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.

]]>I’ve developed two statistical tools so far in ultimate:

1) The adjusted fantasy numbers from above. I’m attempting to aapproximate every fantasy stat’s impact on the probability of scoring a point. Once I’ve done that, I total those up. Your score is the expected increase in goals scored you caused through your presence on the field.

The nice thing about this measure is that it works regardless of who is on the field with who. It’s also very easy to get probabilistic approximations here, because every event I’m considering ends a posession (by turnover or score). Its weakness is that it fails to measure things like clogging or letter your player get open long (or conversely, shutting your man down or creating space).

You could expand this approach to include all the statistics you mention in your blog entry, but this would require developing a reasonable answer to questions like, “how much does the completion of a 20 yard pass affect the probability of scoring on a given posession/point”? Not an easy question, obviously, but unless we have some answer there then including passing yardage in some sort of aggregated statistic amounts to a bunch of hand-waving (IMNSHO).

2) Most of my efforts have been developing another statistical tool, what I call adjusted plus-minus (or, when I’m working in rate form in stead of in counting form, relative efficiency). You can probably guess the basics, although I’ve mentioned it before on Idris’s blog, as well as the ultistats yahoogroup. The only things you consider are who is on the field, whether you start on O or D, who your opponent is, and whether you score. If I get around to it, I’d like to add adjustments for how close the game is at any given point, so that blowout points receive a smaller weight. All the times I’ve used this stat so far, I have not corrected for which teammates are on the field. Ideally, I’d like to adjust for that, which makes the calculation of relative efficiencies a overdetermined linear system of equations.

I think that this path of analysis is more meaningful, at least until we have a LOT of play-by-play data to work with, so we can really measure the value of a swing or the cost of a covered in-cut. It’s biggest weakness is that two players can only be distinguished by the points where they are not in together. If Jim and Count are in all the same points, then they will appear identical by this measure even if one plays terribly and one plays great.

]]>I have been trying to use something similar to this for years, but the problem that it always comes up against is the quality of the competition. That is, your second level offensive players come in against a weaker team and score 100% of the time vs. your top line scoring far less successfully against superior competition.

Specifically, if there is a role-player on the top offensive line who plays a bigger role on the second string, his scoring percentage will be inflated. this is in addition to the obvious second-string only player who will have a great scoring percentage.

Perhaps categorizing stats by quality of opponent? Difficult to be consistent though… Plus, the stats wouldn’t be relevant from team to team…

(I wish I had enough knowledge to truly follow all of the info you work with here)

]]>The x-asis is based on “yardage”, y-axis on how one contributed on end of the possession. Dotted ends are players as receivers.

As an example Jenni scored a lot of goals and gained a lot of yardage as a receiver. Hence the blue end of the line is in the upper right hand corner. However her passes did not advance the disc as much. Hence the green end is lower. She could be categorized as a deep. Kossu and Reija are examples of handlers contributing positively (scoring and passing more goals than turning the disc over).

But still it gives no relative valuation of yardage vs. goals/turnovers.

]]>When i was in college, our coach had a few stats he kept - offensive scoring percentage (ie, scores per posession) offensive power percentage (% of scores per first offensive possesion per point), defensive osp (%of goals allowed per opponent possession) and defensive power percentage (not allowing the opponent to score on their first possession).

it’s then easy to compare stats between players on the same time and also possible to create coefficient to compare players on different teams to each other (something similar to ‘park effects’ in baseball)

I think it’s a good overall system because most teams have goal scorers and goal throwers, but players who’s job it is to be a threat, work the disc through the middle, draw coverage, etc, are just as responsible for creating the goal as anyone else*.

]]>How important is a goal pass? Was it a one-yard pass or a sixty-yard huck? Did the cutter bust free of his defender, or was he left uncovered in a zone? The only thing we can say for certain is that a goal pass traveled forward some number of yards (or yard). Note the difference from a hit in baseball (which can’t result from an error) or a TD pass from a quarterback (who at least marshaled his team down the field).

The situation is a bit like trying to define a Dow Jones average without having an idea of the stock price. I think we should first aim for a passing rating (something like yards thrown with a bonus for goals) and a receiver rating (something like yards caught with a bonus for goals), then define an individual’s offensive contribution as some combo (or sum) of these two. (Of course, we don’t have yard stats in ultimate yet, but we might soon be able to give a rough rating of short, medium, long for 1, 2, or 3 points.)

One could get more sophisticated by trying to quantify how an individual adds to the overall offense through “intangible” contributions. One way might be to find whether the team scores a greater percentage of the time when a given player is in than when he is out (i.e., you could multiply the previous index by the ratio of scoring percentages). This number might come closer to what you’re looking for.

To test, you can take videotapes of games and calculate the index for various players. If the obvious offensive stars come out on top nearly every time, then maybe it makes sense.

Hmm… this is an old thread. Maybe I’ll expand on this idea elsewhere….

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