SEBA Projections

Updated MLS, USL, and NWSL SEBA projections through August 27

Below are the updated season forecasts using data from games through August 27.

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 14th to 16th while ESPN has Philadelphia remaining at 16th.

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).

For comparison, the current roster expectations for maximum weight for the Union 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 have decreased from 1.9% to 0.7%.

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-East-6th value.

The most common number of points required to make the playoffs in the East’s 6th slot has decreased from 50 to 49 while the most common number of points simulated for the Union decreased from 40 to 38.

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 semi-finals and conference finals. This gives those clubs a better chance at notching large victories which carry over.

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

In the U.S. Open Cup, poor teams with a higher propensity to earn a draw are given an advantage as they are more likely to reach penalty kicks, which are a complete tossup. Conversely, good teams with a higher propensity to earn a draw have a disadvantage for the same reason.

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 are probabilities for each category of outcomes for Philadelphia:

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 opponent’s. 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 their opponent. A value of 0.5 means that Philadelphia is half as likely to score as their 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 28 20 3821 0.188 0.660 0.471
2 Oguchi Onyewu 5 28 23 4590 0.098 0.549 0.451
3 Jack Elliott 4 31 27 5114 0.070 0.546 0.475
4 Giliano Wijnaldum 3 18 15 2907 0.093 0.557 0.464
5 Warren Creavalle 2 4 2 334 0.539 1.078 0.539
6 Derrick Jones 2 12 10 1588 0.113 0.680 0.567
7 Fabian Herbers 1 8 7 635 0.142 1.134 0.992
8 Andre Blake 1 26 25 4590 0.020 0.510 0.490
9 Raymon Gaddis 1 26 25 4530 0.020 0.517 0.497
10 CJ Sapong 0 35 35 6100 0.000 0.516 0.516
11 Ilsinho 0 21 21 3002 0.000 0.630 0.630
12 Alejandro Bedoya -1 30 31 5457 -0.016 0.495 0.511
13 Haris Medunjanin -2 36 38 6651 -0.027 0.487 0.514
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 Fabinho -4 18 22 3600 -0.100 0.450 0.550
17 Fafa Picault -4 20 24 3403 -0.106 0.529 0.635
18 Keegan Rosenberry -4 10 14 1897 -0.190 0.474 0.664
19 Roland Alberg -4 12 16 1567 -0.230 0.689 0.919
20 Joshua Yaro -4 5 9 942 -0.382 0.478 0.860
21 Marcus Epps -5 5 10 824 -0.546 0.546 1.092
22 Jay Simpson -5 4 9 350 -1.286 1.029 2.314
23 Richie Marquez -8 8 16 1953 -0.369 0.369 0.737
HomeAway Player Net + MINS Net/90 +/90 -/90
1 Home Chris Pontius 11 21 10 2486 0.398 0.760 0.362
2 Home Andre Blake 9 19 10 2610 0.310 0.655 0.345
3 Home Alejandro Bedoya 8 22 14 3207 0.225 0.617 0.393
4 Home CJ Sapong 8 24 16 3517 0.205 0.614 0.409
5 Home Giliano Wijnaldum 6 14 8 1949 0.277 0.646 0.369
6 Home Oguchi Onyewu 6 20 14 3060 0.176 0.588 0.412
7 Home Jack Elliott 6 21 15 3184 0.170 0.594 0.424
8 Home Haris Medunjanin 6 25 19 3951 0.137 0.569 0.433
9 Home Derrick Jones 4 9 5 1018 0.354 0.796 0.442
10 Home Ilsinho 4 17 13 2156 0.167 0.710 0.543
11 Home Fabian Herbers 3 4 1 241 1.120 1.494 0.373
12 Home Keegan Rosenberry 3 8 5 907 0.298 0.794 0.496
13 Home Roland Alberg 2 8 6 855 0.211 0.842 0.632
14 Home Raymon Gaddis 2 17 15 2822 0.064 0.542 0.478
15 Home Fabinho 1 11 10 1890 0.048 0.524 0.476
16 Home Richie Marquez 0 6 6 912 0.000 0.592 0.592
17 Home Fafa Picault -1 12 13 1917 -0.047 0.563 0.610
18 Home Adam Najem -1 1 2 123 -0.732 0.732 1.463
19 Home Marcus Epps -2 4 6 700 -0.257 0.514 0.771
20 Home Joshua Yaro -2 3 5 440 -0.409 0.614 1.023
21 Home Jay Simpson -2 3 5 305 -0.590 0.885 1.475
22 Home Warren Creavalle -2 0 2 158 -1.139 0.000 1.139
23 Home John McCarthy -3 6 9 1350 -0.200 0.400 0.600
HomeAway Player Net + MINS Net/90 +/90 -/90
1 Away Warren Creavalle 4 4 0 176 2.045 2.045 0.000
2 Away John McCarthy 0 4 4 720 0.000 0.500 0.500
3 Away Raymon Gaddis -1 9 10 1708 -0.053 0.474 0.527
4 Away Oguchi Onyewu -1 8 9 1530 -0.059 0.471 0.529
5 Away Adam Najem -1 0 1 19 -4.737 0.000 4.737
6 Away Jack Elliott -2 10 12 1930 -0.093 0.466 0.560
7 Away Derrick Jones -2 3 5 570 -0.316 0.474 0.789
8 Away Joshua Yaro -2 2 4 502 -0.359 0.359 0.717
9 Away Fabian Herbers -2 4 6 394 -0.457 0.914 1.371
10 Away Fafa Picault -3 8 11 1486 -0.182 0.485 0.666
11 Away Chris Pontius -3 7 10 1335 -0.202 0.472 0.674
12 Away Giliano Wijnaldum -3 4 7 958 -0.282 0.376 0.658
13 Away Marcus Epps -3 1 4 124 -2.177 0.726 2.903
14 Away Jay Simpson -3 1 4 45 -6.000 2.000 8.000
15 Away Ilsinho -4 4 8 846 -0.426 0.426 0.851
16 Away Fabinho -5 7 12 1710 -0.263 0.368 0.632
17 Away Roland Alberg -6 4 10 712 -0.758 0.506 1.264
18 Away Keegan Rosenberry -7 2 9 990 -0.636 0.182 0.818
19 Away Haris Medunjanin -8 11 19 2700 -0.267 0.367 0.633
20 Away CJ Sapong -8 11 19 2583 -0.279 0.383 0.662
21 Away Andre Blake -8 7 15 1980 -0.364 0.318 0.682
22 Away Richie Marquez -8 2 10 1041 -0.692 0.173 0.865
23 Away Alejandro Bedoya -9 8 17 2250 -0.360 0.320 0.680

