed=y&:display_count=no
With all the chatter about how insane foul points are this year, here’s a tool that shows all the matches where foul points decide the match
http://public.tableausoftware.com/views/Fouls/TeamMatchesDecidedByFouls?:embed=y&:display_count=no
Just as an FYI i just checked my team (179) and Match 40 of Orlando we lost due to foul points, but the chart shows different
Can anyone tell me why no information from NH Granite State District are available?
131 has data from their second event, but not GSD.
Fixed! Thanks for the info!
It was a bad calculation: if the match would be a tie without foul points, it was previously counted as a “Not Decided by Fouls”
1266, 2339, and 3328 won San Diego Qualifier #28 because of foul points.
Brandon, the FRC Spy data (which is taken from twitter), says that 1266, 2339, and 3328 (the blue alliance) lost 90 to 53 and there were no fouls. You can verify here: http://www.chiefdelphi.com/forums/frcspy.php?
Is there some way to indicate that a tie would have been won or lost because of the penalty? We (1511) had one of those in match 47 at the NY Tech Valley regional. Because ties are just lines at the axis, I can’t tell if it has color.
Here’s a complete list (well, as complete as the Twitter data anyway).
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Thank You. This is a great tool.
This was working better yesterday. Now the MAR Mt Olive matches are not present.
The following NJFLA match outcomes were affected by foul points:
3448 Sun, 02 Mar 2014 14:50:27 +0000 NJFLA E 18 90 132
3495 Sun, 02 Mar 2014 11:45:14 +0000 NJFLA E 5 132 121
3540 Sat, 01 Mar 2014 18:27:30 +0000 NJFLA Q 67 50 61
3556 Sat, 01 Mar 2014 17:56:45 +0000 NJFLA Q 62 78 102
3634 Sat, 01 Mar 2014 15:35:37 +0000 NJFLA Q 41 80 70
3637 Sat, 01 Mar 2014 15:31:13 +0000 NJFLA Q 40 66 50
3682 Sat, 01 Mar 2014 14:25:48 +0000 NJFLA Q 32 61 105
3693 Sat, 01 Mar 2014 14:10:47 +0000 NJFLA Q 31 55 55
3714 Sat, 01 Mar 2014 13:34:11 +0000 NJFLA Q 29 86 75
3722 Sat, 01 Mar 2014 13:21:15 +0000 NJFLA Q 27 60 40
3757 Sat, 01 Mar 2014 12:14:39 +0000 NJFLA Q 17 55 12
Penalties per match can only be determined based on twitter data. GSD had issues both with their twitter feed and the webcast, so I’m assuming there was something up with the internet at the high school.
Looks like Groton also had issues with Twitter also. Shame they don’t bring a hotspot into smaller events like this, as they’ve been running into this issue for several years now.
How difficult would it be to simply store the data locally and make it available later?
Does anybody have contact info for the person(s) within FIRST who would have the authority to make this happen and might be open to such a change?
I’m curious to see which team(s) have been most affected by foul points. Which teams have lost the most matches due to fouls? Which teams have won the most due to fouls?
Is it possible to compare this year to 2013? I remember some scores from last year with high foul counts. But I wasn’t paying as much attention to when those were the cause of a loss. I know of at least one match last year when we lost because of it. There may have been others. This year I know there was only one such match for us so far. For that reason I am starting to question how much different this year is than last year.
A suggestion:
Even if some of these are from elimination matches, you could organize it as a qualifications-like league table:
Team WfL TfL LfW LfT TfW TfL Points
Where XfY is X from Y (eg WfL is Win from Loss) in number of games, and points is the cumulative points change (eg a WfL will give you +2, a TfL = +1, and a LfW = -2).
It could also be more useful to separate it in to two tables, with one dedicated to benefits (ie points gained) and the other to costs (ie points lost).
moved foul point analysis here:
Cheers!
On first glance, it seems 245, 399 and 4776 (-10) have been hurt the most, while 4500 (+12) has benefitted from foul points the most. (Not quite useful analysis so far)
moved foul point analysis here: