Yesterday, I scored out the D1 men’s pre selection psych sheet. This exercise gives us some idea of how teams stand heading into the meet, but there are some major flaws. Swimmer’s entry times are based on their best time this season. Many top swimmers haven’t fully rested yet, or have rested some, but not in every event they entered. It’s relatively easy to predict that these swimmers will be faster at the big meet. So I decided to try a projection that factors that in.
I grabbed the top collegiate time for every swimmer* that qualified for the meet individually and re scored. I left relays with their current seed times. Still no diving. This resulted in the biggest point jump for Texas who went from 4th with 322 to 1st with 398.5. NC State remained 2nd with 372.5. Indiana are 3rd with 337.5, Florida 4th with 323, and Cal are 5th with 319.
Before anyone jumps to conclusions and declares Cal are out of contention, it’s useful to discuss the flaws of this method. Mainly that it severely underrates top freshmen. For example, Cal’s Ryan Hoffer is scored at 0 points by his collegiate best times. However, if swims his lifetime best times in the 50 and 100 free (18.71, 41.23) suddenly he scores 31 points. Even a small improvement in his third event, the 100 fly, will bump him into the points there as well. Accounting for Hoffer’s ability bumps Cal to 350 points and he isn’t Cal’s only freshman with big potential. Sean Grieshop, Bryce Mefford, Daniel Carr, and Trenton Julian were all ranked in SwimSwam’s top 20 recruits in this year’s class and have qualified for the meet. Texas and Florida also have multiple freshmen at the meet that were highly ranked coming into the year.
In the same way that a scored psych sheet isn’t a straight forward prediction of the outcome, this isn’t either. It’s missing diving. Some swimmers are likely to go much faster or much slower than the times used here. This is one more data point that can help us set expectations.
In this version, Caeleb Dressel of Florida leads individuals with 60 points followed by Joseph Schooling of Texas with 52, Ryan Held of NC State with 49.5, Mark Szaranek of Florida with 49.5, Ian Finnerty of Indiana with 49 and Felix Auboeck of Michigan with 49. Individual scores are listed below the team scores.
Best Time Scores
| Best Time Points | Psych Points | Difference | |
| Texas | 398.5 | 322 | 76.5 |
| NC State | 372.5 | 385 | -12.5 |
| Indiana | 337.5 | 350.5 | -13 |
| Florida | 323 | 302 | 21 |
| California | 319 | 340.5 | -21.5 |
| Michigan | 207 | 218.5 | -11.5 |
| Southern Cali | 185 | 166 | 19 |
| Stanford | 147.5 | 129.5 | 18 |
| Louisville | 136 | 152 | -16 |
| South Carolina | 114 | 84 | 30 |
| Auburn | 109 | 144 | -35 |
| Alabama | 107 | 101 | 6 |
| Minnesota | 90 | 95 | -5 |
| Tennessee | 88 | 80.5 | 7.5 |
| Georgia | 79.5 | 49.5 | 30 |
| Texas A&M | 70.5 | 83 | -12.5 |
| Harvard | 60 | 77 | -17 |
| Ohio St | 59.5 | 75 | -15.5 |
| Florida St | 48 | 53 | -5 |
| Arizona | 47 | 60.5 | -13.5 |
| Missouri | 45 | 47 | -2 |
| Arizona St | 43 | 43 | 0 |
| Virginia | 31 | 46 | -15 |
| Grand Canyon University | 26 | 27 | -1 |
| Notre Dame | 25.5 | 37.5 | -12 |
| Cornell | 22 | 27 | -5 |
| Denver | 15 | 9 | 6 |
| Purdue | 11 | 0 | 11 |
| Georgia Tech | 9 | 9 | 0 |
| Missouri St. M | 6 | 12 | -6 |
| Pacific | 6 | 6 | 0 |
| West Virginia | 4.5 | 8 | -3.5 |
| Virginia Tech | 3 | 7 | -4 |
| Utah | 2 | 7 | -5 |
| Loyola University Maryland | 2 | 5 | -3 |
| Kentucky | 0 | 2 | -2 |
| Penn | 0 | 2 | -2 |
| UNLV M | 0 | 2 | -2 |
Individuals
| School | Best Time Points | |
| Dressel, Caeleb | Florida | 60 |
| Schooling, Joseph | Texas | 52 |
| Held, Ryan | NC State | 49.5 |
| Szaranek, Mark | Florida | 49.5 |
| Finnerty, Ian | Indiana | 49 |
| Auboeck, Felix | Michigan | 49 |
| Seliskar, Andrew | California | 46 |
| Bentz, Gunnar | Georgia | 45 |
| Lanza, Vini | Indiana | 42.5 |
| Haas, Townley | Texas | 42 |
| Vazaios, Andreas | NC State | 40.5 |
| Shebat, John | Texas | 40 |
| Stewart, Coleman | NC State | 36 |
| Gonzalez, Hugo | Auburn | 33 |
| Pieroni, Blake | Indiana | 32 |