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

Player Net + MINS Net/90 +/90 -/90
1 Antoine Hoppenot 7 21 14 1008 0.625 1.875 1.250
2 Conor Casey 7 64 57 8348 0.075 0.690 0.615
3 Chris Pontius 6 72 66 11168 0.048 0.580 0.532
4 Oguchi Onyewu 5 28 23 4590 0.098 0.549 0.451
5 Jack McInerney 5 35 30 5403 0.083 0.583 0.500
6 Jack Elliott 4 31 27 5114 0.070 0.546 0.475
7 Fred 3 8 5 689 0.392 1.045 0.653
8 Giliano Wijnaldum 3 18 15 2907 0.093 0.557 0.464
9 Brian Brown 2 6 4 328 0.549 1.646 1.098
10 Derrick Jones 2 12 10 1588 0.113 0.680 0.567
11 Ethan White 2 36 34 6056 0.030 0.535 0.505
12 Matt Kassel 1 2 1 139 0.647 1.295 0.647
13 Matthew Jones 1 3 2 450 0.200 0.600 0.400
14 Gabriel Farfan 1 6 5 643 0.140 0.840 0.700
15 Fabian Herbers 1 34 33 3686 0.024 0.830 0.806
16 Ilsinho 1 47 46 6175 0.015 0.685 0.670
17 Michael Lahoud 0 36 36 5294 0.000 0.612 0.612
18 Warren Creavalle 0 36 36 5485 0.000 0.591 0.591
19 Keon Daniel 0 22 22 3481 0.000 0.569 0.569
20 Carlos Valdés 0 11 11 1946 0.000 0.509 0.509
21 Bakary Soumaré 0 3 3 504 0.000 0.536 0.536
22 Zac MacMath -1 88 89 15930 -0.006 0.497 0.503
23 Vincent Nogueira -1 81 82 13892 -0.006 0.525 0.531
24 Brian Sylvestre -1 18 19 3330 -0.027 0.486 0.514
25 Austin Berry -1 9 10 1667 -0.054 0.486 0.540
26 Rais Mbolhi -1 1 2 270 -0.333 0.333 0.667
27 Walter Restrepo -1 3 4 162 -0.556 1.667 2.222
28 Roger Torres -1 1 2 68 -1.324 1.324 2.647
29 Corben Bone -1 0 1 12 -7.500 0.000 7.500
30 Jeff Parke -2 37 39 6795 -0.026 0.490 0.517
31 Haris Medunjanin -2 36 38 6651 -0.027 0.487 0.514
32 Rais M’bolhi -2 9 11 1800 -0.100 0.450 0.550
33 Pedro Ribeiro -2 4 6 611 -0.295 0.589 0.884
34 Anderson Conceicão -2 0 2 180 -1.000 0.000 1.000
35 Adam Najem -2 1 3 142 -1.268 0.634 1.901
36 Charlie Davies -2 2 4 52 -3.462 3.462 6.923
37 Raymond Lee -2 0 2 24 -7.500 0.000 7.500
38 Sheanon Williams -3 95 98 17177 -0.016 0.498 0.513
39 Leonardo Fernandes -3 4 7 690 -0.391 0.522 0.913
40 Cristián Maidana -4 62 66 10287 -0.035 0.542 0.577
41 Ken Tribbett -4 33 37 5681 -0.063 0.523 0.586
42 Michael Farfan -4 29 33 4822 -0.075 0.541 0.616
43 Joshua Yaro -4 25 29 4288 -0.084 0.525 0.609
44 Fafa Picault -4 20 24 3403 -0.106 0.529 0.635
45 Sébastien Le Toux -5 119 124 19366 -0.023 0.553 0.576
46 Amobi Okugo -5 87 92 16050 -0.028 0.488 0.516
47 Alejandro Bedoya -5 43 48 7989 -0.056 0.484 0.541
48 Kléberson -5 6 11 1303 -0.345 0.414 0.760
49 Marcus Epps -5 5 10 824 -0.546 0.546 1.092
50 Jay Simpson -5 4 9 350 -1.286 1.029 2.314
51 Tranquillo Barnetta -6 54 60 9529 -0.057 0.510 0.567
52 Eric Ayuk -6 19 25 3165 -0.171 0.540 0.711
53 Aaron Wheeler -6 9 15 1730 -0.312 0.468 0.780
54 CJ Sapong -7 104 111 17994 -0.035 0.520 0.555
55 Zach Pfeffer -7 15 22 2210 -0.285 0.611 0.896
56 Andre Blake -8 81 89 15300 -0.047 0.476 0.524
57 Keegan Rosenberry -8 63 71 11779 -0.061 0.481 0.542
58 Maurice Edu -9 71 80 13587 -0.060 0.470 0.530
59 John McCarthy -10 24 34 5220 -0.172 0.414 0.586
60 Roland Alberg -10 34 44 4367 -0.206 0.701 0.907
61 Leo Fernandes -10 12 22 2082 -0.432 0.519 0.951
62 Danny Cruz -11 47 58 7279 -0.136 0.581 0.717
63 Fabinho -12 133 145 24158 -0.045 0.495 0.540
64 Fernando Aristeguieta -12 13 25 2909 -0.371 0.402 0.773
65 Steven Vitória -13 19 32 4410 -0.265 0.388 0.653
66 Andrew Wenger -14 53 67 8723 -0.144 0.547 0.691
67 Richie Marquez -15 87 102 16736 -0.081 0.468 0.549
68 Raymon Gaddis -20 151 171 28749 -0.063 0.473 0.535
69 Brian Carroll -21 119 140 22471 -0.084 0.477 0.561

The following shows the analysis using player-single-seasons since 2013 when data became available.

