Player Value Recap 2018: Refining a System for Ranking MLS Players

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By Dave Ladig (@davelaidig)

Creating an all-encompassing player value metric is an ongoing process, with more data adding more insight and texture to its meaning. But the challenges are worthwhile. The ability to compare players from different positions on equal footing, like PER for the NBA or WAR for MLB, allows one to test assumptions for what makes a team successful, how players fit together, and where resources might best be spent. If you haven’t already, read my pieces from last year (here are parts one and two). But this is an update on my progress to creating a metric to describe how game actions affect game outcomes, based on the context of team possessions.

Possession as Context

Before analyzing individual actions, one needs a sense of how “possession” is defined. I sought an operational definition that focused on demonstrated control of the ball, which continues until ball control is relinquished or demonstrated by the opponent. In operation, possession:

  • Starts with shot, dribble, completed pass, or incomplete free kick, corner, or throw-in

  • Ends with a shot, opponent offensive action (pass/dribble/shot), or end of half/game

Unlike other definitions, there can only be one shot per possession. Also, I do not keep track of subunits or sequences. And a small percentage of passes are not “in possession”; typically, these are defensive headers or clearances.

Average xG per Possession

With possession defined, I next determined the likelihood of a possession resulting in a goal – when possessing the ball in different parts of the field. To accomplish this, I applied my possession definition to all 374 matches of the 2017 MLS season, covering over 65,000 possessions. I divided the pitch into over 100 zones, and tracked zones used in each possession, and the possession result. I included the location of offensive actions, as well as the location of completed passes (i.e., where a pass recipient gained possession). And I removed duplicate zones from each possession chain. This data provides the average expected possession result (in xG) for each zone, and served as the basis for valuing individual actions.

Zones’ xG per possession value (in the run of play); free kicks and corner kicks have a similar chart, with larger zones

Using the context of the possession as a touchstone, we can value many of the offensive and defensive actions that occur on the pitch. With this, there are two things of note. First, with thousands of match actions divided among 22 players, a “typical” action may not necessarily be very valuable. Second, some actions are basically random. The sport is full of random bounces, odd referee calls, and the like. By choice, I focused on the descriptive aspect (i.e., the ability to describe what happened in a game and who was responsible) instead of predictive utility. Thus, while good players should tend to rise to the top over time because they’re associated with good results – I acknowledge the measure will also capture some “noise.”

Overall Player Rating

The key measure for each player is their overall value rating. This rating is meant to value the sum of all player actions, and their net contribution to the team. Each touch has a starting value (i.e., the expected or average xG per possession from that spot) and the value at the end (i.e., the expected or average xG per possession when the player is done). The value of the player’s touch is the difference between where he started and where/how he ended the touch. On defense, the value is similarly calculated as the difference between the opponents’ likelihood of scoring at the start of the defensive action and the chance of scoring after.

Regardless of the action, the value is measured in Goal Equivalents (GE). In the past, I’ve described the units as xG or xGD equivalents, and some analysts refer to “non-shot xG.” Although, the xG metric is relatively well understood by many, these labels risks confusing true xG values with a metric that does not necessarily involve the act of shooting.

I am using GE here because I use actual xG values in calculating the results of some player touches; and wanted to separate the value units from instances where I meant xG as is commonly understood. Thus, game action values are measured in goal equivalents (GE). And earning +1.00 GE benefits the team like scoring one goal.

Changes in Value Calculations

I’ve made several enhancements since the September articles, largely focused on improving the defensive values and incorporating more defensive actions.

First, I have dropped Block Value as a subcategory. In the initial assessment in September, the relationship between block values and game results was not strong. Using full 2017 and 2018 results, the block values were not related to match results, nor the final table. In fact, the higher block values were associated with poorer performances, and congregated among center backs and defensive midfielders. Considering the purpose of the metric was to account for match results with game actions; I concluded that including block value alongside value created by other defensive actions was misleading. Thus, I dropped the subcategory and blocked shots are not valued.

Second, I have added to Defensive-Turnover Value. Specifically, I have included defensive actions that end a possession, even if they don’t necessarily lead to their own possession with the next touch. In addition, I have added “recoveries” to this category; being careful not to double count possessions started by a defensive action. As a result, this category has updated values, using more game actions than prior versions.

Third, I have created a new Defensive-Pressure Value subcategory. This category captures defensive actions that occur during an opponent’s possession chain, but do not ultimately end the opponent’s possession. For each action type (e.g., interception, tackle, challenge), I compared the average possession result when that action occurs in the zone against the same zone’s average possession result during the run of play. In short, how does the presence of the defensive action change the expected result. The difference is the value assigned to that action.

Also, this category includes fouls committed (that do not lead to penalty kicks). Each zone already had an average possession result for the run of play, and a separate average possession result for a free kick. Because a foul replaces the run of play with a free kick, the difference between the two values was assigned to the player committing the foul. Compared to ending a possession, the Defensive-Pressure effects were small and dependent on location. For example, committing a foul in the attacking third was a small positive (perhaps because it allowed time for defenders to get in position), but a foul just outside one’s own penalty area was a negative (allowing a set piece near goal).

Finally, I added DEFENSE Index which adds the two defensive categories together for convenience. Consequently, the final relationship between the game actions, subcategories, indices, and total value are as described below.

Player Value Subcategories

The main goal of the player value metric is quantifying a player’s overall contribution to a team winning. But recognizing players help teams in different ways; I decided to track where the “value” was coming from. The subcategories below do not overlap with each other.

Subcategory Basic Calculation Purpose
Shot Value Probability of scoring due to the shot taken minus avg xG per possession for the shot zone Intended to measure the value added by the shot itself.
Pass Value Avg xG per possession in the pass recipient’s zone minus avg xG per possession in passer’s zone Intended to measure value added by a completed pass itself.
Turnover/Loss-of-Possession Value (TO/LOP) Avg xG per possession in the zone where last person in possession chain received ball * -1 Intended to measure potential result expected of player; then lost when player lost possession
Movement Value Avg xG per possession in the zone where a player received the ball minus avg xG per possession in the zone of pass/drib/shot (end of the player’s touch) Intended to measure value of advancing the ball at one’s feet (“dribble” or take-on action not required).
Defensive-Turnover Value Opponent avg xG per possession plus own avg xG per possession (depending on possession context) Intended the value of ending an opponent’s possession and/or starting one’s own possession
Defensive-Pressure Value Historical effect on avg xG per possession of similar defensive actions or fouls Intended to capture the effect of a defensive action during a possession that doesn’t end the possession.
F***-Up Value Assigns -0.55 xG equivalents to fouls conceding a PK, +.20 to the sufferer, and assigns values to red cards in accord with Mark Taylor’s formula. Intends to separately value Penalty Kicks and Red Cards; rare, but highly significant, actions that affect results.
Goalkeeping Value Post shot xG (specifically, ASA’s xGK) minus shot result (1 for goal, 0 for save) Intended to value the goal keeping actions for all shots on goal.
Composite Indices
CREATE Index Combination of three subcategories; Pass + TO/LOP + Movement Intended to measure players’ net contribution to the buildup of an offensive attack
DEFENSE Index Combination of two subcategories; Defense (turnovers) + Defense (pressures) Intended to measure players’ net defensive contributions

Validity as a Performance Measurement

With two full years of data, we can examine how well the value ratings mirror good results for teams and players. Considering the primary purpose of the metric is descriptive – the initial test is whether earning higher value scores than an opponent is associated with better game results. And in fact, earning higher values than an opponent in a match is highly associated with outscoring opponents. Over the 765 regular season MLS games played in 2017 and 2018, we have the following correlations:

  • 0.86 - Team value difference and goal difference

  • 0.34 - xGD (using ASA xG) and goal difference

  • 0.71 - Audi Index difference and goal difference (2016 only)

As shown, the team with higher values was highly associated with better match results. Individual game results are “noisy”, but the value metric seems to capture it well, and better than xG comparisons between team. When we look at overall season results, strong relationships remain. Over the 2017 and 2018 seasons, the correlations are:

  • 0.64 - Team season average value and league points

  • 0.63 - Team season average xG and league points

  • 0.44 - Team season average Audi Index and league points (2016 only)

While the course of a season evens out some of the randomness/noise, a team’s value rating is as strongly related to the final table as a team’s xG performance. In addition to correlations, we can express the relationship between value ratings and match results in other ways. The table below shows how much a team outperformed another in the value ratings; and the associated results in goal differential and win percentage.

Home v Away Rating Difference* N Average Goal Difference† Home Win % Draw % Away Win %
Greater than +5.0 13 4.31 100% - -
+4.0 to +4.99 27 3.56 100% - -
+3.0 to +3.99 43 2.72 100% - -
+2.0 to +2.99 110 2.15 95% 4% 1%
+1.50 to +1.99 64 1.55 92% 6% 2%
+1.00 to +1.49 94 0.90 68% 28% 4%
+0.50 to +0.99 88 0.69 58% 37% 5%
+0.00 to +0.49 80 0.23 40% 40% 20%
-0.50 to -0.01 66 0.02 26% 51% 23%
-1.00 to -0.51 56 -0.48 12% 36% 52%
-1.50 to -1.01 47 -0.89 - 30% 70%
-2.00 to -1.51 24 -1.25 4% 13% 83%
-3.00 to -2.01 36 -1.86 - 8% 92%
-4.00 to -3.01 8 -2.38 - - 100%
Below -4.00 9 -4.22 - - 100%
* Positive value (+) means Home team ratings are higher

† Positive value (+) means Home team scored more goals

Broadly speaking, the team with higher value ratings also earn better match results. For example, a home team with 1.1 GE more than its opponent averages around 0.9 goals more than its opponent, and wins about 68% of the time. We see home field effects; i.e., home teams have better results with near equal performance ratings. We also see variability. There is a chance in winning even when one team dominates the performance rating. It’s a small chance of winning for sure – 2% or less for a visiting team with 1.5+ GE less than the home team. But we know that teams sometimes win against the run of play. And home teams get breaks. Thus, the player ratings, when aggregated for whole teams, seems to reflect match results and patterns we know exist. As a result, I consider the value ratings have utility as a performance measure both at the match level and at the season level.

