Karlton Athletic
SILVER
I might be using the wrong app because I can’t find the league rating anywhere… can someone point me in the right direction? I just see teams, clubs and players (which is coming soon apparently).
ECNL. The C stands for Cartel
- Thread starter NorCalUSN
- Start date
That's a lot of words. It sounds like you're agreeing with me + then trying to go off on some tangent.
The quick net is that if teams play each other more often the predictive analysis is more accurate. If teams rarely play each other the predictive analysis isn't as accurate. Which logically makes sense.
You might be seeing confirmation bias in that Socal teams that play and practice all year round tend to win more often than not against other teams that have to deal with snow and cold. But, it's only that.
RandomSoccerFan
PREMIER
No, you are completely and totally wrong. Read it again. Teams that play each other more often are slightly less predictive than teams that play each other less often. It is non-intuitive. But it's correct. It's provably correct. Your logic is failing you here, when you really have to look at the numbers.
Here is the link, posted on this board back in April. As stated above - you are completely free to believe whatever you want to believe, and nobody can tell you different. It doesn't matter if what you believe is wrong and based in incorrect assumptions.
RandomSoccerFan
PREMIER
League rating isn't posted in the app. Mark does the comparison manually on request every once in awhile. The results are posted on the facebook and instagram page. There are slides for national league rankings, as well as some state specific ones, including California.
Reread your post + you are exaggerating.
The find was that that interstate predictability was slightly better than instate teams that played each other more often.
As I said before confirmation bias may be at play. Compare a grouping of CA teams to a grouping of AZ teams. Then compare a grouping of CA teams to another grouping of CA teams. Nothing against AZ but I think we all know which grouping vs the other would be easier to predict the results for.
If you were to compare instate CA to powerhouse locations like TX, GA, FL or the Northeast predictability would go down without league interplay.
I'll make an extreme example of your finding. I bet California vs Alaska predictably is way higher than CA vs CA. This doesn't mean that all interstate predictive results will be better than instate. Just that CA to Alaska is fairly easy to predict.
RandomSoccerFan
PREMIER
You are backtracking and not admitting you were completely wrong. Teams that play each other more often have less predictability than those that play each other less often. Full stop. You said this:
Your logic, uh, isn't. By your logic, instate teams would have higher predictivity than interstate teams, and it turns out to be false. Wrong. Incorrect. However you want to admit that you either learned something or denied the numbers in front of you, it's always your choice.
RandomSoccerFan
PREMIER
This isn't an extreme example, it's again thinking about it wrong. The teams actually have to play to have records. Those records need to be loaded. There needs to be a critical mass of those records to have any validity. If you think that the CA to Alaska contests are outweighing the other interstate games, and the handful of games that are played each year between those two states are upping the predictivity number - your logic isn't shared by most. There are going to be state pairs that play more often, and those that play less often - and many that play never. But on average, those that go out of state have slightly more predictable games than those that stay in state. It overrides the predictive advantage of teams that play each other often - which is the unexpected, yet only, interpretation. Unless someone postulates that teams play teams from out of state more often than teams play teams in their own state - which is quite the interpretation. For what it's worth, interstate games are quite a small portion of games (but still significant) compared to instate games.
What? Look at your original post.
"exclude all in-state games, and measure the predictivity of interstate games exclusively. CA teams playing AZ, TX playing OK, or any other permutation in the country where the opposing teams are in different states."
What's going on is that the CA vs CA teams are generally equally good. CA vs AZ TX or OK or whatever has a bias in that CA teams (in general) are higher level. This makes interstate predictability higher than CA vs CA.
What happens if you take Wisconsin vs Nevada? Or Illinois vs Ohio? Is interstate predictability better or worse than an instate comparable? Are you still comparing everything to CA instate?
What I'm getting at is that your "interstate is more predictable than instate" example happens to align with your expectations.
