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New soccer book using sabermetrics
Posted on 7/27/13 at 10:21 pm
Posted on 7/27/13 at 10:21 pm
Posted on 7/27/13 at 10:29 pm to DEANintheYAY
quote:
Not all goals are created equal. Anderson and Sally devised a stat they call marginal points in which players who score key goals (the first or second for their teams) are worth more. Looking at goal-scoring stats from the Premier League seasons in England in 2009-10 and 2010-11, they found that Chelsea should have paid Sunderland $39 million for Darren Bent rather than sending Liverpool $77 million for Fernando Torres, because Bent’s goals led to more points over those two seasons than any player’s.
I mean, sure, no Torres, but lol Bent
Posted on 7/28/13 at 12:00 am to Friend of OBUDan
This is why sabermetrics is a joke.
Posted on 7/28/13 at 4:21 am to BleedPurpleGold
Say wat? Sabermetrics are a very useful tool. I would like to see how they derived the dollar amts though.
Posted on 7/28/13 at 4:56 am to BleedPurpleGold
quote:
This is why sabermetrics is a joke.
In soccer or baseball?
Posted on 7/28/13 at 6:41 am to Anfield Road
quote:
In soccer or baseball?
In soccer. Soccernomics is as convincing an argument as a 12th grade term paper.
Posted on 7/28/13 at 12:31 pm to BleedPurpleGold
quote:
Soccernomics is as convincing an argument as a 12th grade term paper.
Absolutely disagree. Loved that book.
Posted on 7/28/13 at 12:42 pm to BleedPurpleGold
Soccernomics makes a lot of solid points.
Posted on 7/28/13 at 12:46 pm to BleedPurpleGold
it is certainly things like that which don't do sabermetrics in soccer any favors. or, in your experience, comolli...
Posted on 7/28/13 at 6:39 pm to hendersonshands
quote:Nobody´s saying 12th graders are morons. Many certainly are capable of fashioning and presenting ´solid´ points.
Soccernomics makes a lot of solid points.
Regarding utility or usefulness, I´m with BPyG, about as useful as a 12th grader´s term paper.
This post was edited on 7/28/13 at 6:43 pm
Posted on 7/28/13 at 7:01 pm to Dandy Lion
quote:
Regarding utility or usefulness, I´m with BPyG, about as useful as a 12th grader´s term paper.
I disagree. The data must be contextualized and used appropriately. Saying that Chelsea should have bought Bent instead of Torres is a myopic way to use the data. In that sense they are trying to be controversial, in order to make the claim that statistics CAN be used that way, to justify one thing or another.
Sabermetrics in soccer has to be paired with a discussion about the system employed and about the players within that system, since both can be highly variable, within a team and within in a game. If there is one problem I have with Sabermetrics is that they seemingly apply metrics that might work in other sports but won't apply directly to soccer. Someone needs to come along and say "the way you are understanding data developed from soccer is wrong" for x reason.
Posted on 7/28/13 at 10:52 pm to BleedPurpleGold
Totally disagree. Brazil winning the confed cup is prime example that home field advantage is worth a lot in soccer and that is a main point in that book
Posted on 7/28/13 at 10:53 pm to prison mike 47
quote:
Brazil winning the confed cup is prime example that home field advantage is worth a lot in soccer and that is a main point in that book
Well you don't need a book to realize that.
Posted on 7/28/13 at 11:22 pm to crazy4lsu
No, but it "factualizes" why this is the case, which I thought was pretty fascinating.
Posted on 7/29/13 at 2:18 am to cwil177
The book is a mass of cursory arguments that seem on their face to hold water, but upon a deeper analysis fall apart time and again. I agree with the home field advantage argument, but that wasn't the gist of "the whole book." Some of the stuff like the penalty kick chapter and the home field advantage stuff is very interesting. I just found the rest of it ridiculously easy to poke holes in the arguments. It's very much so a surface analysis. They conveniently gloss over facts that don't agree with their theories.
Posted on 7/29/13 at 4:15 am to BleedPurpleGold
I don't know if Soccernomics was intended to be anything other than a cursory introduction of a relatively new way of understanding the sport. It's certainly not thorough, and I don't think it pretends to be. It's a light read in the style of pop social science that tries to make dense subject matter simple, interesting and engaging for the masses.
That said, I definitely do think there is a place for statistical analysis in the sport, but it needs to be contextualized so that it has meaning. The real problem with the way we read stats right now is that they help us understand why events happen after they happen; they don't seem to be able to predict future events with a greater accuracy than before, at least to my knowledge. Someone out there will figure out a way of distilling that knowledge, but they will have to account for a wide variety of variables, from pitch condition to weather to tactics to formation to style, etc. In that sense the data of the future might read something like: Team A's chance creation rate increases when player X moves into quadrant Z, etc, etc. If over the course of a season you develop a metric of events (for lack of a better word) which more or less happen in the same fashion, you can determine what variables are important and give them more weight.
I think soccer has a game complexity that is equal to chess, because of the wide variety of options a player has when he is with the ball. I'm sure someone good at math could develop the specific number for how many options a soccer player has. So let's say that someone develops a number similar to chess's Shannon number. In this sense you could say that the moves in a soccer game always tend toward originality, as the longer the game is played, the more likely it is that an event will occur which has no precedent (I think in chess they call this an "out of the box" moment).
In soccer, unlike chess, a player isn't limited by directional movement, thus he is free to move the ball anywhere he pleases, theoretically. In a realistic sense, a player is limited by some very specific options.
