NCAA D1 Women Time Change vs Seed at Nationals

  7 Andrew Mering | March 20th, 2017 | College, News

Recently we looked at points change vs the psych sheet at nationals. However, this doesn’t tell the full story of who swam well and who didn’t. A swimmer seeded in the top 8 who adds 2% to their seed time is captured in the points change metric, but a swimmer seeded 20th who adds 2% isn’t. Both should be included in a measure of a team’s performance, so I’ve compiled team’s average percentage time change from their seed times at the meet (this data for the previous 7 years).

As expected from the points change data, the best performing teams were Kentucky, Missouri, and Texas A&M. Kentucky led schools with a reasonable sample size by dropping an average of .23% from 23 swims. Missouri and Texas A&M each dropped an average of .09% from their seed times. None of the other top schools dropped time on average. Stanford added .31%, California added .3%, Georgia added .2%, and Texas added .16%. The worst performing big teams were both from Los Angeles. USC added an average of 1.33% and UCLA added 1.23%.  The field as a whole added time in line with the historical average. The average time change over the last 7 years at this meet was an add of .45%. This year swimmers added an average of .43%.

Kaitlyn Jones of Virginia had the biggest time drop of the entire meet. She dropped 4.88% in her 100 back, going 53.16 from a seed time of 55.89. Next best was Erin Falconer of Auburn who dropped 3.64% in her 100 back (53.97 to 56.01), then Nadine Laemmler of Missouri who dropped 2.73% in the 100 back (51.96 to 50.54), and Amy Bilquist of California who dropped 2.54% in the 100 free (48.97 to 47.55). The biggest time add of the meet was Christina Elmgreen of Florida Gulf Coast who added 4.19% in the 200 IM (1:59.65 to 2:04.66). Longer lists of top adds and drops can be found at the bottom of this article below the tables for whole team average time changes and top/bottom performance for each team.

Team Time Change Averages

Negative is faster, positive is slower. This matches the “dropped time,” “added time” style of talking about performance

Average Time Change At Nationals Number of Times Standard Deviation
UMBC -0.72% 3 0.24%
Davidson -0.39% 3 0.55%
Kentucky -0.23% 23 0.90%
Iowa -0.18% 3 0.66%
Missouri -0.09% 23 1.02%
Texas A&M -0.09% 37 0.93%
Auburn 0.03% 24 1.29%
Louisville 0.04% 19 0.83%
Denver 0.08% 12 1.13%
Michigan 0.10% 26 0.97%
Purdue 0.12% 6 0.64%
Arizona 0.13% 23 0.90%
Drexel 0.14% 2 1.54%
Texas 0.16% 26 0.85%
Georgia 0.20% 37 1.07%
Cincinnati 0.24% 3 0.47%
Indiana 0.29% 16 1.36%
California 0.30% 38 1.26%
Minnesota 0.30% 15 1.43%
UC Davis 0.31% 3 0.18%
Stanford 0.31% 39 0.93%
Penn St 0.32% 8 1.27%
LSU 0.32% 6 0.52%
NC State 0.34% 26 0.73%
Central Conn St 0.35% 2 0.73%
Virginia 0.39% 25 1.62%
Florida St 0.40% 8 0.95%
Harvard 0.43% 3 0.59%
Notre Dame 0.48% 11 0.63%
UNC 0.50% 14 0.89%
Florida 0.50% 21 1.02%
South Carolina 0.50% 5 1.48%
Akron 0.52% 7 0.60%
Virginia Tech 0.54% 12 0.76%
Tennessee 0.57% 20 1.00%
Air Force 0.59% 3 0.43%
Liberty 0.61% 2 0.62%
Arkansas 0.63% 9 0.82%
Eastern Mich 0.63% 4 0.74%
Seattle U 0.68% 3 0.32%
Rutgers 0.70% 4 0.79%
Arizona St 0.78% 9 0.71%
Wisconsin 0.82% 19 1.14%
East Carolina 0.82% 2 0.73%
Buffalo 0.83% 3 0.53%
Northwestern 0.84% 5 1.03%
Ohio St 0.85% 21 1.08%
Boise St 0.88% 8 1.10%
Duke 1.03% 7 0.85%
Pittsburgh 1.04% 8 1.53%
Hawaii 1.05% 6 0.78%
Miami (FL) 1.15% 3 0.85%
Alabama 1.22% 5 0.57%
UCLA 1.23% 18 1.30%
Marshall 1.32% 3 0.24%
Southern Cali 1.33% 36 1.25%
Florida Intl 1.37% 3 0.26%
SMU 1.48% 3 0.72%
Rice 1.63% 2 0.28%
Utah 1.66% 4 0.92%
Wyoming 1.88% 2 0.24%
Florida Gulf 2.01% 4 1.88%

