MLS Week 3: Expected Goals and Attacking Passes

In the coming days, Matthias will be releasing our Expected Goals 2.0 statistics for 2014. You can find the 2013 version already uploaded here. I would imagine that basically everything I've been tweeting out from our @AnalysisEvolved twitter handle about expected goals up to this point will be certainly less cool, but he informs me it won't be entirely obsolete. He'll explain when he presents it, but the concept behind the new metrics are familiar, and there is a reason why I use xGF to describe how teams performed in their attempt to win a game. It's important to understand that there is a difference between actual results and expected goals, as one yields the game points and the other indicates possible future performances. However, this post isn't about expected goal differential anyway--it's about expected goals for. Offense. This obviously omits what the team did defensively (and that's why xGD is so ideal in quantifying a team performance), but I'm not all about the team right now. These posts are about clubs' ability to create goals through the quality of their shots. It's a different method of measurement than that of PWP, and really it's a measuring something completely different.

Take for instance the game which featured Columbus beating Philadelphia on a couple of goals from Bernardo Anor, who aside from those goals turned in a great game overall and was named Chris Gluck's attacking player of the week. That said, know that the goals that Anor scored are not goals that can be consistently counted upon in the future. That's not to diminish the quality or the fact that they happened. It took talent to make both happen. They're events---a wide open header off a corner and a screamer from over 25 yards out---that I wouldn't expect him to replicate week in and week out.

Obviously Columbus got some shots and in good locations which they capitalized on, but looking at the xGF metric tells us that while they scored two goals and won the match, the average shot taker would have produced just a little more than one expected goal. Their opponents took a cumulative eleven shots inside the 18 yard box, which we consider to be a dangerous location. Those shots, plus the six from long range, add up to nearly two goals worth of xGF. What this can tell us is two pretty basic things 1) Columbus scored a lucky goal somewhere (maybe the 25 yard screamer?) and then 2) They allowed a lot of shots in inopportune locations and were probably lucky to come out with the full 3 points.

Again, if you are a Columbus Crew fan and you think I'm criticizing your team's play, I'm not doing that. I'm merely looking at how many shots they produced versus how many goals they scored and telling you what would probably happen the majority of the time with those specific rates.

Team	shot1	shot2	shot3	shot4	shot5	shot6	Shot-total	xGF
Chicago	1	3	3	3	3	0	13	1.283
Chivas	0	3	2	2	3	0	10	0.848
Colorado	1	4	4	2	1	1	13	1.467
Columbus	0	5	1	2	1	0	9	1.085
DC	0	0	1	1	4	0	6	0.216
FC Dallas	0	6	2	0	1	1	10	1.368
LAG	0	0	4	2	3	0	9	0.459
Montreal	2	4	5	8	7	0	26	2.27
New England	1	2	1	8	5	0	17	1.275
New York	2	4	2	0	2	0	10	1.518
Philadelphia	2	5	6	2	4	0	19	2.131
Portland	0	0	2	2	2	1	7	0.329
RSL	0	4	3	0	3	0	10	0.99
San Jose	0	2	0	0	3	0	5	0.423
Seattle	1	4	0	2	2	0	9	1.171
Sporting	2	6	2	2	3	2	17	2.071
Toronto	0	6	4	2	2	0	14	1.498
Vancouver	0	1	1	3	3	0	8	0.476
Team	shot1	shot2	shot3	shot4	shot5	shot6	Shot-total	xGF

Now we've talked about this before, and one thing that xGF, or xGD for that matter, doesn't take into account is Game States---when the shot was taken and what the score was. This is something that we want to adjust for in future versions, as that sort of thing has a huge impact on the team strategy and the value of each shot taken and allowed. Looking around at other instances of games like that of Columbus, Seattle scored an early goal in their match against Montreal, and as mentioned, it changed their tactics. Yet despite that, and the fact that the Sounders only had 52 total touches in the attacking third, they were still able to average a shot per every 5.8 touches in the attacking third over the course of the match.

