For most of the college swimming world, championship season is over. Team’s conference meets marked the pinnacle of their seasons. While those meets were happening, we covered the team battles and individual winner extensively. But many teams aren’t in a position to contend for a conference title. Many swimmers will never win a conference individual title.
A big part of how those swimmers define success is time. Did I go faster? Was this a best time? That’s something we can measure.
Methodology
To get an idea of team’s performance compared to their own previous best times, I grabbed every conference and nationals time from the last 4 years. I then compared a swimmer’s best time from an event at their conference meet this year to their best time in that event from previous year’s conference or nationals meets.
I limited the previous year’s data to conference and nationals because I wanted a guarantee that the only times that I compared to were tapered times in “on” events. A swimmer shouldn’t get credit for a 1 minute improvement in the mile if their only previous mile time was last year at a dual meet. By excluding non championship meets we can avoid that problem entirely.
The downside of this approach is that freshmen improvements aren’t included in the analysis at all. (and yes I know, it would be much better to have them. Adding that in makes this analysis orders of magnitude harder). Also it eliminates possible mid season taper PRs from the baseline data. (that’s more negligible, but with team’s different rest levels mid season, it seemed like a confounding variable I didn’t want to have to consider)
There are a few teams that likely haven’t fully rested yet. The Texas men, for example. For them this analysis isn’t very useful. Their target meet is NCAA’s. However for the vast majority of division 1, that isn’t the case. Schools with only a couple of qualifiers are unlikely to see their numbers change significantly with the inclusion of nationals.
Results
There were 4114 swims at women’s conference meets where the swimmer had a time in that event from a previous conference or nationals meet. Times were a median of .2% faster than previous season times. They were an average of .2% faster and the time change percentages had a standard deviation of 1.6%. Here’s the distribution of time changes:
The Brown men led the way with a median time change of 2.4% faster. VMI were next best with a median time drop of 1.6%. VMI were also 3 for time drops among women’s teams, so this was a pretty good result across the board for them.
The top performing major conference team was Alabama who dropped a median of 1.3%. Florida State were next best with 1.2%.
Arizona State had a weird year. 100% of their eligible swims were faster than last year, but they only had 7 swims that had a previous time at a conference or national meet (swimmers times from before they transferred aren’t considered). They were one of only two teams with fewer than 10 times in the data set. St. Francis was the other .
