Suggestion to improve the alliance choosing program

[Bongle]

It does appear that lower-numbered teams had lower-numbered partners.

Using all the week 2-5 regional results, I found the average team number of each team's alliance partners. The results:

0-numbered teams played with avg team nbrs of 1091, against avg nbrs 1080
100-numbered teams played with avg team nbrs of 1093, against avg nbrs 1095
200-numbered teams played with avg team nbrs of 1073, against avg nbrs 1076
300-numbered teams played with avg team nbrs of 1083, against avg nbrs 1061
400-numbered teams played with avg team nbrs of 1158, against avg nbrs 1157
500-numbered teams played with avg team nbrs of 1147, against avg nbrs 1200
600-numbered teams played with avg team nbrs of 1264, against avg nbrs 1253
700-numbered teams played with avg team nbrs of 1269, against avg nbrs 1192
800-numbered teams played with avg team nbrs of 1268, against avg nbrs 1249
900-numbered teams played with avg team nbrs of 1351, against avg nbrs 1392
1000-numbered teams played with avg team nbrs of 1290, against avg nbrs 1314
1100-numbered teams played with avg team nbrs of 1382, against avg nbrs 1372
1200-numbered teams played with avg team nbrs of 1398, against avg nbrs 1357
1300-numbered teams played with avg team nbrs of 1399, against avg nbrs 1436
1400-numbered teams played with avg team nbrs of 1376, against avg nbrs 1403
1500-numbered teams played with avg team nbrs of 1394, against avg nbrs 1393
1600-numbered teams played with avg team nbrs of 1461, against avg nbrs 1452
1700-numbered teams played with avg team nbrs of 1446, against avg nbrs 1444
1800-numbered teams played with avg team nbrs of 1556, against avg nbrs 1533
1900-numbered teams played with avg team nbrs of 1687, against avg nbrs 1678
2000-numbered teams played with avg team nbrs of 1522, against avg nbrs 1528
2100-numbered teams played with avg team nbrs of 1563, against avg nbrs 1574
2200-numbered teams played with avg team nbrs of 1760, against avg nbrs 1730
2300-numbered teams played with avg team nbrs of 1757, against avg nbrs 1762
2400-numbered teams played with avg team nbrs of 1713, against avg nbrs 1735
2500-numbered teams played with avg team nbrs of 1793, against avg nbrs 1799
2600-numbered teams played with avg team nbrs of 1630, against avg nbrs 1630

I'm fairly sure that unless I have a bug, this indicates that higher-numbered teams indeed don't get to play with lower-numbered teams as often as randomness would suggest, which implies that the match-making algorithm is not blind to team numbers like I would think it should be.


Here's the summing code. I grouped things by hundreds so patterns would show up and it'd be easier to implement. m is a match object, which includes the team numbers of the red and blue alliances. m.red and m.blue are 3-element integer arrays of the alliance. If I have a big glaring bug, please point it out.

for(int x = 0;x < lstMatches.size();x++)
	{
		Match m = lstMatches[x];

		iTeamWithSum[m.blue[0] / 100] += m.blue[1] + m.blue[2];
		iTeamWithSum[m.blue[1] / 100] += m.blue[0] + m.blue[2];
		iTeamWithSum[m.blue[2] / 100] += m.blue[0] + m.blue[1];
		iTeamWithCount[m.blue[0] / 100]+=2;
		iTeamWithCount[m.blue[1] / 100]+=2;
		iTeamWithCount[m.blue[2] / 100]+=2;

		iTeamWithSum[m.red[0] / 100] += m.red[1] + m.red[2];
		iTeamWithSum[m.red[1] / 100] += m.red[0] + m.red[2];
		iTeamWithSum[m.red[2] / 100] += m.red[0] + m.red[1];
		iTeamWithCount[m.red[0] / 100]+=2;
		iTeamWithCount[m.red[1] / 100]+=2;
		iTeamWithCount[m.red[2] / 100]+=2;

	}
	for(int x = 0;x < iTeamsMax;x++)
	{
		cout<<(x*100)<<"-numbered teams played with avg team nbrs of "<<iTeamWithSum[x] / iTeamWithCount[x]<<endl;
	}

[XaulZan11]

Assuming those numbers Bongle provided are correct, that is a problem. I haven't completely gone through how the pairings are assigned, but it appears the team number (or the order of the teams) is a determining factor in the pairings (if not, we wouldn't have the relationship that Bongle provided). Would it be possible to randomly assign each team a number? Like say there are 50 teams at a regional, randomly assign each team a number from 1-50. Then, after the program assigns the pairings, replace the number (1-50) with the team number.