Year Player Net + MINS Net/90 +/90 -/90
1 2013 Antoine Hoppenot 8 16 8 698 1.032 2.063 1.032
2 2017 Chris Pontius 8 28 20 3821 0.188 0.660 0.471
3 2014 Sébastien Le Toux 8 38 30 5429 0.133 0.630 0.497
4 2014 Michael Lahoud 7 14 7 1357 0.464 0.929 0.464
5 2014 Ethan White 6 22 16 3420 0.158 0.579 0.421
6 2017 Oguchi Onyewu 5 28 23 4590 0.098 0.549 0.451
7 2015 Conor Casey 4 7 3 276 1.304 2.283 0.978
8 2014 Conor Casey 4 27 23 3378 0.107 0.719 0.613
9 2014 Cristián Maidana 4 30 26 4460 0.081 0.605 0.525
10 2017 Jack Elliott 4 31 27 5114 0.070 0.546 0.475
11 2014 Fred 3 7 4 648 0.417 0.972 0.556
12 2017 Giliano Wijnaldum 3 18 15 2907 0.093 0.557 0.464
13 2013 Jack McInerney 3 31 28 4947 0.055 0.564 0.509
14 2014 Sheanon Williams 3 38 35 6432 0.042 0.532 0.490
15 2014 Brian Brown 2 6 4 328 0.549 1.646 1.098
16 2017 Warren Creavalle 2 4 2 334 0.539 1.078 0.539
17 2014 Jack McInerney 2 4 2 456 0.395 0.789 0.395
18 2014 Rais M’bolhi 2 4 2 540 0.333 0.667 0.333
19 2017 Derrick Jones 2 12 10 1588 0.113 0.680 0.567
20 2013 Aaron Wheeler 1 2 1 79 1.139 2.278 1.139
21 2013 Matt Kassel 1 2 1 139 0.647 1.295 0.647
22 2016 Matthew Jones 1 3 2 450 0.200 0.600 0.400
23 2017 Fabian Herbers 1 8 7 635 0.142 1.134 0.992
24 2013 Gabriel Farfan 1 6 5 643 0.140 0.840 0.700
25 2016 Vincent Nogueira 1 11 10 1798 0.050 0.551 0.501
26 2016 Ilsinho 1 26 25 3173 0.028 0.737 0.709
27 2017 Andre Blake 1 26 25 4590 0.020 0.510 0.490
28 2017 Raymon Gaddis 1 26 25 4530 0.020 0.517 0.497
29 2014 Vincent Nogueira 1 41 40 7133 0.013 0.517 0.505
30 2014 Maurice Edu 1 43 42 7647 0.012 0.506 0.494
31 2014 Zac MacMath 1 46 45 8190 0.011 0.505 0.495
32 2017 CJ Sapong 0 35 35 6100 0.000 0.516 0.516
33 2014 Andrew Wenger 0 31 31 4459 0.000 0.626 0.626
34 2016 Fabian Herbers 0 26 26 3051 0.000 0.767 0.767
35 2013 Keon Daniel 0 22 22 3481 0.000 0.569 0.569
36 2017 Ilsinho 0 21 21 3002 0.000 0.630 0.630
37 2016 Joshua Yaro 0 20 20 3346 0.000 0.538 0.538
38 2014 Carlos Valdés 0 11 11 1946 0.000 0.509 0.509
39 2013 Bakary Soumaré 0 3 3 504 0.000 0.536 0.536
40 2014 Zach Pfeffer 0 3 3 154 0.000 1.753 1.753
41 2015 Warren Creavalle 0 3 3 420 0.000 0.643 0.643
42 2014 Leonardo Fernandes 0 1 1 104 0.000 0.865 0.865
43 2015 Fred 0 1 1 41 0.000 2.195 2.195
44 2014 Raymon Gaddis -1 50 51 9081 -0.010 0.496 0.505
