Payton Jordan Invitational - analysis on every runner and race (distance)

Teenage wastelandi wrote:

Donaghu for Oregon HS gir GOAT wrote:

Plz include Ella Donaghu, HS distance phenom and Stanford recruit, in your calculations.

+1

____NAME______TEAM______realTime__adj_Time__Gain

Ella Donaghu___Unattached___4:15.27____4:11.92__3.35

the sands wrote:

stats.gangsta_the_real_1 wrote:

For example, this runner ran a 1500m in 3:34.18 with these splits:

38.97, 53.28, 61.51, 60.42

The optimizer shows that person could have run 3:26.89. Is that really "extremely optimistic" or "flat out fantasy" in relation to the capabilities of that runner?

Those are El G's splits (in reverse order) for the Athens final. Any two data points for one runner mean little. The second place finisher, Lagat, of course, also has a 3:26 PB. But the model over-predicts for the rest of the field, who essentially tracked El G's pace, while running the last 700m that much slower. None of the 3-8 place runners in that final (Silva on down) ever ran as fast as the model would predict.

Well of course, that's the whole point of the optimizer. It shows what you're capable, not necessarily what you'll run.

Where is the womens 5k heat 3?

the sands wrote:

stats.gangsta_the_real_1 wrote:

For example, this runner ran a 1500m in 3:34.18 with these splits:

38.97, 53.28, 61.51, 60.42

The optimizer shows that person could have run 3:26.89. Is that really "extremely optimistic" or "flat out fantasy" in relation to the capabilities of that runner?

Those are El G's splits (in reverse order) for the Athens final. Any two data points for one runner mean little. The second place finisher, Lagat, of course, also has a 3:26 PB. But the model over-predicts for the rest of the field, who essentially tracked El G's pace, while running the last 700m that much slower. None of the 3-8 place runners in that final (Silva on down) ever ran as fast as the model would predict.

You're misinterpreting what this calculator does. It doesn't predict. It calculates. I.e., it doesn't predict what someone will run or what their career PB will be. It calculates what they could have run on a particular race day. The calculator results are not supposed to line up with their career PBs. It might or might not, but the two things are independent of each other.

As you can see in the Payton Jordan results, runners don't always pace themselves smoothly. In world class 1500m time trials, runners are usually leaving 1-2 seconds worth of time out there. Consider that when you compare the PBs of the 2004 Athens 1500m finalists to the results of the optimizer for those races.

Also, do you have exact reliable splits for each runner that day? Saying that "the field essentially track El G's pace, while running the last 700m slower" is not accurate. On the first lap, there is a ~1 second gap from the front to the back. That changes the results of the calculator.

If you put all of the difference into the final 700m, then you're going to get bad results. Accounting for what I said in the previous two paragraphs, the numbers match up pretty well with their PBs.

However, Rui Silva was just on another level that day! I think I heard he finished faster than El G and Lagat. That was likely the race & fitness of his life. With some better positioning, he may have won the race.

Interesting Stuff wrote:

Well of course, that's the whole point of the optimizer. It shows what you're capable, not necessarily what you'll run.

It allegedly shows you what a given runner is capable of. With runners' entire careers as data controls, of course it matters whether it is empirically accurate in that regard; otherwise how do you know whether it's "garbage in, garbage out"?

And bear in mind that 1500m Oly finals are rarely the setting for PBs, not just because of tactics but because of the demands of the rounds. For the model to predict that everyone in the top 8 of the Athens final was capable of running either their PB (top two) or faster than their PB (3-8) on the day is not a convincing advertisement for the model's accuracy.

So out of 100-200 elite runners, not a single one got within two seconds/mile of their full ability on the day? That is truly remarkable...or stupid.

Like the concept, thanks for posting!

Do you have a website- or are you taking requests? I have a high school 1600m I want to "optimize" for one runner (not the whole field).

I am asking for it too wrote:

Like the concept, thanks for posting!

Do you have a website- or are you taking requests? I have a high school 1600m I want to "optimize" for one runner (not the whole field).