Texas added the most time among major conference teams. They added a median of .4% and an average of .7% at Big 12s. That would indicate they have quite a bit of time left to drop at nationals. This is very similar to how they swam at Big 12s last year when they added an average of .7% to their previous conference/nationals bests. Texas won the national title last year, so their substantial time add isn’t cause for concern.
All Teams Data
Negative is faster. Positive is slower.
| Median Change | Average Change | Number of Times | % that were Faster | |
| Brown | -2.4% | -2.1% | 19 | 84% |
| Virginia MI | -1.6% | -2.2% | 29 | 93% |
| Alabama | -1.3% | -1.0% | 34 | 68% |
| Florida St | -1.2% | -0.9% | 21 | 76% |
| Missouri | -1.1% | -1.2% | 42 | 83% |
| Penn St | -1.1% | -0.9% | 32 | 81% |
| Arizona St | -1.1% | -1.2% | 7 | 100% |
| Davidson | -1.0% | -0.8% | 49 | 69% |
| Hawaii | -1.0% | -1.1% | 31 | 81% |
| Auburn | -0.9% | -1.1% | 32 | 84% |
| NJIT | -0.9% | -0.9% | 25 | 72% |
| UCSB | -0.9% | -1.0% | 41 | 78% |
| Pittsburgh | -0.9% | -0.7% | 26 | 69% |
| Kentucky | -0.8% | -0.7% | 46 | 70% |
| Eastern Ill | -0.8% | -1.1% | 24 | 54% |
| Arizona | -0.8% | -1.0% | 22 | 82% |
| California | -0.8% | -0.8% | 32 | 72% |
| Purdue | -0.8% | -0.4% | 36 | 67% |
| Pacific | -0.7% | -0.6% | 34 | 59% |
| Boston College | -0.7% | -0.6% | 32 | 69% |
| Delaware | -0.7% | -0.5% | 35 | 66% |
| Holy Cross | -0.7% | -0.5% | 44 | 64% |
| Florida | -0.6% | -0.8% | 23 | 83% |
| Xavier | -0.6% | -0.7% | 40 | 65% |
| Loyola MD | -0.6% | -0.4% | 28 | 68% |
| Duke | -0.6% | -0.5% | 34 | 71% |
| Fairfield | -0.6% | -0.4% | 24 | 58% |
| Penn | -0.6% | -0.4% | 27 | 67% |
| Brigham Young | -0.6% | -0.5% | 34 | 59% |
| Utah | -0.6% | -0.5% | 18 | 67% |
| Virginia | -0.6% | -0.5% | 40 | 68% |
| Cincinnati | -0.6% | -0.3% | 32 | 69% |
| Miami Ohio | -0.5% | -0.6% | 39 | 74% |
| Fla Atlantic | -0.5% | -0.4% | 29 | 59% |
| Cal Poly | -0.5% | -0.6% | 29 | 62% |
| Rider | -0.5% | -0.8% | 37 | 68% |
| Columbia | -0.5% | -0.8% | 30 | 67% |
| Wisconsin | -0.5% | -0.4% | 35 | 63% |
| Georgia | -0.5% | -0.5% | 38 | 66% |
| Lafayette | -0.5% | -0.5% | 14 | 71% |
| Tennessee | -0.5% | -0.4% | 35 | 63% |
| UMBC | -0.4% | -0.1% | 31 | 61% |
| Bucknell | -0.4% | 0.0% | 36 | 53% |
| Manhattan | -0.4% | -0.4% | 19 | 74% |
| Denver | -0.4% | -0.4% | 40 | 63% |
| Lehigh | -0.4% | -0.5% | 37 | 68% |
| GWU | -0.4% | -0.1% | 36 | 58% |
| U.S. Navy | -0.4% | -0.6% | 41 | 76% |
| Texas A&M | -0.4% | -0.5% | 26 | 58% |
| Maine | -0.4% | -0.3% | 41 | 66% |
| Michigan St | -0.4% | 0.0% | 37 | 57% |
| Mt St Marys | -0.4% | -0.5% | 30 | 57% |
| Niagara | -0.4% | 0.0% | 28 | 61% |
| Wis.- Milwaukee | -0.4% | -0.1% | 43 | 58% |
| NC State | -0.4% | -0.2% | 38 | 61% |
| Michigan | -0.3% | -0.3% | 42 | 62% |
| East Carolina | -0.3% | 0.0% | 29 | 66% |
| TCU | -0.3% | -0.3% | 24 | 54% |
| American | -0.3% | -0.4% | 11 | 64% |
| Massachusetts | -0.3% | -0.3% | 33 | 58% |
| Army | -0.3% | 0.0% | 31 | 52% |
| Georgia Tech | -0.3% | -0.4% | 29 | 59% |
| Monmouth | -0.3% | -0.4% | 21 | 62% |
| Indiana | -0.3% | -0.2% | 33 | 64% |
| Bryant U | -0.3% | 0.3% | 30 | 57% |
| Binghamton | -0.2% | -0.1% | 43 | 63% |