Repeating previous caveats, I’m not arguing that player value ratings are objectively better than xG stats for all purposes. I only point out that xG stats are well understood and serve as a baseline comparison. And adding possession context to individual games actions leads to a stronger relationship with individual match results than xG alone.

While focusing on an overall rating, early on it became clear that identifying the source of the value was important, which led to the subcategories. And for the sake of transparency, constructive feedback, and determining utility; I’ve included some basic data on how the subcategories relate to goal differential (for matches) and points (for seasons).

The following chart reports average team values in 2017 and 2018 matches. In addition, I’ve included the single variable correlation with the categories and match goal difference. Further, I ran a multiple regression with each of the subcategories and match goal difference as the dependent variable. For this example, I used only home teams to simplify the contrasts, and avoid exaggerating relationships by double counting matches. In the correlations and in the multiple regression, no variable directly represented opponent efforts. Finally, I report the correlations between the categories and teams’ season point total.

Category (2017 & 2018 combined) Team Avg Per Game Correlation w Match Goal Diff Multiple Regression Coefficients (GD): Home Teams Correlation w League Points Team Season Avg
N 1326 1326 765 39
Total Player Value 1.43 0.637 N/A 0.644
Shot Value 0.94 0.447 1.19‡ 0.606
Pass Value 1.07 0.007 -0.2 0.456
TO/LOP Value -1.49 0.154 0.99‡ -0.325
Movement Value 0.22 0.259 0.52† 0.322
Defense – Turnover Value 0.89 0.075 0.41* 0.137
Defense – Pressure Value -0.02 0.054 0.91† -0.082
F-Up Value -0.13 0.595 1.37‡ 0.345
GK Value -0.05 0.444 0.87‡ 0.37
CREATE Index (Pass+TO+Move) -0.2 0.165 N/A 0.423
DEFENSE Index (Def-TO+Def-Press) 0.87 0.08 N/A 0.098

Some categories are clearly influential and intuitive. Total player value (even with no accounting for opponent performance) is strongly related with match and season results. Shot value seems to matter as well, even when incorporating my distinctions from traditional xG ratings. Goalkeeper value is a match level xGK – Goals Allowed calculation, and has obvious bearing on results. And F-ups are the category that captures red cards and penalties; which are relatively rare actions with an outsized effect on results. But other categories get a little complicated.

The team averages are listed to provide some context to the size of the different categories, which along with variance affects the relationships. For example, turnovers and loss of possessions (TO/LOP) are always negative because ending a possession eliminates a potential (even if small) chance of scoring. And most possessions end with a TO/LOP and not a shot. As a result, it’s a common event, and relatively evenly distributed between teams (not between players necessarily). Further, receiving the ball in a dangerous location means more GE are lost by the turnover. But really bad teams do not get the ball into dangerous places as much as good teams (accommodating high-risk high-reward styles was one reason I made the CREATE Index). All this makes the TO/LOP relationship seem weaker at the team level and inversely related to season results when examined alone.

Another example is Pass Value. Over the course of a season, it has a stronger relationship to league points than a keeper’s shot stopping. But in individual matches the effect is small and not significant. I believe this is due to game state effects. Pass value mirrors common language of playing “negatively” or “positively.” Aggressively completing passes into dangerous zones creates positive values, playing conservative passes back lead to negative values. In other words, a winning team might have lower pass values for a match, and a team chasing the game might try more aggressive passes. And that seems to show up at the match level analysis. Notably, when the dependent variable is xG differential and not actual goal differential, then Pass Value has a significant positive relationship with results (not shown).

Defense is one of the more difficult areas to measure. Yes, there are defensive actions recorded. But using the Sherlock Holmes metaphor – it’s the dog that didn’t bark which is key. In this case, the shots not taken, or taken poorly. For Defense – Turnovers, the correlation went down (compared to previous report of 2017 values alone). The result of the multiple regression equation is marginally significant and in the right direction. However, it’s worth emphasizing that twice as many defensive actions were added since the last report. As this metric is built from the bottom up, I believe there is inherent value in the broader approach.

The Defense – Pressure category captures actions that do not lead to a turnover or new possession. Based on the historical analysis, the values are not as large, or vary like other categories. As a result, it seems to get swallowed up when examined alone (basically a “no relationship” result), but shows up with a statistically significant effect in the multiple regression. As with the previous review, valuing defensive actions remains an area to target for improvement.

Overall, earning a higher player value (in goal equivalents) is related to a better chance at winning, both when looking at matches and season results. Breaking out the source of value adds some insight as the categories are not uniformly related to results, and caution and additional context is needed when applying the subcategory values to team results.

Predicting Future Player Performance

If we have a valid measure of player contribution to team performance, we would expect some level consistency over time. And in an update from previous articles, we can now compare players 2017 values to their full 2018 value.

As mentioned in previous articles, there are caveats to interpreting these correlations. First, there is evidence that poor 2017 performers dropped out of the 2018 population. This effect could make relationships seem smaller. And there are potential variables that could increase or decrease the size of the relationships. In other words, it’s possible there is some important context I’m missing. But since we have to start somewhere, here are the observed year to year correlations for the improved value metric.

2017 & 2018 All players Field players
Year to Year 500+ minutes 1000+ minutes
Correlations (N=283) (N=179)
Total Player Value 0.493 0.523
Shot Value 0.864 0.885
Pass Value 0.774 0.763
TO/LOP Value 0.908 0.903
Movement Value 0.612 0.649
Defense – Turnover Value 0.839 0.82
Defense – Pressure Value 0.33 0.378
F-Up Value 0.13 0.13
GK Value (GKs only) 0.274 N/A
CREATE Index (Pass+TO+Move) 0.755 0.716
DEFENSE Index (Def-TO+Def-Press) 0.801 0.776

The overall rating seems moderately consistent from year to year, and seems sensitive enough for players with 500+ minutes. Turning to the subcategories, there is a variety of relationships reflecting the diverse game events. The F-Up category (red cards, and PKs conceded/drawn) has a very small relationship from one year to the next. Considering the large values associated with these events, we can get an idea where some “noise” is coming from. Goalkeeper value (which reflects xGK) does not have a strong relationship, another example of keepers being difficult to evaluate.

But some subcategories are consistent, at least in this two-year sample. Shot value is similar between years; and a side analysis had SV per 90 as slightly more consistent than xG per 90. The CREATE Index (combining the possession-attack related categories) also had a strong correlation between 2017 and 2018. Same can be said for the DEFENSE Index. It seems that subcategories may be relevant for scouting players with particular profiles.

Considering that the measure is optimized for describing match results, which often have significant unpredictable events like red cards; it’s possible that correlations are too fine of a tool for the data. For a broader view using a “blunter” tool, I updated the hit-miss ratios. These show the percentage of players in a category to meet a specific criterion the next year. For categories, I labeled the 2017 players by their percentile (using the overall value rating per 90, and all players with 500+ minutes in 2017). And for the “hit” condition, I used both an above average rating in 2018, and a top 25% rating in 2018 as success criteria.

2017 Category 2018 Above Avg (GE per 90) 2018 Top 25% (GE per 90) 2018 Avg (GE per 90) Pop’n dropout rate from 2017 (GE per 90)
Top 10% in GE 87% 64% 0.30 9%
Top 30% in GE 72% 47% 0.23 27%
Middle 40% in GE 47% 22% 0.11 32%
Bottom 30% in GE 37% 14% -0.01 42%
Bottom 10% in GE 42% 13% -0.11 44%

While the relationship from one year to the next might not be as strong as desired, players in better categories tend to do better the following year. Further, the natural selection may hide some of the utility as well. The hit ratio for the bottom groups may be artificially inflated by the poor performers that did not get a chance in 2018.

All in all, the player value metric appears to reflect actual match and season results. Some subcategories touch consistent skills and others seem more random; with higher confidence on the offensive side of the ball. The overall rating allows direct comparisons between different positions. The shot value category isolates shot performance, and is not heavily influenced by penalties or goalkeeper performance. Player movement with the ball is captured, distinct from any action labels. And defense actions are placed in the context of a possession; i.e., did the defense action work or not.

Considering the benefits, the value metric has utility in measuring performance, and should only improve with more data and testing. And here are the results of the 2018 season.

2018 Results (players with 500+ minutes)

All values are reported in goal equivalents (GE) per 90 minutes.

