SEBA Projections

MLS, USL, and NWSL projections through March 4th

Below are the initial 2018 MLS season forecasts using data from games through March 4, 2018.

*I’ve made a change to the model to factor in distance traveled to include USL, legacy NASL, and NWSL clubs in addition to MLS. However, distances are treated differently for each league to account for the likely difference in relaxation/ease-of-travel that different levels can afford to provide their players.

Interactive Charts

MLS forecasts

MLS forecasts over time

USL forecasts

USL forecasts over time

Power Rankings

The “Power Rankings” we concoct are the actual “strength” of the team according to competitive expectations. They are computed showing average expected points result if every team in MLS played every team both home and away.

SEBA has Philadelphia in 7th.

Currently, Philadelphia’s performance expectation is set by the following distribution of minutes:

Philadelphia +/- for 2018

Philadelphia Plus/Minus

plyrNetGoalsPlusGoalsMinusGoalsMINSNetGoalsPer90PlusGoalsPer90MinusGoalsPer90
Cory Burke6292314270.3781.8291.451
Alejandro Bedoya5494427770.1621.5881.426
Jay Simpson4401582.2782.2780
Mark McKenzie3302716290.1661.6571.492
Raymon Gaddis3434022980.1171.6841.567
John McCarthy22090220
Keegan Rosenberry2464427730.0651.4931.428
Olivier Mbaizo110851.0591.0590
Derrick Jones1543850.2341.1690.935
Haris Medunjanin1414025300.0361.4581.423
Warren Creavalle0111168101.4541.454
Marcus Epps0101060901.4781.478
Auston Trusty-149503060-0.0291.4411.471
Borek Dockal-137382397-0.0381.3891.427
Anthony Fontana-11299-0.9090.9091.818
Fafa Picault-240422190-0.0821.6441.726
Andre Blake-347502970-0.0911.4241.515
Ilsinho-31316985-0.2741.1881.462
Fabinho-3710714-0.3780.8821.261
Fabian Herbers-347428-0.6310.8411.472
David Accam-417211213-0.2971.2611.558
Matthew Real-415236-1.5250.3811.907
Jack Elliott-518231430-0.3151.1331.448
CJ Sapong-631372321-0.2331.2021.435
Shows the goal differential for each player while they were on the field

 

Playoffs probability and more

Philadelphia has increased from a 53.1% to a 67.9% chance of making the playoffs.

Next is the distribution of Philadelphia’s points against the MLS East’s sixth seed in all the simulations.

 

Philadelphia increased from a 3.2% to a 7.2% chance of winning the Supporters’ Shield.

The Union has increased from a 3.4% to a 5.8% chance of winning the MLS Cup.

* I now have assumed to exclude NASL clubs from USOC as their season was cancelled, and I would guess that those clubs that moved to the NPSL are too late to attempt qualification.

Philadelphia’s chances of having a U.S. Open Cup win increased from 4.2% to 4.5%.

 

The following 2018 CCL Champion odds do not include Tuesday night’s two matches.

The 2019 CONCACAF Champions League qualification from the U.S. was supposed to be the 2017 & 2018 winners of the U.S. Open Cup and the MLS Cup. This is why Kansas City has already qualified. However, since Toronto (not eligible for a U.S. qualification slot) won the MLS Cup, that slot (and any other future Canadian or Kansas City wins) will be given to the club with the most regular season points over both 2017 and 2018.

Philadelphia has increased from an 8.1% to a 11.8% chance of qualifying for the 2019 edition of the CONCACAF Champions League.

The following are the probabilities of each final playoffs placement for Philadelphia.

The following shows the summary of the simulations in an easy table format.

TeamConfptsWLTGFGAGDpowerRkpwrScrPlyfsMLSCupShldUSOCChmpCANChmpCCLQualCCLChmpconfRegSeasFinishregSeasFinishplyfFinish
New York Red BullsEast71227562332921.85210030.71000NA69.70112.9
Atlanta UnitedEast69217670442611.87110024.100NA100NA223.3
Sporting Kansas CityWest62188865402531.73610014.900NA100NA133.6
Seattle SoundersWest591811552371551.6131008.100NA8.10244.4
Los Angeles FCWest57169968521641.651007.700NA7.7NA355.2
FC DallasWest5716995244861.5711004.200NA4.20466.4
New York CityEast561610859451481.5471003.300NA3.3NA376.7
Portland TimbersWest54151095448671.5541001.800NA1.8NA588.2
DC UnitedEast511411960501091.5211003.500NA3.5NA497.3
Columbus CrewEast51141194345-2111.41000.700NA0.7NA5109.3
Philadelphia UnionEast50151454950-1101.4161000.600NA0.6NA6119.9
Real Salt LakeWest49141375558-3151.3191000.300NA0.3NA61210.9
Los Angeles GalaxyWest481312966642141.3220000NA0NA71313
Vancouver WhitecapsWest47131385467-13161.227000NA00NA81414
Montreal ImpactEast46141644753-6121.388000NA00NA71515
New England RevolutionEast411013114955-6181.1890000NA0NA81616
Houston DynamoWest381016858580171.199000100NA100NA91717
Minnesota UnitedWest36112034971-22210.9630000NA0NA101818
Toronto FCEast36101865964-5131.342000NA100100091919
Chicago FireEast3281884861-13191.0680000NA0NA102020
Colorado RapidsWest3181973663-27220.9260000NA00112121
Orlando CityEast2882244374-31200.9750000NA0NA112222
San Jose EarthquakesWest2142194971-22230.8060000NA0NA122323

Next is a table showing the difference between this forecast and the last forecast.