[GaryVoshol]

Using raw team numbers as integers does not create a fair test. Some regionals have very low team numbers, with only a few rookies (Detroit). Some have very high team numbers, mostly rookies (Hawaii, Minnesota, Oklahoma). The two can't be equated directly; they skew the distribution. You would have to figure out a way to assign an equivalent rank to each team number in each regional.

[SteveJanesch]

Originally Posted by Bongle

Here's the summing code. I grouped things by hundreds so patterns would show up and it'd be easier to implement. m is a match object, which includes the team numbers of the red and blue alliances. m.red and m.blue are 3-element integer arrays of the alliance. If I have a big glaring bug, please point it out.

Bongle,

I'm not sure, but I think your code includes the numbers of all three teams on each alliance. If you're calculating the average team number of only of the teams you're paired with, wouldn't you want to keep your own team number out of the average? If that's the case, it'll flatten out the "who you are paired with" average with respect to your own team number. It won't change your opponent's average team number, though. I could easily be reading the code wrong. I thought what was important was the average team number of who you were paired with, which should ideally be constant versus team number, not the whole alliance, which your own team number will influence.

- Steve

[Bongle]

Originally Posted by stj_1533

Bongle,

I'm not sure, but I think your code includes the numbers of all three teams on each alliance. If you're calculating the average team number of only of the teams you're paired with, wouldn't you want to keep your own team number out of the average? If that's the case, it'll flatten out the "who you are paired with" average with respect to your own team number. It won't change your opponent's average team number, though. I could easily be reading the code wrong. I thought what was important was the average team number of who you were paired with, which should ideally be constant versus team number, not the whole alliance, which your own team number will influence.

- Steve

Well, when it's doing the summing for the team's you're with, it leaves your team out of your bucket. iTeamWithSum is an array of 26 elements. iTeamWithSum[0] represents the sum of all alliance partners of (0-99) numbered teams, iTeamWithSum[1] is the sum of all alliance partners of (100-199) numbered teams, and so on. So to say that "188 (a 100-199 team) had team numbers 865 and 703", the code would be iTeamWithSum[1] += 865 + 703.

Example: Let's say we have an alliance of 1114, 1503, and 1680.

It would do:
iTeamWithSum[11] += 1680 + 1503 // 1114 was paired with 1680 and 1503
iTeamWithSum[15] += 1114 + 1680 // 1503 was paired with 1114 and 1680
iTeamWithSum[16] += 1114 + 1503 // 1680 was paired with 1114 and 1503

average[11] = (1680 + 1503) / 2; // note that this isn't affect by 1114 being on the 1114/1503/1680 alliance

So the actual team number that we're doing the indexing by is not included in the average, and thus shouldn't effect the final average (for that bucket).

Good argument though, keep them coming. We don't want to make a fuss if my code is simply wrong.

[StevenB]

Originally Posted by Bongle

It does appear that lower-numbered teams had lower-numbered partners. I'm fairly sure that unless I have a bug, this indicates that higher-numbered teams indeed don't get to play with lower-numbered teams as often as randomness would suggest, which implies that the match-making algorithm is not blind to team numbers like I would think it should be.

I don't think this is the case. As Gary pointed out,

Originally Posted by GaryVoshol

Some regionals have very low team numbers, with only a few rookies (Detroit). Some have very high team numbers, mostly rookies (Hawaii, Minnesota, Oklahoma).

It might be best to do this on a regional-by-regional basis, and see what you get. For example, the average team number at OKC was 2077. Almost everyone had a high team number, and thus their partners also had high numbers.

[Rick TYler]

Originally Posted by StevenB

It might be best to do this on a regional-by-regional basis, and see what you get. For example, the average team number at OKC was 2077. Almost everyone had a high team number, and thus their partners also had high numbers.

Perhaps you could normalize team numbers by representing team numbers as plus or minus from the median (mean?) team number at the regional. Or even use ordinals of a team-number stacking at each regional. Isn't statistics fun?