His website is

Marco Brolo wrote:

Where is the womens 5k heat 3?

Women's 5000 Section 3

______NAME__________TEAM_________realTime__adj_Time__Gain

1_Juliana_Gomes_dos_Santos_Posso_Sports_15:54.78_15:43.74__11.04

2_Natosha_Rogers____New_Balance______15:58.95__15:50.99__07.96

3_Kate_Spencer________Asics_Sydney____15:59.73__15:45.45__14.28

4_Louise_Small___________Afd___________16:00.95__15:52.28__08.67

5_Mara_Olson__________adidas_Rme_____16:03.14__15:53.62__09.52

6_Lianne_Farber____New_Balance_NorCal__16:07.84__15:50.22__17.62

7_Kinsey_Gomez________Idaho__________16:11.17__15:59.93__11.24

8_Olivia_Pratt_________Butler____________16:16.01__16:02.81__13.20

9_Gemma_Hillier-moses____Unattached____16:24.67__16:04.55__20.12

10_Natasha_LaBeaud______Skechers_____16:27.87__16:08.38__19.49

11_Emily_Bean__________Mississippi______ 16:30.33__16:12.25__18.08

12_Danielle_Winslow_____Boston_College__16:31.64__16:11.48__20.16

13_Gemma_Maini_______Unattached______16:37.27__16:11.62__25.65

14_Margo_Malone_______Syracuse________16:44.35__16:17.92__26.43

15_Shaylyn_Tuite________Syracuse_______16:58.12__16:36.28__21.84

Can you add the 10 mile and/or 15K into your input/output distances?

