Michigan Girls Preseason Composite XC Team Rankings

This content requires a MileSplit Pro plan.
Join Now and select any plan for instant access!

MileSplit Pro
Join Now


Find out who our data based ranking system projects in the preseason as the top returning girls cross country squads in the state of Michigan.

RankTeamScoreHighestLowestWeakness
1Troy (MI)7.642183200m Top 4
2Pinckney (MI)8.891235000m 1-5 Gap (1:19.60)
3Northville (MI)9.62405000m 1-3 Gap (1:19.00)
4Clarkston (MI)10.24215000m 1-3 Gap (35.30)
5Saline (MI)10.63345000m 1-2 Gap (37.65)
6Birmingham Seaholm (MI)11.82385000m 1-4 Gap (1:42.74)
7Rockford (MI)12.12395000m 1-3 Gap (1:16.52)
8Lansing Catholic (MI)12.41495000m 1-5 Gap (3:03.00)
9Holland West Ottawa (MI)13.41311600m 1-4 Average (5:34.85)
10Traverse City West (MI)14.45231600m 1-4 Average (5:31.23)
11Mt. Pleasant Sacred Heart (MI)15.24391600m 1-4 Average (5:36.88)
12Ann Arbor Pioneer (MI)17.33495000m 1-2 Gap (1:57.80)
13Traverse City St. Francis (MI)18.39365000m 1-3 Gap (1:09.44)
14Battle Creek Lakeview (MI)18.411325000m 1-2 Gap (32.00)
15Lake Orion (MI)19.610305000m 1-3 Gap (1:00.00)
16Highland Milford (MI)19.77395000m 1-2 Gap (55.00)
17Brighton (MI)20.41255000m 1-2 Gap (21.30), Not Enough Data
18Salem (MI)22.21405000m 1-4 Average (19:44.00)
19Saugatuck (MI)22.89495000m 1-5 Average (20:09.80)
20Bay City Western (MI)23.43365000m 1-5 Average (19:50.40)
21Romeo (MI)23.61465000m 1-4 Average (19:48.20)
22Okemos (MI)23.68343200m 1-4 Average (12:30.03)
23Ann Arbor Gabriel Richard (MI)23.811375000m 1-4 Average (19:42.40)
24Midland Dow (MI)23.87505000m 1-5 Gap (3:11.50)
25White Lake Lakeland (MI)24.07405000m 1-4 Gap (1:46.00)
26Benzie Central (MI)24.413381600m 1-4 Average (5:36.48)
27Grand Rapids Forest Hills Northern (MI)26.212445000m 1-5 Gap (2:12.30)
28East Grand Rapids (MI)26.44413200m 1-4 Average (12:36.76)
29Traverse City Central (MI)276485000m 1-2 Gap (1:57.80)
30Grand Rapids Christian (MI)27.42491600m 1-4 Average (5:43.13)
31Fenton (MI)28.314475000m Top 4, Not Enough Data
32Grand Haven (MI)28.518465000m 1-2 Gap (1:22.90)
33Oxford (MI)29.52425000m 1-5 Average (20:04.70)
34Otsego (MI)30.212315000m 1-4 Average (19:33.12), Not Enough Data
35Grand Rapids Forest Hills Eastern (MI)30.76415000m 1-5 Gap (2:08.20)
36Dearborn Divine Child (MI)30.719383200m 1-4 Average (12:33.07)
37Hudsonville (MI)30.816425000m 1-3 Gap (1:32.57)
38Grandville (MI)32.413465000m 1-5 Gap (2:37.10)
39Rochester (MI)34.06485000m 1-4 Average (19:50.43), Not Enough Data
40DeWitt (MI)34.82395000m 1-4 Gap (1:45.90), Not Enough Data
41Portage Northern (MI)36.228425000m 1-5 Gap (2:11.00)
42East Lansing (MI)36.517395000m 1-4 Average (19:43.53), Not Enough Data
43Saginaw Heritage (MI)40.019455000m 1-4 Average (19:47.00), Not Enough Data
44Mason (MI)41.435485000m Top 4, Not Enough Data
45Remus Chippewa Hills (MI)42.515505000m 1-4 Average (19:57.45)
46Whitehall (MI)435495000m Top 5, Not Enough Data

What are composite team rankings?

A few years ago, MileSplit developed a data based number-cruncher system to rank cross country teams called "composite" team rankings. The rather complicated algorithm takes into account both cross country and track seasons, based on various categories and weights. It even indicates what the computer believes the biggest weakness is at this point.

Teams that did not have much of a track season or did not have at least four of their top distance runners out for track may see their scores drop. However, teams that busted it and looked great this past spring will show higher. Hopefully it is a good balance to predict who is strong coming in! It does not necessarily take into account any new freshman or transfers.

The score represents the team's weighted composite average rank across all categories. The highest column represents the highest ranking they received in a category, and conversely the lowest is the worst ranking they received in a category.

If you pull up the XC Team Scores page, you'll see a link to "Composite" scoring. This is a type of scoring that gives a team a rank on a number of different categories, with different weights on each:

  • XC 5K Team Rank (normal)
  • XC 5K 1-5 Split
  • XC 5K 1-5 Average
  • XC 5K 1-4 Rank (normal)
  • XC 5K 1-4 Split
  • XC 5K 1-4 Average
  • XC 5K 1-3 Split
  • XC 5K 1-2 Split
  • Outdoor 1600m Top 4 (normal)
  • Outdoor 1600m Top 4 Average
  • Outdoor 3200m Top 4 (normal)
  • Outdoor 3200m Top 4 Average

By using all of these factors and weighting them appropriately, we should get a really good and balanced idea of who are the best teams. This is especially designed for returning teams.

Comments