TeamConfptsWLTGFGAGDpowerRkpwrScrPlyfsMLSCupShldUSOCChmpCANChmpCCLQualCCLChmpconfRegSeasFinishregSeasFinishplyfFinish
New York Red BullsEast0.30.1-0.1-0.1-1.7-0.7-100.01701.455.60NA0.40-0.6-0.6-0.1
Atlanta UnitedEast-1.6-0.50.6-0.1-12.1-3.10-0.0470-3.4-55.60NA0NA0.60.60.3
Sporting Kansas CityWest10.4-0.2-0.20-0.20.200.0302.400NA0NA-0.5-0.5-0.4
Seattle SoundersWest0.40.2-0.1-0.1-0.10.2-0.4-10.01701.500NA1.50-0.7-0.7-0.7
Los Angeles FCWest-0.8-0.20.4-0.2-0.20-0.20-0.0090-0.300NA-0.3NA00-0.1
FC DallasWest-1.7-0.50.7-0.2-0.70.7-1.4-1-0.020-1.700NA-1.701.11.10.9
New York CityEast1.10.4-0.3-0.11.1-0.11.2-10.04801.300NA1.3NA-0.4-1-0.8
Portland TimbersWest-1.4-0.40.6-0.2-0.70.3-12-0.0450-1.100NA-1.1NA0.20.70.5
DC UnitedEast-0.4-0.4-0.40.8-1.7-1.701-0.02500.700NA0.7NA-0.8-1.1-1
Columbus CrewEast0.60.2-0.1-0.10.91.2-0.3-1-0.0017.9000NA0NA-0.3-1.3-0.6
Philadelphia UnionEast-1-0.30.4-0.1-0.11.1-1.20-0.0090-0.400NA-0.4NA1.411
Real Salt LakeWest000000010.013700.200NA0.2NA-0.7-0.8-1.6
Los Angeles GalaxyWest-2.2-0.70.8-0.1-0.61.2-1.81-0.059-70-0.700NA-0.7NA0.72.12.4
Vancouver WhitecapsWest1.50.6-0.4-0.20.3-0.7100.023000NA00NA0-0.8-0.8
Montreal ImpactEast-1.1-0.30.5-0.1-1.3-0.9-0.51-0.024-7.900NA00NA0.11.31.3
New England RevolutionEast1.20.5-0.3-0.1-0.9-1.30.510.0180000NA0NA0-0.1-0.1
Houston DynamoWest2.40.8-0.7-0.11.2-0.61.8-10.0480000NA0NA-0.9-1.1-1.1
Minnesota UnitedWest-0.5-0.10.2-0.11.20.90.31-0.0230000NA0NA0.90.90.9
Toronto FCEast1.70.6-0.5-0.12.1-13.1-20.055000NA000-0.20.10.1
Chicago FireEast-0.4-0.4-0.40.8-1.7-1.700-0.0280000NA0NA0.20.20.2
Colorado RapidsWest1.90.7-0.5-0.20.7-0.71.400.0350000NA000-0.6-0.6
Orlando CityEast-0.2-0.10.1-0.1-0.7-1.71-1-0.0060000NA0NA00.60.6
San Jose EarthquakesWest-0.3-0.10.2-0.10.2-0.10.40-0.0060000NA0NA000

The following shows the difficulty for each team’s remaining schedule

The following shows the expectations for upcoming Philadelphia matches:

USL
Power Rankings

SEBA has Bethlehem starting out in 18th. However, the model has no data on changes to squads, so this is largely reflective of last season’s finish (and model changes such as distance traveled).

Playoffs probability and more

We start off with Bethlehem at 49.2% chance of making the playoffs. (Sorry for the cutoff table, I’m working on it, but the interactive charts linked above should be easier to read)

We give Bethlehem a 2.9% chance of claiming the USL regular season title.

Bethlehem’s odds of winning the USL championship start off at 3.1%.

The following shows the probability of each post-playoff ranking finish:

The following are the upcoming expectations for Bethlehem matches.

 

NWSL

The following are the initial projections for the 2018 NWSL season. Like the USL, these are based solely on matches from 2017 and the additional factor of distance traveled.

The following shows the strength of schedule, as we project.

 

Below is the table form of the above. “AdvPerc” is the percentage advantage in points that a team has over the league average, so higher is an easier schedule.

2 Comments

  1. Chris, what informed your “baseline” assumption about each team’s chances? In other words, what was your starting point, before any Week 1 match results?

    • Chris Sherman says:

      As far as the team’s assessments (excluding other factors such as home/away and distance traveled), it was based upon the previous season’s results, weighted by how many of the players in those matches were still on the team’s roster.

      Last week’s post of power rankings should give you a sense for how the model considered them prior to week 1 (although there were a few CCL matches). If you look at the interactive chart’s pwrScr over time, you should have a sense for how the model’s opinion of teams change throughout the course of the season.

      Did I answer your question?

Leave a Reply

Your email address will not be published.

*

%d bloggers like this: