Does travel distance affect results in MLS?

By Brendan Kent (@brendankent)

A key feature of Major League Soccer that differentiates it from European leagues is the physical distance between the teams. While the two-conference system reduces the average distance an away team travels for a match (teams play in-conference opponents three times per year and out-of-conference opponents once), MLS teams still spend some weeks traversing several time zones.

Back of the napkin rendering of MLS stadium locations. Obviously not perfectly to scale, but you get the drift: Western Conference teams travel farther.

In the 2013/2014 MLS Season teams traveled an average of 1,058 miles to away matches‒by this I mean the distance between the two teams’ stadiums. Houston traveled the furthest on average, at 1,345 miles per away match. The average distance traveled to away matches by each team in the 2013/2014 season is shown below. Unsurprisingly, Western Conference teams tend to travel further.

This raises the question, does travel distance effect results? In other words, can we expect teams to perform better away from home when that away match is closer to home?

In answering this question, I used multivariate regression with data from the 2013/2014 MLS regular season. Away results were used as the dependent variable to control for the significant influence of general home advantage. To control for the relative strength of the two teams, I used end of season point differential with a minor adjustment: I removed the result of the game in question to prevent any bias. I also removed any matches between LA Galaxy and Chivas USA from the data because one team was technically playing an ‘away’ game at their home stadium when they went head-to-head.

I ran several variations of the regression, all of which used away results as the dependent variable and the adjusted point differential as one of the independent variables. First, I added distance between the two teams’ stadiums as a second independent variable. This proved to be insignificant, with a p-value of .31.

Next, I ran the regression replacing distance between stadiums with the difference in time zones between the stadiums. This again proved to be an insignificant variable, returning a p-value of .5.  

Finally, I replaced the time zone variable with an indicator variable for whether the teams were two or more time zones apart. Again, insignificant, this time with a p-value of .73.

This tells us that when we control for general home advantage and the relative strengths of two teams, the physical distance between two teams has no effect on results from a statistical standpoint. This is not to say that travel takes no toll on players, it probably does, but the difference between Vancouver traveling to Portland and Vancouver traveling to New York is statistically insignificant when we control for the strength of the two teams.

DC United: Model Breakers or Just Lucky?

By Kevin Minkus (@kevinminkus)

D.C. United's defense, according to advanced metrics, is not very good. As of this weekend, their Expected Goals Against (xGA) sit at 15.7, their Total Shot Ratio (TSR) is .415, and they've allowed 156 shots in 11 games. According to these stats, they should find themselves near the bottom of the Eastern Conference standings. But, miraculously, they're not. Instead, they're at the top of the standings, with 21 points through 11 games. They've allowed only nine goals, tied for fewest in the league.

Some consider this to mostly be a product of luck; after all, they've outperformed their xGA by nearly seven goals, and their PDO is at 1058. Given the quality of shots they've faced this season, the probability of them allowing nine goals or fewer is just three percent. These numbers suggest they are due for a regression sometime soon. But there's a problem. The numbers said the same thing last season, and that regression never happened. In 2014, D.C. United finished first in the East with 59 points, third most in the league. They had just a two percent chance of outperforming expected goals allowed by the margin that they did (about 12 goals). A question on many MLS analysts' minds is “How?”

At least part of this over-performance probably can be attributed to luck (and maybe unconscious biases in the data). You can't ever really rule it out entirely. But a much greater part of it has a simple, straightforward explanation: they actually do defend well, even though it doesn't show up in the stats in ways we might expect it to.

The chart below shows expected goals against minus goals against for 2014, as well as the probability of at least that level of over or under performance occurring. Teams with a highly positive xGA-GA are “lucky”, teams with a highly negative xGA-GA are “unlucky”. The right-most column is the percentage of shots against that are off target or blocked.

2014 Expected goals against minus goals against for 2014

Team	xGA-GA	Chances of Allowing at Least that Many Goals	% of Shots Blocked or Off Target
RSL	15.12	0.005	64.16%
DCU	12.62	0.022	63.97%
SJ	9.08	0.085	62.64%
FCD	4.67	0.28	66.06%
POR	4.44	0.259	61.54%
CLB	3.90	0.248	62.68%
NYRB	1.36	0.425	62.42%
HOU	1.23	0.464	62.50%
SKC	1.04	0.459	60.17%
NE	0.43	0.521	62.62%
VAN	0.13	0.525	66.29%
PHI	-0.21	0.535	61.48%
CHI	-0.84	0.592	61.80%
LA	-3.08	0.764	61.25%
SEA	-4.47	0.821	63.86%
TOR	-5.82	0.873	63.89%
MTL	-7.70	0.917	58.08%
CHV	-14.47	0.993	60.64%
COL	-14.92	0.993	55.82%

The two teams that over-performed xGA the most, D.C. United and Real Salt Lake, also were in the top four for highest percentage of off target or blocked shots against. The correlation between these two variables is pretty high for 2014. For the years 2011 to 2014, it isn't quite as high, but is still significant. That is, the teams that over-perform expectations the most generally force higher than expected numbers of off target and blocked shots. This, of course, makes sense; shots that miss or get blocked can't become goals. United is once again forcing a lot of misses this season, as their percentage of shots that are off target or blocked stands at about 62 percent.