Biggest Drop and Add for Each Team

Biggest Drop At Nationals Biggest Time Add At Nationals
Name Event Time Change Seed Time Nationals Time Name Event Time Change Seed Time Nationals Time
Air Force Miller, Genevieve 500 Freestyle 0.30% 4:38.98 4:39.82 Miller, Genevieve 200 Freestyle 1.09% 1:47.19 1:48.36
Akron Myers, Madison 100 Freestyle -0.49% 49.33 49.09 Szynal, Luka 200 Backstroke 1.27% 1:52.94 1:54.37
Alabama Scott, Bailey 50 Freestyle 0.50% 21.84 21.95 Nonnenberg, Mia 500 Freestyle 2.08% 4:43.20 4:49.08
Arizona Lohman, Kennedy 200 Breastroke -1.43% 2:14.21 2:12.29 Rumrill, Mackenzie 200 IM 2.44% 1:59.39 2:02.30
Arizona St Fisch, Claire 100 Freestyle -0.63% 49.2 48.89 Simonovic, Kat 500 Freestyle 1.49% 4:40.53 4:44.70
Arkansas Macias, Ayumi 500 Freestyle -0.52% 4:42.39 4:40.92 Tatlow, Chelsea 200 IM 1.97% 1:59.50 2:01.86
Auburn Falconer, Erin 100 Backstroke -3.64% 56.01 53.97 Falconer, Erin 200 Backstroke 2.75% 1:54.40 1:57.55
Boise St Martin, Katelyn 100 Freestyle -0.16% 48.98 48.9 Chard, Emma 1650 Freestyle 2.86% 16:17.11 16:45.05
Buffalo Burns, Megan 100 Freestyle 0.44% 48.14 48.35 Burns, Megan 200 Freestyle 1.43% 1:46.07 1:47.59
California Bilquist, Amy 100 Freestyle -2.54% 48.79 47.55 Li, Celina 400 IM 3.34% 4:06.76 4:15.00
Central Conn St Garber, Maddy 200 Breastroke -0.17% 2:13.35 2:13.12 Garber, Maddy 100 Breastroke 0.87% 1:00.01 1:00.53
Cincinnati Keire, Jackie 50 Freestyle -0.14% 22.19 22.16 Keire, Jackie 100 Freestyle 0.77% 47.95 48.32
Davidson Lankiewicz, Elise 100 Freestyle -1.00% 49.11 48.62 Lankiewicz, Elise 200 Freestyle 0.09% 1:45.14 1:45.23
Denver Andison, Bailey 400 IM -1.74% 4:07.40 4:03.09 Myers, Maddie 400 IM 2.68% 4:09.54 4:16.24
Drexel Bernhardt, Rachel 100 Breastroke -0.95% 1:00.25 59.68 Bernhardt, Rachel 200 Breastroke 1.22% 2:10.62 2:12.22
Duke Rusch, Maddie 100 Freestyle -0.28% 49.32 49.18 Goldman, Leah 200 IM 2.05% 1:57.33 1:59.74
East Carolina Norrman, Vendela 100 Breastroke 0.31% 1:01.58 1:01.77 Norrman, Vendela 200 Breastroke 1.34% 2:10.28 2:12.02
Eastern Mich Mitcheltree, Alexis 200 Backstroke -0.11% 1:56.74 1:56.61 Mitcheltree, Alexis 100 Backstroke 1.37% 52.57 53.29
Florida Burns, Hannah 200 IM -1.59% 2:00.23 1:58.32 Yambor-Maul, Alyssa 200 Butterfly 2.01% 1:55.68 1:58.01
Florida Gulf Latham, Katie 50 Freestyle -0.36% 22.34 22.26 Elmgreen, Christina Kaas 200 IM 4.19% 1:59.65 2:04.66
Florida Intl Bertelli, Letizia 100 Freestyle 1.16% 48.32 48.88 Ruele, Naomi 100 Backstroke 1.66% 52.87 53.75
Florida St Lovemore, Tayla 100 Butterfly -0.81% 52.02 51.6 Lovemore, Tayla 50 Freestyle 1.81% 22.1 22.5
Georgia Stewart, Kylie 200 Backstroke -2.01% 1:54.00 1:51.71 McCann, Meryn 500 Freestyle 2.44% 4:38.62 4:45.41
Harvard Dahlke, Miki 100 Freestyle -0.25% 48.9 48.78 Dahlke, Miki 50 Freestyle 0.83% 22.8 22.99
Hawaii Hansen, Bryndis 200 Freestyle 0.09% 1:46.93 1:47.03 Hansen, Bryndis 100 Freestyle 1.83% 48.55 49.44
Indiana Goss, Kennedy 500 Freestyle -1.96% 4:41.64 4:36.13 Pressey, Bailey 400 IM 3.92% 4:10.16 4:19.96
Iowa Sougstad, Emma 100 Breastroke -0.88% 59.31 58.79 Sougstad, Emma 200 IM 0.44% 1:57.98 1:58.50
Kentucky Freriks, Geena 100 Freestyle -1.34% 49.32 48.66 Casey, Kendal 500 Freestyle 1.73% 4:40.81 4:45.66
Liberty Finnigan, Alicia 100 Butterfly 0.17% 53.74 53.83 Finnigan, Alicia 200 Butterfly 1.05% 1:56.15 1:57.37
Louisville Comerford, Mallory 200 Freestyle -1.32% 1:41.70 1:40.36 Houck, Abbie 500 Freestyle 1.44% 4:42.41 4:46.47
