NCAA October Performance: Who is Faster so Far?

Last week I looked at how much valuable information we can glean by looking at October times from D1 swimmers. The short answer: we can tell some. It’s better to be faster this time of year and worse to be slower, but slow swims are no guarantee of failure and fast swims are no guarantee of success. If you want the long answer, go read that article.

October has come to a close, so we now have all of the October times data from this season that we’re going to get. So, who is looking fast? Who is off their game? Let’s dive into the data.

I looked at every D1 swimmer who had a time in an event last October, this October, and at their conference meet or nationals last year. I then compared their fastest October times from each year in those events. The conference/nationals requirement is to make sure that these are primary events for swimmers and not something they are doing for fun/experience or at a pentathlon meet or something.

For example, Pawel Sendyk of Cal was 20.94 in the 50 free last October and he swam the event at nationals. This October he was 19.79, an improvement of 5.5% (that is: (19.79-20.94)/20.94)

On average men were .3% faster this year than last year in October (2510 swims) and women were .1% slower (4469 swims).

The team level data is where things get really interesting. That can give us some insight into how tired teams are and how well they’ve actually been swimming.

The top women’s team by time change so far is Akron with an average time improvement of 2.9% on 26 swims. This explains Akron’s surprisingly high early ranking. Currently they are 10th in the (way-too-early-not-enough-data-yet-to-be-very-useful) Swimulator computer rankings. CSCAA ranked them 22nd. It seems they are ranked that high due to unusually fast October times. The reason they were so fast appears fairly clear cut: they suited for the Akron Zips Classic, and they didn’t last year because this was the first year the meet existed. Other teams that were at that meet also appear on high on the October improvements lists (Cleveland St., Miami (OH)).

Suiting for early season meets appears to be the explanation for a good chunk of the teams with large early season time differences. Among power conference women’s teams, the best performers have been Virginia (1.4% faster, 34 swims) and USC (1.2% faster, 32). This is at least in part because of their participation in the SMU Classic. Swimmers at that meet were suited and the competition is fast. Due to the entry limit, may of the swimmers that participated for USC this year weren’t there last year explaining their improvement. Also, USC suited for the Trojan Invite this year.

In the case of Virginia, they weren’t at SMU last year, so everyone who participated for them didn’t have a suited meet in October last year. For two of last year’s SMU participants, the Michigan women (1.7% slower, 21) and the UCLA women (2.5% slower, 14), their non participation this year likely can go a long way toward explaining their slower average performances this year-both are in the bottom 5 women’s teams.

The more interesting teams are the ones with large October time differences that didn’t suit up this year or last. The likely explanation of the change for those teams is a different training plan. The Texas men, for example, have been an average of 1.2% faster this October than last October (32 swims). This is likely because, according to assistant coach Wyatt Collins, they haven’t been going as hard in practice as last year. Their rivals the Cal men were even better, improving an average of 2.4% on 30 swims. It appears something is different in Berkeley this year as well.

Most of the big differences can be explained by something simple. Suiting, easier or harder training, the whole team getting food poisoning, etc. However, as I mentioned before, ignoring all that important context, under neutral conditions it is better to be faster this time of year than to be slower. Read the team average change table below with that in mind: it’s better to be in the green than in the red, but if there has been a large change in how a team is performing, there’s probably a reason. Something changed.

I also looked at the October time changes for swimmers who were ranked in the top 10 in an event last season. The men were an average of 1% faster (32 data points) than last year and the women were an average of .8% slower (46 data points). Those sample sizes are probably too small to trust the average too much. The complete time change tables for returning top 10 swimmers are below the team change data at the end of the article.

The Cal men have quite a few improvements among their top guys. The top 3 October improvements among returning top 10 swimmers were all Cal men. The standout among the top returning women is Lindsey Kozelsky of Minnesota who has been quite a bit faster in the breast events (3% faster in the 100, 1.2% faster in the 100) while her team has been .1% slower on average. That type of against-team-trend improvement is a good sign.

Data

Team October Time Change Average

negative is faster, positive is slower, Comparing swimmers to themselves. Best time in the month of October this year and last year. Only in events they swam at end of year taper meets. Number is the number of data points for each team.