[StevenB]

Originally Posted by Kimberly

When an alliance consists of 2 rookie teams and 1 non-rookie team, it seems really unfair to the non-rookie team - especially if there aren't any rookies on the opposing alliance.

My suggestion would be to add to the algorith the "rookie factor" where rookies are not put on the same alliance if at all possible. Also, when an alliance has a rookie team, the alliance they're playing against would also have a rookie team.

I respectfully disagree. In some matches the teams will be evenly balanced. In other matches they won't be balanced at all. That isn't a bad thing. You have to be able to adapt and learn from each match, whether you have helpful pairings or not.
The match scheduling has been pretty good this year. Sure, some teams will end up with somewhat easier schedules than others. That happens in every other sport too.

[Cow Bell Solo]

I know at the MN Regional this weekend at least half if not more were Rookies, Also alot of the rookie teams have a better robot and drivers then 5 year old teams, it all depends on the team not there experiance, this isn't true all the time but there were a good amount of good rookie teams at the MN Reg.

[Kimberly]

There seems to be some confusion about what I actually mean when I say it would be better to have the rookie teams more evenly distributed on alliances.

What I am NOT saying is rookies are worse than experienced teams.

What I AM saying is rookies are less experienced than experienced teams.

Sometimes the student is better than the teacher, but that doesn't mean the student can't still learn from the teacher because experience does count for something.

Having said that though, if the data provided is correct, there does seem to be a correlation between the number of rookie teams on an alliance and how the alliance performs, so if it's possible to reduce the "unfairness" to the non-rookie teams by even a little bit through distributing the rookie teams more evenly, why in the world would anyone not want to?

Excepting, of course, the difficulty in writing the program that can do this and all the other functions too. A valid and reasonable argument against my suggestions, btw. If good programmers say it's too hard to factor rookies into the algorithm and still have it work as well as it does now, then it's too hard.

[Kims Robot]

Originally Posted by Kimberly

What I am NOT saying is rookies are worse than experienced teams.

What I AM saying is rookies are less experienced than experienced teams.

I see what you are saying in general, but Im not sure this would work for long, or even in a lot of cases. As FIRST grows and has now been around since 1992, there are more and more teams being started with mentoring teams, more rookie teams attending preseason events, more rookie teams being founded by FIRST college graduates or past FIRSTers that have moved, so the level of the rookie teams has drastically increased from years ago. There are also more and more rookies going to multiple events, at which point they may have enough experience to perform more like a veteran team. The other factor is there are many older teams that go through upheaval, and either loose sponsorship for a year, come back into being, or just have a complete switch of students and/or mentors. At any of these points, the lower number team may "act" like a rookie, and in some of the ones I mentioned earlier, the rookie team may "act" like a veteran. The year that our team was a rookie team, I was amazed with how much the rookies in our area had done and how much they appeared like veteran teams, especially on the field.

While I understand what you mean, especially in certain regions, I dont think this would work as well overall, based on what we have seen with past match algorithms based on number, and the level of many of the rookie teams these days.

[GaryVoshol]

Kimberly may have a point - they got a really unlucky schedule at GLR. In their first 4 matches, they were paired with another rookie. They were paired with a "powerhouse" veteran team just once, 469 - and in that match they came up against 494 and 67.

To contrast, I looked at Rush's schedule (lowest team number at GLR). They saw rookies on the field only twice - in the same match, one on each alliance. 33 played with a rookie once, never against one.

503, in the middle of the list of team numbers, played with and against rookies and very low-number veterans in many of their matches. 573 had a similar schedule.

2676, the highest team number at GLR, played with and against rookies in most of their schedule.

I looked at the West Michgan schedule and saw a similar clustering of rookie teams, and again of low-number teams.

We've seen that the "maximize time between matches" constraint often results in a team playing with another team in one match and against them the following. That's not unreasonable.

Does the schedule algorithm shuffle the teams before slotting them into the schedule? Or does it start in numeric order? As we saw last year, strict team order doesn't equate to team strength. But did the schedule inadvertently create semi-tiers?

[Bongle]

Originally Posted by GaryVoshol

...

Hmmm... I've even seen griping from a low-numbered team (I think it was 48) mentioning that they were in the first match of a regional multiple times.

Wait 10 minutes, I'll re-jigger my OPR calculator to get an idea of the average team # that a team plays with.