ASA Podcast XVIII: The One Where We Discuss Defense Influencing Shots

Family in town, waiting out this baby and being home doing nothing sure has made me lazy. So lazy that I really didn't get around to editing this and putting it together until last night. My apologies for the late posting. This week we discuss the USMNT and their romp in eastern Europe, a bit about Montreal and Omar Gonzalez. Then we transition to some discussion about whether goal keepers can influence shots on target. It's all some interesting stuff with a lot of giggling by me because I coined a new nickname for Drew.

 

[audio http://americansocceranalysis.files.wordpress.com/2013/08/asa-episode-xviii.mp3]

Noisy Finishing Rates

As a supplement to the stabilization analysis I did last week, I wanted to add the self-predictive powers of finishing rates—basically soccer’s shooting percentage. Team finishing rates can be found both on our MLS Tables and in our Shot Locations analysis, so it would be nice to know if we can trust them. Last week I split the 2012 and 2013 seasons in half and assessed the simple linear relationships for various statistics between the two halves of each season across all 19 teams. Now I have 2011 data, and we can have even more fun. I included bivariate data from both 2011 and 2012 together, leaving out 2013 since it is not over yet. It is important to note that I am not looking across seasons, only within seasons. To the results!

Stat Correlation Pvalue
Points

0.438

0.7%

Total Attempts

0.397

1.5%

Blocked Shots

0.372

2.3%

Shots on Goal

0.297

7.4%

Goals

0.261

11.9%

Shots off Goal

0.144

39.5%

Finishing

0.109

52.1%

Surprisingly, to me at least, a team’s points earned has been the most stable statistic in MLS (by my linear definition of stability). Not so surprising to me was that total attempts is also one of the most stable. Look down at the very bottom, and you’ll find finishing rates. Check out the graph below:

 Finishing Rates Stabilization 2011-2012

Some teams finish really well early in the season, then flop. Others finish poorly, then turn it on. But there’s no obvious to pattern that would allow us to predict second-half finishing rates. In fact, the best prediction for any given team would be to suggest that they will regress to league average, which is exactly what our Luck Table does. It regresses all teams’ finishing rates in each zone back to league averages, then calculates an expected goal differential.

On a side note, you might be asking yourself why I don't just use points to predict points. Because this: while the correlation between first-half and second-half points is about 0.438, the correlation between first-half attempts ratios and second-half points is slightly stronger at 0.480. Also, in a multiple regression model where I let both first-half attempts ratio and first-half points duke it out, first-half attempts ratio edges out points for winner of the predictor trophy.

Estimate Std. Error T-stat P-value
Intercept 1.7019 5.97 0.285 77.7%
AttRatio 13.7067 6.32 2.17 3.7%
Points 0.3262 0.19 1.691 10.0%

And since this is a post about finishing rates...

Estimate Std. Error T-stat P-value
Intercept -2.243 7.75 -0.29 77.4%
AttRatio 18.570 5.71 3.26 0.3%
Finishing% 63.743 50.08 1.27 21.2%

A good prediction model (on which we are working) will include more than just a team's attempts ratio, but for now, it is king of the team statistics.

Signal and Noise in MLS

Some Nate Silver guy wrote a whole book about "signal" and "noise" in data, so it must be important, right? Sports produce a lot of statistics, and it turns out that some of those statistics are pretty meaningless---that is, pretty noisy. A pitcher's ERA is sitting below 3.00 after eight starts, but he has more walks than strikeouts. Baseball sabermetricians will tell you that the low ERA is mostly noise, but that the high walk rate is a signal for impending doom. An MLS team leads the league in points per match, but it allows more shots than it earns for itself (note: this team is called "Montreal Impact"). Soccer nerds like me will tell you that its position in the standings is mostly noise, and that its low shots ratio is a signal for impending doom---or something worse than first place, anyway.

The reasoning behind both examples above is basically the same. Pitchers' ERAs, like soccer teams' points earned, are highly variable and unpredictable, while strikeout-to-walk ratios and shots ratios are more consistent. It's better to put your money on something consistent and easy to predict, rather than something variable and hard to predict. Duh, right?

So here's why we like shots data 'round these parts. Below I have provided two charts of MLS data, one from 2012 and one from 2013. I split each season into two parts and then measured the linear predictive power of each stat on itself. Did teams that scored lots of goals early in the season also score lots of goals later in the season? That's the kind of question answered here.