Player Overall Rating (p90) Shot Value (p90) Pass Value (p90) TO/LOP Value (p90) Move Value (p90) Def-TO Value (p90) Def-Press (p90) F-Up Value (p90) GK Value (p90) CREATE Index (p90) DEF Index (p90)
Josef Martinez 0.422 0.49 0 -0.12 0.02 0.02 0 0.02 0.00 -0.101 0.017
Stefan Frei 0.417 0 0.05 -0.05 0 0.01 0.01 0 0.39 0.006 0.018
Maximiliano Moralez 0.409 0.23 0.32 -0.25 0.04 0.09 -0.01 0.01 0.00 0.102 0.073
Miguel Almiron 0.406 0.29 0.18 -0.24 0.08 0.08 -0.01 0.02 0.00 0.023 0.070
Cristian Techera 0.401 0.39 0.19 -0.2 0.02 0.04 -0.01 -0.03 0.00 0.008 0.033
Carlos Vela 0.399 0.27 0.27 -0.25 0.06 0.03 0 0.01 0.00 0.081 0.030
David Villa 0.395 0.42 0.16 -0.29 0.06 0.04 0 0 0.00 -0.066 0.043
Romell Quioto 0.386 0.21 0.29 -0.21 0.06 0.03 0 0 0.00 0.147 0.026
Joao Plata 0.382 0.28 0.22 -0.26 0.07 0.06 0 0.02 0.00 0.020 0.058
Sebastian Saucedo 0.358 0.21 0.14 -0.14 0.08 0.05 -0.01 0.03 0.00 0.073 0.040
Johnny Russell 0.356 0.25 0.2 -0.23 0.08 0.03 0 0.02 0.00 0.048 0.031
Tosaint Ricketts 0.355 0.46 -0.04 -0.1 0.01 0.03 0 0 0.00 -0.130 0.024
Sebastian Giovinco 0.355 0.34 0.19 -0.29 0.08 0.03 0 0 0.00 -0.018 0.026
Ismael Tajouri-Shradi 0.354 0.17 0.21 -0.16 0.08 0.06 0 0 0.00 0.122 0.063
Romain Alessandrini 0.351 0.2 0.36 -0.28 0.03 0.06 -0.01 -0.02 0.00 0.108 0.056
Giovani dos Santos 0.348 0.26 0.17 -0.16 0.06 0.02 0 0 0.00 0.069 0.021
Graham Zusi 0.338 0.07 0.26 -0.16 0.05 0.11 0 0 0.00 0.157 0.109
Alberth Elis 0.336 0.33 0.13 -0.26 0.09 0.04 0 0 0.00 -0.032 0.037
Diego Rubio 0.335 0.44 0.04 -0.2 0.02 0.03 0 0 0.00 -0.139 0.034
Bradley Wright-Phillips 0.334 0.41 0.07 -0.24 0.05 0.03 0 0.01 0.00 -0.111 0.023
Valeri Qazaishvili 0.332 0.28 0.1 -0.18 0.08 0.05 0 0 0.00 0.004 0.049
Santiago Mosquera 0.329 0.24 0.15 -0.2 0.06 0.07 0 0 0.00 0.020 0.071
Zoltan Stieber 0.324 0.16 0.23 -0.17 0.05 0.07 -0.01 0 0.00 0.104 0.064
Nicolas Lodeiro 0.319 0.06 0.43 -0.28 0.04 0.08 -0.01 0.01 0.00 0.185 0.062
Raul Ruidiaz 0.311 0.48 -0.04 -0.16 0 0.03 0 0 0.00 -0.191 0.027
Ola Kamara 0.299 0.33 0.06 -0.18 0.03 0.04 0 0.01 0.00 -0.093 0.045
Darren Mattocks 0.298 0.36 0 -0.18 0.08 0.03 0 0.01 0.00 -0.100 0.029
Diego Valeri 0.294 0.18 0.31 -0.3 0.04 0.05 -0.01 0.01 0.00 0.061 0.041
Ryan Hollingshead 0.294 0.14 0.15 -0.12 0.02 0.1 0 0 0.00 0.048 0.108
Diego Fagundez 0.291 0.18 0.28 -0.23 0.03 0.03 -0.01 0 0.00 0.083 0.023
Kevin Kratz 0.29 0.2 0.11 -0.08 0.04 0.11 -0.01 -0.08 0.00 0.067 0.101
Diego Rossi 0.288 0.31 0.07 -0.21 0.07 0.04 -0.01 0.01 0.00 -0.069 0.036
Yoshimar Yotun 0.286 0.04 0.34 -0.21 0.03 0.09 -0.01 0.01 0.00 0.166 0.072
Bill Hamid 0.285 0 0.05 -0.03 0 0.02 0 0 0.23 0.027 0.023
Marc Rzatkowski 0.279 0.09 0.29 -0.22 0.02 0.1 -0.01 0 0.00 0.092 0.095
Wayne Rooney 0.278 0.38 0.15 -0.29 0.02 0.02 0 0 0.00 -0.120 0.017
Adama Diomande 0.273 0.45 0.03 -0.23 0.02 0.01 0 0 0.00 -0.181 0.009
Hector Villalba 0.27 0.26 0.12 -0.22 0.05 0.07 -0.01 0 0.00 -0.054 0.061
Sebastian Blanco 0.265 0.21 0.2 -0.27 0.06 0.05 -0.01 0.01 0.00 -0.006 0.046
Albert Rusnak 0.263 0.15 0.19 -0.15 0.04 0.03 0 0 0.00 0.080 0.029
Mauro Diaz 0.258 0.09 0.37 -0.29 0.05 0.04 0 0 0.00 0.127 0.043
Marco Farfan 0.257 0 0.14 -0.09 0.02 0.14 0.05 0 0.00 0.065 0.192
Michael Barrios 0.257 0.18 0.14 -0.18 0.08 0.05 -0.01 0.01 0.00 0.038 0.035
Jimmy Medranda 0.256 0.05 0.12 -0.13 0.05 0.17 -0.01 0 0.00 0.043 0.165
Victor Rodriguez 0.255 0.19 0.17 -0.22 0.06 0.07 -0.01 0 0.00 0.010 0.058
Lucas Janson 0.251 0.38 0.02 -0.21 0.02 0.04 0 0 0.00 -0.168 0.041
Matt Turner 0.244 0 0.05 -0.03 0 0.01 0 0 0.22 0.017 0.011
Miguel Ibarra 0.242 0.15 0.16 -0.12 0.03 0.05 -0.01 -0.03 0.00 0.075 0.047
Alphonso Davies 0.242 0.13 0.1 -0.15 0.06 0.09 0 0.01 0.00 0.018 0.091
Jonathan Osorio 0.241 0.2 0.05 -0.14 0.05 0.09 0 0 0.00 -0.045 0.090
Yordy Reyna 0.241 0.19 0.17 -0.14 0.05 0.05 -0.01 -0.06 0.00 0.077 0.037
Alejandro Romero Gamarra 0.238 0.15 0.25 -0.23 0.03 0.06 -0.01 0 0.00 0.042 0.048
Chris Mavinga 0.238 0 0.05 -0.05 0.01 0.2 0.03 0 0.00 0.015 0.224
Philippe Senderos 0.238 0.18 0.05 -0.04 0.01 0.1 0.01 -0.07 0.00 0.018 0.109
Felipe Gutierrez 0.233 0.2 0.1 -0.23 0.03 0.11 -0.01 0.02 0.00 -0.094 0.102
Darwin Quintero 0.232 0.21 0.31 -0.4 0.07 0.04 0 0 0.00 -0.010 0.036
Fabrice-Jean Picault 0.231 0.24 0.07 -0.22 0.07 0.08 0 0.01 0.00 -0.090 0.078
Ronald Matarrita 0.23 0.11 0.21 -0.18 0.01 0.08 -0.01 0 0.00 0.049 0.073
Aleksandar Katai 0.229 0.19 0.14 -0.22 0.07 0.04 -0.01 0.01 0.00 -0.011 0.033
Jonathan dos Santos 0.228 0.04 0.16 -0.11 0.01 0.13 -0.01 0 0.00 0.066 0.123
Larrys Mabiala 0.228 0.06 0.06 -0.05 0.01 0.11 0.04 0 0.00 0.015 0.148
Evan Bush 0.227 0 0.05 -0.04 0 0.01 0.01 0 0.18 0.019 0.026
Jeff Attinella 0.226 0 0.07 -0.03 0 0.04 0.01 0 0.14 0.038 0.051
Drew Moor 0.225 0.01 0.11 -0.07 0.02 0.16 0 0 0.00 0.050 0.167
Nick Hagglund 0.224 0.09 0.06 -0.08 -0.01 0.1 0.05 0 0.00 -0.022 0.151
Bill Poni Tuiloma 0.22 0.06 0.06 -0.04 0.01 0.12 0.01 0 0.00 0.034 0.123
Maximiliano Urruti 0.219 0.25 0.05 -0.18 0.03 0.06 -0.01 0.02 0.00 -0.104 0.054
Derrick Etienne 0.218 0.31 0.08 -0.25 0.05 0.04 -0.01 0 0.00 -0.120 0.032
David Guzman 0.217 0.09 0.14 -0.11 0.02 0.09 -0.01 0 0.00 0.045 0.079
Laurent Ciman 0.21 0.05 0.09 -0.06 0.01 0.13 -0.01 0 0.00 0.042 0.119