It could imply a few different things. Such as it tells me that Seattle took advantage of their opportunities in taking shots and even with allowing of so many shots they turned those into opportunities for themselves. They probably weren't as over matched it might seem just because the advantage that Montreal had in shots (26) and final third touches (114). Going back to Columbus, it seems Philadelphia was similar to Montreal in the fact that both clubs had a good amount of touches, but it seems like the real difference in the matches is that Seattle responded with a good ratio of touches to shots (5.77), and Columbus did not (9.33).

These numbers don't contradict PWP. Columbus did a lot of things right, looked extremely good, and dare I say they make me look rather brilliant for picking them at the start of the season as a possible playoff contender. That said their shot numbers are underwhelming and if they want to score more goals they are going to need to grow a set and take some shots.

Team	att passes C	att passes I	att passes Total	Shot perAT	Att%	KP
Chicago	26	17	43	3.308	60.47%	7
Chivas	32	29	61	6.100	52.46%	2
Colorado	58	27	85	6.538	68.24%	7
Columbus	53	31	84	9.333	63.10%	5
DC	61	45	106	17.667	57.55%	3
FC Dallas	34	26	60	6.000	56.67%	2
LAG	43	23	66	7.333	65.15%	6
Montreal	63	51	114	4.385	55.26%	11
New England	41	29	70	4.118	58.57%	7
New York	57	41	98	9.800	58.16%	6
Philadelphia	56	29	85	4.474	65.88%	10
Portland	10	9	19	2.714	52.63%	3
RSL	54	32	86	8.600	62.79%	3
San Jose	37	20	57	11.400	64.91%	3
Seattle	33	19	52	5.778	63.46%	5
Sporting	47	29	76	4.471	61.84%	7
Toronto	30	24	54	3.857	55.56%	6
Vancouver	21	20	41	5.125	51.22%	2
Team	att passes C	att passes I	att passes Total	ShotpT	Att%	KP

There is a lot more to comment on than just Columbus/Philadelphia and Montreal/Seattle (Hi Portland and your 19 touches in the final third!). But these are the games that stood out to me as being analytically awkward when it comes to the numbers that we produce with xGF, and I thought they were good examples of how we're trying to better quantify the the game. It's not that we do it perfect---and the metric is far from perfect---instead it's about trying to get better and move forward with this type of analysis, opposed to just using some dried up cliché to describe a defense, like "that defense is made of warriors with steel plated testicles" or some other garbage.

This is NUUUUUuuuuummmmmbbbbbbeeerrrs. Numbers!

MLS PWP: Team Performance Index through Week 3

I hope all are enjoying my PWP series here at American Soccer Analysis. With Week 3 completed, I have at least two games worth of data for every team in MLS, and now it's time to begin offering up the cumulative PWP Strategic Index and all that goes with it. Wasting no time, here's the initial diagram on how things look after at least two games:

Observations:

Given what happened the first few weeks, it should be no surprise that Columbus lead the pack early on with Houston second, and (like last year) a strong early start for FC Dallas.

What may be surprising to some is where Toronto falls in this Index; it should be noted that in both games played this year, Toronto have had just 32.46% possession (Seattle) and 37.68% possession (D.C. United).

What this indicator helps point out is how different Toronto is playing compared to others while still taking points - in both cases Toronto have opted to sit back and cede possession in order to capitalize on opponents losing their shape. How well that continues to work for them remains to be seen, but for now Bradley has been absolutely correct in his analysis/offering to MLS: you don't need to have a majority of possession to win a game.

As for the bottom dweller, note the familiar spot for D.C. United. It would seem those off-season transactions have yet to bear fruit, and it might not be t0o long before coach Ben Olsen sees the door if United don't start turning things around.

How about some of the other teams in the middle? Well New York and Portland have both opened up exactly like they did last year with two points in three games. What may be most troubling for both is a lack of scoring. We'll see how that unfolds, as it is likely that Thierry Henry and Tim Cahill will score sooner rather than later.

With respect to LA Galaxy, I watched their game this weekend against Real Salt Lake, and it appeared to me that it was all about Robbie Keane and his single-handed goal (with Donovan lurking) versus a solid Real Salt Lake team effort. If Joao Plata doesn't go off injured in that game, I'd have been a betting man that RSL would have taken three points from LA.

Other lurkers here are Seattle, Colorado and Vancouver. Recall last year that the defense of Vancouver kept them from the Playoffs (45 goals against). This year things are starting a wee bit different, as they had a great defensive battle with New England this past weekend.

