Nathaniel’s soccer team played a night game in Santa Clara a couple of weeks ago, at the field complex right beside Levi’s Stadium. (We lost, 0-3. It’s been a tough season.)
Nathaniel is active in a tournament this coming weekend. Bracket link:
- Surf Cup Thanksgiving, U12B, North San Diego
Regional cross-country championships are on Sunday in Fresno. We’re waiting to see which of our teams qualified.
Nathaniel is active in a tournament next weekend. Bracket link:
- Cerritos Memorial, U11B Nike, Long Beach and vicinity
Ben’s running in the 3M meet this weekend.
Nathaniel is active in a tournament this weekend. Convenient link to his bracket:
- Spring Showcase, U12B Gold, Redwood City
Ben is waiting for the youth track season to start.
I’ve been working out a minor idea involving the control of some household actions based on local time, but relative to sunrise and sunset rather than a naive time of day. Simon Kennedy’s Astral is a Python module that can compute these times, but its examples focus on retrieval of locations from major cities. Most places aren’t major cities in the module’s list, so I spent a little time to read the source to determine what other entry points were enabled.
It turns out that the
Location class is perfect for those times when you can’t associate a site with a major city, but know the latitude and longitude (from GPS or a mapping service, for example). An example usage follows:
#!/usr/bin/python """Example of using astral with a location not in the module's built-in catalog.""" import astral import datetime # From A. Mariano, MacOS units(1), 1993. FT_PER_METRE = 3.2808399 # Construct our location. Longitude west and latitude south are # negative. los_altos = astral.Location(info=("Los Altos", "USA", 37.3681, -122.0975, "US/Pacific", 157/FT_PER_METRE)) # "civil", which means 6 degrees below the horizon, is the default # value for computing dawn and dusk. But this usage shows how to # set it before calculation. los_altos.solar_depression = "civil" tomorrow = datetime.date.today() + datetime.timedelta(1) result = los_altos.sun(date=tomorrow) for k in ["dawn", "sunrise", "noon", "sunset", "dusk"]: print "%7s %s" % (k, result[k])
My 2012 instructions for building node.js on OpenIndiana still work on recent bits. Now to write some software.
I learned that Logitech has brought the Squeezebox product line to end to life. I’m not prepared to replace (or research what might replace) our current audio infrastructure, so I’m experimenting with a piCorePlayer install to see if a low cost, Raspberry Pi solution can solve this problem, much like OpenSprinkler has addressed home irrigation.
We went on a drive today, to try Fletch’s Chicago-style hot dogs and sliders. They make their own dipping sauce, which has a touch of spice and went well with the fries. I couldn’t muster the appetite, but there is a Beard Papa on the very same block of 2nd Ave.
I just started the spring season in my recreational soccer league last weekend. And I’m still using Adidas’s miCoach system to record statistics in each game. Earlier I showed a couple of individual sessions, with image captures from the miCoach site. Here I’m going to summarize the season, and compare it to the previous season (Summer 2013). Top line summary of the season
- 11 games, 9/8 – 12/29
- 8 goals
- Compared to 1 in Summer 2013
- All on turf
For the quantities tracked by the Speed Cell, we have a group of measurements, summarized as follows
- Distance: 4.0 ± 0.6 miles per game, with a best of 5.1 miles on 12/1. (Improved over Summer 2013, 3.9 ± 0.5 mpg.)
- Sprints: 20.5 ± 4.5 sprints per game, with a best of 30 on 12/1. (Improved over Summer 2013, 18.5 ± 4.7 spg.)
- Maximum speed: 15.0 ± 0.4 mph, with a best of 15.52 mph on 9/15 and 10/6. (Improved over Summer, 2013 14.5 ± 1.1 mph.)
miCoach offers badges (“achievements”) for various performance levels: on 12/1, 5.1 miles of total distance and 0.55 miles of “hi intensity distance” resulted in the Pro achievements for those two categories. Still haven’t reached the Pro mark for Maximum Speed, which I suspect will require a burst over 16 mph. (On 6/9, 3.9 miles of distance and 15.17 mph maximum speed earned the respective Club achievements. On 7/14, 0.38 miles of high intensity distance earned the corresponding Club achievement.)
I was pretty happy with my showing on December 1, but Sean Ingle’s Sunday article in The Guardian, on Chelsea and England defender Ashley Cole, shows how low on the performance scale these numbers are:
The additional physical demands are clear from Prozone’s data. In 2003-04 Premier League full-backs made an average of 29.5 sprints – any movement greater than seven metres a second – over a game. This season that figure is exactly 50. A decade ago the average recovery time for a full-back between high-intensity activities – any movement greater than 5.5m/s, or a three-quarters speed run – was 56.4sec. Now it is 40.4sec.
(Of course, I’m ten years older than Ashley Cole, so I am pleased just to be on the field, uninjured, and possessing a little bit of pace.)
It’s going to be hard to compare the spring season with this one, as the new team only has part-time goalkeepers, and I’ll have to take many more shifts in net. (The two times I played keeper during the fall were 13 sprint games.) Other than working on my metrics, my only goal for this season is to finally score one on the grass fields.
As I mentioned in the sprinkler repair/upgrade post, Northern California continues its now multi-year drought. The United States Drought Monitor currently labels the conditions as D2 – severe drought.