2012 MLS Stat R2 Pvalue 2013 MLS Stat R2 Pvalue
Blocked Shots 37.1% 0.6% Shots off Goal 34.8% 0.8%
Total Attempts 26.1% 2.5% Total Attempts 34.5% 0.8%
Goals 20.3% 5.3% Shots on Goal 29.4% 1.7%
Points 20.1% 5.5% Points 4.1% 40.7%
Shots on Goal 18.2% 6.9% Blocked Shots 1.7% 60.0%
Shots off Goal 3.6% 43.7% Goals 1.5% 61.6%

As an example of what this means, let's consider the attempts stat. Remember that an attempt is any effort in the direction of the goal, so basically an attempt is any shot---on target, off target, or blocked. In each of the past two seasons, MLS teams' attempts totals in the first half of the season were able to help predict their attempts totals in the second half, explaining 26.1% and 34.5% of the variability in second-half attempts, respectively. Those might not seem like high percentages of explanation, but the MLS season is short, and statistically significant predictors are hard to find.

In baseball, such "self-predictors" have been referred to as "stabilization." Stabilization is important because, as mentioned above, stabilization means that a stat is consistent, and that a team is likely to replicate its results in the future. This MLS season, points earned during the first 10 matches were essentially worthless at predicting points earned in the second 10 games. Even over the 34 games each team played in 2012, the stabilization for points earned was not as strong as that of attempts or goals scored.*

The next step is figuring out what predicts future points earned, since it does a pretty lame job of predicting itself. But I'll leave that for another post after I have gathered data going back a few more seasons. The number one takeaway here is that some stats can only tell us what happened, but not what will happen. There is another group of stats that are doubly important because they also stabilize---predicting themselves using smaller sample sizes. Those stabilizing stats (like shot attempts) are the signal amid the sea of noise known most places as "football."

Seattle has only played 21 games, so I cannot do 11-and-11 splits, yet.  Also, as for why shots off goal and blocked shots have essentially switched places, I would wager that's more due to how they are (somewhat) subjectively categorized, but who knows. 

Game of the Week: Montreal Impact at Chicago Fire

So why this game, you ask. Real Salt Lake is hosting Houston, and the Revs travel to play the Wiz, but I picked this game instead. Despite a negative goal differential, I like Chicago in this one. I smell upset. Our MLS tables tell me that Chicago ranks 5th in the league in attempts ratio at 1.07, earning nearly 10% more shot attempts than its opponents on average. When we account for where those shots are coming from, our shot location data suggests that Chicago's goal differential should be pretty even: -0.04 expected goal differential (xGD) per game if I regress finishing rates 100%. Basically Chicago is an average team with a little bad own-goals luck. However, as I've been preaching all year, Montreal's play is seemingly unsustainable, and it is playing on the road. Despite the most points per game in MLS, Montreal owns the third-worst attempts ratio in the league, and an expected goal differential of -0.20 goals per game. The Impact may very well be the second-best team on the pitch come Saturday.

Chicago Fire Shots Data

For Locations Goals GoalDistr SOGDistr OffDistr BlksDistr AttDistr Finish% ExpGoals
One 5 19.2% 6.9% 2.6% 2.9% 4.2% 41.7% 4.0
Two 16 61.5% 31.7% 31.3% 17.6% 28.2% 20.0% 14.3
Three 3 11.5% 19.8% 16.5% 20.6% 18.7% 5.7% 3.2
Four 1 3.8% 22.8% 19.1% 25.0% 21.8% 1.6% 2.8
Five 1 3.8% 18.8% 28.7% 33.8% 26.4% 1.3% 1.6
Six 0 0.0% 0.0% 1.7% 0.0% 0.7% 0.0% 0.1
Total 26 26.0
Against Locations Goals GoalDistr SOGDistr OffDistr BlksDistr AttDistr Finish% ExpGoals
One 8 29.6% 11.5% 9.4% 2.0% 8.6% 34.8% 7.7
Two 9 33.3% 26.4% 29.7% 15.7% 25.9% 13.0% 12.3
Three 6 22.2% 23.0% 14.8% 31.4% 20.7% 10.9% 3.3
Four 0 0.0% 13.8% 13.3% 21.6% 15.0% 0.0% 1.8
Five 3 11.1% 21.8% 32.0% 29.4% 28.2% 4.0% 1.6
Six 1 3.7% 3.4% 0.8% 0.0% 1.5% 25.0% 0.2
Total 27 26.9
Luck -0.1