Women’s Teams     Men’s Teams    
Team Average Change Number Team Average Change Number
Akron -2.9% 26 Lehigh -4.4% 17
Buffalo -2.4% 34 Miami Ohio -4.2% 19
Virginia MI -2.1% 13 Cleveland St -2.9% 26
Miami Ohio -1.9% 29 Army -2.5% 22
Iona Coll -1.8% 30 Southern Cali -2.4% 19
James Madison -1.8% 19 California -2.4% 30
Virginia -1.4% 34 Towson -2.1% 26
Towson -1.3% 30 SMU -1.9% 17
Southern Cali -1.2% 36 Saint Peters -1.9% 14
San Diego St -1.2% 38 TCU -1.8% 7
Drexel -1.2% 19 St. Louis -1.8% 31
LSU -1.2% 30 Binghamton -1.6% 27
Wyoming -1.1% 26 Virginia MI -1.5% 21
UNC Asheville -1.1% 24 Providence -1.4% 28
UNLV -1.0% 15 Mt St Marys -1.3% 22
Cleveland St -1.0% 27 St. Francis -1.3% 5
TCU -1.0% 8 Texas -1.2% 32
Illinois -1.0% 21 Iona Coll -1.2% 24
Old Dominion -0.9% 12 George Mason -1.2% 25
La Salle -0.9% 16 U.S. Navy -1.1% 53
Missouri -0.9% 26 Stanford -1.1% 19
Canisius -0.9% 21 St. Bonaventure -1.1% 36
Lehigh -0.9% 19 Tennessee -1.1% 20
Evansville -0.9% 36 Incarnate Word -1.1% 19
San Jose St -0.8% 42 UMBC -1.0% 25
Boise St -0.8% 36 Utah -1.0% 19
Northwestern -0.7% 20 Connecticut -1.0% 14
Indiana State -0.7% 35 Wisconsin -0.9% 27
Fresno State -0.7% 48 La Salle -0.9% 17
Arkansas -0.6% 17 Loyola MD -0.9% 36
Iowa -0.6% 25 Ohio St -0.9% 17
Florida St -0.6% 20 Western Ill -0.9% 15
Indiana -0.6% 26 Fla Atlantic -0.9% 13
Kansas -0.5% 30 Georgetown -0.9% 18
Pepperdine -0.5% 23 Virginia -0.8% 31
Army -0.5% 18 Howard -0.8% 15
Grand Canyon -0.5% 12 UCSB -0.8% 24
Providence -0.5% 42 CSUB -0.6% 36
Ohio St -0.4% 18 Florida -0.6% 26
West Virginia -0.4% 33 Seattle U -0.6% 29
LIU Brooklyn -0.4% 19 East Carolina -0.5% 20
Manhattan -0.4% 14 Evansville -0.5% 15
IUPUI -0.3% 32 Georgia -0.5% 19
Howard -0.3% 8 Wyoming -0.5% 21
Incarnate Word -0.3% 26 William & Mary -0.5% 28
Cincinnati -0.3% 29 Holy Cross -0.4% 29
Georgetown -0.3% 12 GWU -0.4% 19
Tulane -0.3% 30 Davidson -0.4% 31
UMBC -0.3% 31 Kentucky -0.4% 34
Liberty -0.2% 34 Monmouth -0.3% 9
Xavier -0.2% 44 Missouri -0.3% 30
California -0.2% 39 IUPUI -0.3% 27
Duke -0.2% 18 Cincinnati -0.2% 28
Idaho -0.2% 27 Bryant U -0.2% 20
UN Omaha -0.2% 24 Boston U -0.2% 23
NC State -0.2% 32 Boston College -0.2% 38
SMU -0.2% 20 LSU -0.2% 23
Marist -0.2% 15 Denver -0.2% 30
Mt St Marys -0.2% 17 Arizona St -0.2% 10
UC Davis -0.2% 30 SIUC -0.2% 11
Georgia Tech -0.2% 30 Rider -0.2% 21
William & Mary -0.2% 20 Illinois-Chicago -0.2% 22
Auburn -0.1% 23 Brigham Young -0.1% 26
Rutgers -0.1% 26 Marist -0.1% 26
Miami FL -0.1% 7 Grand Canyon -0.1% 20
Northern Iowa -0.1% 30 Louisville -0.1% 23
Oregon St -0.1% 13 Gardner-Webb -0.1% 13
Ball State -0.1% 25 Florida St 0.0% 19
Northeastern -0.1% 29 Canisius 0.0% 30
Air Force -0.1% 41 Cal Poly 0.0% 12
Valparaiso -0.1% 17 Massachusetts 0.0% 34
Penn St -0.1% 18 Duke 0.0% 22
Butler -0.1% 24 Georgia Tech 0.0% 27