RandomSoccerFan
PREMIER
Let's take a different tack. Let's assume there are teams A & teams B. They play every day, for 100 days in a row. One needs to predict the result for the 101st game between these two teams. The prediction is probably going to be pretty good, but it is of course not going to predict reality without error. We can track results, and there will be a percentage that it gets right, and a percentage that it gets wrong. Odds are it's going to be right a reasonably high percentage of time - but whatever the percentage is, it can be calculated.
Let's take those same two teams, team A & team B. They instead play every day, for 200 days in a row. One needs to predict the result for the 201st game between those two teams. Like before, the prediction is going to be pretty good, but as before, it isn't going to predict reality without error. We can track results, and figure out the percentage it gets right and the inverse percentage it gets wrong.
Is the prediction for the 201st game better than the prediction for the 101st game? Maybe it is very slightly better, but it is well within the margin of error and randomness that it probably doesn't matter a whit. Same for 501st game, 1001st game, etc. So if 100 games is as good as 200 games, how low can the amount of games go while the prediction (predictivity) still is going to be very similar? Against most people's (including mine) intuition, the number is quite low - and after 6-8 games, the prediction for youth soccer isn't going to get any better. The reasonably simple model has as much information as it needs to predict how the next game will go.
And it turns out that they don't have to be teams A and teams B. If you rate team A against all the teams it plays, and you rate team B against all the teams it plays, figuring out how A & B will do against each other is the same mathematical model. You're right, that there intuitively would be some drift - and teams that are so far apart from each other that it is more than 6 steps of Kevin Bacon to find intermediate opponents, could have ratings that aren't relevant to each other and shouldn't be compared. But it sure looks like the interconnectivity of youth soccer in the US means that the differences in geography and opponents are such that teams can be compared, no matter what. The model still works, and all indications show that to be true.
One way to figure out if this intuition is true, false, or undetermined is to figure out teams that play each other rarely and compare the predictivity to those that play each other more often. Splitting the games into those that are in-state versus those that are out-of-state is exactly that. It's two data sets, one with teams that play each other more often - and one where they rarely play each other. The intuition and expectation would be that the ones that play each other more often would have measurable additional predictivity. But the results remain clear - they don't. One way to poke holes in that hypothesis is to do what you've done and say that inter-state games are going to be more predictable (e.g. the comparative ratings are more accurate) because the games are easier to predict if 1 state is stronger than another state. By doing this, the assumption is that the difference between states somehow outweigh the amount of assumed drift by teams that don't play each other often. It is quite the assumption, but let's assume that it turns out to be true - in that case the assumption must be that the potential drift due to little to no direct opponents isn't nearly as significant as the difference between states.
The kicker is that it doesn't matter which assumption is accurate - all that matters is the predictive results for all games played, and the various stratifications that are shown to show the different classes of results. Whether states as a block are more predictable than individual teams that don't play each other as often, or states as a block are less predictable than individual teams that don't play each other as often, only one of those can be true. At scale, I don't think it matters much either way, and the differences in predictivity are minor, if measurable - but either way, enough data and more questions can be asked and answered.
The better a rating/ranking system is, the better the predictivity should be, and the inverse is also true. As you almost assuredly understand, it is very possible to rate/rank youth soccer teams, against all of the hand-wringing and teeth-gnashing of those that feel that it's impossible, wrong, or unpredictable. And getting a game prediction wrong is very, very possible - and it does not show that the model (any model) is wrong, it just is a piece of data that can be measured and collated, giving a precise answer for how many games it is expected to get right, and how many it will get wrong. Those that discount all of this entirely either haven't thought this through, or are incapable of understanding math. Those that feel that this is accurate - but it's a bad idea in general to rate/rank teams because knowing the actual strength of teams is in itself harmful - have a defensible position, and it's certainly their prerogative. Personally I think they should be also arguing for not keeping score of goals in games, but I'd assume they'd think that would be a crazy extension of their thinking.
