Below are the updated season forecasts using data from games through June 18, 2017.

**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 MLS team, both home and away, and taking the average per team.

Both SEBA and ESPN have the Union declining from 13th to 16th while both MLSSoccer.com and Soccer America have the Union declining from 18th to 19th. Meanwhile, SI has Philadelphia remaining at 13th while SBI has Philadelphia remaining at 17th.

For those interested in how Philadelphia’s matches are weighted in the model (especially if skeptical about why SEBA’s are so 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 26.5% to 11.3%.

The following shows the simulation distribution for the points earned by the sixth place MLS East club, joined with 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 6th slot is 48. The most common number of points simulated for the Union is 40.

Philadelphia’s odds to win the Supporters’ Shield have decreased from 0.2% to 0.0%.

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 banking large victories which carry over.

The Union’s chances of winning the MLS Cup have decreased from 1.2% to 0.4%.

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 toss-up. Conversely, good teams with a higher propensity to earn a draw have a disadvantage for the same reason.

The simulations include the draw results for the 5th and 6th rounds.

Despite the win over Harrisburg, Philadelphia’s odds of winning the U.S. Open Cup have decreased from 5.9% to 3.1% thanks to the unlucky draw which faces Philadelphia away at NYRB and, if they win, the winner of New England-DC United on the road.

The chances of Canadian teams qualifying for the 2018 CCL are as follows (USA teams remain set as qualified last year):

Philadelphia’s chances for qualifying in 2017 for the 2019 edition of the CONCACAF Champions League have decreased from 7.1% to 3.5%.

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 post-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.

It might 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; 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

The following is a new addition in development. I’ve built a new model, totally separate from the model governing the SEBA system (which is everything else in this article), which estimates the probabilities of goals occurring at different times during a match and how that results in changing win/tie/loss probabilities.

This is currently set up in a very different fashion than it will be, using a number of team stats, but currently assuming that all team stats were performed at the same rate throughout the match. For example, if a match had seven corner kicks in it, the model would be unaware as to when they happened and therefore assume that 7/90 corner kicks happened per minute. Likewise, a red card would be assumed to happen 1/90 per minute even if it actually occurs late in the game.

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.

Eventually I will also back-run all of the old matches, when the model is as I’d like it to be.

##### 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 error will decline when more ties occur, as there is a less severe penalty for ties.

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 increase from 15th to 11th. It has Harrisburg City remaining at 26th.

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 increased from 56.4% to 76.0% and Harrisburg City’s odds of making the playoffs have decreased from 20.7% to 13.5%.

Bethlehem’s odds at the Regular Season Title have improved from practically 0% to 0.1%.

Bethlehem’s odds at becoming the USL Champion increased from 1.8% to 2.8% while Harrisburg City’s decreased from 0.6% to 0.2%:

The USOC odds for USL clubs alone are as follows.

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 the 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 (https://phillysoccerpage.net/2017/03/03/2017-initial-seba-projections/)*

I have to admit that I like this better, especially how things are trending, after a Union win than after a loss. 🙂