Jaylin Lindsey 0.209 -0.01 0.17 -0.1 0.06 0.09 0 0 0.00 0.129 0.086
Keegan Rosenberry 0.208 0.03 0.15 -0.11 0.01 0.14 -0.01 0 0.00 0.048 0.129
Nicolas Mezquida 0.206 0.18 0.07 -0.15 0.02 0.08 -0.02 0.02 0.00 -0.055 0.063
Gregory Van der Wiel 0.206 0 0.12 -0.06 0.03 0.1 0.02 -0.02 0.00 0.094 0.128
Leandro Gonzalez Pirez 0.206 0.05 0.12 -0.09 0.02 0.14 -0.01 -0.02 0.00 0.050 0.129
Sean Davis 0.204 0.01 0.19 -0.14 0 0.14 0 0 0.00 0.056 0.137
Felipe 0.204 0.04 0.21 -0.16 0.02 0.11 -0.02 0 0.00 0.069 0.095
Latif Blessing 0.204 0.17 0.05 -0.16 0.04 0.11 -0.01 0 0.00 -0.066 0.096
Michael Bradley 0.203 0.02 0.16 -0.1 0.02 0.13 -0.01 -0.01 0.00 0.075 0.123
Jack Price 0.202 0.04 0.18 -0.12 0.01 0.1 -0.01 0 0.00 0.066 0.097
Guram Kashia 0.202 0.02 0.08 -0.08 0.01 0.17 0 0 0.00 0.011 0.167
Francois Affolter 0.202 0 0.04 -0.04 0 0.21 -0.01 0 0.00 0.001 0.202
Magnus Wolff Eikrem 0.201 0.11 0.25 -0.25 0.04 0.06 -0.01 0 0.00 0.041 0.048
Auro 0.2 0.02 0.18 -0.17 0.05 0.12 -0.02 0.01 0.00 0.063 0.101
Ilsinho 0.199 0.12 0.17 -0.24 0.09 0.07 -0.01 0 0.00 0.019 0.061
Tesho Akindele 0.198 0.22 0.08 -0.16 0.02 0.05 0 0 0.00 -0.062 0.044
Victor Vazquez 0.198 0.17 0.15 -0.22 0.04 0.06 0 0 0.00 -0.030 0.059
Joe Willis 0.197 0 0.06 -0.04 0 0.01 0.01 0 0.16 0.022 0.017
Matt Besler 0.197 0 0.12 -0.05 0.03 0.08 0.02 0 0.00 0.099 0.097
Ezequiel Barco 0.195 0.16 0.15 -0.19 0.08 0.04 -0.01 -0.03 0.00 0.032 0.029
Haris Medunjanin 0.195 0.05 0.25 -0.17 0.03 0.08 -0.01 -0.04 0.00 0.110 0.074
Pedro Santos 0.195 0.12 0.28 -0.28 0.04 0.08 0 -0.04 0.00 0.045 0.076
Daniel Royer 0.195 0.34 0.09 -0.24 0.02 0.03 0 -0.05 0.00 -0.129 0.031
Luis Robles 0.194 0 0.06 -0.04 0 0.02 0 -0.02 0.17 0.019 0.026
Jack Elliott 0.194 0.05 0.05 -0.06 0.02 0.13 0.02 -0.01 0.00 0.006 0.148
Magnus Eriksson 0.193 0.1 0.23 -0.22 0.03 0.06 -0.01 0 0.00 0.037 0.051
Julian Gressel 0.193 0.11 0.17 -0.18 0.02 0.09 -0.02 0 0.00 0.015 0.067
Andy Polo 0.192 0.13 0.04 -0.11 0.06 0.08 0 0 0.00 -0.012 0.078
David Accam 0.191 0.22 0.05 -0.17 0.05 0.06 -0.01 0 0.00 -0.076 0.050
Bacary Sagna 0.191 0.08 0.16 -0.14 0.02 0.07 0 0 0.00 0.045 0.065
Lamine Sane 0.19 0.03 0.02 -0.03 0.01 0.13 0.02 0 0.00 0.002 0.156
Borek Dockal 0.189 0.13 0.24 -0.26 0.04 0.05 0 0 0.00 0.018 0.041
Luciano Acosta 0.189 0.15 0.16 -0.25 0.07 0.06 -0.01 0 0.00 -0.017 0.053
Michael Boxall 0.189 0.03 0.03 -0.03 0 0.12 0.03 0 0.00 0.010 0.145
Andrew Farrell 0.189 0.01 0.21 -0.13 0.02 0.09 -0.02 0.01 0.00 0.098 0.070
Jahmir Hyka 0.189 0.17 0.04 -0.12 0.05 0.06 -0.01 0 0.00 -0.037 0.056
Nemanja Nikolic 0.188 0.26 0 -0.1 0 0.02 -0.01 0.01 0.00 -0.098 0.015
Rod Fanni 0.187 0.01 0.04 -0.05 0.01 0.16 0.01 0 0.00 -0.005 0.177
Daniel Salloi 0.187 0.25 0.05 -0.19 0.04 0.03 0 0.01 0.00 -0.104 0.029
Aaron Long 0.187 0.05 0.05 -0.05 0.01 0.12 0.01 0 0.00 0.007 0.131
Harold Cummings 0.186 0 0.04 -0.04 0.01 0.15 0.03 0 0.00 0.011 0.176
Marlon Hairston 0.185 0.02 0.19 -0.14 0.02 0.07 0.02 0 0.00 0.074 0.087
Mike Grella 0.183 0.13 0.14 -0.19 0.06 0.06 -0.02 0 0.00 0.020 0.036
Milton Valenzuela 0.183 0.04 0.16 -0.14 0.01 0.1 0.01 0 0.00 0.035 0.106
Cristian Penilla 0.183 0.21 0.16 -0.24 0.05 0.04 0 -0.04 0.00 -0.028 0.041
Saphir Taider 0.182 0.13 0.14 -0.17 0.05 0.08 -0.01 -0.03 0.00 0.016 0.068
Gerso Fernandes 0.181 0.22 0.05 -0.22 0.06 0.06 -0.01 0.01 0.00 -0.109 0.056
Aaron Kovar 0.181 0.14 0.14 -0.16 0.03 0.05 -0.01 0 0.00 0.001 0.042
Walker Zimmerman 0.18 0.05 0.06 -0.05 0.01 0.12 0 -0.02 0.00 0.024 0.121
Djordje Mihailovic 0.18 0.11 0.14 -0.15 0 0.09 -0.01 0 0.00 -0.007 0.079
Maynor Figueroa 0.179 0.07 0.09 -0.08 0.02 0.08 0.01 0 0.00 0.022 0.092
Brad Knighton 0.179 0 0.08 -0.04 0 0.01 0 0 0.13 0.044 0.006
Ethan Finlay 0.179 0.26 0.12 -0.18 0.01 0.06 -0.01 -0.08 0.00 -0.056 0.051
Mauro Manotas 0.178 0.33 0 -0.18 0.02 0.02 0 -0.01 0.00 -0.156 0.017
Harrison Afful 0.177 0.04 0.18 -0.16 0.03 0.09 0 0 0.00 0.053 0.087
Yamil Asad 0.177 0.15 0.12 -0.18 0.03 0.06 -0.01 0 0.00 -0.026 0.056
Osvaldo Alonso 0.177 0.02 0.08 -0.07 0.02 0.11 0 0 0.00 0.038 0.115
Jorge Villafana 0.176 0.01 0.15 -0.1 0.01 0.1 0.01 0 0.00 0.060 0.110
Tim Parker 0.176 0.04 0.08 -0.04 0 0.11 0.01 -0.02 0.00 0.041 0.116
Samuel Armenteros 0.175 0.31 0.03 -0.22 0.03 0.03 0 0 0.00 -0.158 0.025
Roland Lamah 0.174 0.15 0.11 -0.16 0.02 0.04 0 0 0.00 -0.024 0.044
Matt Hedges 0.174 0.06 0.06 -0.04 0.01 0.11 0 -0.02 0.00 0.027 0.109
Tyrone Mears 0.173 0.05 0.18 -0.14 0.03 0.07 -0.01 0 0.00 0.061 0.065
Ignacio Piatti 0.172 0.18 0.07 -0.24 0.09 0.07 -0.01 0.01 0.00 -0.081 0.061
Russell Canouse 0.172 0.05 0.03 -0.06 0.01 0.13 0 0 0.00 -0.018 0.137
Liam Ridgewell 0.171 0.01 0.13 -0.07 0.01 0.09 0.01 -0.01 0.00 0.063 0.103
Corey Baird 0.171 0.22 0.04 -0.14 0.02 0.02 0 0.01 0.00 -0.079 0.017
Zarek Valentin 0.171 0 0.12 -0.07 0.01 0.11 0.01 0 0.00 0.061 0.112
Dominique Badji 0.171 0.26 0 -0.17 0.04 0.03 0 0.02 0.00 -0.134 0.029
Yefferson Quintana 0.17 0.06 0.05 -0.07 0.01 0.11 0.01 0 0.00 -0.019 0.125
Mark-Anthony Kaye 0.169 0.09 0.08 -0.13 0.01 0.12 -0.01 0 0.00 -0.032 0.109
Ashley Cole 0.169 0.04 0.13 -0.09 0.01 0.09 0 -0.01 0.00 0.049 0.097
Marco Delgado 0.167 0.06 0.11 -0.15 0.02 0.13 0 0 0.00 -0.018 0.123
Brett Levis 0.166 0 0.14 -0.1 0 0.12 0 0 0.00 0.045 0.121