45 2016 Fabinho -1 45 46 8037 -0.011 0.504 0.515
46 2013 Amobi Okugo -1 42 43 7644 -0.012 0.495 0.506
47 2013 Brian Carroll -1 41 42 7467 -0.012 0.494 0.506
48 2013 Raymon Gaddis -1 37 38 6659 -0.014 0.500 0.514
49 2017 Alejandro Bedoya -1 30 31 5457 -0.016 0.495 0.511
50 2013 Conor Casey -1 30 31 4694 -0.019 0.575 0.594
51 2015 Brian Sylvestre -1 18 19 3330 -0.027 0.486 0.514
52 2014 Austin Berry -1 9 10 1667 -0.054 0.486 0.540
53 2014 Antoine Hoppenot -1 5 6 310 -0.290 1.452 1.742
54 2014 Rais Mbolhi -1 1 2 270 -0.333 0.333 0.667
55 2016 Walter Restrepo -1 3 4 162 -0.556 1.667 2.222
56 2013 Roger Torres -1 1 2 68 -1.324 1.324 2.647
57 2014 Corben Bone -1 0 1 12 -7.500 0.000 7.500
58 2016 Tranquillo Barnetta -2 43 45 7773 -0.023 0.498 0.521
59 2013 Zac MacMath -2 42 44 7740 -0.023 0.488 0.512
60 2016 Chris Pontius -2 44 46 7347 -0.024 0.539 0.563
61 2013 Jeff Parke -2 37 39 6795 -0.026 0.490 0.517
62 2017 Haris Medunjanin -2 36 38 6651 -0.027 0.487 0.514
63 2016 Brian Carroll -2 36 38 6414 -0.028 0.505 0.533
64 2015 Fabinho -2 32 34 5706 -0.032 0.505 0.536
65 2016 Warren Creavalle -2 29 31 4731 -0.038 0.552 0.590
66 2013 Fabinho -2 10 12 1669 -0.108 0.539 0.647
67 2014 Pedro Ribeiro -2 4 6 611 -0.295 0.589 0.884
68 2014 Andre Blake -2 0 2 180 -1.000 0.000 1.000
69 2016 Anderson Conceicão -2 0 2 180 -1.000 0.000 1.000
70 2016 Eric Ayuk -2 0 2 180 -1.000 0.000 1.000
71 2017 Adam Najem -2 1 3 142 -1.268 0.634 1.901
72 2016 Charlie Davies -2 2 4 52 -3.462 3.462 6.923
73 2015 Raymond Lee -2 0 2 24 -7.500 0.000 7.500
74 2016 Richie Marquez -3 53 56 9810 -0.028 0.486 0.514
75 2016 Andre Blake -3 50 53 9270 -0.029 0.485 0.515
76 2013 Sheanon Williams -3 40 43 7439 -0.036 0.484 0.520
77 2016 CJ Sapong -3 39 42 6934 -0.039 0.506 0.545
78 2014 Fabinho -3 28 31 5146 -0.052 0.490 0.542
79 2015 Vincent Nogueira -3 29 32 4961 -0.054 0.526 0.581
80 2013 Sébastien Le Toux -3 29 32 4867 -0.055 0.536 0.592
81 2015 Sheanon Williams -3 17 20 3306 -0.082 0.463 0.544
82 2016 Sébastien Le Toux -3 20 23 3254 -0.083 0.553 0.636
83 2017 John McCarthy -3 10 13 2070 -0.130 0.435 0.565
84 2013 Michael Lahoud -3 6 9 1033 -0.261 0.523 0.784
85 2013 Leonardo Fernandes -3 3 6 586 -0.461 0.461 0.922
86 2016 Leo Fernandes -3 4 7 335 -0.806 1.075 1.881