Let's say a LB wins the ball from a RW, and attempts to recycle play. His most likely options are to clear the ball down the line toward the opposition zone, or recycle the ball, play out of the back, and help move the ball up the field in a manner which minimizes the chance that possession is lost. While these are his most likely options, I, the observer, am only guessing based on prior experience watching other LB's in similar positions behave. He could do the unwise thing and attempt to dribble out of the back, but that seems statistically improbable. So he passes back to the GK, who moves the ball to the RB. Now the options that are presented to the RB are entirely dependent on the formation of the team, the tactics of the moment, the style of the team, the abilities of the players in his team, the abilities of the players of the opposing team, the weather, the pitch condition, the score, the time of the game, his position on the pitch, his team's position on the table/place in the cup/place in the tie. All these variables, and some I probably haven't accounted for, can play into the decision he makes with the ball, or they can be completely ignored. If one was thorough enough, you could account for every decision in a match this way, with the hope that you could glean some number or algorithm that would reduce the complexity of the game to a manageable number, and thus make the statistic which corresponds directly to a specific event, unrestrained in its application.
I hope this makes sense.
That said, I definitely do think there is a place for statistical analysis in the sport, but it needs to be contextualized so that it has meaning. The real problem with the way we read stats right now is that they help us understand why events happen after they happen; they don't seem to be able to predict future events with a greater accuracy than before, at least to my knowledge. Someone out there will figure out a way of distilling that knowledge, but they will have to account for a wide variety of variables, from pitch condition to weather to tactics to formation to style, etc. In that sense the data of the future might read something like: Team A's chance creation rate increases when player X moves into quadrant Z, etc, etc. If over the course of a season you develop a metric of events (for lack of a better word) which more or less happen in the same fashion, you can determine what variables are important and give them more weight.
I think soccer has a game complexity that is equal to chess, because of the wide variety of options a player has when he is with the ball. I'm sure someone good at math could develop the specific number for how many options a soccer player has. So let's say that someone develops a number similar to chess's Shannon number. In this sense you could say that the moves in a soccer game always tend toward originality, as the longer the game is played, the more likely it is that an event will occur which has no precedent (I think in chess they call this an "out of the box" moment).
In soccer, unlike chess, a player isn't limited by directional movement, thus he is free to move the ball anywhere he pleases, theoretically. In a realistic sense, a player is limited by some very specific options.
Let's say a LB wins the ball from a RW, and attempts to recycle play. His most likely options are to clear the ball down the line toward the opposition zone, or recycle the ball, play out of the back, and help move the ball up the field in a manner which minimizes the chance that possession is lost. While these are his most likely options, I, the observer, am only guessing based on prior experience watching other LB's in similar positions behave. He could do the unwise thing and attempt to dribble out of the back, but that seems statistically improbable. So he passes back to the GK, who moves the ball to the RB. Now the options that are presented to the RB are entirely dependent on the formation of the team, the tactics of the moment, the style of the team, the abilities of the players in his team, the abilities of the players of the opposing team, the weather, the pitch condition, the score, the time of the game, his position on the pitch, his team's position on the table/place in the cup/place in the tie. All these variables, and some I probably haven't accounted for, can play into the decision he makes with the ball, or they can be completely ignored. If one was thorough enough, you could account for every decision in a match this way, with the hope that you could glean some number or algorithm that would reduce the complexity of the game to a manageable number, and thus make the statistic which corresponds directly to a specific event, unrestrained in its application.
I hope this makes sense.
Posted on 7/29/13 at 12:20 pm to crazy4lsu
Solid post Crazy. I might buy this book just because it provides some interesting insight regardless of whether it is true sabermetrics.
Posted on 7/29/13 at 12:41 pm to crazy4lsu
quote:
I don't know if Soccernomics was intended to be anything other than a cursory introduction of a relatively new way of understanding the sport. It's certainly not thorough, and I don't think it pretends to be.
Well that one's on me then. I was led to believe it was supposed to be an intellectually engaging and encompassing read. I was disappointed when it turned out to be much less engrossing than I thought it was supposed to be.
quote:
That said, I definitely do think there is a place for statistical analysis in the sport, but it needs to be contextualized so that it has meaning.
I think this is the core problem with the "science." Contexualization misses the mark more often than not. I agree that it can be used effectively if someone tailors it properly to a system and its players. I just find more often than not, any attempt to do so is ineffective.
Posted on 7/29/13 at 12:48 pm to crazy4lsu
In all honesty, I don't see where/how that knowledge/metric would be either useful or applicable. The sheer scope of a contextualized metric like you suggest simply seems impractical to me.
Too much of soccer is instinctual to be quantified. Maybe there are a handful of players on another intellectual level who see the field as quadrants and players as cones, and play the ball or make a run based on crude statistical probability. But by and large, I feel like players simply make the logical pass or run, and having a stat that predicts that just seems worthless to me.
Too much of soccer is instinctual to be quantified. Maybe there are a handful of players on another intellectual level who see the field as quadrants and players as cones, and play the ball or make a run based on crude statistical probability. But by and large, I feel like players simply make the logical pass or run, and having a stat that predicts that just seems worthless to me.
This post was edited on 7/29/13 at 12:50 pm
Posted on 7/29/13 at 1:33 pm to LSUSOBEAST1
quote:
In all honesty, I don't see where/how that knowledge/metric would be either useful or applicable. The sheer scope of a contextualized metric like you suggest simply seems impractical to me.
Ha, I didn't mean to I imply my example was meant to be applicable. I was simply thinking through a process by which you could account for variables and determine which are the most important.
I also agree on the instinctual part. Since the game is fluid, muscle memory and instinct are paramount in the sport. But people who insist upon statistical analysis will still break down decision making. I'm actually on your side with this, as I don't believe you can actually develop a comprehensive metric for the sport that reinforces what you see and accurately predicts the future. Again statistical analysis in soccer can tell us why things happened in the past but not in the future.
If we want to really understand future events, we would have to study decision making processes and no Economist is really interested in that.
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