LSU Troskot, Leah 100 Freestyle -0.60% 48.53 48.24 Kopcso, Kara 200 Butterfly 0.92% 1:55.66 1:56.72
Marshall Rowe, Sirena 100 Freestyle 1.15% 49.53 50.1 Rowe, Sirena 50 Freestyle 1.59% 22.08 22.43
Miami (FL) Algee, Angela 200 Butterfly 0.44% 1:58.38 1:58.90 Algee, Angela 100 Butterfly 2.10% 52.01 53.1
Michigan Deloof, Gabby 50 Freestyle -2.35% 23.01 22.47 McCann, Carolyn 100 Breastroke 1.84% 1:00.33 1:01.44
Minnesota Horejsi, Lindsey 100 Breastroke -1.48% 58.9 58.03 Zeiger, Brooke 400 IM 3.82% 4:02.71 4:11.99
Missouri Laemmler, Nadine 100 Backstroke -2.73% 51.96 50.54 Suek, Ellie 400 IM 1.61% 4:10.33 4:14.37
NC State Brumbaum, Kayla 200 Breastroke -1.55% 2:07.53 2:05.55 Labonge, Natalie 50 Freestyle 1.73% 22 22.38
Northwestern Gruest Slowing, Valerie 1650 Freestyle -0.65% 16:01.28 15:55.01 Postoll, Melissa 400 IM 1.85% 4:08.76 4:13.37
Notre Dame Mulquin, Catherine 100 Backstroke -0.79% 52.13 51.72 Smith, Katie 50 Freestyle 1.57% 22.25 22.6
Ohio St Li, Zhesi 50 Freestyle -0.88% 21.48 21.29 Vargo, Taylor 100 Breastroke 3.46% 59.86 1:01.93
Penn St McHugh, Ally 1650 Freestyle -0.91% 16:07.69 15:58.92 Sowinski, Katelyn 200 Butterfly 3.19% 1:55.97 1:59.67
Pittsburgh Rathsack, Lina 100 Breastroke -1.34% 59.8 59 Rathsack, Lina 200 IM 2.94% 1:57.66 2:01.12
Purdue Meitz, Kaersten 1650 Freestyle -0.71% 16:10.41 16:03.48 Meitz, Kaersten 200 Freestyle 0.99% 1:44.78 1:45.82
Rice Schillinger, Marie-Claire 200 Breastroke 1.44% 2:10.92 2:12.80 Schillinger, Marie-Claire 100 Breastroke 1.83% 1:00.13 1:01.23
Rutgers Koprivova, Vera 200 Backstroke -0.18% 1:53.94 1:53.74 Stoppa, Francesca 200 Butterfly 1.37% 1:56.20 1:57.79
Seattle U Wittenauer-Lee, Blaise 100 Breastroke 0.31% 1:00.52 1:00.71 Wittenauer-Lee, Blaise 200 IM 0.88% 1:59.95 2:01.00
SMU Samardzic, Matea 200 Backstroke 0.65% 1:52.69 1:53.42 Erasmus, Marne 100 Butterfly 1.93% 51.42 52.41
South Carolina Barksdale, Emma 200 Breastroke -0.36% 2:11.30 2:10.83 Dirrane, Kersten 200 Breastroke 3.11% 2:08.58 2:12.58
Southern Cali Mann, Becca 500 Freestyle -1.30% 4:42.44 4:38.77 Vose, Kirsten 200 IM 3.87% 1:55.91 2:00.39
Stanford Manuel, Simone 100 Freestyle -1.73% 46.36 45.56 Drabot, Katie 500 Freestyle 2.13% 4:35.69 4:41.56
Tennessee Brown, Erika 100 Freestyle -1.76% 49.33 48.46 Cefal, Michelle 200 Butterfly 2.19% 1:56.18 1:58.72
Texas Cox, Madisyn 200 Breastroke -1.13% 2:07.21 2:05.77 Evans, Joanna 500 Freestyle 1.95% 4:36.97 4:42.37
Texas A&M Rasmus, Claire 100 Freestyle -1.44% 49.35 48.64 Gonzalez-Hermosillo, Monika 200 Breastroke 1.97% 2:11.50 2:14.09
UC Davis Laughlin, Solie 200 Backstroke 0.10% 1:54.48 1:54.60 Laughlin, Solie 200 IM 0.43% 1:59.74 2:00.25
UCLA Grover, Katie 100 Freestyle -0.91% 49.28 48.83 Mack, Linnea 50 Freestyle 3.41% 21.67 22.41
UMBC Escobedo, Emily 100 Breastroke -0.91% 59.02 58.48 Escobedo, Emily 200 IM -0.46% 1:55.66 1:55.13
UNC Moffitt, Hellen 100 Butterfly -0.96% 50.86 50.37 Baldwin, Caroline 100 Backstroke 1.50% 51.38 52.15
Utah Colleou, Stina 100 Breastroke 0.88% 1:01.29 1:01.83 Colleou, Stina 200 Breastroke 2.89% 2:09.90 2:13.66
Virginia Jones, Kaitlyn 100 Backstroke -4.88% 55.89 53.16 Tafuto, Vivian 200 IM 4.12% 1:58.80 2:03.70
Virginia Tech Nazieblo, Klaudia 400 IM -1.22% 4:15.77 4:12.65 Hicks, Chloe 200 Backstroke 1.63% 1:53.70 1:55.55
Wisconsin Unicomb, Jess 50 Freestyle -0.79% 22.79 22.61 Grindall, Dana 100 Butterfly 2.46% 52.08 53.36
Wyoming Harutjunjan, Maria 200 Breastroke 1.71% 2:11.53 2:13.78 Harutjunjan, Maria 100 Breastroke 2.06% 59.85 1:01.08