Let's take those same two teams, team A & team B. They instead play every day, for 200 days in a row. One needs to predict the result for the 201st game between those two teams. Like before, the prediction is going to be pretty good, but as before, it isn't going to predict reality without error. We can track results, and figure out the percentage it gets right and the inverse percentage it gets wrong.
Is the prediction for the 201st game better than the prediction for the 101st game? Maybe it is very slightly better, but it is well within the margin of error and randomness that it probably doesn't matter a whit. Same for 501st game, 1001st game, etc. So if 100 games is as good as 200 games, how low can the amount of games go while the prediction (predictivity) still is going to be very similar? Against most people's (including mine) intuition, the number is quite low - and after 6-8 games, the prediction for youth soccer isn't going to get any better. The reasonably simple model has as much information as it needs to predict how the next game will go.
And it turns out that they don't have to be teams A and teams B. If you rate team A against all the teams it plays, and you rate team B against all the teams it plays, figuring out how A & B will do against each other is the same mathematical model. You're right, that there intuitively would be some drift - and teams that are so far apart from each other that it is more than 6 steps of Kevin Bacon to find intermediate opponents, could have ratings that aren't relevant to each other and shouldn't be compared. But it sure looks like the interconnectivity of youth soccer in the US means that the differences in geography and opponents are such that teams can be compared, no matter what. The model still works, and all indications show that to be true.
One way to figure out if this intuition is true, false, or undetermined is to figure out teams that play each other rarely and compare the predictivity to those that play each other more often. Splitting the games into those that are in-state versus those that are out-of-state is exactly that. It's two data sets, one with teams that play each other more often - and one where they rarely play each other. The intuition and expectation would be that the ones that play each other more often would have measurable additional predictivity. But the results remain clear - they don't. One way to poke holes in that hypothesis is to do what you've done and say that inter-state games are going to be more predictable (e.g. the comparative ratings are more accurate) because the games are easier to predict if 1 state is stronger than another state. By doing this, the assumption is that the difference between states somehow outweigh the amount of assumed drift by teams that don't play each other often. It is quite the assumption, but let's assume that it turns out to be true - in that case the assumption must be that the potential drift due to little to no direct opponents isn't nearly as significant as the difference between states.
The kicker is that it doesn't matter which assumption is accurate - all that matters is the predictive results for all games played, and the various stratifications that are shown to show the different classes of results. Whether states as a block are more predictable than individual teams that don't play each other as often, or states as a block are less predictable than individual teams that don't play each other as often, only one of those can be true. At scale, I don't think it matters much either way, and the differences in predictivity are minor, if measurable - but either way, enough data and more questions can be asked and answered.
The better a rating/ranking system is, the better the predictivity should be, and the inverse is also true. As you almost assuredly understand, it is very possible to rate/rank youth soccer teams, against all of the hand-wringing and teeth-gnashing of those that feel that it's impossible, wrong, or unpredictable. And getting a game prediction wrong is very, very possible - and it does not show that the model (any model) is wrong, it just is a piece of data that can be measured and collated, giving a precise answer for how many games it is expected to get right, and how many it will get wrong. Those that discount all of this entirely either haven't thought this through, or are incapable of understanding math. Those that feel that this is accurate - but it's a bad idea in general to rate/rank teams because knowing the actual strength of teams is in itself harmful - have a defensible position, and it's certainly their prerogative. Personally I think they should be also arguing for not keeping score of goals in games, but I'd assume they'd think that would be a crazy extension of their thinking.
rainbow_unicorn
PREMIER
90% of the arguments on Socalsoccer.com would go away if Carlsbad7 switches over to an ECNL team...it would be so quiet
RandomSoccerFan
PREMIER
Unfortunately when the arguments go away, the forum goes away, from what I've seen in the past across a host of once-vibrant forums. If everyone agreed about everything, there wouldn't be much to talk about.