| William & Mary | -0.2% | -0.3% | 44 | 61% |
| Yale | -0.2% | -0.2% | 30 | 57% |
| Iowa | -0.2% | 0.0% | 35 | 51% |
| Incarnate Word | -0.2% | -0.2% | 37 | 62% |
| Ohio St | -0.2% | 0.0% | 34 | 53% |
| Georgetown | -0.2% | -0.2% | 39 | 51% |
| UNC Wilmington | -0.2% | 0.3% | 41 | 54% |
| Iona Coll | -0.1% | 0.0% | 35 | 51% |
| Evansville | -0.1% | -0.4% | 43 | 58% |
| IUPUI | -0.1% | 0.1% | 34 | 53% |
| Illinois-Chicago | -0.1% | -0.1% | 39 | 54% |
| LSU | -0.1% | -0.3% | 25 | 64% |
| SMU | -0.1% | 0.2% | 38 | 55% |
| Ball State | -0.1% | 0.2% | 33 | 52% |
| Colgate | -0.1% | 0.4% | 25 | 52% |
| CSUB | 0.0% | 0.0% | 43 | 53% |
| Saint Peters | 0.0% | 0.0% | 29 | 55% |
| Wyoming | 0.0% | 0.1% | 28 | 54% |
| Virginia Tech | 0.0% | -0.2% | 28 | 50% |
| Wis.- Green Bay | 0.0% | 0.1% | 33 | 52% |
| Boston U | 0.0% | -0.3% | 30 | 50% |
| Northwestern | 0.0% | -0.1% | 37 | 49% |
| Villanova | 0.0% | -0.1% | 32 | 50% |
| La Salle | 0.0% | 0.3% | 27 | 48% |
| Canisius | 0.0% | 0.2% | 46 | 43% |
| Howard | 0.0% | -0.3% | 35 | 49% |
| Harvard | 0.0% | 0.3% | 28 | 46% |
| St. Francis | 0.0% | 0.0% | 8 | 50% |
| Minnesota | 0.1% | 0.1% | 40 | 45% |
| Towson | 0.1% | 0.2% | 40 | 48% |
| Grand Canyon | 0.1% | 0.3% | 31 | 48% |
| Southern Cali | 0.1% | 0.6% | 29 | 45% |
| SIUC | 0.1% | 0.2% | 31 | 45% |
| St. Bonaventure | 0.1% | 0.1% | 31 | 42% |
| Air Force | 0.1% | 0.3% | 21 | 43% |
| Notre Dame | 0.1% | 0.2% | 29 | 41% |
| Connecticut | 0.1% | 0.4% | 24 | 46% |
| St. Louis | 0.2% | 0.0% | 33 | 48% |
| Western Ill | 0.2% | 0.2% | 21 | 38% |
| Louisville | 0.2% | 0.3% | 36 | 39% |
| Stanford | 0.2% | -0.4% | 31 | 48% |
| South Carolina | 0.2% | -0.2% | 31 | 42% |
| West Virginia | 0.2% | 0.2% | 45 | 42% |
| South Dakota | 0.3% | 0.4% | 33 | 42% |
| Missouri St. | 0.3% | 0.2% | 33 | 24% |
| George Mason | 0.3% | -0.1% | 28 | 46% |
| Dartmouth | 0.3% | 0.3% | 24 | 38% |
| UNC | 0.3% | 0.6% | 27 | 30% |
| Oakland | 0.4% | 0.2% | 30 | 40% |
| Drexel | 0.4% | 0.1% | 41 | 41% |
| Texas | 0.4% | 0.7% | 41 | 39% |
| Gardner-Webb | 0.4% | 0.8% | 22 | 36% |
| Seton Hall | 0.4% | 0.7% | 22 | 45% |
| Cornell | 0.5% | 0.2% | 29 | 38% |
| South Dakota St | 0.5% | 1.0% | 38 | 37% |
| Seattle U | 0.5% | 0.3% | 35 | 40% |
| Providence | 0.5% | 0.4% | 34 | 29% |
| Princeton | 0.5% | -0.2% | 21 | 38% |
| Valparaiso | 0.7% | 0.7% | 27 | 33% |
| Cleveland St | 1.0% | 0.9% | 31 | 29% |
| Marist | 1.0% | 0.9% | 39 | 23% |
| UNLV | 1.1% | 0.8% | 15 | 33% |
| Fordham | 1.1% | 1.3% | 33 | 21% |
| Old Dominion | 1.3% | 1.6% | 14 | 29% |
Thanks for taking the time to put this together. It would be very interesting to see the pattern of improvement by programs over several seasons under the same coach. Hope you can continue to report on this in the future.
The Brown coach got fired this afternoon.
This set specifically excludes a huge set of significant improvements many teams see: those among their freshmen and first-time conference attendees.
I get that it would’ve been harder, but that seems like a critical flaw in this analysis.
Isn’t it easier for a slower team to drop time (ie Brown) while harder for a faster team (Texas)?
