Below are the updated season forecasts using data from games through May 21, 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.

SEBA has the Union improving from 10th to 7th. ESPN moves Philadelphia up from 18th to 11th. Soccer America moves Philadelphia up from 15th to 12th.

In case you’re interested in how the Union have jumped so far in the model, I’m showing the matches sort by their weight in the model:

‘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 caused the match result, as indicated by the goal differential), and ‘rostWght’ (how similar the roster deployments for both teams were compared with current trends).

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 29.3% to 45.2%.

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 overlap is where the Union are likely to make the playoffs.

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 46.

Philadelphia’s odds to win the Supporters’ Shield increase to 1.0% from 0.4%.

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 1.9% to 3.0%.

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 following is updated with the second round completed and the fourth round’s draw decided. In the fourth round, Philadelphia will host the winner between Harrisburg City Islanders and Reading United.

Philadelphia’s odds of winning the U.S. Open Cup have increased from 3.6% to 6.2%.

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 increased from 5.4% to 9.0%.

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.

Remaining home field advantage will be important here. 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 distinct 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 decrease to 21st from 16th. It has Harrisburg City moving down to 26th from 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 50.7% to 44.9% and Harrisburg City’s odds of making the playoffs have decreased from 47.2% to 40.8%.

Bethlehem’s chances at winning the Regular Season Title have decreased from 0.5% to 0.2% while Harrisburg City’s odds have decreased from 0.4% to 0.1%.

Bethlehem’s odds at becoming the USL Champion decreased to 1.5% from 2.6% while Harrisburg City’s decreased to 1.6% from 2.1%:

The USOC odds for USL clubs alone are as follows.

Despite Harrisburg defeating Ocean City in the second round, their odds have decreased from 0.7% to 0.3% for drawing away matches against both Reading and (if they make defeat Reading) Philadelphia.

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.

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 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/)