Montreal Impact Shots Data

For Locations Goals GoalDistr SOGDistr OffDistr BlksDistr AttDistr Finish% ExpGoals
One 4 12.1% 5.6% 1.1% 1.5% 3.0% 50.0% 2.7
Two 18 54.5% 30.6% 33.3% 25.4% 30.2% 22.5% 14.3
Three 5 15.2% 25.9% 18.9% 10.4% 19.6% 9.6% 3.1
Four 4 12.1% 18.5% 15.6% 23.9% 18.9% 8.0% 2.3
Five 2 6.1% 19.4% 31.1% 38.8% 28.3% 2.7% 1.6
Six 0 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0
Total 33 23.9
Against Locations Goals GoalDistr SOGDistr OffDistr BlksDistr AttDistr Finish% ExpGoals
One 4 12.9% 5.9% 2.8% 2.9% 3.8% 33.3% 4.0
Two 15 48.4% 39.6% 32.4% 10.3% 29.9% 16.0% 16.7
Three 5 16.1% 14.9% 9.0% 11.8% 11.5% 13.9% 2.2
Four 6 19.4% 19.8% 13.8% 26.5% 18.5% 10.3% 2.7
Five 0 0.0% 18.8% 39.3% 45.6% 34.1% 0.0% 2.2
Six 1 3.2% 1.0% 2.8% 2.9% 2.2% 14.3% 0.3
Total 31 28.1
Luck 6.2

Analysis Evolved Podcast: Episode XVI The One Where We Have A Guest

This week we welcome Chris Gluck as our first guest on the podcast. We talk possession, basic attacking principles and stats. Later on we mention #DempseyWatch. I talk about it from a Seattle perspective, discussing how the team could afford to make him the richest player in MLS history. We also talk a little Gold Cup final recap, and we wrap up the final segment with some Marrying, Boffing, and Killing of  Kris Boyd. Enjoy! [audio http://americansocceranalysis.files.wordpress.com/2013/08/asa-episode-xvi.mp3]

MLS, Home Field Advantage And Success Rates

This week on the podcast we talked a bit about home field advantage and the fact that it undeniably exists. But while it does exist, the question then becomes to what extent does it exist? What teams have taken advantage of it over the past few years? I went back as far as 2008 to collect some data. I'm not sure what this data all means, but I feel that it, at the very least, gives us a bit of commentary on those teams that had success and failure on their home pitches. I broke clubs into two groups: teams that scored more than 40 points and teams that scored less than 40 points. Then I compiled some key home stats for the two groups.

A quick key for the stats used:

%home won, is the percentage of possible games at home at the team won. Duh, right?

%home points is the percentage of its home games in which the team nabbed any points (draw or win).

%total of points is how many points from home games contributed to their total points accrued over the season.

Then GD is just goal differential.

Team Above 40 Points

2008
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
1 Houston 15 10 4 1 30 14 34 51 66.67% 93.33% 66.67% 16 13
2 Chivas USA 15 7 4 4 21 15 25 43 46.67% 73.33% 58.14% 6 -1
3 Real Salt Lake 15 8 6 1 24 10 30 40 53.33% 93.33% 75.00% 14 1
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
1 Columbus 15 11 2 2 30 15 35 57 73.33% 86.67% 61.40% 15 14
2 Chicago 15 7 3 5 23 17 24 46 46.67% 66.67% 52.17% 6 11
3 New England 15 6 4 5 24 20 22 43 40.00% 66.67% 51.16% 4 -3
4 Kansas City 15 9 4 2 22 15 31 42 60.00% 86.67% 73.81% 7 -2

Team Below 40 Points

2008
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
5 New York Red Bulls 15 9 3 3 30 20 30 39 60.00% 80.00% 76.92% 10 -6
6 DC United 15 9 2 4 32 19 29 37 60.00% 73.33% 78.38% 13 -8
7 Toronto FC 15 6 7 2 17 12 25 35 40.00% 86.67% 71.43% 5 -9
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
4 Colorado 15 7 3 5 22 14 24 38 46.67% 66.67% 63.16% 8 -1
5 FC Dallas 15 5 6 4 23 19 21 36 33.33% 73.33% 58.33% 4 4
6 San Jose 15 6 4 5 22 19 22 33 40.00% 66.67% 66.67% 3 -6
7 Los Angeles 15 6 5 4 35 27 23 33 40.00% 73.33% 69.70% 8 -7