That makes sense to me, if we view swimming as asymptotic (which I think most of us do, whether it’s accurate or not).
I think it’s more individual based. Haas is a huge Taper swimmer. He will go from 1:35 in season to 1:29ish. Not everyone does that
Also distance vs sprinter.
Also, if you aren’t training that well then your taper won’t see a huge drop. Much like a times on a travel taper.
Texas effectively doesn’t have a conference meet. Just an in season intrasquad meet that several other teams happen to attend.
You’re right, big 12 is a joke. How could a power 5 conference only have 3 teams. They need some other schools to start fielding teams
It is a big advantage that they alone among the supposed contending teams do not have to break a sweat to win their conference. If the other contending teams want to win their respective conference meet, which most do, they have to rest and or shave many more swimmers than Texas which in turn allows Texas to concentrate on NCAAs more than the other teams.
Explain why Texas would approach the conference meet any different if they were in the SEC or PAC 12 vs the Big 12-9.
You can’t. Because any such argument revolves around the idea that they would view the conference title as an equal or greater goal than the national title.
That’s ludicrous in any conference.
The goal of the Texas team is to win NCAA’s. They just happen to also win the conference title every year. But if forced to choose between winning conference or ncaa every team would choose ncaa’s if it was reasonable.
The Texas team as currently constructed would approach being in the SEC or PAC conference meet the same as big 12.… Read more »
Texas fans would describe conference meets as meaningless lmao.
Btw schools do care about getting conference championships. You get a banner, get to sell your worth to your school’s AD. Not saying that Cal or other elite teams go all in for winning them, but they also don’t train completely through it as much as Texas does. Would Texas approach conferences differently if they were in a competitive conference? Who knows, but if they trained through it still, they wouldn’t get the best of both worlds they currently enjoy. It’s not a knock on Texas, but it is advantageous to not have any real competition for the conference meet. Less pressure, and easier to focus on the big meet.
But… Read more »
Didn’t say they weren’t important. Just not as important.
Ok, so 100% of swimmers at Indiana, Cal and Florida tapered for conference? Got it
You’re right, you can’t expect swimmers to compete at successfully at an emotionally charged high level competition just a month after another emotionally charged high level competition.
USA Swimming, take note. Schedule Olympic trials six months out of Olympics since we are at a disadvantage
So, you think a team that has a chance to win NCAA’s should 100% taper for conference meet instead?
And you think teams in the SEC:Big10:PAC are at a disadvantage by being in a hyper-competitive conference meet one month before NCAA’s? Consequently, The USA is at a competitive disadvantage with their Olympic Trials set up?
Think that through. Why is the greatest swimming nation in the world knowingly putting themselves at a competitive disadvantage by having two hyper competitive meets one month apart?
Maybe because they aren’t? Because that’s not really a problem?
I would guess no one is 100% tapered for their conference. I think it is more the grind of being in a substantial conference with a conference schedule, followed by a conference championship, followed by NCAAs, that most teams are subjected to while Texas is not. Should everyone be gearing for an NCAA victory, of course yes. But does any other conference have the luxury of sitting out their conference, no. Not if they want to keep their program funded.
You act like we’re playing football. You’re swimming. It’s always you against the Clock. You’re not wrestling the guy in the lane next to you.
You develop a year long strategy for your racers with the goal to peak at NCAA’s. That’s goal 1, goal 2 and goal 3. You do everything with that in mind. Texas as currently constructed would adopt the exact same strategy as they do now in any other conference. If they finished 4th in SEC and 1st in NCAA they would be thrilled. If they finished 1st in SEC and 3rd in NCAA they would be devastated.
Again, if the “intense” conference meet 4 weeks out from NCAA was a competitive disadvantage you… Read more »
I’m glad that isn’t actually a real tattoo. It would be a very unfortunate one.
Unrelated to this article. Why is Shaine Casas swimming 100 and 200 fly instead of 100/200 back? He’s 45.9/1:39 in backstroke, while he’s 45.9/1:42 in fly events. It seems like he would have a much better shot at scoring in the backstroke events and it’s not like they have a conflicting schedule. Confused by this event choice.
They entered his twin from a different dimension