ASA Spring MLS Top-50 Ranking: 40-31

By Harrison Crow (@harrison_crow)

Yesterday, I introduced the who, what, where and why of this ranking, which was culled from the ballots of team front office personnel, MLS players, journalists, league analysts and other MLS experts. I'm sure there are going to be some disagreements, so hit us up on twitter or leave a comment below.

Check back later in the week for the rest of the rankings:
Monday: 50 through 41
Tuesday: 40 through 31
Wednesday: 30 through 21
Thursday: 20 through 11
Friday: 10 through 1

----------------------

ExpG: Expected Goals according to our player rankings
ExpA: Expected Assists
ExpSaves: Expected Goals Against minus Actual Goals Against according to our goalkeeper rankings.
Touch%: Percent of team's touches while on the field
TxGp90: Total Expected Goals per 90 minutes
Shots Created: Shots + Key Passes

40. Wil Trapp - Midfielder, Columbus Crew
Total Score: 228

39. Ignacio Piatti - Midfielder, Montreal Impact
Total Score: 243

38. Perry Kitchen - Midfielder, DC United
Total Score: 250

37. Graham Zusi - Midfielder, Sporting KC
Total Score: 253

36. Blas Perez - Forward, FC Dallas
Total Score: 279

35. Dillon Powers - Midfielder, Colorado Rapids
Total Score: 285

34. Matias Laba - Midfielder, Vancouver Whitecaps
Total Score: 293

33. Chad Marshall - Defender, Seattle Sounders
Total Score: 312

32.  Osvaldo Alonso - Midfielder, Seattle Sounders
Total Score: 316

31. Ike Opara - Defender, Sporting KC
Total Score: 327

ASA Spring MLS Top-50 Ranking: 50-41

By Harrison Crow (@harrison_crow)

Earlier today, I introduced the who, what, where and why of this ranking, which was culled from the ballots of team front office personnel, MLS players, journalists, league analysts and other MLS experts. I'm sure there are going to be some disagreements, so hit us up on twitter or leave a comment below.

Check back later in the week for the rest of the rankings:
Monday: 50 through 41
Tuesday: 40 through 31
Wednesday: 30 through 21
Thursday: 20 through 11
Friday: 10 through 1

----------------------

ExpG: Expected Goals according to our player rankings
ExpA: Expected Assists
ExpSaves: Expected Goals Against minus actual Goals Against according to our goalkeeper rankings.
Touch%: Percent of team's touches while on the field
TxGp90: Total Expected Goals per 90 minutes
Shots Created: Shots plus Key Passes

50. Tyler Deric - Goalkeeper, Houston Dynamo TOTAL SCORE: 160

49. Ethan Finlay - Right Midfielder, Columbus Crew
Total Score: 161

48. Juninho - Central Midfielder, LA Galaxy
Total Score: 169

47. Felipe Martins - Attacking Midfielder, New York Red Bulls
Total Score: 171

46. Chris Tierney - Left Fullback, New England Revolution
Total Score: 172

45. Justin Meram - Left Midfielder, Columbus Crew
Total Score: 174

44. Matt Hedges - CentERback, FC Dallas
Total Score: 184

43. Kendall Waston - Centerback, Vancouver Whitecaps
Total Score: 188

42. Gyasi Zardes - Forward, LA Galaxy
Total Score: 189

41. Chris Wondolowski - Forward, San Jose Earthquakes
Total Score: 223

ASA Spring Top-50: An Introduction

by Harrison Crow (@Harrison_Crow)

Today is an exciting day. Later this afternoon we have the distinct privilege to unveil our first quarterly* Major League Soccer Top-50 player ranking. We sent out 40 ballots to team front office personnel, MLS players, journalists, league analysts and other MLS experts. It is a diverse group that encapsulates all perspectives of MLS analysis; including those working behind-the-scenes trying to improve their clubs, players on the field, journalists and analytically focused writers. These are people that know the league better than anyone else, and it's because they live and breathe MLS.

Ranking anything is a delicate and sometimes obtuse task. We're not usually into doing things that are subjective and rather opaque in process but the results seemed to be worth stepping outside of our normal domain to achieve. 

We set before our participants a listing of 346 unique individual players from around the league. The criteria for that pool was a minimum of 45 minutes played by April 15th, roughly 15% into the season. Because of our criteria, players such as Diego Valeri, Jermaine Jones and Mike Magee were not available for selection. Though, and this is purely based upon the amount of e-mails that I received from those submitting ballots, I'm pretty certain all would have ended up high on this list.

The hardest thing about trying to establish who is best when populating a list such as this is figuring out how you really define "best". I think this was the hardest thing for our group to really attempt to discern as they conducted their own ballot.

Instead of me trying to encapsulate the entire group's thought process behind how they filed their ballot, here are a few quotes behind various submissions.

"I put goal-scoring at a premium, and that includes setting up goals"

"My methodology in the rankings as I looked through the players was, "Would you rather have Player X or Player Y?" Then I would rank them accordingly."