Women's 10000m Section 1

________NAME_________TEAM___________realTime__adj_Time__Gain

1_Irene_Cheptai____________Nike________31:15.38__31:01.38__14.00

2_Caroline_Chepkoech___Boulder_Wave____31:16.38__31:01.83__14.55

3_Ayuko_Suzuki_____Japan_Post_Group___31:18.16__31:05.53__12.63

4_Marielle_Hall_________Nike____________31:37.45__31:19.39__18.06

5_Karoline_Bjerkeli_Grovda__Jrs_Sports_M__31:37.91__31:19.22__18.69

6_Buze_Diriba__________Adidas__________31:38.61__31:13.80__24.81

7_Miyuki_Uehara_______Daiichi_Seimei_____31:38.80__31:21.02__17.78

8_Kellyn_Taylor________Naz_Elite_________31:40.70__31:25.88__14.82

9_Veronica_Inglese_______C.S._Esercito___31:42.02__31:28.30__13.72

10_Liz_Costello________New_Balance______31:43.79__31:28.85__14.94

11_Alexi_Pappas_______Nike/Oregon______31:46.85__31:35.97__10.88

12_Hanami_Sekine____Japan_Post_Group__31:48.90__31:36.88__12.02

13_Erin_Finn_________Unattached________31:51.84__31:45.16__06.68

14_Riko_Matsuzaki_____Sekisui_Kagaku____31:55.26__31:46.67__08.59

15_Dominique_Scott____Arkansas_________31:56.84__31:45.24__11.60

16_Diane_Nukuri______Asics_____________31:57.99__31:40.15__17.84

17_Jordan_Hasay____Nike_Oregon_Project__31:58.33__31:45.81__12.52

18_Alia_Gray_________Roots_Runnin______31:59.23__31:48.82__10.41

19_Clémence_Calvin___Unattached________32:06.23__31:51.75__14.48

20_Kasumi_Nishihara____Yamada_________32:07.77__31:55.67__12.10

21_Lanni_Marchant_____Asics_Canada_____32:10.00__31:53.29__16.71

22_Ines_Melchor_Huiza___Federación_P____32:10.99__31:56.22__14.77

24_Yuka_Miyazaki_______Kyudenko_______32:24.07__32:05.82__18.25

25_Carolina_Tabares__Equipo_Porvenir_____32:24.73__32:05.88__18.85

26_Alice_Wright_______New_Mexico_______32:36.11__32:20.20__15.91

27_Rachel_Cliff_______Thunderbirds/Asics__32:45.68__32:28.32__17.36

28_Brenna_Peloquin___Boise_State________33:03.48__32:43.61__19.87

29_Rei_Ohara_________Tenmaya_________33:31.47__32:58.43__33.04

30_Gladys_Tejeda________Federación_P___33:38.59__33:06.54__32.05

31_Rachel_Hannah______U_of_Toronto____34:29.55__33:54.68__34.87

Women's 10000m Section 2

_______NAME_________TEAM___________realTime__adj_Time__Gain

1_Sarah_Lahti_______Global_Sport______31:54.87__31:37.80__17.07

2_Margarita_Hernández__Uaemex_Mexico___32:11.04__31:55.55__15.49

3_Sara_Slattery_______Brooks___________32:13.03__31:57.08__15.95

4_Elaina_Balouris__________Baa________32:32.61__32:14.82__17.79

5_Becky_Wade__________Asics__________32:32.85__32:18.64__14.21

6_Mary_Davies_________Brooks__________32:33.55__32:16.02__17.53

7_Sarah_Pagano_________Baa___________32:55.98__32:34.17__21.81

8_Angela_Bizzarri________Brooks_Beasts___33:04.10__32:38.51__25.59

9_Katy_Moen_________Unattached________33:05.31__32:40.92__24.39

10_Meghan_Peyton____Team_Usa_Min_____33:05.33__32:46.68__18.65

11_Susanna_Sullivan____Capital_Area_____33:05.62__32:41.40__24.22

12_katja_goldring______Skechers_Per_____33:08.90__32:55.67__13.23

13_Elinor_Kirk________New_Balance______33:16.74__32:57.38__19.36

14_Laura_Batterink___McGill_Olympic_Club__33:23.90__32:57.79__26.11

15_Monika_Juodeskaite__Oklahoma_State___33:23.93__33:02.46__21.47

16_Megan_Curham______Unattached______33:30.83__33:07.91__22.92

17_Kotomi_Takayama___Impress_Running___33:34.86__33:11.23__23.63

18_Kathya_Mirelle____Mexico-FMAA______33:37.16__33:06.71__30.45

19_Bridget_Lyons____Atlanta_Track_Club___33:39.88__33:15.75__24.13

20_Joanna_Thompson_Zap_Fitness/Reebok_33:53.65__33:33.80__19.85

21_Jamie_van_Lieshout__Global_Sport_____34:18.73__33:45.74__32.99

22_Regan_Ward_______Unattached_______34:41.71__34:15.43__26.28

23_Abbie_McNulty______Stanford_________35:01.85__34:43.88__17.97

Men's 10000m Section 1

________NAME______TEAM_____________realTime__adj_Time__Gain

1_Bernard_Lagat____Nike_______________27:49.35__27:30.42__18.93

2_Suguru_Osako___Nike_Oregon_Project___27:50.27__27:30.64__19.63

3_Futsum_Zienasellassie_Northern_Arizona__27:52.70__27:34.26__18.44

