Clueless Finn

In social network analysis world there are tools to visualize static networks, like friendship, communication, trade, etc. Examples of such tools are Pajek and Netdraw and more programmatic tools like GUESS. However if one wants to visualize the change of network over time, possibilities are much more limited. One can for example calculate various network metrics (like centrality, betweenness) on different time points and plot these values on a graph. Another possibility is to use SoNIA. Some time ago I experimented with that tool using ultimate passing data as a data set.
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During the weekend I decided to write down the definite answer for individual offensive ratings. After spending way too much time without being able to formulate even one paragraph for that post, I was forced to downgrade my objective. Now I just hope to start some discussion on this topic. Quite lame, I have to admit.
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After writing about Jam-Kaos game I started thinking of what is the scoring efficiency in high level games in general. I have no recent data on that (except from a few selected games). However some time ago I got from Sholom Simon some RUFUS stats from mid-90s.
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Watched the “final” of Norcal sectionals today. Short story: Kaos won 13-11.

A little longer recap. Kaos started well and Jam started a little wobbly. The score was soon 3-0 for Kaos, altough both teams had turned it over; Jam 5 times (few hucks and a drop), Kaos two times. Then Jam answered with 3-0 run of its own. First half in general was shaky on both sides. Kaoes got to the half leading 7-5. At that point the trunover total was Kaos 6, Jam 8.

On the second half both teams played a little better (less turnovers), although in some points there were quite a few calls. I guess the total turnover count was 9-10 to Kaos (resulting in 59% and 52% scoring efficiency).

Some notables. On Kaos Tyler had a great game. Chris McManus made a sweet layout catch (Brandice got at a great photo of that). Both teams had nice pressure defenses, but also a few major mistakes (guy cutting deep was not being covered).

I compiled from-to matrices for all the Team USA goals (who passed to whom) and similar matrices for passes just before goal passes - so called second assists. (I wanted to compare these passes and their correlation to the total number of passes, as there was some time ago short discussion in the ultimate statistics Yahoo group about value of 2nd assists compared to goal passes).

Similar results as yesterday. Goal passes correlated closer to total passes, although the correlation was not high (Pearson correlation only 0.476 compared to 0.381 for second-assist passes). However there was virtually no correlation between 1st and second assists - sort of interesting.

Another interesting tidbit was that the highest number of goal passes in one game from one person to another was 2. In the summary matrix the highest number was 5 (Watson to Ziperstein and Ziperstein to Eastham). And there was no player who passed at least one goal to every other player. It is no surprise that Namkung was closest to reach this milestone (no goal passes to Fontenette though) - as one probably remembers Namkung was the bridge between male and female players.

I started studying if goal passes (from person a to person b), or “leading passes” (the pass from player a to player b, which precedes a goal pass, similar to second assist in ice hockey) are correlated to the numbeor of passes between these players. I started with the final game, and used tools in UCINET (Quadratic Assigment Procedure or somthing along the lines). To my surprise the goal passes had stronger correlation to the distribution of all passes than the “leading passes”. In any case the correlation was not especially strong. The sum of goal and leading passes had a little stronger correlation, which was not a big surprise as this way I was comparing bigger chunck of the passes to the all passes of the game. I will post the numbers of this analysis over the whole tournament as soon as I get that done (probably tomorrow).

Tarr asked for data showing how many passes one person throws to another on average while they are on the field. Turns out not so many. Here is sample from the Final game Team USA against Australia. The numbers in the cells are calculated dividing the total number of passes between players (directional) with a number describing how many points the players were on the field at the same time and during which points USA had possession. So if the players were on the field during a defence-only point, those points were discarded from the calculations.
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