"I fused production, expected production, raw talent and health. ... The game is still about goals, and the guys who create and or score them are the most talented players on the pitch."

"I imagined it as one giant MLS draft"

"This was me hunting through some of the stats I think are valuable and combining that with the eye test to this point in the season"

Our calculation of the rankings was simple. Each submission had 50 placements and each place corresponded with a reverse point score. First place earned 50 points, second place earned 49 points, ect, All counted, 176 of 346 individuals received at least a point, with 39 points needed to even break into the top-100.

Suffice to say this wasn't a clear cut journey. There was deliberation, time taken to think through each decision and in the end not everyone agrees on every player. I think that's okay as this isn't necessarily a fact based adventure but a conversation starter.

It's very hard and nearly impossible to come up with a single metric in this sport that properly measures all the events across the pitch to compare players. This endeavor can help us attempt to bridge this gap in identifying difference makers albeit in a bit of a crude manner. I think with the right people involved with this project, it helps push us towards at least identifying who those players are.

I want to thank all of those that participated in this project and were gracious with their time. I hope that this is just our first go round and that we can expect this to serve as a barometer for players in MLS in the future. 

*Expect our next rankings to be released in August.

The state of MLS Goalkeeping

By Bill Reno (@letsallsoccer)

For those unfamiliar with ASA’s goalkeeping stats, the long explanation can be found here. But the short of it is that the “G - xG” stat column, Goals Allowed Minus Expected Goals, is how many additional goals goalkeepers are giving up versus expectation. A negative number means they are allowing fewer goals than expected, saving their team that many goals relative to an average keeper, while a positive number means they aren’t performing up to the standard MLS goalkeeper and are leaking easy goals. While the statistics do not include extracurricular activities (crosses, distribution, cutting off through balls, major blunders), they still represent an objective look at how MLS goalkeepers are performing this year specific to shot stopping.

It’s no secret MLS goalkeepers have struggled this year. Nearly every goalkeeper has given up some hair-pulling, eye-rolling goal at some point in the season. While the errors have several teams scrambling for a starter who can handle the workload, the shot stopping hasn’t been an issue for most teams. Really, there’s only five-ish goalkeepers that have struggled with the main part of goalkeeping. The other 27 haven’t completely crippled their respective teams.

Keeper	Team	Min	SOG	GA	xGA	GmxG
Bill Hamid	DCU	670	35	5	8.92	-3.92
Clinton Irwin	COL	963	37	9	11.94	-2.94
David Ousted	VAN	1064	38	9	11.87	-2.87
Jaime Penedo	LA	766	42	8	10.48	-2.48
Bobby Shuttleworth	NE	954	34	10	12.17	-2.17
David Bingham	SJ	965	43	10	11.92	-1.92
Jeff Attinella	RSL	191	19	5	6.89	-1.89
Adam Larsen Kwarasey	POR	957	32	9	10.44	-1.44
Josh Saunders	NYC	861	40	11	12.06	-1.06
Nick Rimando	RSL	776	21	5	5.99	-0.99
Stefan Frei	SEA	860	45	9	9.83	-0.83
Andrew Dykstra	DCU	157	10	2	2.62	-0.62
Tyler Deric	HOU	1063	45	13	13.33	-0.33
Travis Worra	DCU	39	1	0	0.04	-0.04
Ryan Meara	NYC	95	7	1	1.04	-0.04
Jon Busch	CHI	196	9	3	2.96	0.04
Brian Sylvestre	PHI	94	10	3	2.92	0.08
Eric Kronberg	MTL	97	3	2	1.92	0.08
Steve Clark	CLB	856	37	10	9.91	0.09
Tim Melia	SKC	193	5	1	0.86	0.14
Chris Konopka	TOR	195	6	2	1.82	0.18
Evan Bush	MTL	382	20	6	5.69	0.31
Sean Johnson	CHI	571	20	7	6.64	0.36
Luis Robles	NYRB	858	31	9	8.53	0.47
Dan Kennedy	FCD	288	10	3	2.38	0.62
Brian Rowe	LA	286	8	3	2.36	0.64
Rais Mbolhi	PHI	486	19	9	8.33	0.67
Chris Seitz	FCD	672	27	10	8.17	1.83
John McCarthy	PHI	474	19	9	6.91	2.09
Donovan Ricketts	ORL	866	31	12	9.52	2.48
Joe Bendik	TOR	574	36	11	8.11	2.89
Luis Marin	SKC	771	30	11	7.97	3.03

At first, 32 seemed like a lot of goalkeepers to be used this early into a season. Exactly half the teams in the league have used at least two goalkeepers, and that's before completing even a third of the season! Over the last four years, MLS has seen forty different goalkeepers on average, so if anything we should be expecting to see even more by the end of the year, especially if certain starters keep up their folly rates. It’s not completely reflective of ability, but to paint a clearer picture, here are the five worst games by MLS goalkeepers so far.

