In anticipation of the 2021 Women’s NCAA Swimming & Diving Championships, we’ve scored out the psych sheets. But exactly how much does that tell us?
In an effort to see how teams typically perform relative to their seeded points, we’ve gone back over the past several years to come up with team-by-team averages of points gained or lost from seed.
Our chart below averages the 2019, 2018 and 2017 seasons, with each team’s gain/loss from seeded points listed. Of course, the most recent season (2020) is missing one major data point, as the actual NCAA meet itself was canceled amid the coronavirus pandemic.
A few notes on these numbers:
- The numbers are swimming points only – we’ve factored out diving, where no good version of a psych sheet exists.
- Points gained from seed are listed in green, while points lost from seed are listed in red.
- Obviously, there are plenty of outside factors that play into each of these numbers, and they aren’t a hard and fast predictor of future seasons’ outcomes. But we can at least identify multi-year trends as we try to diagnose why those trends exist.
- The biggest caveat here is that we’re calculating by total points – in order to lose significant points from seed, you also have to have a lot of seeded points. Same goes for the teams at the top, because you can’t move up 50+ points from seed without qualifying lots of individuals and some relays. So you’ll mostly see big-name teams at both extremes, if only because those are the teams with bigger NCAA groups and more ability to move up or down at the meet itself.
- Where zeroes are listed, a team had athletes at the NCAA meet and finished right on their psych sheet projection, even if that projection was zero. A blank space typically means a team had no swimmers or relays at NCAAs that year, and we didn’t factor that into their average as a zero.
If our embedded chart with the colors isn’t loading, here’s a more basic version of the data:
| Team | Average | 2019 | 2018 | 2017 |
| Louisville | 64.17 | 70.5 | 58.5 | 63.5 |
| Missouri | 32 | -18 | 23 | 91 |
| Stanford | 20.83 | 63.5 | 50.5 | -51.5 |
| Georgia | 16.83 | -28 | 34 | 44.5 |
| Arizona | 14.33 | 37.5 | 16 | -10.5 |
| Duke | 13 | 41 | -2 | 0 |
| Kentucky | 12.67 | -1.5 | -7.5 | 47 |
| Iowa | 8 | 8 | ||
| Texas A&M | 7.83 | -22 | -39 | 84.5 |
| Penn State | 7.67 | 10 | 6 | 7 |
| California | 6 | 44.5 | 6.5 | -33 |
| Minnesota | 5.83 | 50.5 | -4 | -29 |
| Northwestern | 4.83 | 6.5 | 0 | 8 |
| Eastern Michigan | 4.75 | -8.5 | 18 | |
| FGCU | 4 | 4 | ||
| Pitt | 4 | 4 | ||
| UMBC | 4 | 4 | ||
| San Diego State | 3 | 9 | 0 | 0 |
| Purdue | 2.83 | 0 | 13.5 | -5 |
| Alabama | 2.67 | 10 | -2.5 | 0.5 |
| Denver | 2.67 | 0 | 5 | 3 |
| South Carolina | 2.5 | 8 | -0.5 | 0 |
| Indiana | 1 | -25.5 | -3.5 | 32 |
| Air Force | 1 | 1 | ||
| Drexel | 1 | 1 | ||
| Virginia | 0.67 | 41 | 1 | -40 |
| Notre Dame | 0.67 | -4 | 4 | 2 |
| Navy | 0.5 | 1 | 0 | |
| Texas | 0.33 | -31.5 | 17.5 | 15 |
| Miami (OH) | 0 | 0 | 0 | |
| Nevada | 0 | 0 | 0 | |
| Wyoming | 0 | 0 | ||
| Arizona State | 0 | 6 | 1 | -7 |
| UConn | 0 | 0 | — | |
| FIU | 0 | 0 | 0 | 0 |
| Miami | 0 | 0 | 0 | 0 |
| Nebraska | 0 | 0 | 0 | 0 |
| Arkansas | 0 | 0 | 0 | |
| Kansas | 0 | 0 | ||
| Boise State | 0 | 0 | ||
| UMass | 0 | 0 | ||
| Virginia Tech | -0.67 | 5 | -9 | 2 |
| West Virginia | -1 | 0 | -2 | |
| Cincinnati | -1 | -1 | ||
| Rutgers | -1.67 | -5 | 0 | 0 |
| Akron | -2 | -8 | 4 | |
| LSU | -2.17 | -6.5 | 0 | 0 |
| Hawaii | -2.25 | -8 | 3.5 | |
| Yale | -3 | -6 | 0 | |
| UCLA | -3.33 | 0 | 2 | -12 |
| Florida State | -3.5 | 5 | -12.5 | -3 |
| Ohio State | -6.33 | 5 | -17 | -7 |
| Michigan | -6.5 | 19 | -29.5 | -9 |
| Wisconsin | -12 | 10 | -14 | -32 |
| UNC | -17.33 | -22 | -5 | -25 |
| Florida | -25.25 | -49.5 | -1 | |
| Auburn | -27.17 | -51 | -41.5 | 11 |
| NC State | -27.83 | -55 | 0.5 | -29 |
| Tennessee | -51 | -76 | -27 | -50 |
| USC | -51.5 | -21.5 | -58 | -75 |