Florida -0.1% 28 Drexel 0.1% 30
Georgia -0.1% 20 Virginia Tech 0.1% 24
South Dakota St -0.1% 17 Oakland 0.1% 10
Tennessee -0.1% 28 Minnesota 0.1% 28
Ark.-Little Rock -0.1% 33 Missouri St. 0.2% 25
Pittsburgh 0.0% 29 Indiana 0.2% 26
Missouri St 0.0% 27 Alabama 0.2% 21
CSUB 0.0% 30 Xavier 0.2% 37
Northern Colo 0.0% 11 Wis.- Milwaukee 0.2% 33
Massachusetts 0.0% 42 Northwestern 0.3% 23
Boston College 0.0% 39 South Dakota St 0.3% 19
GWU 0.0% 25 South Dakota 0.4% 11
Siena 0.1% 24 West Virginia 0.4% 45
Lafayette 0.1% 4 Michigan 0.4% 18
Eastern Mich 0.1% 28 Delaware 0.5% 20
Connecticut 0.1% 25 Iowa 0.5% 23
Iowa State 0.1% 31 American 0.5% 13
Purdue 0.1% 32 Lafayette 0.5% 8
East Carolina 0.1% 10 UNC Wilmington 0.5% 23
Bucknell 0.1% 26 Air Force 0.5% 25
Loy. Marymount 0.1% 46 Manhattan 0.6% 6
Minnesota 0.1% 33 Auburn 0.6% 18
St. Louis 0.1% 17 Eastern Ill 0.6% 11
Florida Gulf 0.1% 30 NJIT 0.6% 15
U.S. Navy 0.1% 48 Michigan St 0.6% 25
Holy Cross 0.2% 29 Wis.- Green Bay 0.6% 20
Bowling Green 0.2% 25 Seton Hall 0.7% 18
Wisconsin 0.2% 32 Valparaiso 0.7% 22
North Florida 0.2% 20 Hawaii 0.7% 23
American 0.2% 19 Pacific 0.8% 36
Davidson 0.2% 31 Pittsburgh 0.8% 26
Cal Poly 0.3% 20 Niagara 0.8% 13
Washington St. 0.3% 17 UNC 0.8% 24
George Mason 0.3% 25 UNLV 0.8% 7
Bryant U 0.3% 31 Old Dominion 0.8% 12
Vermont 0.3% 25 Penn St 0.9% 25
Hawaii 0.3% 15 Fordham 0.9% 17
Ohio 0.3% 29 Purdue 1.0% 27
Fairfield 0.4% 48 Bucknell 1.0% 18
Western Ill 0.4% 17 Notre Dame 1.0% 22
Vanderbilt 0.4% 21 NC State 1.0% 31
Richmond 0.4% 14 South Carolina 1.1% 14
Rice 0.4% 31 Colgate 1.1% 33
Texas 0.4% 38 Texas A&M 1.2% 22
Rider 0.4% 21 Fairfield 3.6% 5
Texas A&M 0.4% 28
Binghamton 0.4% 29
UCSB 0.4% 46
Michigan St 0.5% 33
Gardner-Webb 0.5% 30
North Texas 0.5% 24
Kentucky 0.5% 32
Toledo 0.5% 20
Arizona 0.5% 21
Houston 0.5% 41
Sacred Heart 0.6% 36
Utah 0.6% 34
St. Francis 0.6% 17
Wis.- Green Bay 0.6% 20
Delaware 0.6% 22
South Carolina 0.6% 20
Arizona St 0.6% 29
Niagara 0.6% 14
Monmouth 0.6% 11
Duquesne 0.6% 33
New Hampshire 0.6% 19
Colgate 0.7% 27
Brigham Young 0.7% 33
Florida Intl 0.7% 28
Illinois St 0.7% 27
Saint Peters 0.7% 3
Loyola MD 0.7% 48
Central Conn St 0.7% 18
Denver 0.7% 34
New Mexico St 0.8% 32
St. Bonaventure 0.8% 12
San Diego 0.8% 32
Louisville 0.8% 41
UNC 0.9% 20
Nevada 0.9% 23
Fordham 0.9% 25
GA Southern 1.0% 36
Pacific 1.0% 33
Marshall 1.0% 30
Colorado St. 1.0% 32
Campbell 1.1% 29
Wis.- Milwaukee 1.1% 16
Boston U 1.1% 19
Fla Atlantic 1.1% 6
Notre Dame 1.1% 35
Illinois-Chicago 1.1% 28
Oakland 1.2% 24
Nebraska 1.2% 28
Brown 1.3% 14
Seton Hall 1.3% 25
Virginia Tech 1.4% 21
St. Francis Pa. 1.4% 42
New Mexico 1.4% 15
Youngstown St 1.4% 21
Wagner 1.4% 23
Eastern Ill 1.5% 11
Stanford 1.5% 7
South Dakota 1.6% 20
UNC Wilmington 1.7% 20
Michigan 1.7% 21
Alabama 1.8% 22
Seattle U 2.0% 28
UCLA 2.5% 14
Cornell 2.6% 13