| Mahmoud, Akaram | South Carolina | 32 |
| Ipsen, Anton Oerskov | NC State | 32 |
| Switkowski, Jan | Florida | 31.5 |
| Quah, Zheng | California | 31 |
| Roberts, Jonathan | Texas | 31 |
| DeVine, Abrahm | Stanford | 30.5 |
| Becker, Bowen | Minnesota | 30 |
| Apple, Zachary | Auburn | 29 |
| Carter, Dylan | Southern Cali | 29 |
| Farris, Dean | Harvard | 29 |
| McHugh, Conner | Minnesota | 28 |
| Shoults, Grant | Stanford | 26 |
| Kaliszak, Luke | Alabama | 25 |
| Wich-Glasen, Nils | South Carolina | 25 |
| Jackson, Tate | Texas | 25 |
| Brock, Levi | Indiana | 24 |
| Katz, Austin | Texas | 23 |
| Minuth, Fynn | South Carolina | 23 |
| Evdokimov, Alex | Cornell | 22 |
| Yeadon, Zach | Notre Dame | 22 |
| Ringgold, Brett | Texas | 20.5 |
| Ress, Justin | NC State | 20.5 |
| Vargas Jacobo, Ricardo | Michigan | 20 |
| Acosta, Marcelo | Louisville | 19 |
| Sweetser, True | Stanford | 19 |
| Litherland, Jay | Georgia | 19 |
| Castillo Luna, Mauro | Texas A&M | 18.5 |
| Stuart, Hennessey | NC State | 18 |
| Mulcare, Patrick | Southern Cali | 17 |
| Vissering, Carsten | Southern Cali | 17 |
| Lynch, Justin | California | 17 |
| Craig, Cameron | Arizona St | 17 |
| Ransford, Pj | Michigan | 17 |
| Nikolaev, Mark | Grand Canyon University | 16 |
| Hoppe, Connor | California | 16 |
| Tribuntsov, Ralf | Southern Cali | 15 |
| Loncar, Anton | Denver | 15 |
| Josa, Matthew | California | 15 |
| Acevedo, Javier | Georgia | 14.5 |
| Samy, Mohamed | Indiana | 14 |
| Stevens, Peter | Tennessee | 14 |
| Montague, Jacob | Michigan | 14 |
| Claverie, Carlos | Louisville | 14 |
| Condorelli, Santo | Southern Cali | 14 |
| Wright, Justin | Arizona | 14 |
| Powers, Paul | Michigan | 14 |
| Lense, Noah | Ohio St | 13.5 |
| Fantoni, Gabriel | Indiana | 13 |
| Baqlah, Khader | Florida | 13 |
| Albiero, Nicolas | Louisville | 12 |
| Sendyk, Pawel | California | 12 |
| Egan, Liam | Stanford | 12 |
| Pomajevich, Sam | Texas | 12 |
| Khalafalla, Ali | Indiana | 11 |
| Amaltdinov, Marat | Purdue | 11 |
| Perry, Sam | Stanford | 11 |
| Rooney, Maxime | Florida | 11 |
| Almeida, Brandonn | South Carolina | 11 |
| Harty, Ryan | Texas | 11 |
| Schubert, Ted | Virginia | 10 |
| Glinta, Robert | Southern Cali | 9 |
| Norman, Nick | California | 9 |
| Swanson, Charlie | Michigan | 9 |
| Somov, Evgenii | Louisville | 8 |
| Peribonio, Tom | South Carolina | 8 |
| Reid, Christopher | Alabama | 7 |
| White, Evan | Michigan | 7 |
| Thomas, Mike | California | 7 |
| Bish, Blair | Missouri St. M | 6 |
| Kaleoaloha, Kanoa | Florida St | 6 |
| Grieshop, Sean | California | 6 |
| Novak, Brennan | Harvard | 6 |
| Newkirk, Jeff | Texas | 6 |
| Cope, Tommy | Michigan | 6 |
| Dobbs, Chatham | Arizona | 5 |
| Holoda, Peter | Auburn | 5 |
| Bekemeyer, Cody | South Carolina | 5 |
| Ogren, Curtis | Stanford | 5 |
| Delakis, Paul | Ohio St | 5 |
| Armstrong, Jake | West Virginia | 4.5 |
| Poti, Zachary | Arizona St | 4 |
| Bonetti, Brock | Texas A&M | 4 |
| Molacek, Jacob | NC State | 4 |
| Loschi, Moises | Georgia Tech | 4 |
| Ferraro, Christian | Georgia Tech | 4 |
| Wielinski, Jacob | Missouri | 4 |
| Lawless, Ben | Florida | 4 |
| Szabo, Norbert | Virginia Tech | 3 |
| Howard, Robert | Alabama | 3 |
| Schreuders, Mikel | Missouri | 3 |
| Wieser, Chris | Arizona | 3 |
| McHugh, Sam | Tennessee | 3 |
| Thorne, Nick | Arizona | 3 |
| Plaschka, Justin | Notre Dame | 2.5 |
| Ungur, Paul | Utah | 2 |
| Cono, Ben | Loyola University Maryland | 2 |
| Tybur, Jonathan | Texas A&M | 2 |
| Gurevich, Etay | Louisville | 2 |
| Clark, Joe | Virginia | 1 |
| Whitacre, Robert | Notre Dame | 1 |
| Babinet, Jeremy | Michigan | 1 |
| Pumputis, Caio | Georgia Tech | 1 |
| Coetzee, Ryan | Tennessee | 1 |
| Higgins, Walker | Georgia | 1 |
| Harting, Zach | Louisville | 1 |
*I didn’t get times for transfers from their previous school, only their current school. Shouldn’t change much
wpDiscuz
How did you handle the relay times – same as psych sheet?
fun. doesn’t look like you credited Purdue with their 11 points scored by Amaltdinov in the 200 breast
Updated. Thanks.
