Thoughts.

I started typing this out as a response to Brad’s comment on the last post about goal setting, and it grew into its own post.

Brad,

Thanks for the link.

I agree that flexibility with goal setting is important; I find that this comes naturally when you add timeframes. If my goal is to train twice a week for a month, I naturally have to re-evaluate it at month’s end (or perhaps sooner, if I can’t meet the weekly requirement). It’s important to note that a lot of research out there has goal SETTING as the important thing–as they say, shoot for the stars and you might land on the moon. We don’t have to do everything we set out to do, but it gives us much-needed direction. It’s OK to change direction to continue working towards what makes the most sense for you.

A lot of issues that the article you linked gets at have to do with the nature of the goals set. For one, they’re entirely outcome-based. Profit. Market share. These are goals that depend on somebody else, namely the consumer, to meet. They’re also goals that are handed down from on high–so you’re setting goals that may or may not be realistically achieveable, are not entirely under the control of your employees…and then expecting magic. It’s no wonder indeed that it leads to problems.

I’m a really big fan of focusing on process. The example of Southwest, who worked to cut down their turnaround time to 10 minutes–that’s something entirely in their control, something their employees can manage with enough practice and improvement (assuming 10 minutes is not a wholly unrealistic number), is a good process goal. They didn’t say “let’s double our profits by reducing turnaround”–that doesn’t necessarily follow, but because they focused on the process they still made good things happen.

There’s a saying, “that which is measured, improves.” It doesn’t say it improves organically, just that it improves, and I think that’s the trap a lot of the corporate goal-setting falls into (and incidentally is why I’m very, very leery of incentive-based restructuring of the American healthcare system). We need to be very careful of what we choose to place stock in measuring (this same warning applies to stat-keeping as well).

In response to my post on scorekeeping, Chad commented saying he was working on an iphone app to help with score and especially stat keeping.

The beta is now good to go, and he’s looking for testers/feedback. Directions below:

I’ve finally finished a beta of my ultiStats iPhone app. I’m contacting you because I thought you might be interested in helping me out by testing it.

If you’d like to help, I need your device ID. Follow the directions in the paragraph entitled “Adding Beta Testers” in the following link and send me the code (I think it’s 40 digits).

I’ll then send you an e-mail with the application attached and some instructions.

I’m looking for you to find bugs in the program, suggest design changes that might make it easier to use or look better, or even suggest features that I have not considered that you might like to see.

Installing future bug fixes may require you to delete your database, losing all of your stored statistics. Please do not rely on these early releases for long-term stat tracking.

There are many features that I would still like to add, but I need to get this first version out.

If you don’t have an iphone/ipod touch but you know someone who does that might be interested, feel free to forward this to them.

Thanks for your help,
-Chad

His email can be found in the comments of the aforementioned post.

While writing the last post about energy demands in ultimate¹, it struck me that there is a LOT of potential data to be mined just looking at scoring trends, play durations, etc, but the data isn’t there currently–nobody really tracks that sort of thing (at least, not publicly).

We need more descriptive score keeping than the simple “X-X” final total. In much the same way that baseball scores by innings, or tennis has set-by-set counts (or really any sport has at least some temporal division), ultimate needs something more robust to help keep the fan clued in. I’ve broken it down below into a few phases based on ease of incorporation:

Phase 1, (I hope) obviously, is reporting scores at halftime. You get this all the time in written-up recaps; why not on score reporter or tournament result sites?

I’m not saying it has to be done all the time–hell, at plenty of tournaments even final scores go unreported–but at bigger tournaments that have a fan following, it’s the bare minimum to be done to build something of a “box score” and give an at-a-glance view of how the game went. Did team X cruise out to a big halftime lead before blowing it at the end? Did team A stay neck and neck with the #1 seed through the first half and fall back to earth in the second? These are stories that are out there, but often go un(der)reported.

I’m thinking a parenthetical–i.e., Team A 15(8) – Team B 12(2)–would be pretty simple and easy to incorporate into the current SRT structure.

Phase 2 is generating a score report that can really capture the flow of scoring throughout a full game, and I have just the method in mind:

Enter The Hardball Times’ sparklines (I’m amending to scorelines for ultimate’s use).