(Note: this table was edited due to an error in the CAA data. )

Top 10 Men From 2018 Season October Time Change

Name School Event Taper Time 2017 Oct Time 2018 Oct Time Change
Sendyk, Pawel California 50 Free 18.94 20.94 19.79 -5.5%
Hoffer, Ryan California 50 Free 18.97 21.29 20.17 -5.3%
Julian, Trenton California 200 Fly 1:40.63 1:50.88 1:45.77 -4.6%
Jackson, Tate Texas 100 Free 41.27 46.51 44.45 -4.4%
Seliskar, Andrew California 200 Breast 1:50.42 2:02.06 1:56.81 -4.3%
Stewart, Coleman NC State 100 Fly 44.84 49.06 47.14 -3.9%
Katz, Austin Texas 200 Back 1:37.53 1:47.03 1:44.44 -2.4%
Stewart, Coleman NC State 100 Back 44.54 47.39 46.26 -2.4%
Becker, Bowe Minnesota 50 Free 18.69 20.15 19.77 -1.9%
Katz, Austin Texas 100 Back 44.99 49 48.18 -1.7%
Vargas Jacobo, Ricardo Michigan 500 Free 4:11.11 4:24.08 4:19.92 -1.6%
Mefford, Bryce California 200 Back 1:38.48 1:46.98 1:45.34 -1.5%
Nikolaev, Mark Grand Canyon 100 Back 44.71 47.97 47.25 -1.5%
Molacek, Jacob NC State 100 Free 41.55 44.6 44.18 -0.9%
Bish, Blair Missouri St. 100 Breast 51.93 56.21 55.78 -0.8%
Baqlah, Khader Florida 200 Free 1:31.96 1:35.56 1:34.84 -0.8%
Becker, Bowe Minnesota 100 Free 41.61 44.63 44.31 -0.7%
Haas, Townley Texas 200 Free 1:29.5 1:38.46 1:37.85 -0.6%
Yeadon, Zach Notre Dame 1650 Free 14:34.6 15:20.2 15:17.1 -0.3%
Cope, Tommy Michigan 200 Breast 1:51.87 1:59.84 1:59.97 0.1%
Jackson, Tate Texas 50 Free 18.95 20.28 20.34 0.3%
Finnerty, Ian Indiana 200 Breast 1:50.17 1:59.5 2:00.05 0.5%
Finnerty, Ian Indiana 100 Breast 49.69 53.19 53.49 0.6%
Vazaios, Andreas NC State 200 IM 1:39.97 1:45.9 1:46.65 0.7%
Lanza, Vini Indiana 100 Fly 44.5 46.68 47.14 1.0%
Lanza, Vini Indiana 200 Fly 1:39.75 1:44.18 1:45.27 1.0%
Minuth, Fynn South Carolina 500 Free 4:10.51 4:25.28 4:28.45 1.2%
Vazaios, Andreas NC State 200 Free 1:31.32 1:36.32 1:37.63 1.4%
Acosta, Marcelo Louisville 500 Free 4:11.61 4:22.59 4:26.39 1.4%
Lanza, Vini Indiana 200 IM 1:40.82 1:46.56 1:48.11 1.5%
Haas, Townley Texas 500 Free 4:08.6 4:26.01 4:29.97 1.5%
Mulcare, Patrick Southern Cali 200 Back 1:38.43 1:40.44 1:42.94 2.5%