Would it be possible to re-run this analysis including freshman by using their best time, or best time +- X% where X represents the historical average change from pre-NCAA lifetime bests to NCAA performances as a freshman? I think it would make the prediction model a little more robust
I agree. An ideal projection includes them. The problem is that while collegiate best times are easy to get, pre collegiate best times are hard to get.
No, they’re actually really easy to get. Use the best times by event/SCY filter.
https://www.usaswimming.org/Home/times/individual-times-search
Using that requires you to look up the swimmers one at a time. Many names return multiple swimmers so you need to check each one until you find the correct one. Times 50 or so freshmen. Way too much work. Also lots of high school swim times are missing from that database.
Vs college swimming data which I already have every time swum the last 10 years in a file on my computer and can look up the times for everyone on the psych sheet with 10 lines of code. Easy.
I agree. The data wouldn’t be worth it, thanks for making that clear.
Especially because as soon as he did the work, every swimmer, parent, and alumni from every team that didn’t come out on top would bolt over here to tell him that his effort was meaningless ;-).
I would assume all the yards times swim are in the swims database same as the colleges use
My men’s Prediction
50 free
1. Dressel 18.08
2. Held 18.40
3. Hoffer 18.52
100 free
1. Dressel 39.73
2. Pieroni 40.81
3. Hoffer 40.90
200 free
1. Haas 1:29.98
2. Pieroni 1:30.22
3. Farris 1:30.94
500 free
1. Auboeck 4:06.56
2. Haas 4:06.88
3. Shoults 4:09.55
1650 free
1. Auboeck 14:15.12
2. Ipsen 14:28.93
3. Shoults 14:30.20
100 back
1. Shebat 44.16
2. Stewart 44.37
3. Katz 44.57
200 back
1. Katz 1:36.44
2. Shebat 1:37.89
3. Gonzalez 1:38.65
100 breast
1. Finnerty 50.39
2. Mchugh 51.42
… Read more »
I’m surprised you received 15 downvotes… for what?
I think you’re accurate on some except I fully believe Pieroni will win the 200 and break 1:30. I also think you’re overestimating Shebat.
I agree on both points above and also on hoffer, Hugo and Katz. I *hope* I’m wrong but expecting a 40.9 and 18.5 from hoffer and 1:36 2back and 3:33 4im from frosh seems a bit lofty. Actually most of the times I think are a bit fast but the placing I could get behind. ??
Probably because he has Schooling winning the 2 Fly. Didn’t he not even score in this event last year, or was it the year before? My other thought is because those times are sooo fast. Although, every year it become more and more ridiculous how fast this meet has become. Or, maybe not having Dean Farris winning every event?? There seems to be a lot of his fans around here.
My men’s Prediction
50 free
1. Dressel 18.08
2. Held 18.40
3. Hoffer 18.52
100 free
1. Dressel 39.73
2. Pieroni 40.81
3. Hoffer 40.90
200 free
1. Haas 1:29.98
2. Pieroni 1:30.22
3. Farris 1:30.94
500 free
1. Auboeck 4:06.56
2. Haas 4:06.88
3. Shoults 4:09.55
1650 free
1. Auboeck 14:15.12
2. Ipsen 14:28.93
3. Shoults 14:30.20
100 back
1. Shebat 44.16
2. Stewart 44.37
3. Katz 44.57
200 back
1. Katz 1:36.44
2. Shebat 1:37.89
3. Gonzalez 1:38.65
100 breast
1. Finnerty 50.39
2. Mchugh 51.42
… Read more »
Yale wasn’t scored yet have three people going to NC’s?
They don’t have anyone whose best time would put them in the top 16.
Pile on more expectations on Ryan Hoffer. Can he surpass his high school records at his first NCAAs?
Every tenth of a second counts in his events. Getting to the finals is Step 1 for Ryan.
Thanks Mr. Mering for another interesting article. Also, when you came out with your article predicting NCAA cut times, people were criticizing them as being too fast, but they were remarkably accurate. https://swimswam.com/estimating-ncaa-d1-nationals-cut-times/
Thanks.
The men’s times were really close to the predicted times, but the women’s times were noticeably faster than predicted (all cuts were within prediction confidence intervals, but 13/13 were faster than predicted time). I looked into the women’s miss a bit. The error appears to be partly because of the cut line being lower this year and partly due to model calibration. There was a couple year lull in long term overall improvement among top NCAA women swimmers and my training data included mostly the lull. This year was back on track, so the model undershot by a bit. Here’s the write up: https://swimswam.com/d1-womens-national-cuts-faster-year-mean-anything/