Luis Silva 0.165 0.35 -0.02 -0.22 -0.01 0.05 0 0.02 0.00 -0.246 0.045
Cristian Roldan 0.164 0.1 0.09 -0.13 0.02 0.1 -0.01 0 0.00 -0.021 0.089
Raymon Gaddis 0.164 0.02 0.08 -0.06 0.02 0.11 -0.01 0 0.00 0.037 0.102
Anton Tinnerholm 0.164 0.04 0.16 -0.15 0.03 0.07 0 0 0.00 0.045 0.074
Florian Jungwirth 0.163 0.03 0.08 -0.09 0.01 0.13 0 0 0.00 -0.002 0.130
Chris McCann 0.163 0.04 0.12 -0.07 0.01 0.11 0 -0.03 0.00 0.052 0.101
Niko Hansen 0.163 0.19 0.05 -0.17 0.05 0.04 0 0 0.00 -0.067 0.039
Shea Salinas 0.162 0.01 0.17 -0.15 0.05 0.09 0 -0.01 0.00 0.072 0.089
Reggie Cannon 0.162 0.02 0.1 -0.07 0.03 0.09 0 0 0.00 0.055 0.091
Brooks Lennon 0.162 0.01 0.16 -0.11 0.04 0.07 0 0 0.00 0.077 0.071
Mohamed El-Munir 0.16 0.03 0.12 -0.16 0.05 0.19 0 -0.07 0.00 0.005 0.193
Ibson 0.16 0.08 0.09 -0.14 0.02 0.12 -0.02 0 0.00 -0.019 0.098
Jeisson Vargas 0.16 0.21 0.03 -0.14 0.02 0.04 0 0 0.00 -0.088 0.037
Danilo Silva 0.159 0.03 0.05 -0.07 0.05 0.09 0.01 0 0.00 0.028 0.099
Chris Mueller 0.159 0.12 0.1 -0.17 0.06 0.04 0 0.02 0.00 -0.011 0.034
Steven Beitashour 0.158 0.02 0.14 -0.1 0.01 0.09 0.01 -0.02 0.00 0.053 0.103
Rolf Feltscher 0.157 0.02 0.19 -0.13 0.01 0.06 0 0 0.00 0.071 0.063
Brent Kallman 0.156 0.03 0.03 -0.03 0.01 0.1 0.03 0 0.00 0.008 0.123
Justin Morrow 0.156 0.06 0.1 -0.11 0.02 0.09 0 0 0.00 0.011 0.084
Diego Chara 0.155 0.03 0.07 -0.07 0.02 0.11 0 0 0.00 0.011 0.109
Jorge Corrales 0.155 0 0.15 -0.09 0.01 0.08 0.01 0 0.00 0.063 0.089
Sebastian Lletget 0.154 0.07 0.15 -0.15 0.01 0.08 -0.01 0 0.00 0.009 0.077
Gyasi Zardes 0.153 0.29 -0.03 -0.17 0.03 0.01 0 0.02 0.00 -0.169 0.015
Justin Meram 0.153 0.15 0.14 -0.24 0.07 0.04 0 0 0.00 -0.027 0.033
Michael Parkhurst 0.153 0 0.05 -0.04 0.02 0.13 0.01 -0.02 0.00 0.027 0.141
Nick DeLeon 0.152 0.04 0.09 -0.09 0.01 0.11 -0.01 0 0.00 0.014 0.100
Rasmus Schuller 0.152 0.05 0.03 -0.11 0.01 0.19 -0.02 0 0.00 -0.066 0.169
Harry Shipp 0.151 0.16 0.09 -0.16 0.02 0.06 -0.02 0 0.00 -0.052 0.044
Jacori Hayes 0.151 0.07 0.04 -0.11 0 0.17 -0.02 0 0.00 -0.063 0.147
Connor Lade 0.151 0 0.15 -0.12 0.01 0.1 0 0 0.00 0.048 0.102
Sacha Kljestan 0.151 0.12 0.14 -0.18 0.04 0.04 0 0 0.00 -0.005 0.034
Cristhian Paredes 0.15 0.08 0.04 -0.1 0.01 0.12 0 0 0.00 -0.042 0.114
Kevin Garcia 0.15 0.01 0.09 -0.08 0.01 0.08 0.03 0 0.00 0.024 0.114
Luis Argudo 0.15 0.08 0.09 -0.15 0.03 0.06 -0.01 0.05 0.00 -0.021 0.043
Paul Arriola 0.149 0.14 0.11 -0.2 0.04 0.08 0 -0.02 0.00 -0.054 0.081
Yangel Herrera 0.149 0.12 0.06 -0.17 0.01 0.17 -0.04 0 0.00 -0.104 0.138
Tony Tchani 0.149 0.01 0.08 -0.1 0.03 0.13 0 0 0.00 0.004 0.130
Jay Chapman 0.148 0.17 0.1 -0.22 0.02 0.09 0 0 0.00 -0.111 0.091
Adolfo Machado 0.148 -0.02 0.12 -0.07 0.02 0.13 0 -0.03 0.00 0.063 0.129
Adam Lundqvist 0.148 0 0.18 -0.11 0.02 0.07 -0.01 0 0.00 0.079 0.066
Ryan Telfer 0.147 0.04 0.15 -0.16 0.03 0.07 0 0.02 0.00 0.026 0.062
Jefferson Savarino 0.147 0.16 0.08 -0.18 0.05 0.05 -0.01 0 0.00 -0.044 0.035
Diego Campos 0.146 0.08 0.18 -0.18 0.03 0.04 -0.01 0 0.00 0.033 0.034
Jesus Medina 0.145 0.22 0.07 -0.21 0.03 0.05 -0.01 0 0.00 -0.116 0.044
Andrew Wenger 0.144 0.11 0.11 -0.16 0.01 0.08 0 0 0.00 -0.043 0.080
Edgar Castillo 0.143 0.03 0.13 -0.15 0.04 0.09 -0.01 0.01 0.00 0.024 0.085
Shkelzen Gashi 0.143 0.12 0.18 -0.19 0.01 0.03 -0.01 0 0.00 -0.007 0.028
Bastian Schweinsteiger 0.143 0.05 0.15 -0.15 0.02 0.1 0 -0.02 0.00 0.019 0.096
Jesse Gonzalez 0.143 0 0.05 -0.04 0 0.01 0 0 0.12 0.017 0.007
Nick Rimando 0.143 0 0.05 -0.05 0 0.01 -0.01 0 0.13 0.011 0.006
Raheem Edwards 0.143 0.12 0.12 -0.2 0.01 0.11 -0.01 0 0.00 -0.075 0.099
Oscar Boniek Garcia 0.143 0.01 0.14 -0.09 0.02 0.1 -0.02 -0.02 0.00 0.075 0.082
Dave Romney 0.143 0.02 0.13 -0.1 0.01 0.1 0 -0.02 0.00 0.047 0.100
Jonathan Mensah 0.141 0.02 0.05 -0.05 -0.01 0.12 0.01 0 0.00 -0.001 0.126
Michael Mancienne 0.139 0.01 0.04 -0.03 0.01 0.1 0 0 0.00 0.021 0.106
Alejandro Bedoya 0.139 0.08 0.12 -0.1 0.03 0.07 0 -0.05 0.00 0.049 0.065
Claude Dielna 0.138 0.07 0.11 -0.13 0.01 0.13 -0.01 -0.05 0.00 -0.012 0.123
Ricardo Clark 0.137 0.04 0.07 -0.08 0.01 0.09 0.01 0 0.00 -0.001 0.097
Joe Mason 0.137 0.28 -0.01 -0.17 0.02 0.03 -0.01 0 0.00 -0.154 0.016
Will Johnson 0.137 0.09 0.07 -0.09 0.01 0.09 -0.01 -0.02 0.00 -0.007 0.080
Darwin Ceren 0.137 0.02 0.06 -0.07 0.01 0.14 -0.01 -0.01 0.00 -0.006 0.128
Cory Burke 0.137 0.27 0.01 -0.22 0.06 0.04 0 -0.02 0.00 -0.151 0.035
Tommy Smith 0.136 0.04 0.05 -0.06 0.01 0.1 0 -0.02 0.00 0.006 0.105
Quincy Amarikwa 0.136 0.29 0.04 -0.24 0.01 0.03 0 0 0.00 -0.188 0.032
Abu Danladi 0.136 0.27 0 -0.26 0.05 0.08 0 0 0.00 -0.214 0.075
Cristian Martinez 0.136 0.09 0.16 -0.22 0.04 0.08 -0.01 0 0.00 -0.019 0.065
Tomas Martinez 0.135 0.17 0.11 -0.19 0.03 0.05 -0.01 -0.02 0.00 -0.051 0.042
Chris Schuler 0.135 0.03 0.01 -0.03 0 0.11 0.01 0 0.00 -0.011 0.118
Carlos Ascues 0.134 -0.01 0.03 -0.04 0.01 0.13 0.01 0 0.00 0.002 0.140
Scott Sutter 0.133 0.02 0.12 -0.12 0.02 0.08 0.01 0 0.00 0.025 0.087
Victor Cabrera 0.133 0.03 0.05 -0.06 0.03 0.23 0 -0.14 0.00 0.023 0.224
Andres Flores 0.132 0.05 0.1 -0.11 0.01 0.07 0 0.01 0.00 -0.002 0.069
Tim Melia 0.132 0 0.05 -0.03 0 0.01 0 -0.03 0.13 0.017 0.012
Ben Sweat 0.131 0.03 0.1 -0.12 0.02 0.08 0.01 0 0.00 0.008 0.090