All those thoughts being said here's how the teams stack up in the PWP Strategic Attacking Index:

Observations:

Columbus Crew, FC Dallas and Houston are the new guys on the block this year--as compared to last year--with RSL, LA Galaxy, Seattle, Colorado, New York and Vancouver returnees to the top spots.

Missing from the potent attack side so far this year (foremost) are Sporting Kansas City and Portland. One may recall that Chivas USA had a good start last year, but then the Goats seemed to wander off and join D.C. United as the season wore on.

Of note is where Toronto sits. In playing a counterattacking style, parts of their PWP will naturally fall lower down the list than other more possession-based teams. It will be fun to track how they progress in PWP this year.

For the defensive side of PWP here's how things stand today:

Observations:

With Columbus doing so well in attack it's no surprise that their opponents aren't... so here's where the real grist begins when peeling back defending activities.

Note that Houston, Seattle, Colorado, and Sporting Kansas City are in the top five, while FC Dallas, high up in attack, isn't quite so high in defending. Will that gap create issues again this year? Pareja was noted as having a pretty tight defense in Colorado. Will there be personnel changes in Dallas?

Oddly enough, a top defender in my view for Portland was David Horst. I'm still not sure why he was moved to Houston, but given their early season success, his big presence in the back has certainly improved that team. Can David remain healthy? Hard to say, but continued presence by the big guy should garner some interest, I hope, in some USMNT training after the World Cup is completed this year. It's never too early to plan for the future.

As for the bottom dwellers, note again that Chivas USA are the bottemost. They may have improved their attack this off-season, but if they can't stop the goals against, that attack will mean nothing when it comes to Playoff crunch time.

In closing...

It remains early, and I've every belief that this table will adjust itself a bit more as time passes and points are won and lost. The intent is not necessarily to match the League Tables, but to offer up a different perspective on teams' abilities that are reasonable when viewing team performance.

Check out my PWP Week 3 Analysis, as well as my New York Red Bulls-centric PWP weekly analysis for New York Sports Hub. If time permits please join me on twitter as I offer up thoughts during nationally-televised matches this year.

All the best, Chris

MLS Possession with Purpose Week 3: The best (and worst) performances

Here's my weekly analysis for your consideration as Week 3 ended Sunday evening with a 2-nil Seattle victory over Montreal. To begin, for those new to this weekly analysis, here's a link to PWP. It includes an introduction and some explanations; if you are familiar with my offerings then let's get stuck in.

First up is how all the teams compare to each other for Week 3:

Observations:

Note that Columbus remains atop the League while those who performed really well last year (like Portland) are hovering near the twilight zone. A couple of PKs awarded to the opponent and some pretty shoddy positional play defensively have a way of impacting team performance.

Note also that Toronto are mid-table here but not mid-table in the Eastern Conference standings; I'll talk more about that in my Possession with Purpose Cumulative Blog later this week.

Also note that Sporting Kansas City are second in the queue for this week; you'll see why a bit later.

A caution however - this is just a snapshot of Week 3; so Houston didn't make the list this week but will surface again in my Cumulative Index later.

The bottom dweller was not DC United this week; that honor goes to Philadelphia. Why? Well, because like the previous week, their opponent (Columbus) is top of the heap.

So how about who was top of the table in my PWP Strategic Attacking Index? Here's the answer for Week 3:

As noted, Columbus was top of the Week 3 table again this week, with FC Dallas and their 3-1 win against Chivas coming second, and Keane and company for LA coming third.

With Columbus taking high honors, and all the press covering Bernardo Anor, it is no surprise he took top honors in the PWP Attacking Player of the Week. But he didn't take top honors just for his two wicked goals, and the diagram below picks out many of his superb team efforts as Columbus defeated Philadelphia 2-1.

One thing to remember about Bernardo; he's a midfielder and his game isn't all about scoring goals. Recoveries and overall passing accuracy play a huge role in his value to Columbus, and with 77 touches he was leveraged quite frequently in both the team's attack and defense this past weekend.

