Squash Analytics: Men’s College Rankings by David Keating November 15, 2019
Today’s match between UVA and GWU crucial for both teams.
Should you put any stock in the College Squash Association (CSA) ranking of teams at the beginning of the season?
That ranking is done by polling all the varsity coaches and certainly
the coaches are aware of who got what new recruits. All of them know
how to evaluate talent.
But with the incredible depth now seen among the top teams, a simple
look at the rankings might lead you to believe there are more
differences between the teams than is actually the case. This article
will present another way to rank the teams. And it will offer a more
informed look at the magnitude of the differences between the top teams.
According to the CSA, “total votes determin[e] the order of the
preseason rankings.” The CSA reported that “Similar to last season, the
men’s poll exhibited more movement [compared to the 2018-2019
final rankings after the national championships] than the women’s
rankings. The headliner is Princeton’s jump into the top 4 after
finishing 8th in last year’s tournament, all thanks to a strong
recruiting class and top returning players. George Washington (No. 8)
and Virginia (No. 9) will be right on the bubble all year long, hoping
to stave off Dartmouth and Drexel, who both dropped relative to last
season.” University of Virginia also jumped up three-spots.
There’s another way to rank the top teams. I’m not claiming it’s
better, it’s just another method. One can compare the teams using the
skill level ratings computed by US Squash for each player on each of
the teams. Using this data, I built a computer model that simulated
each of the teams playing the other right now. When I tested the model
during the national team championships earlier this year, it did a
better job than the seeding in predicting the final outcome.
Still, this method is hardly foolproof. There are several flaws I’ve
identified and there are probably more. The most obvious is that the
rosters listed online might not be accurate for each team. I didn’t ask
the coaches about that. If the player is listed on the CSA roster, I
assumed he will play for the team this season.
First, the skill ratings for many players are often not based on many
meaningful college matches. Often matches are lopsided (e.g., Harvard
playing Tufts) and that doesn’t help the rating algorithm as much as
closely contested matches. And for international players who are
freshmen, the skill ratings are often based on even less data. However,
this flaw will become less important as the season progresses. This
year many, if not most, teams are posting their challenge ladder
matches online in the US Squash software, a commendable development
that will lead to much more accurate skill ratings and help discourage
stacking.
Second, I’m skeptical that junior skill ratings are directly comparable
to college player skill ratings. Many juniors only play juniors, and
few play adults on a regular basis. Fewer still play any college age
players in matches that post to the US Squash system – there simply are
very few opportunities for that to happen. I’m guessing that junior
ratings are overstated compared to college ratings. That could well
introduce a large source of error among American freshmen.
Third, no one knows how much differences in skill predict the outcome
of the match. For example, if the difference in skill rating is 0.1,
does that mean the higher rated player has a 60% chance of winning, or
some other chance? My model makes informed assumptions for that, but no
one has yet analyzed the data.
Finally, the model is fairly rudimentary in predicting a team’s odds of
winning, which is actually a tough statistical analysis that I don’t
have the knowledge yet to make. But my rough estimates have proven
highly predictive in the past.
With all these caveats out of the way, I compared the CSA ranking of the top 10 teams with my model’s ranking.
Here are the top 10 teams according to the CSA:
#1 Harvard University
#2 Trinity College
#3 University of Pennsylvania
#4 Princeton University
#5 Yale University
#6 University of Rochester
#7 Columbia University
#8 George Washington University
#9 University of Virginia
#10 Dartmouth College
My computer model ranks these same teams as follows:
#1 Harvard University
#2 University of Pennsylvania
#3 Trinity College
#4 Princeton University
#5 Yale University
#6 University of Rochester
#7 University of Virginia
#8 George Washington University
#9 Columbia University
#10 Dartmouth College
As you can see the two methods produce remarkably similar
results. Under the computer model, Penn moves up to #2 and Trinity
moves down to #3. UVA trades places with Columbia to jump from #9 to #7.
Under either ranking system, the UVA match at GWU today is shaping up
to be a crucial match for both teams. A loss by one won’t ruin their
chances at the top division, but it would make it somewhat more
difficult.
One thing is clear from my computer model, and probably to all the
coaches of the top 10 teams – Harvard is clearly going to be the team
to beat. Six of its nine starters sport skill ratings above 6.0. No
other team has more than three. The #9 Harvard player would be at
ladder position #5, or better, for any of the other top teams. My model
predicts
Harvard would win each match against the other top 10 teams by at least
a 6-3 margin and nearly all the teams would be lucky to get two
individual wins. An upset is possible, most likely if Harvard is
playing an away match and/or down a player, though the team has
incredible depth beyond the #9. My model predicts 65 individual match
wins of a possible 81 against the other top 10 teams. No other team
would win more than 49. By the end of the season, it may well become
the best collegiate team ever.
Penn, Trinity, and Princeton are all closely matched. Each of those
dual matches would be near toss ups, with the home team likely holding
a tiny edge. (An earlier analysis I did for Daily Squash Report appears
to indicate home court is worth on average about 0.66 extra
individual match wins.) Yale appears to be a slight step behind those
three, but certainly capable of pulling an upset, especially at home.
Yale in turn appears to be a slight step above Rochester, UVA, GWU and
Columbia. The #6 to #9 teams appear to be closely matched, with
Rochester perhaps just a bit stronger and Columbia just a bit weaker
than UVA and GWU. All four of those teams behind Yale have a decent
chance of pulling an upset against the Bulldogs, especially if Yale
plays an away match.
Other factors beyond team control such as injuries, illness or home
court advantage might well determine dual match results or the
end-of-season rankings, which are built on head-to-head match results
over the season.
But there are other things in the team’s control. How hard do the
players work? Do they eat and sleep well? How good is the coaching
staff? How good is the between game coaching and are the strategies for
each opponent?
Lurking just below the top 10 teams are some highly capable teams that
could easily break into the top 10, but I didn’t have time to analyze
the other teams in the rankings.
The season is just beginning, but it promises to showcase the best
college squash in history. There will be many exciting matches this
season to watch. The early season rankings are just that, early. It
will be interesting to see how the season progresses and how it ends.
Coming soon, a look at the top 10 women’s college teams.
The views expressed by the
author are his alone, based on his experiences and observations. They
do not represent the views or positions of any organizations with which
he is affiliated. All information used for the article is available to
the general public.