1. Rais M’Bolhi vs Sporting Kansas City - The last time M’Bolhi started for Philadelphia, he proved that the strategy of “letting more goals go in to forget the last one” doesn’t really hold up. M’bolhi managed to jump under a cross for the first goal, then do an Oscar-winning impersonation of a traffic cone for the final six minutes of stoppage time, turning a 2-1 win into a 3-2 loss.

2. Sean Johnson vs San Jose - A rough twenty minutes to start the game, to say the least. A cross is floated in and Johnson’s three inch vertical isn’t enough to punch it out. If that wasn’t bad enough, he follows that goal by muffing a soft bouncer, which of course San Jose pounced on.

3. Chris Seitz vs Portland - A game that easily could be number one if his pass back to Portland wasn’t to an offside player. After successfully dodging a bouncing shot for a goal, he whiffs on the skate save in the closing minutes. To be fair, Portland should have won the game, but Seitz didn’t really help the FC Dallas cause in this game.

4. Joe Bendik v Chicago - Three times Bendik can’t get down fast enough. While they’re all hit with pace, you have to think it’s going to be a task for Bendik to earn the starting spot back once he’s 100% healthy again.

5. John McCarthy v Columbus - Fortunately the scoreline wasn’t close for the Union rookie. On the opening goal, McCarthy over-anxiously rushes the shooter, and then scrambles on the third goal to find proper technique. McCarthy will be fine down the road, but this is one game he’ll want to learn from.

It’s easy to single out the worst games, but it’s not like the rest of the crop have been great. Bingham handed over on a late goal early in the season and Hamid did the same thing more recently. Kwarasey globetrotted a ball to Dempsey for a goal. Shuttleworth misplayed a trickling ball. Deric shot on his own goal. Ricketts was caught not paying attention. Busch essentially own goals a shot. Kennedy seemed more interested in tackling than saving. Penedo is starting to show his age. Marin has looked pretty clueless at times. Robles was yet another goalie to struggle with a cross. I don’t know what Saunders was expecting on this goal. Rimando did the infamous dive-out-of-the-way dive. And of course Meara pulled out some acrobatics to concede this goal.

Sure, this is a hyper focus on goalkeepers’ worst moments this season. We’ve seen several stellar saves - looking at you, David Ousted - but the excessive amount of goalkeeping errors really makes you wonder how far MLS has come from when Beckham was taking advantage of goalkeepers in the late 2000s.

To end on a positive note, there is still a race for goalkeeper of the year going on. Last month I wrote that it was Bill Hamid’s award to lose, and I now I am already backtracking on that statement. David Ousted has brought his A-game this year and is now in the driver’s seat. Another month into the season and we’re dropping the ASA GOTY Power Rankings from ten spots down to nine.

1. David Ousted - Vancouver only has a two goal differential and thanks in large part to Ousted. How long can he keep it up?

2. Stefan Frei - Easily would win “most improved” over the last two years. If Seattle makes a deep run, Frei could bump to number one with some hardware.

3. Bobby Shuttleworth - Struggled last game (see above) but his hair could propel him to the top.

4. Luis Robles - Red Bulls need a good goalkeeper more than Robles needs the award so he’ll get plenty of chances to earn it.

5. Bill Hamid - Signs are pointing to a transfer but if he sticks around the USMNT tag will put him in the discussion alone.

6. Steve Clark - Underplaying his talent so far and should bump up later in the season.

7. Nick Rimando - Incredibly unlikely, but “hey, it could happen.”

8. Clint Irwin - Fought off Zac MacMath for the starting spot and now is looking like his old 2013-self.

9. Tyler Deric - Erratic as all get out, but if he put it together he has the talent to contend.

Toronto FC: Road Warriors

By Matthias Kullowatz (@mattyanselmo)

Team	xPoints
TOR	73.8
SEA	52.2
MTL	52.0
SJ	50.6
NYRB	49.1
NE	47.9
COL	47.5
VAN	46.1
CLB	46.1
SKC	44.5
FCD	43.1
LA	42.5
ORL	42.2
POR	41.6
RSL	40.1
DCU	39.2
PHI	38.6
NYC	38.6
CHI	38.0
HOU	37.1

I thought my computer had spit out an error when it told me Toronto FC was the best team in MLS. To the right you can see the power rankings that I was too scared to publish in their typical location without an accompanying article. These are the number of points teams would be expected to earn if the 34-game season started today and each team played a balanced schedule. Toronto may or may not be one of the best teams in MLS, but here's why the computer thinks so.

After last weekend's 1 - 0 win in Philadelphia, Toronto finally completed its seven-game road trip to start the 2015 campaign, a difficult way to start the season which was necessitated by construction to expand BMO Field. That type of road trip typically only happens in MLB or the NBA if the rodeo is in town. The model gives teams bonuses when they have played fewer than half their games at home, assuming that, had they gotten more home games, their expected goals stats would be better. 

