Opta's F24 data(the most advanced stuff they have for MLS) doesn't keep track of all player's positions, just ball events. Some guys incorporate a ton more stuff like some things you mention. Here is a good write up of all the factors in a more 'advanced' xG model.
Thanks. I’ll read that. My point I suppose is not that xG is useless (or analytics), just that xG is currently not what a lot of fans including myself think/thought it is: an approximation of who should have won the game. Whereas what it really is = a rough approximation of score based on some simplistic metrics based solely on shots. Over the course of a season it’ Probably pretty helpful, but over the small sample size of one game it can be pretty limited.
It sort of measures whether or not the GK had a great day, and or whether the shot takers were good or poor. Stats can help us quantify certain elements of the game. I'd like to see successful versus unsuccessful throw-in data. That is how many times did your team retain possession on a throw-in, and how many times did they lose possession. And you'd have to consider was the ball lost after three or four touches? Was that the dribbler's fault? Or the thrower's fault? So even something as simple and easy to calculate as that could become much more complex when you get into the nitty-gritty. None of this is really scientific yet, but we're starting to get there. Go Quakes!! Fire Stahre!! - Mark
Oh it's scientific. It's just that the science can get better. The perfect is the enemy of the good and all that.
I just finished reading Sebastian Abbot’s “The Away Game”. Don, this is the book you bought for me at Abbot’s book signing at Avaya. This is an excellent book, and I recommend it for those interested in how Qatar’s Aspire Academy evaluated players for its “Football Dreams” program. Abbot also researched other programs and discusses the newer technologies, which even try to measure soccer intelligence and ability to see the game. The program started recruiting players in 2007 and was terminated in 2016. Abbot covers players from the first 2 classes. For those of you who want to learn how coaches and such evaluate players, read the first half of the book where players are recruited, and the ending pages where he discusses various reasons why players failed as pros. The last thing — when I read that the discrepancy in age cheating was 3-7 years, the first player I thought about was Freddy Adu. I always thought he looked 20 when he was supposedly 14. Too bad they didn’t do MRI wrist measurements back then. I guess they started doing it around 2009.
One of my favorite baseball podcasts took a pretty nice detour towards soccer and MLS and interviewed Harrison Crow about the state of soccer analysis, etc. Highly recommended for both its content and just because the podcast host, Carson Cistulli, is a national treasure. https://www.fangraphs.com/blogs/fangraphs-audio-harrison-crow-of-american-soccer-analysis/
Thanks for posting, that was a very helpful episode — an Analytics 101 approach to soccer analytics. I may have to listen to it again! However, I was a little bothered by 2 statements about 9 minutes from the end: first, that Harrison Crow likes Marco Urena, and second that he said Urena “used to play with Carlos Wondolowski”. Bit of a gaf there...
Haha, yeah, I caught that as well. Not sure if you've ever had to go on and do a recording like that before, but it is surprisingly easy to misspeak. I found it more amusing than anything else. My only note to them about Urena would have been that the Quakes had to basically choose among Wondolowski, Urena, and Hoesen to leave exposed for the expansion draft, and leaving Urena open was clearly the right choice based on actual results. Those kinds of details are easy to overlook when you aren't focused on one team like many of us on this board are.
This company, IMPECT.com has a metric for “packing” where they analyze how often attackers outplay defenders, getting behind them with a pass or dribble. I’m unclear as to why such a metric would not already be in OPTA data, but they say they use 60 college students to collect the data, requiring 6 hours of analysis per game.
This team is a disgrace. I can't believe the Quakes can't find , scout or pick a group of college all stars or even free agents and/or MLS veterans , groom them to win more than four MLS games. Its just not believable that it can't be done. I'm sure they can find 11 players somewhere. This is ridiculous!
Metrics meaning tactics? Ok here is a tactic, play Catenaccio and counterattack! We wouldn't lose 1-5 at home!
no falvo, metrics as in measuring individual player performance via sensor monitoring, OPTA type tools, etc. Not tactics, not rants about the ownership...
Player Sensoring or monitoring? Isn’t that monitoring a players progress or what he does in s game? I don’t get it.
look back at the beginnings of this thread... Metrics, statistics, data, moneyball, that kind of thing. We've pretty much stayed on topic, altho i think we may have also journeyed into areas of sports medicine.
Some of this statistical analysis by Colin Etnire just feels so wrong compared to the eye test, such as Godoy being one of the best midfielders in MLS, and Magnus being a good attacking asset. But other than those two players, the rest of the analysis seems pretty true. Whaddya all think? 1046868322912997381 is not a valid tweet id