Anton Nedyalkov 0.131 -0.01 0.07 -0.09 0.02 0.12 0.01 0 0.00 0.009 0.128
Ike Opara 0.13 0.04 0.07 -0.08 0.01 0.12 0 -0.03 0.00 0.005 0.120
Emmanuel Boateng 0.129 0.11 0.11 -0.19 0.04 0.06 -0.01 0 0.00 -0.039 0.057
Dominic Dwyer 0.129 0.3 0 -0.23 0.02 0.01 0 0.02 0.00 -0.202 0.013
Wilfried Zahibo 0.128 0.08 0.07 -0.14 0.01 0.11 -0.01 0 0.00 -0.056 0.101
Sebastien Ibeagha 0.128 0.01 0.04 -0.04 0 0.1 0.01 0 0.00 0.009 0.113
Yohan Croizet 0.128 0.16 0.08 -0.22 0.04 0.07 0 0 0.00 -0.098 0.068
Will Bruin 0.127 0.23 0.02 -0.17 0.01 0.03 0.01 0 0.00 -0.146 0.045
Michael Murillo 0.126 0.04 0.17 -0.19 0.02 0.1 -0.02 -0.01 0.00 0.006 0.087
Danny Hoesen 0.126 0.25 0.04 -0.21 0.03 0.02 0 0.01 0.00 -0.152 0.018
Fernando Bob 0.126 0 0.03 -0.06 0 0.18 -0.01 -0.02 0.00 -0.020 0.171
Samuel Piette 0.125 0 0.03 -0.06 0.01 0.15 0 0 0.00 -0.020 0.144
Tristan Blackmon 0.125 0.04 0.08 -0.09 0.01 0.09 -0.01 0 0.00 -0.002 0.083
Jakob Nerwinski 0.124 0 0.13 -0.09 0.02 0.07 0 0 0.00 0.050 0.069
Scott Caldwell 0.124 0.13 0.03 -0.12 0.01 0.12 -0.01 -0.02 0.00 -0.090 0.112
Leonardo 0.123 0 0.05 -0.04 0.01 0.09 0 0 0.00 0.027 0.091
Zlatan Ibrahimovic 0.123 0.34 0.11 -0.33 0 0.03 0 -0.02 0.00 -0.225 0.028
Aaron Maund 0.123 -0.02 0.03 -0.03 0 0.09 0.04 0 0.00 0.004 0.135
Jason Hernandez 0.121 -0.02 0.03 -0.05 0.01 0.14 0.01 0 0.00 -0.005 0.145
Oniel Fisher 0.121 0.02 0.11 -0.08 0.01 0.08 0 -0.03 0.00 0.042 0.086
Dax McCarty 0.12 0 0.05 -0.09 0.01 0.14 -0.01 0 0.00 -0.017 0.134
Federico Higuain 0.12 0.12 0.29 -0.3 0.02 0.03 0 -0.03 0.00 -0.002 0.031
Alexander Callens 0.12 -0.01 0.08 -0.06 0.02 0.11 0 -0.02 0.00 0.041 0.109
Alexander Ring 0.12 0.02 0.06 -0.13 0.01 0.18 -0.01 -0.02 0.00 -0.054 0.167
Nick Besler 0.119 0.02 0.04 -0.04 0.02 0.1 0.01 -0.03 0.00 0.022 0.111
Francisco Calvo 0.119 0.03 0.1 -0.1 0.01 0.14 -0.02 -0.03 0.00 -0.002 0.117
Tyler Adams 0.119 0.02 0.09 -0.12 0.01 0.12 -0.01 0 0.00 -0.020 0.115
Oriol Rosell 0.119 0.02 0.08 -0.11 0.02 0.12 0 0 0.00 -0.020 0.118
Alejandro Silva 0.118 0.16 0.1 -0.24 0.03 0.07 -0.01 0 0.00 -0.105 0.061
Christopher Durkin 0.118 0.02 0.02 -0.07 0 0.14 0.01 0 0.00 -0.049 0.151
Kevin Ellis 0.118 0.04 0.08 -0.09 0 0.11 0.01 -0.03 0.00 -0.009 0.115
Kim Kee-Hee 0.117 0 0.05 -0.05 0.01 0.11 0 0 0.00 0.013 0.105
Gustav Svensson 0.117 0.03 0.04 -0.08 0.01 0.12 -0.01 0 0.00 -0.023 0.110
Jerome Thiesson 0.117 0 0.18 -0.14 0.02 0.1 0.01 -0.04 0.00 0.056 0.103
Amro Tarek 0.116 0.01 0.07 -0.07 0.01 0.13 -0.01 -0.03 0.00 0.009 0.124
Joao Moutinho 0.116 0.03 0.1 -0.1 0.01 0.13 0.01 -0.07 0.00 0.016 0.140
Ager Aketxe 0.116 0.06 0.15 -0.22 0.05 0.07 0 0 0.00 -0.011 0.068
Tommy Thompson 0.116 0.07 0.05 -0.14 0.02 0.09 0 0.03 0.00 -0.070 0.090
Maxime Chanot 0.116 0.03 0.02 -0.04 0.01 0.1 0.01 -0.01 0.00 -0.009 0.108
Julio Cascante 0.116 0.02 0.03 -0.04 0.01 0.11 0 -0.03 0.00 0.000 0.118
Josh Williams 0.115 0.02 0.09 -0.04 0.01 0.06 0 -0.03 0.00 0.060 0.063
Wil Trapp 0.115 0.01 0.09 -0.08 0.02 0.09 0 -0.02 0.00 0.029 0.094
Roger Espinoza 0.115 0.04 0.13 -0.14 0.01 0.09 -0.01 -0.02 0.00 0.005 0.082
Jukka Raitala 0.114 0 0.06 -0.05 0.01 0.12 0.02 -0.04 0.00 0.015 0.140
Reto Ziegler 0.113 0.02 0.09 -0.07 0.01 0.1 0 -0.04 0.00 0.035 0.098
Franco Escobar 0.113 0.03 0.09 -0.1 0.02 0.12 -0.02 -0.03 0.00 0.015 0.095
Aly Ghazal 0.112 0.03 0 -0.06 0 0.15 -0.01 0 0.00 -0.054 0.140
Jimmy Ockford 0.112 -0.02 0.05 -0.07 0.01 0.12 0.02 0 0.00 -0.007 0.141
DaMarcus Beasley 0.11 0.02 0.09 -0.07 0.01 0.11 0.01 -0.05 0.00 0.031 0.110
Chad Marshall 0.109 0.02 0.05 -0.07 0 0.1 0.02 -0.01 0.00 -0.018 0.118
Daniel Lovitz 0.108 0.01 0.15 -0.13 0.02 0.09 0 -0.03 0.00 0.042 0.083
Benny Feilhaber 0.108 0.04 0.12 -0.14 0.02 0.1 -0.01 -0.02 0.00 -0.003 0.086
Ilie Sanchez 0.107 0.03 0.09 -0.11 0.03 0.11 -0.01 -0.03 0.00 0.003 0.106
Lalas Abubakar 0.107 0 0.04 -0.04 0.02 0.11 -0.01 -0.02 0.00 0.028 0.093
Servando Carrasco 0.105 0.02 0.06 -0.1 0 0.12 0 0 0.00 -0.038 0.124
Junior Moreno 0.104 0.01 0.04 -0.05 0 0.12 0 -0.02 0.00 -0.013 0.123
Rudy Camacho 0.103 0.01 0.05 -0.04 0.01 0.08 0.03 -0.03 0.00 0.015 0.107
Pablo Ruiz 0.102 0.03 0.12 -0.14 0.02 0.07 0.01 0 0.00 -0.001 0.076
Marco Urena 0.102 0.31 -0.03 -0.24 0.03 0.01 0 0.02 0.00 -0.232 0.012
Grant Lillard 0.102 -0.01 0.05 -0.07 0.01 0.12 0 0 0.00 -0.013 0.127
Eduard Atuesta 0.101 0.06 0.07 -0.11 0.03 0.12 -0.03 -0.03 0.00 -0.017 0.089
Luis Felipe Fernandes 0.1 0.01 0.02 -0.06 0.01 0.15 -0.02 0 0.00 -0.037 0.129
Justen Glad 0.1 0.01 0.04 -0.06 0.01 0.12 0.01 -0.03 0.00 -0.011 0.131
Jordan Harvey 0.1 -0.01 0.09 -0.07 0 0.09 0 0 0.00 0.024 0.088
Jordy Delem 0.1 0.04 0.01 -0.06 0 0.11 0 0 0.00 -0.043 0.105
Aaron Herrera 0.099 0.01 0.07 -0.09 0.02 0.1 0 0 0.00 0.001 0.091
Jorgen Skjelvik 0.099 0.01 0.07 -0.05 0.01 0.09 0.01 -0.04 0.00 0.025 0.099
Micheal Azira 0.098 0.02 0.03 -0.07 0 0.11 0 0 0.00 -0.028 0.106
Jeff Larentowicz 0.098 0.01 0.02 -0.04 0 0.11 -0.01 0 0.00 -0.017 0.100
Hector Jimenez 0.097 0.03 0.07 -0.09 0.02 0.05 0.01 0 0.00 -0.003 0.068
Kemar Lawrence 0.096 0.02 0.11 -0.11 0.01 0.08 0.02 -0.02 0.00 0.002 0.096
RJ Allen 0.096 0 0.16 -0.13 0.02 0.1 0 -0.05 0.00 0.041 0.101
Krisztian Nemeth 0.096 0.28 0 -0.24 0.01 0.02 0 0.02 0.00 -0.227 0.021