Mizzou is such an interesting case study. Back in the mid-2010s, I remember they would falter at NCAAs relative to seed pretty hugely and consistently. Clearly the coaches turned it around, but now there’s the “dual meet tech suit” experiment that we have yet to see results of… Here’s hoping they find their perfect formula.
Texas also gets flack in the comments for underperformance, but on average, they hold seed. I wonder if perhaps average isn’t the best statistical measure without also including a confidence interval. If you think about it, averages are arbitrary: they have never actually been achieved. Take Texas A&M. They have either significantly exceeded seed by 80+ points, or fell from seed 20-40 points. They have… Read more »
Absolutely revealing that both USC and TENNESSEE have lost an average of over 50 points off seeds at the NCAA meet. Kudos go to the Louisville coaches – they are obviously preparing their team and focus for the NCAA Championship performance! Wow.
I think it could be more accurate if a long timeframe was used (although coaching changes could then have a bigger impact then over a 3 year span.
Well, we don’t actually know if it’s more accurate to do that. To figure out which method is more accurate, we’d have to run some kind of regression study to see what timeframe gives us the most predictive outcome, historically (presuming that’s the goal – to use the data forward-looking).
If the goal is to just look historically for fun, then one is neither more nor less accurate than the other.
That math probably won’t happen before NCAAs, but maybe it could be a future project.
would’ve never guessed in a million years that the Louisville women are the most improved historically at NCAA’s lol the more you know
Their recruiting person owes Jared bigtime.
This is an interesting exercise. Thanks for doing it.
Your “biggest caveat” can be addressed. Divide the points gained or lost by the total points expected to get a ratio (which will be greater or less than one). This statistic alone won’t tell the whole story either because small teams will have extreme (even infinite) ratios. But shown alongside the statistic you already have, the two measures would give a pretty good overall picture.
It would be interesting to see this based on time add or drop instead of place/score. Big data crunch, but you guys are always up to the task! So many factors in play with individuals that rest to make the meet versus elites that can make the meet unrested.
You can see the range of possibilities with this data. It looks like it’s not that uncommon to have 50-70 point swings. I don’t know that I would specifically apply the historical performances to these teams because you have so many variables going on this year. What about showing percentage change from the seeded point total? Dropping 40 points off of 400 isn’t that bad but drop it off 150-200 it looks worse and they drop a lot more places.
missing utah🤔
Will go back and double check to be sure, but this likely means that the Utah women haven’t scored or been seeded to score at the NCAA Championships in the relevant time period.
The Utah women haven’t scored at the NCAA Championships since 2014. I’ll double check, Jordan Anderson would have been the only recent Ute likely to have been seeded to score, though she never did.
Braden is correct – no projected scorers or actual scorers for the Utah women over the past three years.
Stay tuned for our men’s analysis, where (spoiler alert) Utah is included.
Jared’s got the best humor
I believe that Audrey Reimer was projected to score in the 2020 event (200 Back)
That is true! But because of last year’s cancellation, we don’t have the data points to include 2020 in these ranks. I should re-word my comment: no projected scorers or actual scorers for the Utah women over the three years we’re able to analyze here, 2017, 2018 and 2019.
Said no one ever