So much of what makes games into exciting stories is the string of breaks, rises and falls in momentum, or the hard-fought back-and-forth matches, and this metric would capture it perfectly–long gaps in the scoreline denote a string of breaks, whereas the back-and-forth games would have a, dare I say it, beautiful symmetry to their scorelines. Could you imagine how ridiculous the scoreline would’ve looked for Fury’s massive comeback from 10-1 against Riot in the UPA finals last year?²

Even if the scoreline doesn’t make it into mainstream use anytime soon, I imagine it’d be very useful for teams that keep any kind of stats to track their scores (if it isn’t done already)–rather than wonder “did we start off with a 2-0 or 3-0 lead before their zone shut us down?” you can look at the evidence conclusively, and with a few short notes during the game, see concretely what impact your adjustments had on the flow of the game. You could write in the score at set intervals (every 5 ticks for instance) to make it a little easier to track at a glance while still keeping the flow-tracking intact.

The other component I’d like to see go along with this is game time.

Even without shifting to a stopped-time dynamic it’d be possible to track active game duration from pull to last goal caught (or hard cap horn), using a designated scorekeeper with a stopwatch. This would give some indication of, for instance, team A’s offensive dominance with 20-second points while team B struggles to the tune of a minute per score, a prelude to team A’s eventual string of breaks (or team B’s unlikely upset despite the lower efficiency). You might see a break at 10 seconds of play, which would suggest a callahan off the pull or a short turn and quick strike. Even a simple notation of, say, 5-minute play intervals on the scoreline would help to give some idea of how rapid or drawn-out the points were.

Phase 3 moves beyond scorekeeping itself and incorporates stats. This is my baseball bias coming in to play of course, but similar to how at bats are tracked along with hits/runs/RBIs/HRs etc, you could similarly chart points played along with goals caught/assists thrown/Ds (and maybe at a high level, things like hucks and completion % and touches as well). A hockey-like +/-, if refined to account for starting on O or D, would also be a cool stat to see.

Why it’s worth it
Each level takes a greater amount of work to pull off, but each brings with it a greater amount of clarity on “what-happened” syndrome that plagues ultimate today. Outside of following real-time updates, we’re left to get the story secondhand, reading sparse/biased RSD and blog coverage, and unless we know people involved, are generally left unsatisfied. Web coverage is awesome–video feeds, etc–but when you compare the logistics of setting all that up to simply putting a little more effort into score keeping, this is a pretty simple/easy way to boost the profile of tournaments and teams to the casual (and passionate) observer.

What do you think? Would love to hear your thoughts in the comments.

Quite a lot of commentary to see there.

Yours truly has a long treatise up there; I’d caution against taking a lot of the stuff as gospel though.

My methods for generating those percentages you see was a little different than simply comparing % of answers in each group: to try to control for the different sample sizes (as well as respondent sizes) in each group (whether it’s nationals vs. no-nationals or coaches vs. no-coaches) I aggregated response counts and divided by total number of teams, or players if the question entailed roster counts…essentially creating an average rate for either group, and then looked at differences between these rates. I’d encourage caution to the tune of discounting anything below 10% difference or so as being less significant, but some of the differences are really prominent and that’s the stuff I keyed in on in my opening paragraphs as they’re likely significant.

I worked on this all a few months ago…was channeling my college stats pretty hard but converting the survey responses into something SPSS would be able to work with for rigorous analysis proved beyond my time or pleasure.

All that said, if you want to take a look at the original data set (caution: I didn’t make the sheets with prettiness/usability in mind: probably best reserved for people who enjoy playing with excel) you can download the .xlsx (or .xls if you’re incompatible) here.
UPDATE: I’ve been informed that the data is not for sharing, at the lack of consent of the team captains.You might ask the Huddle for permission if you’re interested in working with the data.

I’d love to see somebody with time, desire, and means hit the data hard(perhaps some of the other Huddle contributors already have and it’s not apparent from their commentary), and I’m sure the Huddle would too.

I love Michael Lewis. You’ve likely read Moneyball–people credit James, Beane with developing new statistical approaches, but guess who wrote the book that made it a common concept?

At any rate, there has been a little discussion recently concerning the use of statistics in ultimate, and a comparison with basketball’s newer stats has been made. It’s not the scores made, assists, blocks that define a player’s value, but something beyond that.

Lewis swoops in again to shed some light. Read this article with a mind for ultimate–how are you selfish? How are you part of the system? How do you help your teammates get better?