It's nice having two of us doing this because that means I can check my output too and make sure I haven't bollocked stuff up on one of my many research projects that my crazy code doesn't allowed me to separate out properly.
Also, my keying system is completely cray cray. Like, I have several different "unique keys" for players. One is a unique numeric ID. One is "whatever AFL Tables calls them", which I require uniqueness on and is occasionally consistent with what AFL Tables calls them, which is nice. Then I've got my own system, where they're called <first initial>.<surname>, in lowercase with apostrophes removed. This is not unique, except when you combine it with a year and a team, so brl-r.bastinac (for example) is unique within the year 2019, and this is how I usually refer to players internally*. Of course, you've got a problem if two players on the same team in the same year have the same initial and surname. So when we had Josh Clayton in the team and Jack Clayton in the academy squad and I was doing statistics on both teams Jack Clayton was mostly "Zac Clayton" to me. Sorry "Zac" . Now the bloody Bulldogs have two L. Youngs. Hopefully that doesn't become a problem at some point.
* probably at some point this made sense because I was loading stuff from the Fantasy Freako newsletter and they were just writing out player names like this and my database originally existed for exclusively this purpose.
Also I calculate the age not by storing the age or their date of birth but by storing the number of days between their DOB and Jan 1 1860. Computers usually do this when storing dates** but for some obscure reason I decided to implement this bit myself and to use an unusual epoch at the same time. No idea. Did my DBMS have a datetime format when I first added that column?
** for example, the time per your phone, the servers that run the internet and also the rest of the world, and your router, etc, is the number of seconds between now and Jan 1 1970 in a 32-bit signed int. This means that all computers will run out of dates in 2038 and this, like Y2K***, will be a problem down the line.
*** yes Y2K was a problem. Lots of people spent lots of time fixing it and basically got it pretty much sorted in time.
I really need to work on transitioning this to a new system that's not so bad.
I'd be very interested to see the comparative position if the 200+ games player was excluded. That would be Hodge in our case, not sure who the Eagles player would be. It would bring our average age and games played down considerably.
The stats are remarkably close, with the difference being that the Eagles have a few more real babies (under 25 games), whereas the Lions have more youngsters (25-49 games). None of these really tell us much about what will happen.
R1 Lions vs Eagles at Gabba, 23 March, 2019
(0 to 10 games - Lions 0 vs. Eagles 3) 0 to 24 games - Lions 3 vs. Eagles 6
25 to 49 games - Lions 5 vs. Eagles 1
50 to 99 games - Lions 7 vs. Eagles 6
100 to 199 games - Lions 6 vs. Eagles 8
200+ games - Lions 1 vs. Eagles 1 Extra stats:
Average games played - Lions 92.5 vs. Eagles 93.3 (= -0.8 games on average)
Average age - Lions 25.1 vs. Eagles 25.2 (= -0.1 years on average)
Average height - Lions 188.6cm vs. Eagles 188.5cm (= +0.1cm on average)
Average weight - Lions 88.5kg vs. Eagles 87.5kg (= +0.9kg on average)
Have been reading this thread for years. First time we are starting to look real on the stats sheet. I remember when our average games was often around 60 and sometimes half that of the opposition team.
Hi Jiv and Fan. Just want to thank you two for the stats you did last week. Once they came out my confidence soured. It even meant I was still confident at quarter time (as opposed to wife who was questioning our investment in membership again).