Sabermetrics applying to Soccer

At "once football" you can find many statistical informations about particular match or more matches for players and teams. This is an app for ipad and iphone. If moderators allows i'll put some pictures to see what is offered(many informations in few seconds)

No.
What I was saying was:
if there's a strong correlation between team salary and win/loss/tie record, and clubs 1 to 20 are all as bad at identifying soccer talent, it can not be concluded that "despite the lack of stats" any of them is actually "fairly adept" at "identifying the best soccer talent".
If a select group of people all suck at something to the same degree, it doesn't mean they're actually good at what they suck at.

Yes, one of the many interesting questions one could ask. I wish we had the data to answer questions like these.

Not quite what you may be after by I'm happy to try and add something (apologies if someone else has already added this or something like this from elsewhere):

Data based on the English Premiership between 2003 and 2011 - data taken from here http://www.football-data.co.uk/ - can't remember specifically where they sourced it from originally:

Home Team Goals Scored

0.152 * Home Team Shots On Target (of any kind - e.g. poor or good shot on target from any distance)
+ -0.244 * Home Team Yellow Cards
+ 0.214 * Opponent Team Yellow Cards (so the yellow cards effectively cancel each other out)
+ 0.072 * Home Team Corners

All P Values are less than 0.02
R Squared is 0.9757 (I don't like the factors but I will take it short term)
Standard Error = 8.112

You'd have to Monte Carlo it as you go along or something similar but it's a start

Home Team Goals Conceded

0.131 * Opponent Team Shots (not Opponents Shots on Target)
+ -0.041 * Home Team Corners

All P Values are less than 0.0004
R Squared is 0.971 (again I don't particularly like the factors but anyhow....)
Standard Error = 8.751

My slight alternative to one of the formulas above (again I don't particularly like it, as you have to know the number of goals going forward, but maybe you can more closely correlate this to players within a club?) - it also I suppose allows you to project things from a point within the season based on current data making it relevant to current form?

All data as per the above and the regression is done again on the 2003 through 2011 English Premiership Seasons:

Factor trying to be determined: Home Team Goals Scored

Two factors used:
Home Team Shots on Target (as above of any kind)
Strike% (percentage of successful shots on target - e.g. those that resulted in a goal - number expressed as a full percentage e.g. 26% not 0.26)

0.218*Shots on Target
+228.567*Strike %
+-50.004 (Intercept)

All P Values are statistically significant at less than 0.000000001
R Squared is 0.982
Standard Error = 1.845

You could get a better R Square (0.986, Standard Error 1.79-ish) from using shooting % (shots on target from total shots), but it had a p value of .30 so I dropped it out.

Hey everyone,

I've been reading a lot of the posts on this thread, and am fascinated by all of the good ideas that everybody has. I'm currently working on a project that predicts the outcomes of games in the big 5 European leagues http://www.bloombergsports.com/football/, and I think it pertains to a lot of what you all are talking about. This thread is great keep it going!

Really good reading material... killed off tons of time at work

I would be interested to see more how individual statistics work into equation, to isolate a bit more the performances of each player. Such as stolen balls, passes intercepted, and % of passes completed (which I think it's a great indicator of player performance)

Good book review here ...

http://www.nytimes.com/2012/10/24/b...oks&_r=0&gwh=7832E66ADD4FDBF228E8A9D4EF294BE0

The Signal and the Noise - Nate Silver

I have a metod e predicting league matches that usually works quite. I could try posting some predictions here.

Please do!

Has everyone/anyone here checked out http://www.whoscored.com/ ? The sub-heading on the homepage says "Revolutionizing Football Statistics." Opinions, please.

Off topic, but Silver correctly predicted all 50 states in the 2012 US Presidential race (after going 49/50 four years ago). Obviously, many states are easy to predict, but not all of them. Also, I don't know how early he made his predictions, whether any needed later revision, etc.

He made predictions every day up to the election.

Great site - thanks!
Really fascinating, if expected, how Barca players swept the top 10, and 11 of the top 12 in overall passing accuracy. Lots of good data on that site.

Looking at this site... it saddens me that Fagner left my Vasco da Gama... he's right now one of the top defenders in the Bundesliga, and defending is his weakness!!