While it's a bit crazy to think that Toronto will break the MLS points record with more than 70, it's not crazy to think that maybe they're even better than you, our readers, thought when you ranked them second in the East. Toronto is, after all, fifth in the league in expected goal differential (xGD) despite the fact that--as mentioned before--it hasn't played a single home game. 

Let's play around with some more-intuitive math. In the past five seasons, home teams have outscored away teams by an average of 0.41 expected goals, and this season Toronto has outscored its opponents by an average of 0.18 expected goals per game. If we give Toronto a 0.82 xGD swing, weighted over 3.5 games, then their xGD jumps to 0.59. That would rank them first this season, and either first or second in each of the previous four seasons. 

Toronto is an outlier in both not having played any home games, and having played fewer games than most teams overall. This tends to break regression models. You might notice that the Montreal Impact is also toward the top of the rankings, and not surprisingly, they have played just one home game (25%) and only four total games. Small sample sizes, relative to the rest of the league, are more likely to create outlying results, and that's why the computer is insanely high on those two Canadian clubs. That said, Toronto has put together a very impressive season thus far, even if it doesn't look like it in the standings, and I think it justifies our readers' beliefs that Toronto would be good in 2015. 

The Weekend Kick-off: Orlando City Are Good, New England Is (Probably) Better

by Harrison Crow (@Harrison_Crow)

I'm not sure that many of us knew what to make of Orlando City prior to this season. Sure, they spent some money and got some interesting talents but that doesn't always translate to something or even anything, ask Toronto.  But Kaka has turned out to be maybe the best player in Major League Soccer, Aurélien Collin is still a very good defender and it turns out that Kevin Molino (before ripping his ACL in half) was an above average MLS winger.

Right now, Orlando City looks like a potential playoff team. The big difference between them and, say, the LA Galaxy who are actually sitting in a playoff position is Orlando's horrible home record. Which is all kinds of crazy considering they're sitting second in home attendence with over 39,000.

You have to think that if they can unearth a striker--or maybe just borrow some of Phil Rawlings millions to buy one--who can take advantage of all the chances that Ricardo creates, Orlando may just be a formidable team come the end of this season. Also there is a chance that I might have "borrowed" bath salts from Rafael Ramos.

New England is picking right up where they left off last season. But this time they're doing it in a bit more sustainable fashion. Currently the Red, White and Blue are posting great expected goal numbers in both zero-gamestate situations and overall.

Lee Nguyen isn't overperforming his expected goal numbers astronomically like he did last season, but the Revs don't need him to do that this go-round. Kelyn Rowe is having a break out season, Chris Tierney stepping up to create goal scoring opportunities and Juan Agudelo (who is 3rd in expected goals) is everything they needed him to be last year to be when he went on his sabbatical to the Netherlands, driving a camper between Utrecht and various Scottish cities along the border of England with the hope some magic gate would open to... Stoke (I might have vomitted in my mouth a bit typing that).

New England has seen a once mighty generation come and go with little-to-nothing to show for it. This time around their window is open with the pieces needed to start to fill that trophy case that sits right next to the case of deflated balls that Tom Brady (the greatest of all time! I lost a bet. Don't ask.) has attempted to hide.

The real question is will they be able to keep their expected goals against so low? Don't misunderstand me; it's not as if we're really expecting regression with opposing clubs and their scoring rates. New England is still sitting in the middle of the pack, but considering how horrible they were last year (in the bottom third) and with losing perhaps their top defender... it's just a head-scratcher that they've improved in this manner. I'm going to chalk it up to Jay Heaps and his transition from the Just for Men to Just for Men Touch of Gray.

On a positive note, the team appears set to bring in local boy Will Packwood from Birmingham, England on a discovery claim (assuming they do have sole custody of his rights) which should help supplement a defense that at times has looked unsettled and needing some... well something better. Let's just see how this plays out, Cotton.

Prediction: I'm going with New England on the road with the win.

FANTASY PERSPECTIVE

ORLANDO CITY

Kaka (20.6% Selected, $11.4 Cost)

Ugh, the guy is the best player in the league. The best. Forget what he costs. Best. Player.

Rafael Ramos (12.2% Selected, $5.2 Cost)

So long as you're fine with the unmedicated outbursts that result in red cards from time to time burning your fantasy club to the ground, I'm sure you'll get along fine.

NEW ENGLAND

Bobby Shuttleworth (6.1% Selected, $5.6 Cost)
I think it's funny how much I get bent out of shape about the disparity between the cost of keepers and their points versus any other position. Most people look at Shuttleworth and think "5.6, WHOA, not made of money here" but really considering what any keeper provides versus their costs. There seems to be one right answer.

Lee Nguyen (4.7% Selected, $11.1 Cost)

He's still really good. Probably not $11 good. But good enough to where it might draw a few suckers.