Anyhoo... the Top PWP Defending Team of the Week was Sporting Kansas City. This is a role very familiar to Sporting KC, as they were the top team in defending for all of MLS in 2013. You may remember that they also won the MLS Championship, showing that a strong defense is one possible route to a trophy.

Here's the overall PWP Strategic Defending Index for your consideration:

While not surprising for some, both New England and Vancouver finished 2nd and 3rd respectively; a nil-nil draw usually means both defenses performed pretty well.

So who garnered the PWP Defending Player of the Week?  Most would consider Aurelien Collin a likely candidate, but instead I went with Ike Opara, as he got the nod to start for Matt Besler.  Here's why:

Although he recorded just two defensive actions inside the 18-yard box compared to five for Collin, Opara was instrumental on both sides of the pitch in place of Besler. All told, as a Center-back, his defensive activities in marshaling the left side were superb as noted in the linked MLS chalkboard diagram here. A big difference came in attack where Opara had five shots attempts with three on target.

In closing...

My thanks again to OPTA and MLS for their MLS Chalkboard; without which this analysis could not be offered.

You can follow me on twitter @chrisgluckpwp, and also, when published you can read my focus articles on the New York Red Bulls PWP this year at the New York Sports Hub. My first one should be published later this week.

All the best, Chris

In Defense of the San Jose Earthquakes and American Soccer

Note: This is part II of the post using a finishing rate model and the binomial distribution to analyze game outcomes. Here is part I. As if American soccer fans weren’t beaten down enough with the removal of 3 MLS clubs from the CONCACAF Champions League, Toluca coach Jose Cardozo questioned the growth of American soccer and criticized the strategy the San Jose Earthquake employed during Toluca’s penalty-kick win last Wednesday. Mark Watson’s team clearly packed it in defensively and looked to play “1,000 long balls” on the counterattack. It certainly doesn’t make for beautiful fluid soccer but was it a smart strategy? Are the Earthquakes really worthy of the criticism?

Perhaps it’s fitting that Toluca is almost 10,000 feet above sea level because at that level the strategy did look like a disaster. Toluca controlled the ball for 71.8% of the match and ripped off 36 shots to the Earthquakes' 10. It does appear that San Jose was indeed lucky to be sitting 1-1 at the end of match. The fact that Toluca only scored one lone goal in those 36 shots must have been either unlucky or great defense, right? Or could it possibly have been expected?

The prior post examined using the binomial distribution to predict goals scored, and again one of the takeaways was that the finishing rates and expected goals scored in a match decline as shots increase, as seen below. This is a function of "defensive density," I’ll call it, or basically how many players a team is committing to defense. When more players are committed to defending, the offense has the ball more and ultimately takes more shots. But due to the defensive intensity, the offense is less likely to score on each shot.

Mapping that curve to an expected goals chart you can see that the Earthquakes expected goals are not that different from Toluca’s despite the extreme shot differential.

Given this shot distribution, let’s apply the binomial distribution model to determine what the probability was of San Jose advancing to the semifinals of the Champions League. I’m going to use the actual shots and the expected finishing rate to model the outcomes. The actual shots taken can be controlled through Mark Watson’s strategy, but it's best to use expected finishing rates to simulate what outcomes the Earthquakes were striving for. Going into the match the Earthquake needed a 1-1 draw to force a shootout. Any better result would have seen them advancing and anything worse would have seen them eliminated.

Inputs:

Toluca Shots: 36

Toluca Expected Finishing Rate: 3.6%

San Jose Shots: 10

San Jose Expected Finishing Rate: 11.2%

Outcomes:

Toluca Win: 39.6%

Toluca 0-0 Draw: 8.3%

Toluca 1-1 Draw: 13.9% x 50% PK Toluca = 6.9%

Total Probability Toluca advances= 54.9%

San Jose Win: 32.3%

2-2 or higher Draw = 5.8%

San Jose 1-1 Draw: 13.9% x 50% PK San Jose = 6.9%

Total Probability San Jose Advances = 45.1%

The odds of San Jose advancing with that strategy are clearly not as bad as the 10,000-foot level might indicate. Counterattacking soccer certainly isn’t pretty, but it wouldn’t still exist if it weren’t considered a solid strategy.

