Below are the updated season forecasts using data from games through July 2, 2017 (and does not include the five matches played yesterday).

**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 increasing from 13th to 8th, because, let’s face it, it’s hard to understand who the Union really are. All large-GD-matches have been wins, the Union have changed their lineup significantly from their early losses, and suddenly the picture doesn’t seem so bleak… until you look at the hole the Union have to climb out of in terms of points.

ESPN has Philadelphia increasing from 16th to 14th while MLSSoccer.com has the Union increasing from 18th to 10th. Soccer America has the Union increasing from 18th to 17th.

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 increased from 15.1% to 22.2%.

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 increased from 47 to 48 while the most common number of points simulated for the Union has increased from 41 to 42.

Philadelphia’s odds to win the Supporters’ Shield increase from practically 0% to 0.1%.

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 increased from 0.4% to 0.5%.

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.

Philadelphia has been eliminated from the U.S. Open Cup, having previously had 2.7% odds to win prior to their 5th-round match.

Toronto has re-qualified for the 2018 CCL tournament.

Philadelphia’s chances for qualifying in 2017 for the 2019 edition of the CONCACAF Champions League have decreased from 3.1% to 0.5% with the US Open Cup loss.

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.

As of now, Philadelphia has the hardest remaining schedule, with an expected points-per-game being 10.4% lower than the league’s average remaining schedule.

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

Currently, this model only treats goals as time-dependent. All other stats (discipline, subs, etc.) are treated as being at a constant rate throughout the model. I’d like to change that when I can as much as possible.

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.

##### 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 7th to 9th while Harrisburg City increased from 26th to 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 86.3% to 82.6% and Harrisburg City’s odds of making the playoffs have increased from 11.4% to 24.1%.

Bethlehem’s odds at the Regular Season Title have decreased from 0.2% to 0.1%.

Bethlehem’s odds at becoming the USL Champion decreased from 3.6% to 3.4% while Harrisburg City’s increased from 0.2% to 0.5%:

The USOC odds for USL clubs alone are as follows. FC Cincinnati is the lone remaining USL club.

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

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