Team Above 40 Points

2009
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
1 Columbus 15 9 4 2 25 15 31 49 60.00% 86.67% 63.27% 10 10
2 Chicago 15 5 6 4 16 17 21 45 33.33% 73.33% 46.67% -1 5
3 New England 15 7 4 4 22 16 25 42 46.67% 73.33% 59.52% 6 -4
4 DC United 15 7 5 3 19 14 26 40 46.67% 80.00% 65.00% 5 -1
POS TEAM W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
1 Houston 15 8 6 1 23 13 30 48 53.33% 93.33% 62.50% 10 10
2 Los Angeles 15 7 4 4 18 17 25 48 46.67% 73.33% 52.08% 1 5
3 Seattle Sounders FC 15 7 6 2 21 10 27 47 46.67% 86.67% 57.45% 11 9
4 Chivas USA 15 9 3 3 25 14 30 45 60.00% 80.00% 66.67% 11 3
5 Real Salt Lake 15 9 5 1 34 11 32 40 60.00% 93.33% 80.00% 23 8
6 Colorado 15 8 5 2 25 10 29 40 53.33% 86.67% 72.50% 15 4

Team Below 40 Points

2009
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
5 Toronto FC 15 8 4 3 20 14 28 39 53.33% 80.00% 71.79% 6 -9
6 Kansas City 15 4 5 6 18 20 17 33 26.67% 60.00% 51.52% -2 -9
7 New York Red Bulls 15 5 4 6 24 20 19 21 33.33% 60.00% 90.48% 4 -20
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
7 FC Dallas 15 8 4 3 28 19 28 39 53.33% 80.00% 71.79% 9 3
8 San Jose 15 6 4 5 22 21 22 30 40.00% 66.67% 73.33% 1 -14

Team Above 40 Points

2010
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
1 New York Red Bulls 15 10 2 3 18 9 32 51 66.67% 80.00% 62.75% 9 9
2 Columbus 15 10 2 3 22 12 32 50 66.67% 80.00% 64.00% 10 6
POS TEAM GP W D L F A Home Points Pts %home won %home points %total of points Home- GD GD
1 Los Angeles 15 9 2 4 27 19 29 59 60.00% 73.33% 49.15% 8 18
2 Real Salt Lake 15 11 4 0 31 7 37 56 73.33% 100.00% 66.07% 24 25
3 FC Dallas 15 8 6 1 25 13 30 50 53.33% 93.33% 60.00% 12 14
4 Seattle Sounders FC 15 8 3 4 21 16 27 48 53.33% 73.33% 56.25% 5 4
5 Colorado 15 8 5 2 26 11 29 46 53.33% 86.67% 63.04% 15 12
6 San Jose 15 7 3 5 17 14 24 46 46.67% 66.67% 52.17% 3 1

Team Below 40 Points

2010
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
3 Kansas City 15 7 3 5 25 19 24 39 46.67% 66.67% 61.54% 6 1
4 Chicago 15 4 7 4 14 13 19 36 26.67% 73.33% 52.78% 1 -1
5 Toronto FC 15 6 6 3 19 15 24 35 40.00% 80.00% 68.57% 4 -8
6 New England 15 7 3 5 21 18 24 32 46.67% 66.67% 75.00% 3 -18
7 Philadelphia Union 15 6 6 3 22 16 24 31 40.00% 80.00% 77.42% 6 -14
8 DC United 15 3 1 11 12 25 10 22 20.00% 26.67% 45.45% -13 -26
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
7 Houston 15 6 3 6 25 21 21 33 40.00% 60.00% 63.64% 4 -9
8 Chivas USA 15 6 2 7 19 19 20 28 40.00% 53.33% 71.43% 0 -14