What you're trying to say "interstate competition is more predictive than instate competition" is not always true. This is my issue, it may happen a lot but it's not always the result. Let me explain...
First, bla bla bla about the number of times a team plays each other. What you're describing is called diminishing returns + in this case it's specifically the diminishing returns on predictability as number of games played goes up.
Break down all the states into their own data set. Then take all the teams within that data set and define an overall predictiveness for each state. What you'll find is that some states have a higher predictability and some have a lower predictability.
Now compare the instate predictability of a state against the interstate predictability of another states games in defined matches. There will be 3 potential outcomes.
1. Instate predictability is more than interstate predictability
2. Instate predictability is less than interstate predictability
3. Everything is even (this is the lottery ticket number that never happens)
Heres an example of "interstate competition is more predictive than instate competition" likely being true.
CA vs CA teams = high predictability
UT vs UT teams = lower predictability
CA vs UT team = high predictability
UT vs CA teams =high predictability
The reason CA vs CA teams is highly predictable has nothing to do with the data set size. It's more to do with population density + playing each other more often which evolves a higher level of tactics + play. This creates a greater divide between the better and worse teams. A greater divide between better and worse teams =s a higher predictability if all teams are averaged as a whole.
The reason UT vs UT teams have a lower predictability is exactly the opposite of CA. Less dense population, They don't play each other as often and they don't evolve a higher level of tactics and play. This creates a grouping of data that's all roughly the same. Without a wide spread of better and worse teams you get a lower predictability.
The reason CA vs UT and UT vs CA interstate is easier to predict is because one has a higher predictability and the other has a lower predictability. The variance either way is likely higher than instate high or low predictability.
To invalidate your statement "interstate competition is more predictive than instate competition" all I'd need to do is find 2 states that have roughly the same predictability (high or low) then compare the interstate results. I will be able to cherry pick a situation where the invalidation =s true.
I will admit that there's likely more highly predictive vs lower predictive state examples which validate your statement than there are evenly predictive states that invalidate. This is because between 1 and 100 there's 49 numbers below 50, 1 number 50, and 49 numbers above 50. Put more simply there's a lot more uneven interstate examples then there are even interstate examples.
Have played for/on an ECNL club/team. ;-)
RandomSoccerFan
PREMIER
I hear what you're saying, but I disagree that it's an accurate assumption, and even if it was - I disagree that it's either material or relevant. The goal is to find data sets of teams that play each other often, and teams that play each other less often. These two data sets - and only these two - represent exactly that. When you compare the two data sets - the one with teams that play each other less often, has slightly (but very slightly) more predictivity than the data set that has teams that play each other more often.
It may be surprising, unintuitive, or even shocking to some, but it remains true.
Finding two states where in-state is more predictive than interstate does nothing to prove that this is false. Same as finding two states where in-state is less predictive than interstate. The totals show that interstate, on average, is ever so slightly, tenths of a percent at that point, more predictive than in-state. Hanging your (or my) hat on that extremely small difference wouldn't be terribly useful, and you're right, one or two state pairs could be the tipping point in either direction. But that's not the point. The bigger point - and the one that proves the hypothesis being tested - is that there is pretty much the same predictivity whether you are talking about teams that are from the same state, or from different states. Which is as good a representation as currently exists, of teams that play each other more are pretty much as predictive as teams that play each other less, with the data implying that the ones that play each other less even being slightly more predictive.
I think your assumptions about CA being more predictive than other states ought to be confirmed, and I'll ask for the info by state (as much as feasible for Mark) to show that to be not the case. CA has so many teams, it's the same as dozens of smaller states combined, and likely has similar average to whatever the national average is in terms of variability. It has many of the stronger teams, certainly, but that's heavily weighted toward the pointy end, which is also what this board tends to focus on.
It may be surprising, unintuitive, or even shocking to some, but it remains true.
