http://vlado.fmf.uni-lj.si/pub/networks/pajek/SVGanim/

best, Vlado

Your comment is BTW the first real comment after getting maybe 100 or so spam comments on this blog.

I have to admit that I have had a long (too long) pause on this site and analyzing ultimate with these social networking tools. That’s because I have started in a new job, which has sort of taken all of my energy. Maybe if I start writing again, I will also get real comments. Hmmm???

Hartti

You might try using a longer time window in the sonia animation, then you will see more of the long-term structure/positions of the players.

best,
-skye

Stephen, you are correct, one game is by no means an adequate dataset. I’ll try with Team USA stats this weekend - let’s see if there something interesting.

on a team of 21 players, there are 8000 possible 3-player sequences (you can’t pass to yourself, afterall). without too rigorous of an analysis, let’s just say you’d need to see on the order of 1000-10000 strings to draw any real conclusions.

as for jim’s comment, you don’t expect to see anything with that high of a frequency, unless the offense can run 2- or 3- man weave the length of the field, or huck it right off the pull with high success; even if A-B-C is the bread and butter of the offense, you’ll get just as many X-Y-A, X-A-B, B-C-X and C-X-Y strings, so the frequencies are never going to get *too* large.

Frankly, I’m surprised that there weren’t more 3 person strings with higher frequencies. Early 90s NY probably went Weiss-King-Dobyns 50% of the time.

There’s a few obvious approaches to doing this estimate of how good the team SHOULD do against an opponent:

1) Past scores against that team. This has some value, but if you consistently coast against some bad teams and give up more scores than you should, this won’t correct for it.

2) Use the predicted score from RRI. Works a lot better late in the season.

The best approach would probably be some sort of weighted combination of these, along with the actual score of the game.

We really ought to start up the stats groups again.

I wanted to add that yardage, possession, position, and scoring goals are all important in offense.

One little flaw in Tarr’s method is that it assumes the team plays equally well in all games, so a 13-7 win against a good team in which the starters play well gives as much total credit as a 13-7 win against a bad team in which the backups play like backups. Weren’t you going to try to incorporate an a priori estimate of the other team’s strength?

You are absolutely right that adjusting for opponents’ strength is crucial to any good analysis. Both of my approaches do this.

Here’s an example: I computed the Purdue Women’s team statistics for Great Lakes regionals using the method I posted on the 21st.

Lucy threw 6 goals with no turnovers in Purdue’s 11-1 win over Case Western. For that game, Lucy’s fantasy score by my system was +.91. In other words, despite throwing SIX goals, she didn’t manage to even increase our expected score by ONE goal. Why? Because if those are all throwaways, we probably get the disc back anyway.

By contrast, Kelsey caught two scores and had 3 blocks (with no turns) in the 10-9 win over Illinois (our closest of the weekend). That performance was worth a score of +4.6, demonstrating how important every one of those plays was to the final outcome.

The adjustment for opponent strength in the adjusted plus/minus calculations is a little more explicit — a player’s relative efficiency for a given game is just the team efficiency while he’s on the field, divided by the team efficiency overall. If you do well in a blowout, well, that’s nothing special.