Below are the updated season forecasts using data from games through October 15.
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 declining from 13th to 14th.
The following shows the evolution of SEBA’s power rankings for the MLS Eastern Conference over time:
Playoffs probability and more
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.
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.
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:
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.
| plyr | Net | + | – | MINS | Net/90 | +/90 | -/90 | |
|---|---|---|---|---|---|---|---|---|
| 1 | Chris Pontius | 7 | 34 | 27 | 4864 | 0.130 | 0.629 | 0.500 |
| 2 | Derrick Jones | 4 | 13 | 9 | 1525 | 0.236 | 0.767 | 0.531 |
| 3 | Jack Elliott | 4 | 39 | 35 | 6549 | 0.055 | 0.536 | 0.481 |
| 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 | Oguchi Onyewu | 2 | 29 | 27 | 5040 | 0.036 | 0.518 | 0.482 |
| 7 | Raymon Gaddis | 1 | 30 | 29 | 5172 | 0.017 | 0.522 | 0.505 |
| 8 | Andre Blake | 1 | 34 | 33 | 6030 | 0.015 | 0.507 | 0.493 |
| 9 | CJ Sapong | 0 | 43 | 43 | 7524 | 0.000 | 0.514 | 0.514 |
| 10 | Ilsinho | 0 | 25 | 25 | 3681 | 0.000 | 0.611 | 0.611 |
| 11 | Fabian Herbers | 0 | 8 | 8 | 660 | 0.000 | 1.091 | 1.091 |
| 12 | Alejandro Bedoya | -1 | 37 | 38 | 6717 | -0.013 | 0.496 | 0.509 |
| 13 | Haris Medunjanin | -2 | 44 | 46 | 8091 | -0.022 | 0.489 | 0.512 |
| 14 | Adam Najem | -2 | 1 | 3 | 142 | -1.268 | 0.634 | 1.901 |
| 15 | John McCarthy | -3 | 10 | 13 | 2070 | -0.130 | 0.435 | 0.565 |
| 16 | Roland Alberg | -3 | 13 | 16 | 1581 | -0.171 | 0.740 | 0.911 |
| 17 | Fabinho | -4 | 25 | 29 | 4856 | -0.074 | 0.463 | 0.537 |
| 18 | Keegan Rosenberry | -4 | 14 | 18 | 2593 | -0.139 | 0.486 | 0.625 |
| 19 | Joshua Yaro | -4 | 5 | 9 | 942 | -0.382 | 0.478 | 0.860 |
| 20 | Jay Simpson | -4 | 5 | 9 | 363 | -0.992 | 1.240 | 2.231 |
| 21 | Fafa Picault | -5 | 27 | 32 | 4606 | -0.098 | 0.528 | 0.625 |
| 22 | Richie Marquez | -5 | 15 | 20 | 2943 | -0.153 | 0.459 | 0.612 |
| 23 | Marcus Epps | -6 | 6 | 12 | 936 | -0.577 | 0.577 | 1.154 |
Model Validation
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
SEBA’s 1st round of Playoffs:
Bethlehem%: 27.9%
Louisville%: 72.1%
Bethlehem’s score on the left, Louisville’s score on the top, and the resulting probabilities of each.
Most likely case is Louisville winning 2-0.
Power Rankings
SEBA has the Bethlehem Steel back down from 15th to 17th.
The following shows the evolution of SEBA’s power rankings for the USL East over time.
Playoffs probability and more
Bethlehem’s odds at becoming the USL Champion have increased from 2.3% to 2.4%:
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.
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 (https://phillysoccerpage.net/2017/03/03/2017-initial-seba-projections/)
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