Finding two states where in-state is more predictive than interstate does nothing to prove that this is false. Same as finding two states where in-state is less predictive than interstate. The totals show that interstate, on average, is ever so slightly, tenths of a percent at that point, more predictive than in-state. Hanging your (or my) hat on that extremely small difference wouldn't be terribly useful, and you're right, one or two state pairs could be the tipping point in either direction. But that's not the point. The bigger point - and the one that proves the hypothesis being tested - is that there is pretty much the same predictivity whether you are talking about teams that are from the same state, or from different states. Which is as good a representation as currently exists, of teams that play each other more are pretty much as predictive as teams that play each other less, with the data implying that the ones that play each other less even being slightly more predictive.
I think your assumptions about CA being more predictive than other states ought to be confirmed, and I'll ask for the info by state (as much as feasible for Mark) to show that to be not the case. CA has so many teams, it's the same as dozens of smaller states combined, and likely has similar average to whatever the national average is in terms of variability. It has many of the stronger teams, certainly, but that's heavily weighted toward the pointy end, which is also what this board tends to focus on.
Eagles will be getting ECNL Boys…
We talk about pay to play and how it does not work…It has worked on the Women’s side for years…the USWNT has been at the top for years…on the boys side, pay for play works in other sports…Women’s Soccer gets some of our best women’s athletes…on the boys side too much competition with other sports…our best male athletes usually play football and basketball. Many more options than soccer. I think it is less a pay for play issue than it is that our best males athletes do not gravitate to soccer like other countries. Plenty of pay to play in sports other than soccer. If the kid is talented enough they will get on a team, less the fee.
Not to turn this is into a pay or play argument, but dems fighting words. The USWNT has been slipping for a while and the rest of the world (well, Europe and a few others) have caught up because they focused on building out an academy system. The US days of dominance are now over, and while the US may very well be a power even with pay to play, it will continue to slip as long as we don't recreate an academy system.
On the boys side, football and basketball are not equivalent. Basketball has a gate keeper where height is required while soccer does not (see Messi). Football is a low skilled sport for most positions (QB, receiver and certain other positions exempted) and conditioning is way more important than technical skills as is being top heavy. While it is true that there is some talent left on the table due to basketball and football, given the US sports population dwarfs even a Croatia or Uruguay, that's just an excuse. The real reasons are multifaceted including: a) our academy system is subpar and recruitment is late in the game in comparison to Europe, b) for the best training we have to send players to Europe but for immigration reasons that's difficult to do, c) our well has somewhat been poisoned by two other countries that also have some difficulty, namely England and Mexico, d) pay to play makes it a rich man's sport where talent ID worldwide has been traditionally among working class people, e) the reason for d. is because the academic track is less risky and more lucrative over the long run, when discounted for probability, than a long shot at Europe or an MLS career that pays $80K+ per year, e) our tradition of soccer is a relatively short one and we are only beginning to see some relative fruits now with the Zers and lower millenials...it won't reach full saturation until those kids have kids, f) certain American cultural traits including an aversion to games which don't reward merit and instead punish mistakes, g) America's size which makes it difficult for top talent to play top talent and weather patterns which disrupt the ability to hold continuous training and h) that our governance boards are involved into multifaceted turf wars instead of coming up with an organized pyramid to properly identify and sort talent.
RandomSoccerFan
PREMIER
It's good that you understand soccer - but you are way out of your lane if you really believe what you shared above.
Not at all. My son has been playing soccer nearly his entire life and has yet to truly master it. My nephew is a de at a top 20 school (private) who never played a day in his life before freshman year (formerly water polo) based 100% on his physique, body type and height. The amount of technical training he takes in v my son is laughable, but the conditioning is almost 3x the rate. Most of his private training is focused on conditioning. Also the amount of undercover steroid use at the top 20 is shocking to. I would have presumed it was close to 5% but I put it at closer to 1/4 and that’s setting aside hgh or bulking supplements.