87 2016 John McCarthy -3 0 3 270 -1.000 0.000 1.000
88 2016 Keegan Rosenberry -4 53 57 9882 -0.036 0.483 0.519
89 2014 Amobi Okugo -4 45 49 8406 -0.043 0.482 0.525
90 2016 Ken Tribbett -4 33 37 5681 -0.063 0.523 0.586
91 2015 Richie Marquez -4 26 30 4973 -0.072 0.471 0.543
92 2015 CJ Sapong -4 30 34 4960 -0.073 0.544 0.617
93 2013 Michael Farfan -4 29 33 4822 -0.075 0.541 0.616
94 2017 Fabinho -4 18 22 3600 -0.100 0.450 0.550
95 2017 Fafa Picault -4 20 24 3403 -0.106 0.529 0.635
96 2015 Eric Ayuk -4 19 23 2985 -0.121 0.573 0.693
97 2015 Michael Lahoud -4 16 20 2904 -0.124 0.496 0.620
98 2015 John McCarthy -4 14 18 2880 -0.125 0.438 0.562
99 2015 Ethan White -4 14 18 2636 -0.137 0.478 0.615
100 2016 Alejandro Bedoya -4 13 17 2532 -0.142 0.462 0.604
101 2017 Keegan Rosenberry -4 10 14 1897 -0.190 0.474 0.664
102 2015 Tranquillo Barnetta -4 11 15 1756 -0.205 0.564 0.769
103 2017 Roland Alberg -4 12 16 1567 -0.230 0.689 0.919
104 2016 Raymon Gaddis -4 7 11 1549 -0.232 0.407 0.639
105 2015 Andre Blake -4 5 9 1260 -0.286 0.357 0.643
106 2015 Rais M’bolhi -4 5 9 1260 -0.286 0.357 0.643
107 2017 Joshua Yaro -4 5 9 942 -0.382 0.478 0.860
108 2013 Danny Cruz -5 26 31 4199 -0.107 0.557 0.664
109 2013 Kléberson -5 6 11 1303 -0.345 0.414 0.760
110 2017 Marcus Epps -5 5 10 824 -0.546 0.546 1.092
111 2017 Jay Simpson -5 4 9 350 -1.286 1.029 2.314
112 2014 Danny Cruz -6 21 27 3080 -0.175 0.614 0.789
113 2016 Roland Alberg -6 22 28 2800 -0.193 0.707 0.900
114 2015 Sébastien Le Toux -7 32 39 5816 -0.108 0.495 0.604
115 2014 Brian Carroll -7 16 23 3360 -0.188 0.429 0.616
116 2015 Zach Pfeffer -7 12 19 2056 -0.306 0.525 0.832
117 2014 Leo Fernandes -7 8 15 1747 -0.361 0.412 0.773
118 2014 Aaron Wheeler -7 7 14 1651 -0.382 0.382 0.763
119 2015 Cristián Maidana -8 32 40 5827 -0.124 0.494 0.618
120 2017 Richie Marquez -8 8 16 1953 -0.369 0.369 0.737
121 2015 Maurice Edu -10 28 38 5940 -0.152 0.424 0.576
122 2015 Brian Carroll -11 26 37 5230 -0.189 0.447 0.637
123 2015 Fernando Aristeguieta -12 13 25 2909 -0.371 0.402 0.773
124 2015 Steven Vitória -13 19 32 4410 -0.265 0.388 0.653
125 2015 Andrew Wenger -14 22 36 4264 -0.295 0.464 0.760
126 2015 Raymon Gaddis -15 31 46 6930 -0.195 0.403 0.597
 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
Power Rankings