It’s difficult, but we can also try to simulate what a “normal” possession-based strategy might have looked like in Toluca. In MLS the average possession for the home team this year is 52.5% netting 15.1 shots per game. In Liga MX play, Toluca is only averaging about 11.4 shots per game so they are not a prolific shooting team. They are finishing at an excellent 15.2%, which could be the reason San Jose attempted to pack it in defensively. The away team in MLS is averaging 10.4 shots per game. If we assume that a more possession oriented strategy would have resulted in a typical MLS game then we have the following expected goals outcomes.

Notice the expected goal differential is actually worse for San Jose by .05 goals. Though it may not be statistically significant, at the very least we can say that San Jose's strategy was not ridiculous.

Re-running the expected outcomes with the above scenario reveals that San Jose advances 43.3% of the time. A 1.8% increase in the probability of advancing did not deserve any criticism, and definitely not such harsh criticism. It shows that the Earthquakes probably weren’t wrong in their approach to the match. And if we had factored in a higher finishing rate for Toluca, the probabilities would favor the counterattack strategy even more.

Even though the US struck out again in the CONCACAF Champions League, American's don't need to take abuse for their style of play. After all, soccer is about winning, and in the case of a tie, advancing. We shouldn't be ashamed or be criticized when we do whatever it takes to move on.

Predicting Goals Scored using the Binomial Distribution

Much is made of the use of the Poisson distribution to predict game outcomes in soccer. Much less attention is paid to the use of the binomial distribution. The reason is a matter of convenience. To predict goals using a Poisson distribution, “all” that is needed is the expected goals scored (lambda). To use the binomial distribution, you would need to both know the number of shots taken (n) and the rate at which those shots are turned into goals (p). But if you have sufficient data, it may be a better way to analyze certain tactical decisions in a match. First, let’s examine if the binomial distribution is actually dependable as a model framework. Here is the chart that shows how frequently a certain number of shots were taken in a MLS match.

The chart resembles a binomial distribution with right skew with the exception of the big bite taken out of the chart starting with 14 shots. How many shots are taken in a game is a function of many things, not the least of which are tactical decisions made by the club. For example it would be difficult to take 27 shots unless the opposing team were sitting back and defending and not looking to possess the ball. Deliberate counterattacking strategies may very well result in few shots taken but the strategy is supposed to provide chances in a more open field.

Out of curiosity let’s look at the average shot location by shots taken to see if there are any clues about the influence of tactics. To estimate this I looked expected goals by each shot total. This does not have any direct influence on the binomial analysis but could come in useful when we look for applications.

The average MLS finishing rate was just over 10 percent in 2013. You can see that, at more than 10 shots per game, the expected finishing rate stays constant right at that 10-percent rate. This indicates that above 10 shots, the location distribution of those shots is typical of MLS games. However, at fewer than 10 shots you can see that the expected goal scoring rate dips consistently below 10%. This indicates that teams that take fewer shots in a game also take those shots from worse locations on average.

The next element in the binomial distribution is the actual finishing rate by number of shots taken.

Here it’s plain that the number of shots taken has a dramatic impact on the accuracy rate of each shot. This speaks to the tactics and pace of play involved in taking different shot amounts. A team able to squeeze off more than 20 shots is likely facing a packed box and a defense less interested in ball possession. What’s fascinating then is that teams that take few shots in a game have a significantly higher rate of success despite the fact that they are taking shots from farther out. This indicates that those teams are taking shots with significantly less pressure. This could indicate shots taken during a counterattack where the field of play is more wide open.

Combining the finishing accuracy model curve with number of shots we can project expected goals per game based on number of shots taken.

What’s interesting here is that the expected number of goals scored plateaus at about 18 shots and begins to decline after 23 shots. This, of course, must be a function of the intensity of the defense they are facing for those shots because we know their shot location is not significantly different. This model is the basis by which I will simulate tactical decisions throughout a game in Part II of this post.

Now we have the two key pieces to see if the binomial distribution is a good predictor of goals scored using total shots taken and finishing rate by number of shots taken. As a refresher, since most of us haven’t taken a stat class in a while, the probability mass function of the binomial distribution looks like the following:

Where:

n is the number of shots

p is the probability of success in each shot

k is the number of successful shots

Below I compare the actual distribution to the binomial distribution using 13 shots (since 13 is the mode number of shots from 2013’s data set), assuming a 10.05% finishing rate.

