All My Sports Teams Suck (AMSTS) is the internet’s premier data-and-analytics-driven-somewhat-serious-but-not-really-sports-directed blog out there. Doubt me? You shouldn’t. It’s true.
AMSTS looks to point out interesting data while sifting through massive amounts of noise, with a focus on attempting to present said data in a layman-friendly format. We try to find interesting narratives that aren’t emotionally driven, while looking beyond the box score at the impact of statistics you won’t find on the front page of other sports websites. At the same time, we try to put the right context on the numbers, as not to turn this into a mathematical discussion, or devaluing intangibles such as “momentum” and “being clutch.”
The blog is headed by Chris Ford, a dedicated sports fan who leverages his data skills from his day to day work into his passion for sports. Headquartered in Berlin, Germany, AMSTS can be reached by postal mail at Niebuhrstrasse 72, 10629 Berlin, or via telephone at +49 1575 6797 623. Alternatively, you can reach us through comments, or emails on the blog.
We accept (and love!) guest articles on one-off topics that are interesting to our readers. If you would like to submit a post to AMSTS, please let us know via our contact page today. No previous writing experience is necessary.
8 thoughts on “About AMSTS”
Hi there! I’ve found some interesting stuff on your blog here! I am curious where you get the weekly game attendance data? Cheers!
Sorry for the delayed response. I compiled the attendance myself and with the help of SportsLogbook.com. Thanks for reading!
All your rankings suck!
UConn is the number one team in the USA and has already beaten the crap out of Texas (up by 17 with four minutes to go, qon by 7 after taking staters out), and today we will beat the crap out of NC State. Ga,e at one, stay tuned. (And go see the the AP and Coaches Rankings, and Charlie’s Bracketology.
Hi Dave, thanks for the kind words. The computer rankings only use data available from that season, and doesn’t inherently know that UConn is a good basketball team until enough games in the season have been played for the teams to be closely related to one another. Right now it’s seen two UConn games, one of which they only won by 7. This is by design to avoid human bias in the rankings (leading the computer to over-rank a team before their quality is known).
Considering that UConn has been to the final four every year (that the tournament was played) for the last 14 years, please explain how any person who actually followed WBB would ever place UConn lower than than 4th, EVER? and since UConn finished second in the country last year and gas 11 national championships, why would you ever rank sucky tams like Notre Dame ahead of UConn? So far this year, we played the toughest schedule in the country and are 5-0 beating Texas, NC State, Duke , and Iowa so far. What statistics do you use anyway, How does Penn Stater rank above UConn> How about Notre Dame which hasn’t played any team in the top 25 this year? 4 out of 5 teams were in the top 10 when UConn beat them.
The key to a good statistical model is predictability. So far your model has shown 0 predictability I don’t even know why anyone is publishing it. Your comment that “”the computer models use only the date from that season” is utter nonsense , and even if that were true, which is absolutely is not, which other team in this country has been to the final four every year for the past 14 years? Or has played 4 top ten teams this year and won them all? Or has 11 nataional championships? Nothing but a marajuana haze could explain your rankings.
You’re showing a fundamental lack of understanding as to how computer rankings work, so I’ll explain it a little simpler.
First off, they’re made by computers, with minimal human input beyond the parameters that they’re designed to consider, but the actual act of ranking teams is done without human intervention. The various systems out there tend to fall into two buckets: those with “pre-season bias” that is usually built off of recruiting ratings, or a continuation of the previous year. The second type is “Without bias” where the computer algorithm solves the rankings by only looking at playing events for what has happened this year. Now, in the first 3 games of the season, all it can really look at is a very small amount of cross-pool data, so if UConn beat Texas by 7, while Louisville beat them by 8, the computer is going to initially believe that Louisville is better, until more data fills out the picture.
Now, this is precisely why the NET doesn’t release ratings until January, because they don’t need to hear some guy who doesn’t understand that the computer rankings’ entire universe is the pool of games that have happened and not some obscure variable on historic performance (should UCLA always be #1 in Men’s Computer Rankings due to their number of National Championships?). I post mine from the beginning of the season because it’s fun to see the variability early when there is limited data. I’m sorry that upsets you so much.
Since you insist that my statement that “computer rankings only use the data from that season” is nonsense, please cite a computer ranking that takes in multiple years of performance into their rating data. I’ll wait.
But instead, you will fall back on citing some delusional marajuana [sic, for an immigration lawyer one would think you should be able to use spell check] haze because someone on the internet ranked your precious women’s basketball team at 20 after 2 games, then 11 after 4, and now 2nd in the country after 6.
Have you considered going outside and touching grass (just the regular kind would be fine, but weed might also chill you out) instead of getting mad online like this?
Are we able to calculate an expected MOV using the win or points ratings?
Points rating is points above average, so a predicted MOV would simply be the delta in points rating, plus a home court factor if necessary.