Marcel de Jong 0.096 0 0.19 -0.11 0.01 0.11 0 -0.1 0.00 0.089 0.103
Ebenezer Ofori 0.095 0.02 0.03 -0.06 0.01 0.12 -0.01 -0.02 0.00 -0.024 0.118
Chris Wondolowski 0.095 0.2 0.03 -0.17 -0.01 0.05 0 0 0.00 -0.144 0.043
Nick Lima 0.094 0.01 0.14 -0.15 0.02 0.08 0 0 0.00 0.003 0.078
Daniel Steres 0.094 0 0.07 -0.04 0.02 0.07 0.01 -0.03 0.00 0.051 0.082
Brek Shea 0.094 0.05 0.11 -0.14 0.03 0.05 -0.01 0 0.00 0.000 0.040
Mark McKenzie 0.093 0 0.02 -0.03 0.01 0.11 0 -0.03 0.00 0.007 0.116
Shane O'Neill 0.093 -0.01 0.03 -0.03 0.02 0.09 0.02 -0.02 0.00 0.012 0.110
Tony Rocha 0.092 0.02 0.07 -0.1 0.01 0.11 -0.02 0 0.00 -0.019 0.090
Russell Teibert 0.092 0.02 0.07 -0.09 0.01 0.07 0.01 0 0.00 -0.004 0.081
Jonathan Spector 0.09 0.01 0.07 -0.07 0.01 0.07 0 0 0.00 0.002 0.074
Christian Ramirez 0.089 0.21 -0.01 -0.12 0 0.01 0 0 0.00 -0.125 0.002
Fanendo Adi 0.089 0.26 -0.01 -0.23 0.04 0.03 0 0 0.00 -0.194 0.023
Memo Rodriguez 0.089 0.04 0.15 -0.16 0.03 0.05 -0.02 0 0.00 0.016 0.034
Auston Trusty 0.088 0 0.04 -0.07 0.01 0.11 0.02 -0.02 0.00 -0.019 0.128
Michael Ciani 0.088 0.01 0.02 -0.05 -0.01 0.12 0.03 -0.03 0.00 -0.041 0.149
Carlos Gruezo 0.087 0.04 0.01 -0.07 0.01 0.09 0 0 0.00 -0.040 0.090
Jared Watts 0.087 -0.02 0.01 -0.03 0.01 0.09 0.03 0 0.00 -0.017 0.120
Anibal Godoy 0.087 0.01 0.05 -0.08 0.02 0.13 -0.01 -0.02 0.00 -0.017 0.116
Nouhou Tolo 0.086 0.02 0.08 -0.11 0.03 0.1 0 -0.02 0.00 -0.010 0.101
Florian Valot 0.084 0.17 0.1 -0.24 0.01 0.06 -0.02 0 0.00 -0.132 0.045
Mikey Ambrose 0.084 -0.01 0.12 -0.1 0.01 0.05 0.01 0 0.00 0.031 0.059
Steve Birnbaum 0.084 0.01 0.05 -0.04 0 0.05 0.01 0 0.00 0.008 0.063
Lawrence Olum 0.084 0.01 0.02 -0.05 0 0.13 -0.01 -0.02 0.00 -0.031 0.122
Greg Garza 0.082 0.05 0.1 -0.11 0.01 0.06 -0.01 -0.02 0.00 0.000 0.053
Axel Sjoberg 0.081 -0.02 0.02 -0.03 0 0.09 0.02 0 0.00 -0.014 0.112
Kyle Beckerman 0.081 0.01 0.04 -0.1 0.01 0.13 -0.01 0 0.00 -0.046 0.119
Jordon Mutch 0.081 0.08 0.1 -0.16 0.03 0.04 -0.01 0 0.00 -0.027 0.024
Sean Franklin 0.081 0 0.13 -0.07 0.01 0.08 -0.01 -0.05 0.00 0.059 0.076
Kelyn Rowe 0.079 0.09 0.14 -0.25 0.01 0.09 0 0.01 0.00 -0.102 0.084
Jackson Yueill 0.079 0.04 0.09 -0.12 0.02 0.05 0 0 0.00 -0.010 0.050
Patrick Mullins 0.078 0.15 0.03 -0.17 0.02 0.01 0 0.03 0.00 -0.116 0.010
Seth Sinovic 0.077 0.01 0.12 -0.08 0.04 0.07 -0.01 -0.06 0.00 0.075 0.054
Victor Ulloa 0.076 0.03 0.06 -0.1 0.01 0.11 -0.01 -0.02 0.00 -0.034 0.104
Gaston Sauro 0.076 0 0.05 -0.04 0.01 0.13 0 -0.09 0.00 0.027 0.137
Kendall Waston 0.075 0.05 0.06 -0.08 0.02 0.08 0.03 -0.09 0.00 0.000 0.119
Johan Kappelhof 0.075 0 0.05 -0.07 0.01 0.12 0 -0.03 0.00 -0.013 0.113
Gabriel Somi 0.075 0.01 0.17 -0.16 0.01 0.07 -0.02 0 0.00 0.019 0.048
Eriq Zavaleta 0.074 -0.01 0.04 -0.04 0.01 0.12 0.01 -0.06 0.00 0.022 0.124
Brandon Vincent 0.074 0 0.12 -0.11 0.01 0.06 0.01 -0.02 0.00 0.022 0.063
Marcelo Silva 0.07 0 0.01 -0.04 0 0.1 0 0 0.00 -0.029 0.100
Alvas Powell 0.07 0.06 0.1 -0.19 0.03 0.1 -0.02 -0.01 0.00 -0.058 0.079
Marquinhos Pedroso 0.069 0 0.08 -0.11 0.02 0.1 0.03 -0.04 0.00 -0.022 0.130
Sunny 0.067 0 0 -0.07 0.01 0.13 0 0 0.00 -0.066 0.134
Darlington Nagbe 0.067 0.02 0.02 -0.08 0.02 0.08 0 0.01 0.00 -0.040 0.081
Deklan Wynne 0.066 0 0.1 -0.07 0.01 0.06 0 -0.03 0.00 0.035 0.061
Enzo Martinez 0.066 0.04 0.05 -0.14 0.02 0.09 0 0 0.00 -0.069 0.096
Angelo Rodriguez 0.066 0.2 0.04 -0.23 0.03 0.03 0 0 0.00 -0.159 0.024
Brandt Bronico 0.065 0.04 0.05 -0.11 0.02 0.06 -0.01 0.01 0.00 -0.043 0.053
Jalil Anibaba 0.064 0.01 0.05 -0.05 0 0.1 0 -0.03 0.00 -0.008 0.096
Antonio Mlinar Delamea 0.062 -0.02 0.03 -0.03 0 0.18 -0.01 -0.09 0.00 0.007 0.168
Doneil Henry 0.061 0.01 0.06 -0.06 0 0.17 0 -0.11 0.00 -0.001 0.161
Eric Alexander 0.061 0.02 0.02 -0.07 0.01 0.09 0 -0.01 0.00 -0.041 0.092
Ulises Segura 0.06 0.1 0.04 -0.17 0.02 0.07 0 0 0.00 -0.110 0.068
Alexi Gomez 0.06 0.06 0.09 -0.15 0.02 0.08 -0.02 -0.04 0.00 -0.033 0.066
Alex Muyl 0.06 0.17 0.1 -0.28 0.01 0.08 -0.01 0 0.00 -0.175 0.068
Waylon Francis 0.058 0 0.16 -0.17 0.01 0.06 0.01 0 0.00 -0.007 0.064
Juan Agudelo 0.058 0.14 0 -0.19 0.05 0.07 -0.01 0 0.00 -0.143 0.059
Sam Nicholson 0.057 0.11 0.05 -0.17 0.03 0.05 -0.01 0 0.00 -0.095 0.040
Luis Caicedo 0.056 0.04 0.04 -0.1 0 0.09 -0.02 0 0.00 -0.062 0.077
Danny Wilson 0.055 0.01 0.02 -0.06 0.01 0.08 -0.01 0 0.00 -0.028 0.071
Carter Manley 0.053 0 0.1 -0.13 0.01 0.08 0 0 0.00 -0.026 0.080
Nicolas Hasler 0.053 0.04 0.08 -0.14 0.03 0.06 -0.01 0 0.00 -0.041 0.049
Artur 0.052 0.04 0.04 -0.13 0.02 0.12 -0.01 -0.02 0.00 -0.076 0.110
Mohammed Adams 0.051 0 0.01 -0.07 0 0.14 -0.01 -0.02 0.00 -0.060 0.127
Erik Hurtado 0.051 0.15 0.03 -0.22 0.04 0.04 0 0 0.00 -0.147 0.044
Chris Pontius 0.051 0.09 0.08 -0.19 0 0.07 0 0 0.00 -0.116 0.073
Lee Nguyen 0.05 0.06 0.08 -0.15 0.03 0.05 -0.01 -0.01 0.00 -0.044 0.047
Joseph Mora 0.05 0 0.09 -0.09 0.01 0.08 0 -0.04 0.00 0.011 0.078
Tyler Miller 0.049 0 0.04 -0.04 0 0.02 0.01 -0.02 0.03 0.004 0.029
Handwalla Bwana 0.048 0.09 0.04 -0.21 0.08 0.06 -0.02 0 0.00 -0.082 0.040
Maximiniano 0.047 0.02 -0.02 -0.07 0.01 0.2 -0.01 -0.08 0.00 -0.085 0.191
Alejandro Fuenmayor 0.046 0.05 0.05 -0.07 0.01 0.12 0 -0.12 0.00 -0.004 0.115
Kortne Ford 0.045 -0.01 0.06 -0.08 0.01 0.11 0 -0.04 0.00 -0.012 0.107