Team Above 40 Points

2011
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
1 Sporting Kansas City 17 9 6 2 29 51 52.94% 88.24% 56.86% 10
2 Houston Dynamo 17 10 4 3 33 49 58.82% 82.35% 67.35% 4
3 Philadelphia Union 17 7 9 1 22 48 41.18% 94.12% 45.83% 8
4 Columbus Crew 17 9 5 3 30 47 52.94% 82.35% 63.83% -1
5 New York Red Bulls 17 8 6 3 27 46 47.06% 82.35% 58.70% 6
6 Chicago Fire 17 6 8 3 21 43 35.29% 82.35% 48.84% 1
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
1 LA Galaxy 17 12 5 0 36 67 70.59% 100.00% 53.73% 20
2 Seattle Sounders FC 17 9 4 4 31 63 52.94% 76.47% 49.21% 19
3 Real Salt Lake 17 10 4 3 33 53 58.82% 82.35% 62.26% 8
4 FC Dallas 17 9 3 5 32 52 52.94% 70.59% 61.54% 3
5 Colorado Rapids 17 6 9 2 20 49 35.29% 88.24% 40.82% 3
6 Portland Timbers 17 9 3 5 32 42 52.94% 70.59% 76.19% -8

Team Below 40 Points

2011
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
7 DC United 17 4 8 5 20 39 23.53% 70.59% 51.28%
8 Toronto FC 17 5 8 4 23 33 29.41% 76.47% 69.70%
9 New England 17 4 6 7 18 28 23.53% 58.82% 64.29%
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
7 San Jose 17 5 8 4 23 38 29.41% 76.47% 60.53%
8 Chivas USA 17 5 5 7 20 36 29.41% 58.82% 55.56%
9 Vancouver Whitecaps 17 6 5 6 23 28 35.29% 64.71% 82.14%

Team Above 40 Points

2012
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
1 Kansas City 17 10 4 3 22 12 33 63 58.82% 82.35% 52.38% 10 15
2 DC United 17 12 4 1 37 17 37 58 70.59% 94.12% 63.79% 20 10
3 New York Red Bulls 17 11 4 2 34 18 35 57 64.71% 88.24% 61.40% 16 11
4 Chicago 17 11 3 3 27 18 36 57 64.71% 82.35% 63.16% 9 5
5 Houston 17 11 6 0 31 12 33 53 64.71% 100.00% 62.26% 19 7
6 Columbus 17 11 3 3 28 21 36 52 64.71% 82.35% 69.23% 7 0
7 Montreal Impact 17 10 3 4 31 19 34 42 58.82% 76.47% 80.95% 12 -6
POS TEAM P W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
1 San Jose 17 10 6 1 43 22 31 66 58.82% 94.12% 46.97% 21 29
2 Real Salt Lake 17 11 2 4 27 15 37 57 64.71% 76.47% 64.91% 12 11
3 Seattle Sounders FC 17 11 2 4 27 11 37 56 64.71% 76.47% 66.07% 16 18
4 Los Angeles 17 10 1 6 31 20 36 54 58.82% 64.71% 66.67% 11 12
5 Vancouver Whitecaps 17 8 6 3 25 17 27 43 47.06% 82.35% 62.79% 8 -6

Team Below 40 Points

2012
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
8 Philadelphia Union 17 7 2 8 22 20 23 36 41.18% 52.94% 63.89% 2 -8
9 New England 17 7 6 4 23 15 27 35 41.18% 76.47% 77.14% 8 -5
10 Toronto FC 17 3 5 9 15 25 14 23 17.65% 47.06% 60.87% -10 -26
POS TEAM GP W D L F A Home Points Total Points %home won %home points %total of points Home- GD GD
6 FC Dallas 34 6 8 3 21 16 26 39 17.65% 41.18% 66.67% 5 -5
7 Colorado 34 8 3 6 29 19 27 37 23.53% 32.35% 72.97% 10 -6
8 Portland Timbers 34 7 6 4 24 21 27 34 20.59% 38.24% 79.41% 3 -22
9 Chivas USA 34 3 3 11 9 30 12 30 8.82% 17.65% 40.00% -21 -34

2008-2012 average, Teams above 40 points

avg HP avg TP avg %HW avg %HP avg %of TP
25 41 46.10% 68.59% 50.56%

2008-2012 average, Teams below 40 points

avg HP avg TP avg %HW avg %HP avg %of TP
24 35 37.67% 67.26% 71.09%

There are a few things that I think you could really take from this, starting with A) there is so much parity in this league that it doesn't matter if you are a good team or a bad, a game at home should give you a lot of confidence. B) Good teams, play-off teams, teams who want a chance at the Supporters Shield ... they win road games. The teams above 40 points and teams below 40 points are only separated by their percentage of total points won at home. Meaning that teams above 40 points only took 50% of their teams total points from home games; they won on the road, too. Where as teams that struggled depended on those home games to make them suck less.