The binomial distribution under predicts scoring 2 goals and over predicts all other options. Overall the expected goals are close (1.369 actual to 1.362 binomial). The Poisson is similar to the binomial but the average error of the binomial is 12% better than the Poisson.

If we take the average of these distributions between 8 and 13 shots (where the sample size is greater than 40) the bumps smooth out.

The binomial distribution seems to do well to project the actual number of goals scored in a game, and the average binomial error is 23% lower than with the Poisson. When individually looking at shots taken 7 to 16 the binomial has 19% lower error if we just observe goal outcomes 0 and 1. But so what? Isn’t it near impossible to predict the number of shots a team will take in the game? It is. But there may be tactical decisions like counterattacking where we can look at shots taken and determine if the strategy was correct or not. And a model where the final stage of estimation is governed by the binomial distribution appears to be a compelling model for that analysis. In part II I will explore some possible applications of the model.

Jared Young writes for Brotherly Game, SB Nation's Philadelphia Union blog. This is his first post for American Soccer Analysis, and we're excited to have him!

MLS Prediction Contest - We Have a Winner!

After two weeks of Major League Soccer wins, losses, and, this week, mostly draws, the best predictors were... [googleapps domain="docs" dir="spreadsheet/pub" query="key=0At6qSdpic03PdE4zOE12WWNlSm0zeVBnaXd6SnpDQ0E&output=html&widget=true" width="500" height="300" /]

MLSAtheist and timbertyler tied for first place with 13 correct answers each (out of 20). Normally, we would have gone to the tiebreaker to determine the grand prize winner, but MLSAtheist, a valued contributor to American Soccer Analysis, graciously decided to withdraw his prize eligibility. That leaves timbertyler as the winner of a subscription to MLS Live 2013!

Congratulations to timbertyler; maybe Portland will follow his lead and start amassing some wins of their own.

ASA Fantasy League Update Round 2: A Terrible Case of the Nagbe's

This is your weekly reminder that you're doing MLS fantasy, and if you're taking part in our league you should probably set your rosters so you have an opportunity to win something TBD. And really, since you're probably not doing any work with the NCAA tournament going on, you have some time to make sure your lineup is good to go this week. If you aren't in our league yet, and for some reason you feel the strong need to join, you can do so by figuring out how to use this code: 9593-1668. We grade on a pass/fail scale. If you get in you passed. Here is the current week's worth of data. It's in a jpeg format because, frankly, tables show up for crap on our site and we'll be moving soon enough to this other site that... well, we'll tell you more when we're at that stage.

Here are the main take aways for this week.

- Stop making Darlington Nagbe your Captain.

- Will Bruin continues to make me look stupid.

- I'm average, and if you are below me, you are not doing yourself any favors.

- I'm ahead of both Matthias and Drew, so while I'm the idiot of the podcast I've so far shown to be the better fantasy player.

- I totally lucked out with Zack MacMath this week.

Now the below image is for the week 2 "dream team" which is basically how you could have gotten the most points last week. Interesting that no one from our league sported a 3-5-2 formation this week and that three main formations were kind of cycled through for everyone.

Good luck to you all, and we'll see if we can ever catch up to either Bazzo, Cris Pannullo or Chris Gluck. They look poised to possibly run away with this thing. Hopefully this week will set them back so the rest of us can feel better about ourselves.

xGD in CONCACAF Champions League

Understanding that not everything has to mean something, we still try to provide meaning to things. Deriving meaning becomes infinitely harder when sample sizes are small: what size sample is important when considering a specific set of data? We don't always know, but I present you the CONCACAF Champions League data anyway. Below is the Expected Goals 1.0 data from the group stage of the CCL that I've compiled in the last couple of days.