Top 10 Women From 2018 Season October Time Change

Name School Event Taper Time 2017 Oct Time 2018 Oct Time Change
Kozelsky, Lindsey Minnesota 100 Breast 57.91 1:02.41 1:00.55 -3.0%
Hansson, Louise Southern Cali 50 Free 21.69 22.74 22.1 -2.8%
Oglesby, Grace Louisville 100 Fly 51.07 52.66 51.65 -1.9%
Duncan, Delaney Eastern Mich 100 Breast 58.36 1:02.08 1:01.02 -1.7%
Kozelsky, Lindsey Minnesota 200 Breast 2:07.37 2:15.68 2:14 -1.2%
Oglesby, Grace Louisville 200 Fly 1:53.16 1:55.05 1:53.79 -1.1%
Tetzloff, Alyssa Auburn 100 Free 47.17 49.96 49.76 -0.4%
Jacobsen, Kirsten Arizona 500 Free 4:35.04 5:03.91 5:03.21 -0.2%
Belousova, Anna Texas A&M 100 Breast 58.4 1:01.42 1:01.28 -0.2%
Darcel, Sarah California 200 IM 1:54.5 2:00.96 2:00.72 -0.2%
Galyer, Ali Kentucky 200 Back 1:50.16 1:57.5 1:57.27 -0.2%
Darcel, Sarah California 400 IM 4:03 4:15.56 4:15.32 -0.1%
King, Lilly Indiana 100 Breast 56.25 59.44 59.46 0.0%
Scott, Riley Southern Cali 200 Breast 2:07.29 2:07.99 2:08.13 0.1%
Belousova, Anna Texas A&M 200 Breast 2:05.08 2:13.8 2:13.96 0.1%
Wright, Maddie Southern Cali 200 Fly 1:53.17 1:54.97 1:55.32 0.3%
Gyorgy, Reka Virginia Tech 400 IM 4:04.42 4:18.41 4:19.33 0.4%
Nelson, Beata Wisconsin 200 Back 1:49.27 1:54.28 1:54.76 0.4%
Bonnett, Bailey Kentucky 200 Breast 2:07.17 2:12.26 2:12.85 0.4%
Hansson, Louise Southern Cali 100 Fly 49.8 51.28 51.56 0.5%
Adams, Claire Texas 100 Back 51.13 52.52 52.85 0.6%
Brown, Erika Tennessee 100 Fly 49.85 52.61 52.98 0.7%
Krause, Vanessa Michigan 200 Fly 1:53.31 1:58.11 1:59 0.8%
Hansson, Louise Southern Cali 100 Free 47.41 47.57 47.94 0.8%
Brown, Erika Tennessee 50 Free 21.39 22.65 22.83 0.8%
Rasmus, Claire Texas A&M 200 Free 1:43.01 1:47.96 1:48.84 0.8%
Small, Meghan Tennessee 200 IM 1:53.05 1:59.64 2:00.67 0.9%
King, Lilly Indiana 200 Breast 2:02.6 2:10.13 2:11.32 0.9%
Seidt, Asia Kentucky 100 Back 50.86 53.33 53.83 0.9%
Brown, Erika Tennessee 100 Free 47.08 49.45 49.94 1.0%
Haan, Elise NC State 100 Back 50.42 53.47 54.04 1.1%
Comerford, Mallory Louisville 200 Free 1:39.8 1:41.7 1:42.79 1.1%
McLaughlin, Katie California 200 Fly 1:52.64 1:55.94 1:57.33 1.2%
Comerford, Mallory Louisville 100 Free 46.2 47 47.57 1.2%
Quah, Jing Texas A&M 200 Fly 1:53.05 1:58.58 2:00.05 1.2%
Seidt, Asia Kentucky 200 IM 1:53.04 1:58.83 2:00.51 1.4%
Weitzeil, Abbey California 100 Free 46.63 48.04 48.78 1.5%
Hansson, Louise Southern Cali 200 Free 1:41.81 1:43.19 1:44.8 1.6%
Haughey, Siobhan Michigan 200 Free 1:40.69 1:42.44 1:44.45 2.0%
Haughey, Siobhan Michigan 100 Free 46.91 47.22 48.36 2.4%
Weitzeil, Abbey California 50 Free 21.41 21.63 22.17 2.5%
Tucker, Miranda Michigan 100 Breast 57.93 1:00.35 1:01.88 2.5%
Eastin, Ella Stanford 200 Fly 1:49.51 1:52.83 1:56.53 3.3%
Aroesty, Margaret Southern Cali 200 Breast 2:06.85 2:06.88 2:12.89 4.7%
Tucker, Miranda Michigan 200 Breast 2:06.59 2:09.87 2:16.54 5.1%
Drabot, Katie Stanford 200 Free 1:42.99 1:44.26 1:52.52 7.9%

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Swimming Fan

Great stuff, thanks!

ONEHANDTOUCH

This is really cool Thanks for doing this for us nerds.

Lehigh Swim Parent

Can I get a LEHIGH!?

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