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Statistical Breakdown of Real Salt Lake – Seattle Sounders

October 31, 2011 Sarah Rudd 5 comments

It was rough being a Sounder’s fan last night. Amidst discussions of a CONCACAF Champions League curse, playing at altitude and missing one of their best players of the season in Mauro Rosales, the Sounders had a tough playoff matchup against Real Salt Lake. While most fans would have been surprised if the Sounders had come away with a first leg lead, going down 3-0 was a bit of a shock. Not only did they concede 3 goals for only the third time all season, but they just looked awful. Using Opta’s chalkboards, let’s take a look at what went wrong.

If you chat with me about the statistical analysis of soccer, one of the first phrases out of my mouth is probably “I hate passing percentage”. I still do (because often the numbers are quoted without context and used to “prove” one team is superior to another), but I am going to use some passing stats here to illustrate some points.

Passing Momentum

Total Attempted Passes for each team over time

For the first 30 minutes, Seattle clearly struggled to control the ball and allowed Real Salt Lake to maintain possession and pass the ball around. Why is this important? Seattle is a team that has been competing in 3 tournaments and is playing at altitude that it isn’t accustomed to. Chasing the ball for 30 minutes to start the game is sure to be taxing on already tired legs. It wasn’t until around the75th minute that Seattle started to see a sustained advantage in passes completed, however, that wasn’t so much because of their improved play but because RSL shut it down and tried to protect their two goal lead.

Total Attempted Passes in the final third

Looking at passes just in the final third, again Seattle was the inferior team, failing to get much penetration early on while having to absorb lots of pressure from Real Salt Lake. Seattle had some opportunities towards the end of the first half, but failed to capitalize. Towards the end of the match, Seattle was again getting opportunities in the final third, but their inability to complete a pass really let them down.

Passing Distance

Distribution of passing distances for Seattle Sounders and Real Salt Lake

Why did Seattle have such a hard time completing passes? Whether it was that Real Salt Lake did a good job of closing down the passing channels or Seattle failing to move off the ball and provide options for their teammates is hard to say without going back and rewatching (something I can’t stomach). What is apparent, is that Seattle had to revert to attempting much longer passes than Real Salt Lake. The above graph shows the quartiles of attempted pass distances for each team in 15 minute increments. Throughout the game, but in particular early on, Seattle’s passes were much longer than Real Salt Lake’s. Seattle definitely struggled playing out of the back, with defenders often trying to play the ball down field to alleviate pressure, but failing to connect with a teammate.

Passing Out of the Back

Passing completion in the defensive third. Weight of the line is the average distance of the passes.

There’s a lot going on in the graph above, but basically for the defensive third it shows passing completion and the average distance of complete/incomplete passes. Seattle’s passing completion out of the back is very low with the incomplete passes tending to be much longer than the completed passes.

Midfield Battle

Pass selection for Seattle midfielders

Pass selection for Real Salt Lake midfield

Not surprisingly, RSL’s midfielders were able to complete a high number of short passes while Seattle’s midfield attempted longer passes with little success. Of particular note is that there were long stretches of time where Alvaro Fernandez, Brad Evans and Lamar Neagle failed to complete a pass (hard to tell in the graph, but if there isn’t a dot on the line, there is no pass attempted for that time period and the software just connects points where there where there was data. It’s not just that the Sounders midfield didn’t complete as many passes as Real Salt Lake, it’s that they didn’t see enough of the ball.

Shots

Shot Distances by Type of Shot

While Seattle only managed 5 Shots On Target, they were pretty even with Real Salt Lake in terms of shots taken from 18 yards or less. RSL’s dominance in Shots On Target comes mostly from long distance shots. Seattle was a little unlucky with the goals they conceded and had they been a little more clinical, the scoreline could have been a little more favorable. I like that Seattle was selective in the shots they took and waited for good opportunities while (for the most part) restricting RSL to shooting from the outside.

Summary

The passing stats for the Sounders are atrocious. They allowed Real Salt Lake to dominate possession early on, causing themselves to chase the ball and wear themselves out. Long passes out of the back caused them to bypass the midfield and more often than not return the ball back to Real Salt Lake. Seattle was able to absorb a lot of the RSL pressure and keep them shooting from the outside. The abscence of the two first-choice center backs for RSL plus the possible return of Mauro Rosales bodes well for Seattle. The Sounders are no strangers to scoring three but will find it tough since Real Salt Lake can put 11 behind the ball and protect their 3 goal lead.

