SEBA Projections

Updated MLS, USL, and NWSL SEBA projections through October 1

Below are the updated season forecasts using data from games through October 1.

Power Rankings

The “Power Rankings” we concoct are the “strength” of the team according to its competitive expectations. They are computed by forecasting the expected points (3 x win probability + 1 x draw probability) against every other MLS team – both home and away – and taking the average per team.

SEBA has the Union jumping from 15th to 13th. By comparison, ESPN has Philadelphia remaining at 13th while Soccer America has the Union climbing from 18th to 15th.

For those interested in how Philadelphia’s matches are weighted in the model (especially if skeptical about why SEBA’s rankings can be different from other outlets):

‘wght’ is the actual weight value used in the model, which is a combination of the ‘timewght’ (how long ago the match occurred), ‘goalWght’ (how much luck could have influenced the match result, as indicated by the goal differential), and ‘rostWght’ (how similar the roster deployments for both teams were compared with current trends).

By comparison, the current roster expectations for maximum weight for the Union (and therefore the model’s assessment of ‘who’ Philadelphia is in the model) are currently:

The following shows the evolution of SEBA’s power rankings for the MLS Eastern Conference over time:

Playoffs probability and more

Philadelphia’s playoffs odds increased from 0.3% to 1.1%.

The following shows the simulation distribution for the points earned by the sixth place MLS East club as well as the simulation distribution of points that Philadelphia is expected to earn.

Tiebreakers aside, the Union make the playoffs when >= this MLS Eastern Conference 6th place value.

The most common number of points required to make the playoffs in the East’s 6th slot decreased from 49 to 47 while the most common number of points simulated for the Union increased from 40 to 42.

In part, clubs that score a lot of goals are given an advantage in MLS Cup due to the two-leg aggregate goal format of the conference semifinals and finals. That gives those clubs a better chance at notching large victories which carry over.

The Union’s chances of winning the MLS Cup remain at practically zero.

Philadelphia’s chances for qualifying in 2017 for the 2019 edition of the CONCACAF Champions League remain at practically zero.

Over time, we can see how Philadelphia’s odds for different prizes have changed:

The following shows the probability of each playoff ranking finish:

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

Next, we show how the Remaining Strength of Schedule affects each team.

The “Points Percentage Advantage” shown on the X-axis represents the percentage of points expected over the league average schedule. This “points expected” value is generated by simulating how all teams would perform with all remaining schedules (and therefore judges a schedule based upon how all teams would perform in that scenario).

In short, the higher the value, the easier the remaining schedule.

Accompanying the advantage percentage in the following table is their current standings rank (right now ties are not properly calculated beyond pts/gd/gf; I may fix that, but maybe not for a while), the remaining home matches, the remaining away matches, the current average points-per-game of future opponents (results-based, not model-based), and the average power ranking of future opponents according to SEBA.

The following shows the expectations for upcoming Philadelphia matches:

Last Game Probability Chart

This model finally incorporates changing team statistics due to subs and yellow/red cards.

For the following, the green line represents the odds of a win, the blue line the odds of a tie, and the red line the odds of a loss.

The following shows the changing proportion of Philadelphia’s probability of scoring goals compared with their opponents’. This proportion can only change due to subs, yellow cards, and red cards.

*For example, a value of 2 means that Philadelphia is twice as likely to score as its opponent. A value of 0.5 means that Philadelphia is half as likely to score as its opponent.

The following shows the changing raw probability of the two teams each scoring a goal. Green is Philadelphia’s probability of scoring a goal and red is their opponent’s probability of scoring a goal.

The reason for the spike at the 45th minute is because ’45+x’ is condensed to the 45th minute (therefore increasing the frequency of goals occurring in the 45th minute) to avoid duplication with the actual 46th/47th/etc minute, whereas the same situation does not occur for ’90+x’ minute, for which we actually calculate the addition and attribute the action to the goals to the 91st/92nd/etc minute if they occur.

 Philadelphia +/- Player Analysis

The ‘+’ is a measure counting how many goals were scored by the Union while the player was on the field. The ‘-‘ counts how many goals were scored against the Union while the player was on the field.