This, too, could merely be the product of luck. None of off target percentage, block percentage, or off target plus block percentage is, in general, particularly repeatable from season to season. However, off target plus block percentage is generally a good indicator of save percentage, at least for teams with the highest percentages (for whom it is less likely to be luck):

From these numbers, and from watching them play, the logical conclusion is that D.C. United pressures shooters on the ball, and gets defenders behind the ball, to the extent that they force opposing teams to shoot poorly, resulting in missed shots, blocked shots, and shots that are easy saves for the goalkeeper. Put another way, their defensive pressure causes teams to shoot at percentages below what expected goals models would predict. That D.C. United's defense leads to a high save percentage also means that their PDO will be a poor measure of how “lucky” they are.

As further evidence that this is not just luck, teams that consistently force a lot of blocked and missed shots also tend to be teams that allow a high proportion of shots from crosses. D.C. United and Real Salt Lake, 2014's two most over-performing teams, were also the two teams that faced the highest percentage of shots off crosses. This suggests those teams are making the conscious decision to pack the box and pressure shots, at the cost of allowing space out wide.

Most teams can be considered “good” defensively by the number of shots they give up. A low number of shots typically means a low number of expected goals, which in turn means a low number of goals. Other defenses are considered “good” because they only give up low percentage shots. They may give up a lot of shots, but, because those shots aren't likely to go in, they have low expected goals totals, and they don't allow many actual goals.

D.C. United's defense, as I've tried to show, takes a third way. They give up shots, from good positions, but because of their abnormally high defensive pressure,  those shots are more likely to miss the target or be blocked or saved, and therefore expected goals models overstate their chances of going in. Because of this, most metrics will systematically mis-evaluate the team's defense. This was the case last season, and it is once again the case at the start of this season. Presumably, expected goals models that incorporate defensive positioning would more accurately describe the team's defensive performance, but until the day comes when that data is made publicly available, we won't know for sure.

ASA Spring MLS Top-50 Rankings: Final Thoughts

by Harrison Crow (@Harrison_Crow)

If you haven't yet, check out the top 50 MLS player rankings that we released last week.

The initial conversation that took place when we entered the planning stages for this project had us talking about a top-100. Looking back, that maybe would have lead to a lot less scrutiny. What's the saying: "aim small, miss small?" Had we posted a top-20 more comments and frustration would be directed to those that were included than those left out. I think 50 has been a good mark and I think that's what we're going to stay with.

One the biggest things that has been hardest to discern in this endeavor is what type of value one position has in relation to others. I think we can all agree that attackers have most of our attention, the problem that most of us have is determining at what point does the value of an attack diminish that a defender or goalkeeper becomes just as valuable? Obviously that answer isn't necessarily spelled out in this project but it's something that each of us that had to consider with our vote.

Beyond the existential questions that come with this sort of thing there are some interesting correlations and general observances that come too. Let's talk about some of these things.

POSITIONAL REPRESENTATION

Our top MLS top 50 by position: Keepers: 3 Defenders: 7 Midfielders: 26 Forwards: 14

— AnalysisEvolved (@AnalysisEvolved) May 15, 2015

While we talked about strikers being highly valued (six of 14 finished in the top-10) there were more midfielders included in total. However, you have to consider there are twice as many midfielders on the pitch taking up minutes.

Looking on the defensive side you have Omar Gonzalez finishing 15th as the top defender in the pool and Bill Hamid finished with top honors at 16th overall for keepers.

However, an interesting note is that Chris Tierney made the list (barely) while ranking fourth overall in expected assists while teammate Kelyn Rowe who is having a big season thus far and siting 20th overall in xG+xA.

TEAMS REPRESENTATION

Players in our top 50 by team: SKC, NYRB, CLB 5 SEA, LAG, VAN 4 RSL, FCD, TFC, NE 3 DCU, NYCFC 2 POR, COL, CHI, MON, OCSC, SJ, HOU 1 PHI 0

— AnalysisEvolved (@AnalysisEvolved) May 15, 2015

The biggest take away from this is that no longer can MLS teams have a player or the top 2-3 players and dominate. DC United, while agonizingly frustrating to understand,* exist as a cohesive and effective compilation of talent. Sporting has five players voted in the top-50 and yet sit mid table. In other words, having the best players doesn't translate to having the best team.

Teams need depth and more specifically quality depth. The chart above is another example of why managing your budget and not just counting on landing quality designated players is so very important.

* Check back here tomorrow for more on how DC United consistently "beat" the model.