Analytics, MLS, Playoffs, Seattle Sounders, Statistics

The Curse of CONCACAF Champions League and Squad Management

October 27, 2011 Sarah Rudd 5 comments

During tonight’s MLS Playoff match between the New York Red Bulls and FC Dallas, the “Curse of CONCACAF Champions League” was brought up. FC Dallas has had to play more matches than NYRB this season and came into the match looking a bit fatigued. Since the CONCACAF version isn’t as lucrative as the European version, it is getting the reputation as being a drain on teams. This begs the question, are teams that participate in the Concachampions at a disadvantage when it comes to the MLS playoffs? A Beautiful Numbers Game has a post on the correlation between factors that contribute to winning play-off series. Not surprisingly, number of matches played is important. What hasn’t been discussed is how manager’s deal with squad rotations and what effect does that play on success. Major League Soccer is a parity league, so unlike in Europe where more successful teams can go out and buy new players if they qualify for additional tournaments, MLS teams have similar resources. There is some unknown quantity of allocation money that teams get when they qualify for CCL, but the number of roster spots is fixed. Are teams using their resources differently?

Analytics, Major League Soccer, MLS, Playoffs, Statistics

Good or Lucky? Evaluating MLS’s Top Managers

June 2, 2011 Sarah Rudd 3 comments

There’s been discussion on some of the Sounders FC boards lately about whether or not Sigi Schmid should be fired. One of the common defenses that comes up is that he’s won 2 MLS Cups so therefore he is a good manager. The question is, in a league based on parity with a playoff system that almost everyone qualifies for, how hard is it to win an MLS Cup? Is Sigi Schmid, or other top MLS managers, statistically different from the rest of the league?

To answer these questions I looked at both playoff and regular season performance of Sigi Schmid, Bruce Arena, Frank Yallop, Dominic Kinnear, and Steve Nicol. All of these guys have won the MLS Coach of the Year award, coached in the league for more than 8 seasons and, with the exception of Steve Nicol, who has lost the cup final a remarkable 4 times, won multiple MLS Cups. They are the best that MLS has to offer.

Analytics, Major League Soccer, MLS, Playoffs, Seattle Sounders, Statistics

On the Playoff Race

September 29, 2010 Sarah Rudd No comments

One of the problems I’ve run into with following MLS is figuring out where my team stands in the playoff race. In essence, the top 8 teams make it into the playoffs. In reality, it’s possible for a team not in the top 8 to make it into the playoffs, but the likelihood of that happening is very slim. The full rules for qualifying are availabe here. For most soccer leagues, it’s easy to figure out who the top 8 teams are. They have a single table format with a balanced schedule. Very few teams have played a different number of matches than the other teams in the league. It’s easy to compare apples to apples in this scenario. MLS, on the other hand, has a very bizarre way of scheduling matchs and it’s uncommon for teams to have played the same number of matches at any point in the season. How, then, do you figure out where you stand in the table without running through all the scenarios of “Well, if team A beats team B, and then draws against team C, and we beat team D, then…”.

I’ve several approaches to this problem. The first is to use the average points earned per game as an estimate of what a team will do in their games-in-hand. This is a decent approach for the middle of the season when a team’s performance will be averaged out over a large number of games. Towards the end of the season, this approach doesn’t work as well. For example, if a team is averaged 1.2 PPM, then assuming they will earn 2.4 PPM in their two games-in-hand can be a poor assumption. It’s quite reasonable for them to pick up the full 6.

Another approach is to use Monte Carlo simulations to calculate the possible outcomes of future matches. Sports Club Stats is a good example of this approach. The problem is that their estimates are limited to the quality of the model they use for estimating the outcomes. Your outputs are only as good as your inputs and quite frankly, the discipline isn’t mature enough to have quality inputs just yet.

Baseball has the concept of games back. It’s a beautiful metric that tells you exactly what your team needs to do in order to move into first. Any casual baseball fan can understand it. It’s a simple formula that encapsulates where a team stands no matter how many games-in-hand are in play. It got me thinking about whether or not something similar could be applied to a league with trinary outcomes (win, loss, tie). I tweaked the formula a bit to work with ties and the results are decent but take some getting used to. Instead of the simplicity of a half or full game back, there are now weird situations like being 1/6 of a game back. I’m going to update the results each week for the rest of the season to get a feel for how it works. Below are the latest standings as of 9/27.

Team Name	Points	GP	GB From Playoffs
Los Angeles Galaxy	50	26	-3 2/3
Real Salt Lake	48	26	-3
FC Dallas	46	26	-2 1/3
Columbus Crew	45	26	-2
New York Red Bulls	44	26	-1 2/3
San Jose Earthquakes	39	24	-1
Colorado Rapids	38	25	- 1/6
Seattle Sounders FC	39	26	0
Kansas City Wizards	33	25	1 1/2
Toronto FC	31	26	2 2/3
Chicago Fire	26	24	3 1/3
Philadelphia Union	27	25	3 1/2
Chivas USA	25	25	4 1/6
New England Revolution	26	26	4 1/3
Houston Dynamo	26	26	4 1/3
D.C. United	18	26	7

Both the Sounders and KC are deep in the playoff race after going through some dark times this season. At their worst points during the season the teams were only 2 and 2.5 GB respectively. I think that’s a testament to parity in MLS and the fact that half the teams qualify for the playoffs.