Player Net + MINS Net/90 +/90 -/90
1 Chris Pontius 8 32 24 4499 0.160 0.640 0.480
2 Jack Elliott 5 37 32 6104 0.074 0.546 0.472
3 Derrick Jones 4 13 9 1525 0.236 0.767 0.531
4 Giliano Wijnaldum 3 19 16 3087 0.087 0.554 0.466
5 Warren Creavalle 2 8 6 1014 0.178 0.710 0.533
6 Raymon Gaddis 2 28 26 4722 0.038 0.534 0.496
7 Oguchi Onyewu 2 29 27 5040 0.036 0.518 0.482
8 Andre Blake 2 32 30 5580 0.032 0.516 0.484
9 Ilsinho 1 23 22 3231 0.028 0.641 0.613
10 CJ Sapong 1 41 40 7074 0.013 0.522 0.509
11 Alejandro Bedoya 0 35 35 6267 0.000 0.503 0.503
12 Fabian Herbers 0 8 8 660 0.000 1.091 1.091
13 Haris Medunjanin -1 42 43 7641 -0.012 0.495 0.506
14 Adam Najem -2 1 3 142 -1.268 0.634 1.901
15 Fabinho -3 23 26 4406 -0.061 0.470 0.531
16 John McCarthy -3 10 13 2070 -0.130 0.435 0.565
17 Fafa Picault -4 25 29 4191 -0.086 0.537 0.623
18 Keegan Rosenberry -4 14 18 2593 -0.139 0.486 0.625
19 Richie Marquez -4 13 17 2493 -0.144 0.469 0.614
20 Roland Alberg -4 13 17 1606 -0.224 0.729 0.953
21 Joshua Yaro -4 5 9 942 -0.382 0.478 0.860
22 Jay Simpson -4 5 9 363 -0.992 1.240 2.231
23 Marcus Epps -5 6 11 921 -0.489 0.586 1.075
Model Validation

The following shows the overall net values since 2013 which is when data is available.

The following shows the degree of error by the model vs the error if the model was purely random without intelligence. The x-axis is based on the date from which the forecast was made (this will update throughout the season as more results are finalized and compared with predictions). The ordinal squared error metric (not a traditional metric) is calculated as:

(ProbW – ActW)^2 + (ProbT – ActT)^2 + (ProbL – ActL)^2 +

((ProbW + ProbT) – (ActW + ActT))^2 +

((ProbL + ProbT) – (ActL + ActT))^2

where Prob[W/T/L] is the model’s probability of resulting outcomes and Act[W/T/L] is a 1 or 0 representation of whether it actually happened.

Random errors will decline when more ties occur as there is a less severe penalty for ties.

We should expect random errors to remain relatively constant over time, where our model’s errors will hopefully decline as the season goes on as it gathers new information.

These data points are not fixed until the end of the season due to additional matches adding to them.

USL
Power Rankings

SEBA has the Bethlehem Steel dropping from 15th to 17th while it has Harrisburg City jumping from 25th to 21st.

The following shows the evolution of SEBA’s power rankings for the USL East over time.

Playoffs probability and more

Bethlehem’s playoff odds have decreased from 85.5% to 68.3%.

Bethlehem’s odds at becoming the USL Champion have decreased from 1.1% to 1.0%:

Over time, we can see how the odds for different prizes change for Bethlehem and Harrisburg.

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

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

We can also clarify how the Remaining Strength of Schedule affects each team.

The “Points Percentage Advantage” shown on the X-axis represents the percentage of points expected over the league average schedule. This “points expected” value is generated by simulating how all teams would perform with all remaining schedules (and therefore judges a schedule based upon how all teams would perform in that scenario).

In short, the higher the value, the easier the remaining schedule.

Remaining home field advantage will be significant here. It can also be true that a better team has an ‘easier’ schedule simply because they do not have to play themselves. Likewise, a bad team may have a ‘harder’ schedule because they also do not play themselves.

The table following the chart also shares helpful context with these percentages.

Accompanying the advantage percentage in the following table is their current standings rank (right now ties are not properly calculated beyond pts/gd/gf), the remaining home matches, the remaining away matches, the current average points-per-game of future opponents (results-based, not model-based), and the average power ranking of future opponents according to SEBA.

The following shows the expectations for upcoming matches for both Bethlehem and Harrisburg:

Model Validation

This chart is the same as that in the MLS forecast (except for USL matches instead of MLS).

Remember that these data points are not fixed until the end of the season.

NWSL
Power Rankings

 

Playoffs probability and more

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

The SEBA Projection System is an acronym for a tortured collection of words in the Statistical Extrapolation Bayesian Analyzer Projection System. Check out the first season’s post to find out how it works (http://www.phillysoccerpage.net/2017/03/03/2017-initial-seba-projections/)

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