Biggest Time Drops from Seed

Team Time Change Event Seed Time Nationals Time
1 Jones, Kaitlyn Virginia -4.88% 100 Backstroke 55.89 53.16
2 Falconer, Erin Auburn -3.64% 100 Backstroke 56.01 53.97
3 Laemmler, Nadine Missouri -2.73% 100 Backstroke 51.96 50.54
4 Bilquist, Amy California -2.54% 100 Freestyle 48.79 47.55
5 Deloof, Gabby Michigan -2.35% 50 Freestyle 23.01 22.47
6 Stewart, Kylie Georgia -2.01% 200 Backstroke 1:54.00 1:51.71
7 Goss, Kennedy Indiana -1.96% 500 Freestyle 4:41.64 4:36.13
8 Brown, Erika Tennessee -1.76% 100 Freestyle 49.33 48.46
9 Andison, Bailey Denver -1.74% 400 IM 4:07.40 4:03.09
10 Manuel, Simone Stanford -1.73% 100 Freestyle 46.36 45.56
11 Laemmler, Nadine Missouri -1.65% 200 Backstroke 1:52.65 1:50.79
12 Stewart, Kylie Georgia -1.63% 100 Backstroke 52.65 51.79
13 Burns, Hannah Florida -1.59% 200 IM 2:00.23 1:58.32
14 Brumbaum, Kayla NC State -1.55% 200 Breastroke 2:07.53 2:05.55

