We had some of the 2168 dudes go up to Kickoff this year and get us some pictures. They are available here

So, I really need to get this to be automatic. Here is the latest cast.

Twas The Night Before Kickoff…

View FRC 2012 Team Locations in a full screen map

Christmas 2011 - Inside EWCP

FRC 2010 Control System Beta

FRC Districts - Past, Present, Future

2011 FRC Registration Part 2

The 2012 build and competition season is nearly upon us and the EWCP hopes to make it a little easier for everyone. Tenatively, we’re planning on doing the following casts:

• ‘Twas the night before kick-off’, scheduled for 1/5/12. We’ll be discussing subjects like brainstorming, game analysis, strategic design and more.

• Post Kick-off cast, sheduled for 1/8/12. We’ll be talking about information gathered from some of our EWCP members in reference to the new game. This discussion will be somewhat general as to not influence teams who are still brainstorming.

• Weekly Build Casts, Scheduled for each Sunday During the build season. Each cast will focus on where you should be in the build stage, any new updates that may change the game, etc.

• Weekly Competition Season Casts: Predictions, Recaps, Updates, Tips and Trick

As always, our casts will be hosted via talkshoe at 9pm EST.

“I’m not looking for the best players, Craig. I’m looking for the right ones” - Herb Brookes

Your team’s first meeting after kickoff is the most important meeting you will have all year. You have been presented a problem with an infinite set of solutions, and you’ve only got six weeks to pick one and build it! We think a major reason teams often build less than stellar robots is that they aim too high. By trying to design a robot that can do everything, they often don’t have time to refine any of it, and ultimately build a product that just doesn’t perform. By showing that the average robot doesnt score as many points as you might think, we hope teams will consider simpler mechanisms they can thoroughly debug and test, resulting in better on field performance. To do this, we took data from the @FRCFMS twitter feed. We analyzed 3615 qualification matches from 2010 and 4756 qualifying matches from 2011. The following is what we learned.

• Teams have a hard time scoring.
  • In 2010 and 2011 approximately 20% of alliances scored 0 points after penalties.
  • In 2010 the average robot scored 1.4 points per match. That’s 3 balls every 2 matches, or 2 hangs every 3 matches.
  • In 2011 the average robot scored 11.3 points per match. To put that in perspective, that is the equivalent of launching a first place minibot roughly 2 out of 5 matches or, since many events are 10 matches, 4 first place minibot finishes all event.
• The end game is important and teams often undervalue it.
  • In 2010 a robot hung in roughly 30% of the matches. The 2 points from hanging would have changed the outcome of the match in 30% of matches.
  • In 2011 at least one minibot scored in 67% of matches. Both alliances scored a minibot in 23.3% of matches. It is telling that the average alliance score with minibots was 34.5 but without them was 20.4.
• Avoid penalties like the plague
  • Low scoring alliances are penalized more frequently than higher scoring alliances.
  • In 2010, penalties cost an alliance a win 6% of the time, and turned a win into a tie 10.8% of the time.
  • In 2011, penalties cost an alliance a win 5.1% of the time, and turned a win into a tie 7.6% of the time.

Introduction

This summer when I was an intern in the “real world” I found that Boeing went to great lengths to use data from old programs to create baselines for new programs. With the advent of the FRC field twitter feed, there are mountains of data available, but not a whole lot of public analysis of it done. Many of the results of this are common sense – but I think it is worthwhile to see the analysis bear out the general accepted rule of thumb.

This analysis uses data mined from the 2010 FRCFMS twitter feed. I got my dataset from Andrew Schreiber. After removing elimination matches and what appeared to be test cases, I have data from 3615 matches played over the course of the season.

Points & Penalties

The average robot scored about 1.4 points per match after penalties. It is interesting to note in retrospect that hanging and not acquiring any penalty flags would result in a statistically above average robot. For the record, hanging occurred in 30.5% of matches in this dataset. It is interesting to see the distribution in the first figure, and the clear skew to zero points as a result of penalties.

Avoiding penalties is always a good thing, and avoiding them is something every team tries for. We can see that they clearly bias the score, but in 2010 what was their effect? It turns out that in 6% of matches penalties were the difference between a win and a loss, and in 10.8% of matches they turned what would have been a win into a tie.

Winning and Losing Alliance Scores

By glancing between the penalized figures and the unpenalized ones, a trend quickly becomes clear. Losing alliances (especially low scoring ones) are especially penalty prone. While penalties shift the mean by .3 points for losing alliances, it shifts .15 points for winning ones. However it is especially telling that the 1st quartile shifts from 0 points to 2 points, meaning that many alliances scoring between 0 and 2 points lost those points in penalties. Plotting penalized losing scores and unpenalized losing scores on the same plot drives the point home.

End Game

It is worth noting that 2010 was an especially low scoring FRC game. However, if we consider that the GDC tries to have an end task worth about as many points percentage-wise in every FRC game these numbers could give you some guidance. Remember that 2 points were awarded per robot hanging and 3 per robot that was suspended. This doesn’t consider the opportunity cost of hanging vs. doing something else, but when a hanging robot on the losing alliance could’ve changed the outcome of the match 30% of the time, it seems the end game was significant, despite being worth only 2 balls.

Conclusions

1. Don’t get penalties
2. The average robot can’t accomplish the “main scoring task” that many times, so shoot for a simple mechanism that you can train your driver on, especially if you are under resourced.
3. Consider the end game. It is often left for last and it is often a significant competitive advantage, especially if you are looking to make it to the big dance on the third pick spot.