Good teams win at home, but so do bad teams... unless you are DC United. Sorry, Drew.

Field Dimension, Turf and Home Field Advantage in MLS

During last weekend’s podcast, we discussed home field advantage and where it might come from. There is much literature to suggest that home field advantage comes largely from rowdy home crowds—crowds that both encourage the home team to be more aggressive and encourage the referees to be more biased—but you probably already presumed that. We went on to talk about “home specialists,” or teams that play especially well at home in a given season. An article on the site The Power of Goals theoretically explains why home specialists from any single season tend to be products of statistical noise rather than signal. That’s not to say there aren’t home specialists out there, only that it is nearly impossible to identify them statistically in a single season.

Picking out the teams that have performed markedly better at home, and then retroactively seeking explanations to match the traits of those teams is known as cherry picking, and it’s likely to lead to false conclusions (On the podcast, I recounted an example from the book Naked Statistics by Charles Wheelan as to why this can lead to trouble). Instead, identifying traits of teams and stadia first, and then checking for measurable differences in home performance based on those traits is a more sound approach.

We have mentioned around here before that Houston’s narrow home pitch might have helped the Dynamo to one of the best home records since BBVA Compass Stadium was built in preparation for the 2012 season. Indeed, Houston’s field is the narrowest in the league at 70 yards, and the Dynamo’s home goal differential is a whopping 1.33 goals better at home than on the road. However, the only reason we considered field dimensions was because Houston has performed so well at home.

We went like this:

Extreme split for Houston --> Field Dimensions must matter

But we should have thought like this:

Field Dimensions --> Extreme splits?

To advance the discussion, I gathered data going back to the 2010 season in order to look for explanatory patterns in two observable variables of stadia: field dimensions and surface. If teams are able to train on especially large or especially small fields, or on turf, such differences in the pitches may give home teams a leg up in matches played on those familiar pitches.

It turns out there is not enough evidence that either turf surfaces or field dimensions have much to do with home success.

Surface vs. Home success

There are currently four teams that play on turf: Portland, Seattle, Vancouver and New England. While the Timbers and Whitecaps have dominated at home, The Sounders and Revs have been sub par relative to the league in that department. Considering I didn’t account for the confounding variable that two of these teams play in front of some of the rowdiest fans in MLS, the “turf effect” may not even exist at all. It’s hard to say with only four teams playing on turf, three of which are not even in their adolescence as franchises.

Width vs. Home success

Field dimensions showed minimal effects, as well. Though Houston’s small, 70-by-115-yard pitch has correlated with its home success, that correlation is not true of other small stadia. The next-smallest stadium can be found in Washington D.C.,** but DCU has actually performed a little worse at home relative to the typical league splits. Montreal has the widest pitch at 77 yards, and yet, also has performed well. There is a chance that teams with extreme widths—extremely narrow or extremely wide—have some sort of advantage, but we’re going to have to wait for additional data from Houston and Montreal to be more definitive about that.

The vast majority of MLS pitches, 16-of-19 in fact, are either 74 or 75 yards wide. So even the two extremes in Houston and Montreal are not all that different. Houston could be a team built to play on a narrow pitch, but I’m skeptical that A) Soccer Analytics have come far enough for a general manager to sort that out and B) that 4-5 yards would make such a big difference.

Though I can’t say for sure that the pitch effects are non-existent, I can say pretty confidently that they aren’t pronounced or noticeable in a single season. Right now, I would argue it’s more likely that Montreal and Houston have performed so well at home due to the random variation of only two seasons of data. We will have to wait another few seasons to check on that one.

*Vancouver plays on Astroturf while the other three play on Field Turf.

**DCU’s field at RFK is a little wider at 72 yards, and actually a little shorter at 110 yards. 

PDO: Week 22 Rankings

I dropped the ball a bit last week not updating the tables. Here is how they look as of this past weekend's results.

