MoCoRunning

County Championship Projections - Girls

By: Kevin Milsted

Sunday, October 16, 2022

Below you will

Projected ======================= Pl Name ======================= 1 Varri Higgins 2 Grace Finnegan 3 Avery Graham 4 Alexa Avila 5 Daisy Dastrup 6 Katie Greenwald 7 Madeleine Simmons 8 Hallie Muniz 9 Maya Gottesman 10 Noor Aly 11 Victoria Ketzler 12 Charlotte Chang 13 MacKenzie Raue 14 Rebecca Vasconez 15 Meilani Rodgers 16 Whitney Duhon 17 Maya Ducker 18 Carolyn Hultman 19 Maya Colavito 20 Emily Zanni 21 Nina Forstner 22 Anna Bodmer 23 Elisa Ciriaci 24 Caroline Easley 25 Zuzana Huserova 26 Aanya Tiwari 27 Lauren Gotting 28 Roma Diak 29 Audrey Wychulis 30 Sophie Harjes 31 Aubrey Green 32 Natalie Merberg 33 Abigail Hill 34 Emilie Creighton 35 Phoebe Dainer 36 Chiara Mood 37 Zara Kanold-Tso 38 Christa Sadd 39 Emma Bergfalk 40 Madeline Quirion 41 Peri Nelson 42 Kelsie Miller 43 Olivia Woitach 44 Alyssa Forrester 45 Audrey Inglese 46 Elola Eckford 47 Morgan Kirsch 48 Sabrina Chou Projected Team ======================= School ======================= 1. Thomas S. Wootton 2. Walter Johnson 3. Bethesda Chevy Chase 4. Winston Churchill 5. Walt Whitman Teams with Four Teams with Three Mocorunning's The projections To assign The chart I decided to make Moving right to

HERE. The ranking formula compares runners in every race throughout the season and year after year. The finish times only matter to the extent that finish times of all ranked runners will be compared and scored against all other ranked runners in that race. The most important thing for mobility within the ranking is beating other ranked runners by as many seconds as possible.

you see on this page exactly match the names on the week 7 ranking published on 10/16/2022 with a couple notable changes. Private school runners were removed and the points were replaced with projected finish times for the county championship course at Gaithersburg. Do the points convert directly to 5k times? No, not exactly, but the points do convert to a time scale. 1 second = 2 points. Therefore, if the top ranked runner had 200 points, he or she would be ranked 200 points or 100 seconds above the ranking cutoff. You can assign that top ranked runner any 5k time, and he or she would be 100 seconds (1:40) above the ranking cutoff. Take any two runners and subtract their point totals, divide by two, and you will know how far apart they are expected to finish according to the ranking.

the projected times to all the runners, you really only need to assign a projected time to one runner. Once one runner has an assigned projected time, the point scale dictates the finish times for every other runner within the ranking. It is not the first or last ranked runner that you want to key off of. It is the runners in the middle range that will be the most consistent year after year. The great thing about the county championship is that we have the same exact number of teams entered in the meet at the same time of the year, every year. The caliber of the middle tier varsity runners will not fluctuate very much from year to year, at least, that was my thesis before the COVID-19 pandemic. The sport clearly had a lack of depth in 2021, and the effects of low participation during the pandemic are still lingering.

below shows the county championship varsity race finishers plotted for each year since the course was modified in 2011. With ten years of history on the course, the average line is well-defined. You can see that there have been some slow years (2018 and 2021) and some very fast years (2015 and 2017). Weather played a roll in 2018 and 2021 when the course was particularly wet. The pandemic clearly affected the depth of the varsity race in 2021. Most other years have been dry, and the athletes dictated the speed of the course.

the assumption that we are back to "average" even though the data still points to lingering effects of low participation during the pandemic. Under the assumption that 2022 will be an average year, meaning that this year's middle tier "top 100" varsity high school runners are no better or worse than a typical year, I want my RED projected dots to land on the blue AVG line as closely as possible. I cannot manipulate the curvature of the red dot curve. The curvature of the red dot curve is dictated by the ranking system. I can only move my red line up or down vertically to match the historical results profile of the county championship meet. I adjusted all times up and down until I felt that I had the best overlay with the 10-year average. I aimed to line up the 20th to 40th ranked runners as closely as possible to the average line, paying little attention to the top ranked runners. Doing this exercise ensured that the projections are in line with the history of the meet on this course.

left on the chart below, the red curve bends sharply downwards such that it breaks away from the historical average. This tells me that the top 15 runners have separated themselves from the #16 through #48 runners this season more than what is typical. I was left with a choice. Should I line up the top 15 with the historical average trend and let #20-48 rise much higher than the historical average? Or should I line up the #20-40 runners with the historical average and let the #1-19 runners land much lower than the historical average? I chose the latter to keep consistent with my methodology from past years, which is the optimistic scenario. The pessimistic case is that this is NOT an average year because the sport as a whole and each individual program is still recovering from low participation due to the COVID pandemic. We will find out on Saturday. s/2022xcgirlsprojections.png">


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