Team	xGF	xGA	xGD
Cruz Azul	8.578	4.112	4.466
Toulca	7.528	3.488	4.04
Tijuana	6.617	3.018	3.599
America	6.975	4.017	2.958
Dynamo	5.683	3.417	2.266
LA Galaxy	7.052	4.95	2.102
Sporting KC	4.785	2.699	2.086
SJ Earthquakes	4.768	2.962	1.806
Montreal I.	3.816	8.796	-4.98

To be honest, this is my inventive way to present this information to you. I wanted to do an article about various things concerning CCL, but the problem always kind of leads back to sample size. Four games just isn't that much. The thing is, while you may not be able to draw any solid conclusions from this, it does give us a rough assessment for how Liga MX compares to MLS at this juncture, and it tells us that for the most part, MLS and Liga MX teams are better than the competition.

Mind you, teams have changed between when they qualified for CCL 2013-14 and now. This San Jose Earthquakes squad, for example, has quite a few new faces. Houston also has added a couple of pieces and underwent a some changes in the defensive rotation scheme.

xGD wasn't going to tell us too much about the semi-final matches that were played the last two nights. We knew that it was improbable that even two clubs were going to move forward. Furthermore it seems awkward to even consider that San Jose was the closest to advancing--and had it not been for a bad call, it probably would have.

What xGD did tell us is that all four Mexican clubs performed better in that short period than any of the MLS sides. Sure, a "duh" statement is in order, but this clarifies that point further than a cute 1990's radio morning drive show with catchy sound effects could. Cruz Azul seemed a superior team, for example, as they were nearly two expected goals better than any MLS side. In a short tournament that says something stronger than their actual goal tallies.

Yes, I realize the whole sample size thing, and really it's funny submitting qualifying statements, but it's even more silly to consider that we qualify them despite the fact that we don't actually know if we need to. For all we know xGD stabilizes as a metric at six games or maybe even four. We'll get Matty on that...

Mexico's teams were better, and judging from everything going down on  Twitter and how the fragile psyche of the average US Soccer fan seems almost devastated by this fact, the reality is that MLS is better than it has been. The league has grown so much, and considering the issues that still limit organizations from competing against Mexico, it's surprising how well we really do in this competition.

Now American teams aren't yet on the "elite" level yet. But they are still very good and are nearing the imaginary line of being able to compete on a greater level with Mexico. As the budgets of MLS increase, and the depth charts along with the academies grow deeper, you're only going to see MLS teams get better. Stating that an MLS team will never win the CCL is one of those hyperbole statements that is just crazy to me. I think it's an eventuality at this point that some club somewhere will knock Mexico off it's perch...sooner rather than later.

ASA Podcast: XLI An MLS Week 2 Review

Okay, here is a podcast. This is our weekly podcast that is just simply a podcast. A podcast that stars myself and Drew. A Podcast that I edited to make sound not bad and some what interesting. I encourage you to listen to it. Drew makes lots of good points, I make some too. [audio http://americansocceranalysis.files.wordpress.com/2014/03/asa-podcast-xli-the-do-over.mp3]

Passing: An oddity in how it's measured in Soccer (Part II)

If you read my initial article on "Passing - An oddity in how it's measured in Soccer Part I"; I hope you find this article of value as well as the onion gets peeled back a bit further  to focus on Crosses. To begin please consider the different definitions of passing identified in Part I and then take some time to review these two additional articles (Football Basics - Crossing) & (Football Basics - The Passing Checklist) published by Leo Chan - Football Performance Analysis, adding context to two books written by Charles Hughes in 1987 (Soccer Tactics and Skills) and 1990 (The Winning Formula).   My thanks to Sean McAuley, Assistant Head Coach for the Portland Timbers, for providing these insightful references.

In asking John Galas, Head Coach of newly formed Lane United FC in Eugene, Oregon here's what he had to offer:

"If a cross isn’t a pass, should we omit any long ball passing stats? To suggest a cross is not a pass [is] ridiculous, it is without a doubt a pass, successful or not - just ask Manchester United, they ‘passed’ the ball a record 81 times from the flank against Fulham a few weeks back.”

In asking Jamie Clark, Head Coach for Soccer at the University of Washington these were his thoughts...

"It's criminal that crosses aren't considered passing statistically speaking. Any coach or player knows the art and skill of passing and realizes the importance of crossing as it's often the final pass leading to a goal. If anything, successful passes should count and unsuccessful shouldn't as it's more like a shot in many ways that has, I'm guessing, little chance of being successful statistically speaking yet necessary and incredibly important."