Warren Creavalle 0.045 0 0.01 -0.08 0.02 0.14 0.01 -0.06 0.00 -0.041 0.146
Anthony Blondell 0.043 0.14 0.05 -0.21 0.02 0.02 0 0.03 0.00 -0.145 0.026
Jordan McCrary 0.042 0 0.09 -0.12 0.01 0.11 0 -0.06 0.00 -0.017 0.113
Alex Roldan 0.042 0 0.06 -0.13 0.02 0.11 -0.01 0 0.00 -0.054 0.100
Frederic Brillant 0.04 -0.01 0.02 -0.06 0.01 0.07 0.01 0 0.00 -0.025 0.078
Jonathan Campbell 0.04 0 0.02 -0.05 0 0.1 0.01 -0.05 0.00 -0.030 0.112
Eric Miller 0.038 0 0.11 -0.1 0 0.03 -0.01 0 0.00 0.013 0.027
Clint Dempsey 0.034 0.21 0.07 -0.19 0.02 0.02 0 -0.09 0.00 -0.112 0.022
Matteo Mancosu 0.032 0.13 0 -0.15 0.02 0.03 0 0 0.00 -0.130 0.032
Kelvin Leerdam 0.032 0 0.19 -0.19 0.02 0.05 0 -0.04 0.00 0.022 0.050
Khiry Shelton 0.032 0.17 0 -0.18 0.01 0.03 0 0 0.00 -0.169 0.032
Brandon Bye 0.03 0.02 0.09 -0.15 0.01 0.06 0 0 0.00 -0.047 0.062
Roman Torres 0.03 -0.01 0.05 -0.07 0 0.09 -0.03 0 0.00 -0.022 0.064
Zack Steffen 0.029 0 0.05 -0.02 0 0.03 0 0 -0.03 0.029 0.027
Jose Aja 0.028 0 0.03 -0.07 0.01 0.13 -0.01 -0.05 0.00 -0.037 0.123
Anthony Jackson-Hamel 0.027 0.25 -0.01 -0.23 0 0.03 0 0 0.00 -0.248 0.024
Danilo Acosta 0.026 0.02 0.08 -0.11 0.01 0.08 -0.01 -0.05 0.00 -0.013 0.072
Damir Kreilach 0.026 0.16 0.01 -0.16 0.02 0.04 -0.01 -0.03 0.00 -0.131 0.031
Drew Conner 0.025 0 0.08 -0.11 0.01 0.13 -0.01 -0.08 0.00 -0.016 0.123
C.J. Sapong 0.024 0.18 0 -0.2 0 0.03 0 0.01 0.00 -0.198 0.029
Tim Howard 0.021 0 0.05 -0.04 0 0.02 0.02 -0.04 0.00 0.019 0.039
Stefano Pinho 0.02 0.28 -0.02 -0.22 -0.04 0.02 0 0 0.00 -0.284 0.020
Jordan Hamilton 0.02 0.25 -0.03 -0.23 0 0.02 0 0 0.00 -0.252 0.021
Jo Inge Berget 0.019 0.15 0.03 -0.21 0.02 0.02 0 0.01 0.00 -0.165 0.021
Chris Duvall 0.018 0 0.09 -0.1 0.02 0.06 -0.01 -0.04 0.00 0.010 0.046
Perry Kitchen 0.018 -0.01 0.01 -0.05 0.01 0.09 -0.01 -0.02 0.00 -0.031 0.080
Bismark Boateng 0.018 0.04 0.05 -0.13 0.02 0.11 -0.01 -0.06 0.00 -0.063 0.098
Johan Blomberg 0.018 0.01 0.09 -0.14 0.01 0.07 -0.01 -0.02 0.00 -0.040 0.061
Collen Warner 0.017 0 0.02 -0.08 0 0.12 -0.02 -0.03 0.00 -0.053 0.098
Steve Clark 0.014 0 0.07 -0.03 0 0.01 0 0 -0.03 0.034 0.015
Andre Blake 0.012 0 0.07 -0.03 0 0.01 0.01 -0.02 -0.03 0.037 0.016
Elliot Collier 0.01 0.13 0.03 -0.2 0.02 0.03 -0.01 0 0.00 -0.150 0.025
Sean Johnson 0.009 0 0.05 -0.06 0 0.02 0 -0.02 0.01 -0.006 0.023
Josue Colman 0.009 0.1 0.04 -0.2 0.05 0.03 -0.01 0 0.00 -0.109 0.018
Collin Martin 0.007 0.03 0 -0.09 0.01 0.07 -0.01 0 0.00 -0.082 0.060
Dairon Asprilla 0.003 0.15 0.03 -0.27 0.05 0.04 0 0 0.00 -0.191 0.039
Dillon Serna 0 0.1 0.06 -0.18 0.02 0.04 0 -0.04 0.00 -0.097 0.037
Kellyn Acosta -0.002 0.05 0.09 -0.16 0.02 0.06 -0.01 -0.05 0.00 -0.052 0.053
Eric Remedi -0.004 0.05 0.02 -0.09 0.02 0.07 -0.02 -0.04 0.00 -0.056 0.049
Kei Kamara -0.006 0.22 -0.01 -0.23 -0.02 0.03 0 0.01 0.00 -0.260 0.032
Teal Bunbury -0.006 0.25 -0.02 -0.28 0 0.03 0 0.01 0.00 -0.303 0.035
Kofi Opare -0.01 -0.05 0.02 -0.03 0 0.06 0 0 0.00 -0.016 0.060
Cristian Higuita -0.01 0.08 0.04 -0.13 0.02 0.12 -0.02 -0.12 0.00 -0.072 0.105
Michael Petrasso -0.011 0 0.09 -0.18 0.03 0.06 0 0 0.00 -0.060 0.053
Giles Barnes -0.013 -0.01 0.05 -0.11 0 0.05 0 0 0.00 -0.054 0.050
Jack McBean -0.02 0.14 0.02 -0.23 0 0.04 -0.01 0.02 0.00 -0.205 0.029
Alan Gordon -0.02 0.23 -0.02 -0.23 -0.03 0.03 0 0 0.00 -0.280 0.029
Dejan Jakovic -0.022 0 0.04 -0.06 0.01 0.13 0 -0.15 0.00 -0.013 0.137
Brad Guzan -0.023 0 0.04 -0.05 0 0.04 0 -0.03 -0.03 -0.003 0.037
Bobby Shuttleworth -0.027 0 0.03 -0.03 0 0.01 0 0 -0.03 -0.003 0.010
Luis Solignac -0.033 0.09 0.06 -0.23 0 0.05 -0.01 0 0.00 -0.165 0.044
Richard Sanchez -0.035 0 0.05 -0.03 0 0.02 0 -0.02 -0.06 0.018 0.028
Liam Fraser -0.035 0.01 0.06 -0.09 0.01 0.09 -0.03 -0.08 0.00 -0.029 0.058
Jimmy Maurer -0.036 0 0.04 -0.04 0 0 0 -0.04 0.00 0.002 0.005
Ashtone Morgan -0.04 0.01 0.06 -0.09 0.01 0.06 0 -0.09 0.00 -0.018 0.061
David Bingham -0.04 0 0.1 -0.06 0 0.03 -0.02 0 -0.11 0.048 0.018
Ken Krolicki -0.047 0.03 0 -0.09 0.01 0.07 0 -0.06 0.00 -0.089 0.068
Alexander Bono -0.05 0 0.06 -0.05 0 0.01 0 -0.02 -0.04 0.011 0.003
Rodney Wallace -0.071 0.09 0.01 -0.21 0 0.04 0.01 0 0.00 -0.205 0.049
Jozy Altidore -0.073 0.25 0.01 -0.26 0.04 0.01 0 -0.11 0.00 -0.223 0.009
Joe Bendik -0.09 0 0.05 -0.04 0 0.02 -0.01 0 -0.11 0.014 0.008
Marcus Epps -0.096 0.07 0.03 -0.25 0.02 0.05 0 0 0.00 -0.205 0.042
Matthew Lampson -0.103 0 0.03 -0.03 0 0.05 0 0 -0.15 -0.003 0.051
Fabinho -0.112 0 0.07 -0.14 0.02 0.08 0 -0.13 0.00 -0.051 0.075
Efrain Juarez -0.118 0.01 0 -0.09 0 0.06 -0.01 -0.1 0.00 -0.084 0.054
Yannick Boli -0.145 0.13 0 -0.19 -0.01 0.02 -0.01 -0.09 0.00 -0.193 0.007
Cristian Colman -0.164 0.26 0 -0.33 -0.07 0.02 0 -0.05 0.00 -0.398 0.021
Stefan Marinovic -0.173 0 0.06 -0.02 0 0.02 0 0 -0.23 0.034 0.020
Andrew Tarbell -0.201 0 0.06 -0.05 0 0.01 0.02 -0.02 -0.22 0.010 0.026
David Ousted -0.225 0 0.05 -0.04 0 0.04 -0.02 0 -0.26 0.009 0.021
Brian Rowe -0.271 0 0.07 -0.03 0 0 0 0 -0.31 0.040 0.003
Niki Jackson -0.34 0.04 -0.01 -0.21 0.04 0.03 -0.02 -0.23 0.00 -0.173 0.019
Jake Gleeson -0.367 0 0.06 -0.04 0 0 0.03 0 -0.43 0.030 0.030
Chris Seitz -0.402 0 0.06 -0.03 0 0.01 0 -0.08 -0.36 0.034 0.008
Clint Irwin -0.55 0 0.06 -0.03 0 0.03 0 0 -0.60 0.026 0.025