Team Shots Against GA Sv% SoT GF SH% TSR Points Games PPG PDO
Portland Timbers 89 20 77.53% 101 30 29.70% 0.532 34 21 1.62 1072
New England Rev. 85 19 77.65% 84 22 26.19% 0.497 30 21 1.43 1038
New York Red Bulls 92 27 70.65% 88 29 32.95% 0.489 35 22 1.59 1036
Houston Dynamo 83 20 75.90% 81 22 27.16% 0.494 30 20 1.5 1031
Salt Lake 102 24 76.47% 121 32 26.45% 0.543 37 22 1.68 1029
Dallas 109 27 75.23% 98 27 27.55% 0.473 32 21 1.52 1028
Vancouver Whitecaps 92 29 68.48% 98 32 32.65% 0.516 32 21 1.52 1011
Philadelphia Union 97 30 69.07% 102 32 31.37% 0.513 34 22 1.55 1004
Seattle Sounders FC 80 22 72.50% 76 21 27.63% 0.487 28 19 1.47 1001
Colorado Rapids 92 24 73.91% 91 23 25.27% 0.497 34 23 1.48 992
Montreal Impact 92 29 68.48% 105 31 29.52% 0.533 35 20 1.75 980
Columbus Crew 99 27 72.73% 94 23 24.47% 0.487 23 21 1.1 972
Kansas City 63 21 66.67% 103 29 28.16% 0.620 36 22 1.64 948
San Jose Earthquakes 109 33 69.72% 87 21 24.14% 0.444 27 22 1.23 939
CD Chivas USA 118 37 68.64% 69 17 24.64% 0.369 17 21 0.81 933
L.A. Galaxy 76 27 64.47% 108 30 27.78% 0.587 33 22 1.5 923
Toronto FC 77 29 62.34% 69 17 24.64% 0.473 17 21 0.81 870
Chicago Fire 85 30 64.71% 103 20 19.42% 0.548 25 20 1.25 841
DC 93 35 62.37% 62 8 12.90% 0.400 10 21 0.48 753

Again, Portland, even with their loss, retains their title as the luckiest club in MLS by PDO*. Meanwhile, New England continues to mystify as they pretty much pulled that win together with duct tape, spit and some wood glue. Is Jay Heaps really Macgyver? I'm going to guess no, though as we talked about on the podcast, home field advantage not only helps to place pressure on the ref, but it may also encourage more aggression from the home side. One can only wonder if Jay Heaps is able to simulate this effect with a stirring pep talk prior to the match against a terrible team on the road.

However, just like how Chivas and Toronto have been largely unaffected this season, likely due to some terrible play and a limited talent base, you have to wonder if we are seeing many of these clubs performing at their true rates. I don't think you can completely attribute RSL's finishing success to luck when defensively they have some great pieces and offensively they, again, have some great pieces.

As we watch the year unfold it's going to be rather interesting to see where these clubs end up with playoff spots at seasons end.

 

*PDO here is based on shots on target, not total attempts. 

A Look At The Weekend Performances Within the Final Third

I just recently discovered that the Golazo web application---with up-to-the-minute statistical game information courtesy of MLSsoccer.com---provides details for teams in the final third. Sadly enough they don't keep the data available for long so I took advantage of a late evening to tally up the following numbers over the nine MLS matches this weekend. Below are the passing rates for each team in the final third of the pitch.

 Team Pass Completions Pass Attempts Pass Percentages Opponent
Union 68 105 74.20% Vancouver
RSL 68 98 72.40% NYRB
NYRB 89 131 69.10% RSL
San Jose 60 97 67.50% Portland
Houston 92 141 67.30% Chicago
Portland 91 146 66.40% San Jose
New England 72 121 66.20% DC
DC 69 115 64.10% New England
Sporting KC 75 121 63.50% Montreal
Colorado 64 113 63.30% LA Galaxy
LA Galaxy 69 121 63.20% Colorado
Toronto FC 85 137 62.90% Columbus
Columbus 54 107 62.50% Toronto FC
Seattle 67 117 61.50% Chivas
Chicago 57 103 61.10% Houston
Vancouver 51 102 55.30% Union
Chivas 54 108 52.00% Seattle
Montreal 31 80 50.80% Sporting KC

There really isn't much in the way of true context at this point. Some of this is information is still about style rather than performance, but comparing the results and then applying some added data will be interesting. I wonder what, if anything, can be determined by this type of data.

I'd love to hear inputs from all you smart people.