SEBA has the Bethlehem Steel decreasing from 16th to 18th while it has Harrisburg City remained at 25th.

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 72.0% to 57.3% while Harrisburg City’s odds of reaching the postseason have decreased from 1.5% to 0.4%.

Bethlehem’s odds at becoming the USL Champion have decreased from 1.1% to 0.7% while Harrisburg City’s remained at practically zero:

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

The following are probabilities for each category of outcomes for Bethlehem.

The following are probabilities for each category of outcomes for Harrisburg City:

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 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.

Remaining home field advantage will make a large contribution 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.

As a new feature, we can also 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.

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 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/)

4 Comments

  1. Interesting that the Union had a loss against the #1 team and a draw against #3 and yet were passed in the power rankings by New England who lost to one of the lowest ranked teams, DC.

    • Chris Sherman says:

      yeah, well it’s in part to them losing on the road by only 1 goal, whereas the Union drew at home and lost badly on the road.

      Additionally, the Union’s performance of late isn’t due to much roster change, so it’s looking less likely that this is a blip. I’m not sure of D.C.’s situation

  2. I’m struggling with the significance of the individual player plus/minus data. I mean, they should be very meaningful, but when I look over the last 5 years and see that our top performer is… Antoine Hoppenot???? I have to wonder if maybe this is not as useful of a metric as I thought it was.

    • Chris Sherman says:

      I agree, I don’t think it’s particularly meaningful. However, I do think it is just as good as judging a player for not scoring, so I’d give Pontius a wash over this.

Leave a Reply

Your email address will not be published. Required fields are marked *

*