THE WEEKEND MATCH-UPS

(expected goal differential in even game-states)

FRIDAY

New England Revolution (0.43) @ Orlando City SC (0.06)
Predictions: NE

San Jose Quakes (-0.05) @ Colorado Rapids (-0.30)
Predictions: Draw?

SATURDAY

Real Salt Lake (-0.35) @ Chicago Fire (-0.28)
Predictions: Draw??

Portland Timbers (0.12) @ Montreal Impact (-0.10)
Predictions: Draw???

Sporting KC (0.71) @ DC United (-0.46)
Predictions: SCREW YOU, DCU... I'm going with SKC

Philadelphia Union (-0.26) @ Vancouver Whitecaps FC (-0.06)
Predictions: VAN

Seattle Sounders FC (0.88) @ Columbus Crew SC (0.23)
Predictions: Draw

LA Galaxy (0.06) @ FC Dallas (0.14)
Predictions: FCD

SUNDAY

Houston Dynamo (-0.14) @ Toronto FC (0.13)
Predictions: TFC

New York City (-0.48) @ New York Red Bulls (0.11)
Predictions: NYRB

NERD IMAGERY

I've been saving this for weeks...it's all for you, Toronto.

MLS Roster Rules: The Thorough Examination of A Discovery Claim

By Harrison Crow (@harrison_crow)

Two nights ago we burned some midnight oil and recorded our latest podcast talking about changes being made with the release of the new MLS roster rules. Leave it to Bruce Arena to act on these things the very next day.

Ives Galarcep and Goal.com broke down the story and provided most pertinent details as former US youth international and“West Ham” (mostly seeing time on the reserve squad) midfielder, Sebastian Lletget, is “set to sign” with LA Galaxy. Additionally, if the article is correct, the New England Revolution had a discovery claim on him and will receive $50,000 in allocation compensation as a result.

You may be asking yourself “why does this matter?”, and it's a good question. I think it’s important to highlight a few interesting pieces of information as a result of this maneuver we now understand a bit better.

1. Bruce Arena continues to use the mechanisms at his disposal.

First understand there is only one mechanism at play. Lletget, despite being a former member of the US U-23’s, wasn't acknowledged with the allocation roster, making the attacking midfielder free game for discovery lists, which is where this business with New England enters. The Revolution received a $50,000 allocation because they submitted their bingo card discovery list correctly (I can’t imagine what they’d do with another attacking midfielder).

This isn't anywhere close to New York giving up Eric Alexander and Ambroise Oyongo for the rights to Sascha Kljestan and the reclamation project Felipe Martins (seriously, what a freakin’ coup). We don’t’ know what percent of their allocation budget LA gave up for him, but $50,000 in allocation relative to the total budget or salary cap isn't much. When you consider the minimum youth contract is now $60,000 for a roster spot of player 18-25 it further puts things in positive perspective.

Additionally, we now have some sort of idea how these discovery claims will work too. It’s not as if we didn't have an idea of how this worked before, but it brings more transparency to the process and how it might continue to work in the future relative to these types of situations.

The questions continually ringing in my head are as follows; does this mean LA Galaxy could only sign him because he was on their discovery list? Did they have had to submit a discovery claim on him before signing him? Lastly, if LA used a discovery claim on him does mean they only have six left or do they now gain back a spot Lletget once occupied on their claim list?

These are mostly all questions which are not helpful and ones having little real bearing in the long run. We have no idea of how to answer any of them and we’ll probably never really get straight answers on it but there are interesting thoughts that, if patient, we may see indications in the future.

2. New England randomly gains an additional $50,000 in allocation

As I already mentioned, New England gets paid for pretty much just putting the right guy on the list. Unless they think this off-season they’re going to get reasonable bid offers for Kelyn Rowe or they aren't willing to reward Lee Nguyen with a designated player contract (yep, I believe it’s going to be a thing), then Lletget probably wasn’t going to be of any use to them. They also still have Steve Neumann (picked fourth overall in 2014 MLS Superdraft) who has largely been an afterthought during his short MLS tenure and could also potentially fit into an attacking midfield role.

LA got a good deal, dispensing little in compensation compared to what other teams have surrendered in the past. This wasn't completely one sided as New England also benefited on the fact it had little immediate use for the attacking midfielder and have time to replace him on their discovery lists while earning a bit of monopoly money in the process.

3. There are still US players on the market not listed in the allocation roster.

During the podcast we referenced the subjective nature involved in identifying those on the allocation roster and those who were not mentioned. Three that did not make the list are Alfredo Morales, Will Packwood and Zarek Valentine.

Morales still with FC Ingolstadt 04 in German Division II soccer and could be an interesting snag for a few different MLS teams that need a boast to their central midfield or depth out wide.

Packwood is currently training with New England and could sign soon. The $50,000 discovery claim allocation payment for Lletget should come close to covering the majority of his salary which just makes things seem that much better for New England.

