I have a job for the Atlanta based AUDL team the Atlanta Hustle. I think my title is technically "advance scout" which basically means I'm going to look at film and try to give assessment docs to the coach so he can map out practices and strategy.
Anyway, the GM is a numbers guy, so I think I'll need some numbers/video to back up my assessments. Good thing the later is my strong suit. The former I need some work on, so that is what I have been doing recently.
I have a poor relationship with ultimate statistics. I loved the work that Ultiworld did a long time ago through tracking every flipping pass in NexGen games. But that app is dead, and I don't really know to do with a chart that shows me a team's scoring probability based on field position. I guess it could expose that a team is particularly weak in the coffin corner, but in general I don't know what else I can get from that information.
So this high school season, since I am a sub-called, I decided to play around with statistics for the team. We haven't been having our most successful year so I started tracking the number of unforced errors and the number of possessions. It hasn't really righted the ship, but it is interesting what the numbers show.
First a bit on "unforced errors." In order to get around the tricky subjectivity problem I decided that if the defense doesn't touch the disc it is an unforced error. There are some strange things that fall into that category that make the numbers hard to really analyze. For example a punt in the wind is considered an unforced error, as is a high stall punt that no one touches. Also a jump-ball that the defense doesn't actually touch (but they clearly influenced the play) counts as an unforced error. It would make sense to fix some of these issues, but then we get into the subjectivity of "was that a punt or a huck too far?" Or "how much did the defender actually influence that drop?" These are things that I want to avoid so I'm keeping it pure.
Basically unforced errors are possessions that end with you giving away the disc, which means you aren't making the defense take it from you, which is a bad thing . . . right? So I took the number of unforced errors, divided by the number of possessions and we now have UE% which tells us the percentage of possessions that end in unforced error.
For Paideia that number was frighteningly high (~50% or more at times) which pointed out how we were really beating ourselves. If we could improve on that number then we would at least be asking more of our opponent. I don't want to get into Paideia's season, but it has been having a good impact. One other thing I was able to track was what I am calling our "Conversion Rate" which is just the number of possessions divided by the number of scores. An average of this over multiple games tells us how many times we need the disc (on average) in order to score.
Here is where I feel like I got into something that was useful, tracking possessions. In the past many teams have been concerned with offensive holds and defensive breaks. But a defensive break that requires 4 possessions to score isn't the same as break that only takes one. I think moving away from line-based statistics and moving toward possession based statistics will offer some new insights to analyzing the sport.
After playing with this for a little bit I wondered what was a reasonable UE%? Did it change per level of play? So I set off looking at college games from this season. I have made it through just over 20 games and here is what I have found.
The average UE% of the games that Ultiworld has filmed is around 30.79%. The funny thing is that the average for a winning team isn't any lower than that of a losing team (30.75% vs 30.83%). What is even more interesting is winning the UE% battle isn't a good indicator of success. Plenty of teams have won their games despite having a worse UE% than their opponent. I guess this would speak to the idea of there being "good" turnovers. This metric still gives us a glimpse of how many times on average a good/elite college team will just give you the disc back. Looking at the similar numbers for the college women's game and the club games might provide more support for what we assume (better levels of play make fewer "mistakes").
The real insight came from conversion rate. First of all, looking at the conversion rate of a single game is boring. Because possessions for each team are never more than one away from each other if you win the game you won the conversion rate. This is one of the things I hate about certain statistics like "breaks" and "turns." Guess what, if you get more breaks than your opponent you won the game. If you commit fewer turns, you won the game.
But this metric did offer some insight over a number of games. For example, there seems to be a clear line between the best teams and the next step down. Elite teams (Pitt, Oregon, UNCW) have a conversion rate that is typically sub 2.00. Other teams tend to operate above that mark, with some of the worst being as high as 3+ (which is where my high school team operates at times). It is no surprise that the average for winning teams would be lower than that for losing teams. Conversion Rate basically tells you the number of possessions you need to score (on average) so winning means a lower number. The winning teams have an average of 2.06 while the losing teams have an average of 2.47. There are some teams that lose with a CR below 2. Those are typically good, or at least efficient, games.
What I'm curious about now is how good a predictor average CR for a pair of teams is for the game's outcome. In general does the team with the lowest CR win future games. How should standard deviation of CR play into that calculation. Washington has a poor CR (2.41) but was able to post one sub-2.0 number. Could they get hot and beat an elite team by putting up their best efficiency number (1.83). Pitt (1.76!) has a fairly stable CR, so the likelihood of getting a "bad" game out of them seems low.
I feel like there is some room for innovation there. Given enough data we could look at the effect a "good defensive team's"impact on opponent's CR as compared to that opponent's average CR. Anyway, I have to go. Hopefully I didn't ramble too much.