Attached is the 2010 Team 1114 Championship Database. This year’s database includes full results for every team who competed in the 2010 season as well. We’ve based this version on the current divisions posted at usfirst.org. If these change we will update accordingly. (However if it’s just a couple of additions, we may not release a new version)
The database includes:
An interface allow you pull an individual team’s record
Full listing of awards, record & finish
Team scoring averages
“Calculated Contribution” which is the same calculation being refered to as OPR on these forums. This calculation usings linear alegbra to determine what a team’s average input to their alliance was at each regional. (Only using qualifying match results)
A master sheet for a sortable comparison of all FIRST teams
Master sheets for each division and full divisional assigments
The data we have was all mined from the FIRST website. There may be some errors, but I’m confident the data is 97.1114% accurate. That being said, there were some gaps in the standings for WOR and IL. These regionals will have some funky results, as we pulled the standings straight from usfirst.org, but the alliance selection results from actual observation.
We’ll upload a version for older Excel shortly. It’s a much larger file.
Prior to 2008 we never released any of our regression analysis (Calculated Contribution) that we had been doing since 2004. Since people have become more knowledgeable on the subject we decided to make the change. Please do not take a poor score as a slight or an insult. We simply used the actual scores from matches to perform a calculation. We feel that this tool is the best available metric if you are unable to watch the actual matches. Since none of us can attend every regional, it should be a valuable tool. Regression analysis is much more effective for Breakaway than it was for Lunacy, but still not as good as it was for Overdrive. If you want more details on this, come check out my seminar in Atlanta.
Thanks to Geoff Allan, Ben Bennett and Roberto Rotolo of Team 1114 Stats and Research for creating this year’s database.
According to the divisions breakdown summary, Archimedes is the most stacked division, including the most Chairman’s Award winners.
My bet is that the CA winning team this year comes out from that division.
I see it almost like an exponential curve. Teams that tend to do better over the course of a few events have a habit of leveling off. Whether it be from getting too sure of yourself, falling into a pattern, or just reaching your peak a little too soon, they eventually begin to stabilize. Either that, or in the effort to keep improving they end up becoming weaker.
Continuous improvement =/= continuous work. Sometimes all a team needs is a better mindset between competitions, not a better robot.
I simply can’t fathom how Roberto Rotolo keeps pumping this stuff out year after year with such a high degree of quality too! He’s definitely the backbone to that skilled group of individuals, its nuts, every year!
For a team like ours, that just qualified for the Championship last weekend and is just barely able to send a skeleton crew (7 kids, 4 mentors) down to Atlanta, this data base is an invaluable resource. Plus, we look pretty good!! Thank you for sharing this information with the FIRST community.
This is explained in a few other places on these forums, but it’s probably best that I put an explanation in this thread.
Going into an event like the Championship, it’s impossible to watch video on all 344 teams. So, how can you get an idea of what each team’s scoring potential is? Well, one way to do it would be just to look at their average score per match. Nice and simple, but it only tells you a small part of the story. Since FIRST matches involve alliances, an average score does not isolate the individual performance of a given team. So if Team XYZ repeatedly plays matches with great teams, their average score will not necessarily be an accurate indicator of the team’s performance.
So, how do we isolate the impact of a single team on a match? Simple, using good old linear algebra. For those of your familiar with advanced basketball statistics, the method I’m about to describe is very similar to “adjusted +/-”. Adjusted +/- has become a very popular tool among NBA franchises to try and figure out just how much each player is contributing on the court. NBA teams have long figured out that just looking at how many points a player scores does not always tell you how much they’ve impacted a team towards victory. There have been some great papers presented about this at the MIT Sloan Sports Analytics Conference.
So, here’s how it works. For each alliance during qualifying matches at a regional, we set up an equation. Say Teams i, j, k were on an alliance together and scored s points. Out equation would be
T_i + T_j + T_k = s
where T_i, T_j & T_k are variables representing those teams. So if there are m teams at a regional, and n matches, we now have equations that give us a m x 2n matrix. We then solve this matrix for our variables, and voila you have each team’s Calculated Contribution.
Why is Calculated Contribution so valuable this year? Well, consider a team that plays the midfield and is great at supplying balls to the home zone, but rarely scores them. If your scouts are just tracking goals scored, they might get a big zero. But their Calculated Contribution (if run over a large enough sample size) would should a higher value reflecting the points they helped their alliance score.
There are a lot more subtle details as to why Calculated Contribution is a good tool, and even more that expose some shortcomings. Unfortunately it’s a bit much to go into on CD post while I’m eating lunch. I’ll talk a lot about this at my Effective FIRST Strategy Seminar in Atlanta.
This sheet on an ipod touch will be invaluable. Awesome job. ekk (entered my team) looks like the data on us reaffirms our thoughts that we should focus on defense. This program has even aided in scouting my own team and influencing our stratagy! Now that is a testiment to it’s effectivness.