Valentin is an interesting one as he left MLS at the end of his Generation Adidas contract for Norway, but since joining FK Bodø/Glimt it would seem that he’s been challenged for time on the pitch. Mind you I don’t know a lot about what’s going on in Norway and some of that might be due to injuries but he could be sly candidate for a return to MLS.

When to park the bus in MLS

By Kevin Minkus (@kevinminkus)

Should teams park the bus? When?

Goals change games. Garry Gelade recently wrote two excellent pieces on this phenomenon (found here and here). One of his key findings is that teams that are down a goal increase their shooting rates to try to make up the deficit, while teams that are ahead take fewer shots. The thinking goes that teams that are ahead can afford to let up on the attack in order to better maintain defensive shape, and thus give up fewer high quality chances to their opponents. In other words, they park the bus. Whether this is a sound strategy remains an open question, and, if it is, how early is too early to do it?

As an example, here is what the 2014 Crew looked like in terms of shots when behind, tied, and ahead (hat tip to Garry, once again, for the excellent way to visualize this):

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Let me know on twitter if you'd like to see a different team's graph for any season from 2011 to 2015 - @kevinminkus.

As you can see, Columbus shot less frequently when in the lead. This is a pretty typical trend.

Using logistic regression, we can evaluate the effect of shots and shot quality on a leading team's chances of conceding the next goal. The model I've built, like Garry's, breaks down a game into a sequence of game states. The game begins at 0-0, and each time a goal is scored, a new game state segment begins. My model takes as inputs the number of shots the leading team takes, and the average quality of those shots (using the site's expected goals model) during a segment. It then outputs the probability of that team conceding the next goal.

In general, teams that shoot more are less likely to concede the next goal in a game. Teams that take better shots are also less likely to concede the next goal. If we include only situations where a team is up by one goal, the same results hold. However, if we only look at time frames towards the end of games and where teams are up by one goal- situations where parking the bus would be appropriate- things change. 

To examine the problem this way, I've built separate models using data filtered by when each segment begins. I've filtered the data this way since I'm hoping to answer the question of when a team should start to go into a defensive shell. Using the start time of the segment, I think, is a good though not perfect proxy for this. For example, then, to see whether parking the bus is a good tactic up a goal after 70 minutes, the model is built using data from game segments which begin on or after the 70 minute mark. Note that as a point of interest I've also included whether the leading team is home or away as a variable in the model.

The chart below shows the minute mark I've filtered by, and whether each of the three variables for the leading team- shots, shot quality, and venue (home or away)- has a statistically significant effect on whether that team concedes a goal. 

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Essentially what this shows is after the 63rd minute, taking more shots no longer decreases a leading team's chances of giving up a goal. If a team is looking to see the scoreline out, this would be the time to implement a tactical change by withdrawing into a defensive shell. It still makes sense, however, to take high quality chances as they come, at least until about the 69th minute.

It's also interesting to note that in close games in the second half, being home or away doesn't really help prevent conceding. This appears to be evidence against teams playing differently up one late at home versus up one late away.

If, instead of holding on to the scoreline, a team's goal is to put the game away by scoring an insurance goal, that can be modeled, too. For the chart below I've built logistic regression models for each minute mark, using the same variables. The output now, though, is the probability the leading team scores the next goal.

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The models suggest taking more shots increases a leading team's odds of being next to score until the 71st minute, while taking high quality shots increases a leading team's odds of being next to score until the 77th minute. So, if a team wants one more goal, taking more shots will help until about 19 minutes remaining, and taking high quality shots will help until about 13 minutes remaining.

There's definitely more work to be done in this area. One next step would be to directly evaluate the trade-off between seeing out the score and trying to put the game away by scoring once more.

This analysis also certainly isn't definitive. I've approached the problem this one way, I'm sure there are some flaws with this approach, and I'd love to hear about them, and see other ways to tackle it.

"Positions" are a lie.

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By Benjamin Harrison (@NimajnebKH)

The idea of a player “position” is too inflexible.

We know – as fans – that that there are more than 11 different types of soccer players. We simply assign them titles which match a variety of on field roles, and some of those labels fit better than others. A “defensive” midfielder may also be a holding midfielder, is likely a central midfielder, and could even be a deep-lying playmaker. We may use the more nuanced terminology in a basic narrative description of game play – but there is no standard definition for how those roles might translate into measurable events. Soccer analytics is often left with a set of basic positions to categorize play on the field. These are reflected fairly well in the most basic statistics measured by OPTA. Consider a set of 209 players receiving starts over the 2014 season:   

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The raw data here is collected from whoscored.com. Pass attempts per 90’ accordingly excludes crosses and set pieces. “Defensive actions” are all tackles (successful or not) interceptions, clearances, and blocks. Where deemed useful, I used the position selection option from whoscored (this is an extremely useful tool for reasons that will hopefully become evident over the course of this post) to restrict the player to a dataset which fit into an assembled 11-man lineup (only 11 starters- a potential lineup, were chosen from each team). Although positional differences are apparent in the basic biplot, the accumulation of passes and defensive actions also incorporates aspects of style – the pace of play – which vary considerably by team. To remove team context, I summed up the pass and defense rates by team and converted the axes to share of team actions for the 2014 dataset.