Biggest Time Adds to Seed

Name Team Time Change Event Seed Time Nationals Time
1 Elmgreen, Christina Kaas Florida Gulf 4.19% 200 IM 1:59.65 2:04.66
2 Tafuto, Vivian Virginia 4.12% 200 IM 1:58.80 2:03.70
3 Pressey, Bailey Indiana 3.92% 400 IM 4:10.16 4:19.96
4 Vose, Kirsten Southern Cali 3.87% 200 IM 1:55.91 2:00.39
5 Zeiger, Brooke Minnesota 3.82% 400 IM 4:02.71 4:11.99
6 Weiss, Hannah Southern Cali 3.50% 100 Backstroke 52.56 54.4
7 Moseley, Stanzi Southern Cali 3.49% 200 Freestyle 1:43.98 1:47.61
8 Vargo, Taylor Ohio St 3.46% 100 Breastroke 59.86 1:01.93
9 Mack, Linnea UCLA 3.41% 50 Freestyle 21.67 22.41
10 Li, Celina California 3.34% 400 IM 4:06.76 4:15.00
11 Leach, Hanni Southern Cali 3.32% 200 Backstroke 1:52.58 1:56.32
12 Weiss, Hannah Southern Cali 3.32% 200 Backstroke 1:55.00 1:58.82
13 Vargo, Taylor Ohio St 3.32% 200 Breastroke 2:08.56 2:12.83
14 Sowinski, Katelyn Penn St 3.19% 200 Butterfly 1:55.97 1:59.67
15 Vose, Kirsten Southern Cali 3.13% 200 Breastroke 2:07.65 2:11.65
16 Dirrane, Kersten South Carolina 3.11% 200 Breastroke 2:08.58 2:12.58
17 Mack, Linnea UCLA 3.11% 100 Backstroke 50.56 52.13

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7 Comments on "NCAA D1 Women Time Change vs Seed at Nationals"

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Great data. Excellent work.

The Grand Inquisitor
Thanks for sharing the seven year history and sd’s – useful for adjusting psych sheet forecasts in the future. Using raw diving scores from the various zone qualifying meets turned out to be a useful technique to adjust for diving points as well. Using this method, Minnesota was forecasted to benefit most from diving with 65 points, and they actually scored 95 – by far the biggest over performer as well. UCLA was forecasted at 49 and scored 43; Indiana 40/27, S Carolina 36/4, Nev 30/25, Tex 28/20, Miami OH 25/29, LSU 21/14, Purdue 19/29, Stanford 18/27, Mizzou 18/18, ASU 17/9, Neb 16/15, Northwestern 15/31, Haw 13/0, OSU 12/0, UK 11/4, Air Force 9/0, TAM 7/0, UTAH 6/0, Mich 3/12,… Read more »

This is interesting, but because of total subjectivity in the sport, I’m not sure it is quite as applicable. It might be more predictive to average dive lists for participating athletes from their final dual meet, conference and Zone dive lists (approximately 5 in total), then place them. Greater high level sample size is definitely more predictive of result.

The Grand Inquisitor

To be clear, was not trying to gauge a “performance vs seed” analysis for diving. The forecasted team scores based on psych sheet which were presented excluded any assumptions for diving (a significant limitation if you are really interested in final team placing). What I just shared was simply a test to see if you could improve the accuracy of the forecast if you layered on expected scores for diving based on zone diving scores. It appears to me that this in fact yielded useful results — there may be (probably are) better methods, but I haven’t seen any put forward to this point.

again, still don’t see the purpose in making a chart of ‘biggest time adds’.

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