Louisiana Boys Preseason Composite XC Team Rankings

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

RankTeamScoreHighestLowestWeakness
1Episcopal (LA)2.941103Mile 1-5 Gap (1:02.71)
2Brother Martin (LA)4.441263Mile 1-3 Gap (1:01.10)
3St. Thomas Aquinas (LA)6.922133Mile 1-4 Average (16:51.94)
4Airline (LA)7.152253Mile 1-5 Gap (1:57.02)
5Holy Cross (LA)7.263333Mile 1-2 Gap (36.40)
6Jesuit (LA)9.563193Mile 1-4 Average (17:10.97)
7John Curtis (LA)10.43343Mile 1-3 Gap (1:14.50)
8West Feliciana (LA)11.85383Mile 1-2 Gap (44.20)
9Belle Chasse (LA)11.92193Mile 1-4 Gap (1:06.30)
10Parkway High School (LA)12.96343Mile 1-5 Gap (2:41.00)
11St. Michael (LA)16.210333Mile 1-3 Gap (1:14.50)
12Barbe (LA)16.61313Mile 1-4 Average (17:35.88)
13Dutchtown (LA)16.98303Mile 1-4 Average (17:31.58)
14St. Martin's Episcopal (LA)175413Mile 1-5 Gap (3:11.50)
15Mandeville (LA)17.29383Mile 1-4 Gap (2:01.10)
16Lakeshore (LA)17.46253Mile 1-4 Average (17:18.83)
17St. Paul's School (LA)17.514--
18West Monroe (LA)1810273Mile 1-4 Gap (1:18.00)
19Ascension Catholic (LA)21.29483Mile 1-2 Gap (1:26.30)
20Catholic High (LA)22.05293Mile 1-2 Gap (28.90), Not Enough Data
21St. Thomas More (LA)22.53303Mile Top 4
22Episcopal of Acadiana (LA)23.88213Mile 1-5 Gap (1:40.10), Not Enough Data
23Archbishop Shaw (LA)2414363Mile 1-4 Average (17:47.58)
24Holy Savior Menard (LA)26.55223Mile 1-4 Average (17:15.85), Not Enough Data
25Parkview Baptist (LA)26.911433Mile 1-4 Average (18:01.98)
26Live Oak (LA)27.118373Mile 1-4 Average (17:51.78)
27Zachary (LA)27.23273Mile 1-4 Average (17:20.54), Not Enough Data
28West Ouachita (LA)28.58453Mile 1-5 Gap (3:40.03)
29Byrd, C.E. (LA)31.56503Mile 1-3 Gap (3:10.00)
30St. Louis High (LA)33.316463Mile 1-4 Average (18:06.41), Not Enough Data
31Fontainebleau (LA)33.414473Mile 1-2 Gap (1:25.63), Not Enough Data
32Lafayette High (LA)33.810333Mile 1-4 Average (17:40.96), Not Enough Data
33Ruston (LA)34.511443Mile 1-2 Gap (1:07.50), Not Enough Data
34Newman (LA)3516353Mile 1-4 Average (17:42.99), Not Enough Data
35E.D. White (LA)36.220443Mile 1-4 Gap (2:29.50), Not Enough Data
36Slidell (LA)36.523413Mile 1-4 Gap (2:16.00), Not Enough Data
37Denham Springs High (LA)39.723493Mile Top 4
38Cedar Creek (LA)39.926503Mile 1-2 Gap (1:48.90), Not Enough Data
39Westgate High School (LA)40.121493Mile 1-4 Gap (3:42.65), Not Enough Data
40South Terrebonne (LA)40.325443Mile 1-4 Average (18:03.08), Not Enough Data
41Erath (LA)4131433Mile 1-2 Gap (1:04.13), Not Enough Data
42Natchitoches Central (LA)44.815473Mile 1-4 Gap (3:10.20), Not Enough Data
43Ben Franklin (LA)47.428503Mile 1-5 Gap (4:38.80), 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.


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