4_Abraham_Habte__Posso_Sports_________27:53.38__27:35.12__18.26

5_Sam_Chelanga_____Nike______________27:54.57__27:40.11__14.46

6_Luis_Fernando__Federación_P__________27:54.80__27:41.23__13.57

7_Wiliam_Malel______Impress_Running_____27:56.23__27:42.05__14.18

8_Mogos_Shumay__Posso_Sports_________27:56.98__27:45.24__11.74

9_Jason_Witt_____Hansons-Brooks_Odp____28:19.45__28:03.31__16.14

10_Ben_Bruce_____Hoka_Naz_Elite________28:25.32__28:11.12__14.20

11_Aron_Rono______Unattached__________28:27.04__28:04.05__22.99

12_George_Alex____Zap_Fitness/Reebok___28:32.68__28:16.32__16.36

13_Sean_Quigley___Boulder_Track_Club___28:38.06__28:22.70__15.36

14_Ross_Millington___Global_Sport________28:38.88__28:22.43__16.45

15_Collis_Birmingham__Melbourne_Tr______28:40.84__28:11.14__29.70

16_Brian_Shrader____Saucony___________28:45.55__28:25.73__19.82

17_Luc_Bruchet_____Asics_Canada_______28:45.81__28:24.31__21.50

18_Luke_Caldwell____Melbourne_Tr_______28:45.82__28:21.86__23.96

19_Tim_Ritchie______Saucony____________28:48.67__28:27.18__21.49

20_Pierce_Murphy___Colorado___________28:48.79__28:30.30__18.49

21_Scott_Smith_____Hoka_Naz_Elite_______28:50.85__28:28.97__21.88

22_Yuta_Shitara_____Impress_Running_____28:51.21__28:27.99__23.22

23_Colin_Bennie_____Syracuse___________28:52.72__28:28.21__24.51

24_Eric_Fernandez___Hoka_Naz_Elite______29:02.05__28:40.64__21.41

25_Maverick_Darling__Saucony___________29:03.22__28:38.24__24.98

26_Martin_Hehir____Unat-Syracuse________29:07.64__28:38.92__28.72

27_Ryan_Dohner___Hoka_Naz_Elite_______29:29.44__28:53.58__35.86

Men's 10000m Section 2

NAME______________TEAM____________realTime__adj_Time__Gain

1_Robert_Cheseret___Wcap____________28:31.91__28:15.02__16.89

2_Gabriel_Proctor___Asics/MTC_________28:33.76__28:20.01__13.75

3_Johnnie_Guy_____Southern_Indiana____28:34.79__28:21.93__12.86

4_Andrew_Ronoh____Arkansas__________28:36.20__28:22.76__13.44

5_Erik_Peterson___Butler______________28:44.18__28:31.01__13.17

6_Stephen_Furst___Unattached__________28:45.36__28:27.09__18.27

7_Cameron_Marantz___New_Balance/_____28:46.50__28:33.80__12.70

8_Zach_Zarda______Unattached_________28:46.78__28:33.90__12.88

9_Mikael_Ekvall___Melbourne_Tr_________28:48.22__28:35.77__12.45

10_Ivan_Gonzalez__Equipo_Porvenir______28:56.75__28:40.37__16.38

11_Johnny_Crain___Zap_Fitness_________29:00.81__28:45.58__15.23

12_Brenton_Rowe___Team2012.at________29:15.68__28:57.15__18.53

13_Daniel_Tapia___Asics_Mtc____________29:18.33__29:00.15__18.18

14_Roy_Hoornweg___Global_Sport________29:19.93__28:58.46__21.47

15_Rafael_Bañales__Chavillin_wo_________29:30.91__29:05.03__25.88

16_Alfredo_Santana__adidas_Garde_______29:48.68__29:20.01__28.67

17_Julian_Florez______New_Mexico________30:43.88__30:15.72__28.16

varavaneradave wrote:

I am asking for it too wrote:

Like the concept, thanks for posting!

Do you have a website- or are you taking requests? I have a high school 1600m I want to "optimize" for one runner (not the whole field).

His website is

http://timescalculator.appspot.com

Great site, thanks. I entered times for a HS girl and a D1 guy I know well at three different distances and I think this is very accurate and useful.

"pace optimizer"? come on.

some bullshit math in this thread.

stats.gangsta_the_real_1 wrote:

Anuther dwun runer wrote:

Op: what do you predict for this 1500 time:

400@62, 800@2:08, 1200@3:13, 1500@ 4:10

That was my pr (ran it once that season, was an 800m runner). I really went for broke through 1200m, just trying to hang on as long as possible, since I wasn't sure my fitness level. Obviously I went out a little too hard. I've always thought I was about 4:00 shape.

That was freshman year of college and I never ran a 1500 or mile again.

Without decimal accuracy,

57,62,66,65 (did I do these correctly?)

gives me -> 4:04.6

Actually would have been more like 46, 65, 65, 75 in your format, roughly.