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We’ll be using the 2015 dataset (raw data collected from whoscored as of April 23rd) through the remainder of this post. These 232 data points have been assembled using a slightly different approach – collecting all player statistics with a cutoff of 270 minutes game time, and normalizing individual numbers to the team average. Players who change positions between games should be expected to blur some position-specific distinctions, but major changes in player role are infrequent enough to be overwhelmed by the general trends. Despite the modest differences in method, the two plots exhibit predictably comparable values – there are a finite number of actions teams can take in a game, and a limited number of general tactical formations used in MLS (and soccer, in general).

The modified plot clarifies how the team uses the particular player as a share of its overall play. When the plot is constrained to a team-specific lineup, it can be a useful tool for visualizing average tactical setup, changes between seasons/games, and tactical adjustments to game state (check out the three links for some handy case studies specific to Seattle Sounders play). Positional differences remain apparent, but considerable overlap persists between categories, and their range implies poorly-matched roles. So long as a “midfielder” can have the same share of team actions as both a striker and a central defender, it remains a poor label. Overly broad player categories force the statistical comparison of different player roles having vastly different circumstantial difficulty (see, for example, this study of players with similar attacking midfield roles to Lamar Neagle). Often, difficult behavior is associated with exactly those aspects of play that lead to team success:

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“Chances” are defined here as the sum of all assists, key passes, and shots. Offensive “touches” are the sum of basic passes, cross attempts, and shots. Evaluating player performance based on skill-dependent statistics is dependent upon a thorough assessment of player behavior. We need player typing to be as diverse as on-field roles, and as indifferent to nominal “position” as possible. The statistics used to characterize type should be characteristic of role and as far removed as possible from player quality/skill (e.g., shooting rate should discriminate attacking players, but the ability to generate shots is descriptive of quality, so it is not useful as a role-dependent statistic). Finally, we shouldn't use so many statistics in constructing a model of roles such that the result becomes overfit to specific players or contains redundancy (e.g. including two different types of basic passing rates – say, short passes and long passes – would exaggerate role difference specific to distribution).

For now, with the 2015 dataset, I assessed pass and defense share as described above.Goalkeepers have been excluded (it is interesting to include them in team analysis, but their position label is relatively effective). I also calculated and recorded dribbles/touch (measuring attacking style on the ball) and crosses per touch (wide vs. central play). I then relativized each of these four role indices to its 210-player maximum and performed a hierarchical cluster analysis on the resulting data matrix:  

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I chose a position for pruning the tree (dashed line) that identifies 15 discrete player clusters grouped by role similarity by the four indices (this step is arbitrary this time, but will be automated in the future). Alongside each, I’ve roughly characterized the differences picked up in the analysis on a scale of --- (well-below average) to 0 (average) to +++ (well-above). Notice, if we move the cutoff line to the left to define only 3 groups, these would be primary defenders at the top, wide players in the middle, and central attackers at the bottom. Running a principal components analysis on the same dataset, let’s take a look at the differences between nominal position and cluster identity on the two first axes of variation. 

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The overlap problem with position is considerably reduced (though not absent) with cluster identity. To be useful, the cluster identities must also exhibit superior discrimination of role difficulty. Short pass accuracy is a skill-dependent statistic, but highly variable depending on situation:  

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Here the short pass accuracy by position is compared to that by cluster (cluster 11 is excluded, since it is simply Fabian Castillo – the point guard man who never encountered a ball he didn't want to dribble past an opponent). Many clusters exhibit a substantially tighter range of values than for the position counterparts – remember that these categories have not been defined by any values that explicitly measure skill or quality. Within clusters (or between closely related clusters) players should show similar statistical performance unless otherwise influenced by skill (as shown with the previously linked example concerning Neagle). No matter how well we characterize situational difficulty (e.g. how far from goal a shot is taken, or the direction, location and length of a pass), constraining the performance of peers provides a more complete characterization of expected result.

Providing context for player evaluation is only part of the value of this approach. The performance of individual players is strongly controlled by myriad factors even beyond team and role context. Grouping similar players may allow us to address questions that would be otherwise complicated by sample size. Take, for example, the question of whether any player can be considered to overperform or underperform expected goals.  

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If a style-specific skill in finishing exists, the grouping of similar players – with the resulting increase in sample size – might allow its detection more readily than would be the case measuring goal records for an individual player subject to seasonal noise, team context, and age-related development trends. However, the modest differences between xG and G in the data above should probably be considered a vindication of the model, if anything. Attackers with substantially different on-field roles and shot selection still exhibit predicted finishing success. Still, this approach may warrant further testing in the future with more refined role discrimination and a larger dataset.