Once you've taken the time to read through those articles, and mulled over the additional thoughts from John Galas and Jamie Clark, consider this table.

Stat	Golazo/MLS STATS	Squawka	Whoscored	MLS Chalkboard	My approach	Different (Yes/No)?
Total Passes	369	356	412	309+125 = 434	309+125+9=443	Yes
Total Successful Passes	277	270	305	309	309 + 9 = 318	Yes
Passing Accuracy	75%	76%	74%	NOT OFFERED	71.78%	Yes
Possession Percentage	55.30%	53%	55%	NOT OFFERED	55.93%	Yes
Final Third Passes	141	NOT OFFERED	NOT OFFERED	FILTER TO CREATE	140	Yes
Final Third Passing Accuracy	89/141= 63.12%	NOT OFFERED	NOT OFFERED	FILTER TO CREATE	92/140 = 65.71%	Yes
Total Crosses	35  vs 26 (MLS Stats)	NOT OFFERED	35	35	35	No
Successful Crosses	35*.257=9	NOT OFFERED	9	9	9	No
KEY PASSES	NOT OFFERED	7	9	6	6	Yes
* NOTE: MLS Chalkboard includes unsuccessful crosses as part of their unsuccessful passes total but does not include successful crosses as part of their total successful passes; it must be done manually.

For many, these differences might not mean very much but if looking for correlations and considering R-squared values that go to four significant digits these variations in datum might present an issue.

I don't track individual players but Harrison and  Matthias do, as does Colin Trainor, who offered up a great comment in the Part I series that may help others figure out where good individual data sources might come from.

What's next?

My intent here is not to simply offer up a problem without a solution; I have a few thoughts on a way forward but before getting there I wanted to offer up what OPTA responded with first:

I (OPTA representative) have has (had) a word with our editorial team who handle the different variables that we collect. There is no overlay from crosses to passes as you mentions, they are completely different data variables. This is a decision made as it fits in with the football industry more. Crosses are discussed and analysed as separate to passes in this sense. We have 16 different types of passes on our F24 feed in addition to the cross variable.

So OPTA doesn't consider a cross a pass - they consider it a 'variable'?!?

Well I agree that it is a variable as well and can (and should) be tracked separately for other reasons; but for me it's subservient to a pass first and therefore should be counted in the overall passing category that directly influences a teams' percentage of possession.  Put another way; it's a cross - but first and foremost it's a pass.

(Perhaps?) OPTA (PERFORM GROUP now) and others in the soccer statistics industry may reconsider how they track passes?

I am also hopeful that OPTA might create a 'hot button' on the MLS Chalkboard that allows analysts the ability to filter the final third consistently, from game to game to game, as an improvement over the already useful 'filter cross-hairs'...

In closing...

My intent is not to call out any statistical organizations but to offer up for others, who have a passion for soccer analyses, that there are differences in how some statistics can be presented, interpreted and offered up for consideration.  In my own Possession with Purpose analysis every ball movement from one player to another is considered in calculating team passing data.

Perhaps this comparison is misplaced, but would we expect the NFL to call a 'screen pass' a non-pass and a variation of a pass that isn't counted in the overall totals for a Team and Quarterback's completion rating?

Here's a great exampleon how Possession Percentage is being interpreted that might indicate a trend.

Ben has done some great research and sourced MLS Stats (as appropriate) in providing his data - he's also offered up that calculating possession is an issue in the analytical field of soccer as well.

In peeling back the data provided by MLS Stats he is absolutely correct that the trend is what it is... When adding crosses and other passing activities excluded by MLS Stats the picture is quite different and lends credence to what Bradley offers.

For example--when adding crosses and other passing activities not included by MLS Stats--the possession percentages for teams change, and the R-squared between points in the league table comes out as 0.353, with only 7 of 8 possession-based teams making the playoffs. New York, with most points, New England and Colorado all had possession percentages last year that fell below 50%, and only one team in MLS last year that didn't make the playoffs finished with the worst record (16 points) DC United.

For me, that was superb research - a great conclusion that was statistically supported. Yet, when viewed with a different lens on what events are counted as passes, the results are completely different.

All the best,

Chris

You can follow me on twitter @chrisgluckpwp