The four-index model above warrants more work. Some player groups are very effective, but others clearly could benefit from different weighting prior to clustering and/or additional indices. Take, for example, cluster 15 which mainly incorporates central attacking players with fairly average pass share. The cluster also picked up Vancouver CB Pa Modou Kah, who has exhibited abnormally low pass and defense shares for his role so far in 2015. The present dataset may also suffer from limited sample size (any set of a few games may lead to some very unusual game states and corresponding performance). Nevertheless, preliminary work suggests player typing may be a useful analytical tool.

The Weekend Kick-off: Texas Two Step

by Harrison Crow (@Harrison_Crow)

If there is one thing that we know about sports it's simply that familiarity breeds hate. Classy line... and one that I had to steal because this introduction was, for some absurd reason, killing me. Face it, Houston moving back to the Western Conference this past year probably excited a lot of fans as it could mean a more prominent and possibly resurgent Texas Derby with both clubs meeting more often than once a year.

I feel as if the most quoted thing in connection with the Dynamo is how soon can they get Erik 'Cubo' Torres. I don't want to exaggerate and call them a terrible team, but they haven't had a real good showing of late. Either their defense is terrible, their attack is anemic, or it's some gross combination of the two. The sick thing about this is that our numbers actually indicate that they might actually end up being the better team.

Okay, Mr. Snooty. You can point to the current standings and wag your finger at FC Dallas but indulge me for a moment. Forget about Houston being tied for seventh place in points per game; they have thus far been the inverse of FC Dallas, with a smidge more than an expected goal per match and just less than one expected goal against. This presents the possibility, despite the disparity in the standings, that these two teams are a lot closer than many would readily admit.

I think it's fair to suspect FC Dallas might go on a downward spiral at some point in their 2015 campaign. Not because they're "Dallas" and thus making it something easy to call, but it has to do with the amount of shots their surrendering, the leverage index of those shots and the fact that they are who they are. Also, Dallas becomes unbearable in summer time this according to my own personal research and experience of it being "hot as balls" when I visited.

That being said, Dallas has a great quartet of Mauro Diaz, Tesho Akindele, Blas Perez and Fabian Castillo. While this group has been described with excess hyperbole by many early in the season, it's still a very good grouping of talent that can hurt you very quickly and through multiple delivery methods.

Michele and Diaz are both gifted at delivering from dead balls and set pieces, Castillo and Akindele have tons of physical gifts mixed with fun technical abilities that make watching highlights a joy. Blas Perez is a brute that wins balls in the air and is excellent back to goal. Let's not attempt to convince ourselves that this attack is not going to get better at some point.

I think this game boils down to which team can find the right mix of shots and leverage opportunity. Will Texas finally start taking more regular attempts as they get those opportunities presented or will they squander them looking for the best chance that might not come?

Likewise I think Houston needs to use their creativity to find shots that aren't just shots added to a tally but are meaningful in the way that might increase the probability in their favor.

FANTASY PERSPECTIVE

HOUSTON DYNAMO

Tyler Deric (Selected 17.8% , Cost $5.0)
Surprisingly enough Deric has been a top-three keeper in MLS according to our G -xG rankings. Houston's shots allowed gives credence to the idea that he might just be able to sustain this.

DeMarcus Beasley (Selected 14.9% , Cost $7.1)
Possibly one the best all around fullbacks in MLS and right now the best fantasy full back fake money can buy. The question is have to ask yourself is do you value full backs on defense over centerbacks that have dominated the season thus far?

FC DALLAS

Chris Seitz (Selected 20.8%, Cost $4.9)
A solid keeper in his own right, Seitz ownership mostly spawns from the three clean sheets in the first four matches of the season. But the Dallas defense is allowing a lot of shots which kind of limits his long term value.

Ryan Hollingshead (Selected 20.8%, Cost $5.4)
The injuries sustained by Mauro Diaz directly related to the early minutes which Hollingshead received. His cost is reasonable but with the return of Diaz it's a legit question of how many minutes he's going to regularly see.

THE WEEKEND MATCH-UPS

(expected goal differential in even game-states)

FRIDAY

Dallas FC (0.05) @ Houston Dynamo (-0.10)
Prediction: Draw

San Jose (-0.03) @ Real Salt Lake (-0.41)
Prediction: Draw

SATURDAY

Toronto FC (-0.23) @ Philadelphia Union (-0.06)
Prediction: Draw

Columbus Crew SC (0.30) @ DC United (-0.54)
Prediction: CCSC, FTW!

Colorado Rapids (-0.19) @ LA Galaxy (0.00)
Prediction: Draw

Vancouver (0.06) @ Portland Timbers FC (0.00)
Prediction: Draw

SUNDAY

Chicago Fire (-0.08) @ Sports Kansas City (0.63)
Prediction: SKC!

Seattle Sounders FC (0.04) @ New York City FC (-0.53)
Prediction: Draw

NERD IMAGERY

Yeah, go see it before all your friends do and spoil all the good parts.