Top 12 most stacked returning teams for 2025 XC (Using my own numbered system) Jesuit CA - 177 Beckman CA - 175 Mira Costa CA - 171 Matilda Torres CA - 171 Jserra CA - 170 Mountain View CA - 169 San Clemente CA - 169 Woodbridge CA - 168 Saugus CA - 166 Ayala CA - 166 Cresenta Valley CA - 166 ML King CA - 166 Clovis North CA - 164
Based on my data California Boys teams in 2025 will be the most talented year by far.
Clovis 2023 was hotter than Clovis 2024. Clovis 2023 was 71 degrees Clovis 2024 was 63 degrees that felt like 66.
I'd bet most runners Saturday morning would say that it felt like 86. I wasn't running, but it definitely didn't feel like 63 at the Championship races.
Even if that's true, that doesn't change the fact that is was indeed hotter in 2023. And the improved times in 2024 are partially the result of the cooler weather. It wasn't a big difference, as TullyRunners changed their speed rating scale this year compared to Clovis and the State Meet in 2023, but only reduced it by 1 point (3 seconds). A 15:00 went from 189SR to 188SR. That was the adjustment I was assuming before he published the ratings. Yes, there are other factors that go into the rating, but he frequently uses the same scale for Woodward, and this year a 15:00 (or any other time) was not rated as high as last year. Take it for what you will.
Top 12 most stacked returning teams for 2025 XC (Using my own numbered system) Jesuit CA - 177 Beckman CA - 175 Mira Costa CA - 171 Matilda Torres CA - 171 Jserra CA - 170 Mountain View CA - 169 San Clemente CA - 169 Woodbridge CA - 168 Saugus CA - 166 Ayala CA - 166 Cresenta Valley CA - 166 ML King CA - 166 Clovis North CA - 164
Based on my data California Boys teams in 2025 will be the most talented year by far.
Probably, since these number will likely improve after section finals, state meet and NXN/FL.
Right now, those numbers look a lot like this year's returning teams.
Great Oak 178.2 Martin Luther King 174.0 Buchanan 171.2 Glendora 169.4 Arnold O. Beckman 169.0 Jesuit 168.0 JSerra 167.4 Mira Costa 166.8 Tesoro 166.0 Oaks Christian 165.0 Trabuco Hills 164.0 Bellarmine College Prep 164.0
I don't have much data for 2022, but I have:
Newbury Park 193.8 Great Oak 172.8 Granada 171.8 San Clemente 167.8
Also, in turns of national strength, it isn't just comparing to previous Cal teams, because the last 2 years have seen (in my mind) a huge improvement in returning teams. So yes, this year might end up with the highest average speed ratings for returning runners, but where that puts them nationally compared to past years is hard to judge, and I don't have that data. But, it's a little early to be thinking about rankings for next year.
Great Oak boys 6th in the Southern section. Next year they only bring back 2 of those guys. This year could be the last year (at least for a while) they qualify for state. Last time they didn’t make state was 2012
Top 12 most stacked returning teams for 2025 XC (Using my own numbered system) Jesuit CA - 177 Beckman CA - 175 Mira Costa CA - 171 Matilda Torres CA - 171 Jserra CA - 170 Mountain View CA - 169 San Clemente CA - 169 Woodbridge CA - 168 Saugus CA - 166 Ayala CA - 166 Cresenta Valley CA - 166 ML King CA - 166 Clovis North CA - 164
Based on my data California Boys teams in 2025 will be the most talented year by far.
Probably, since these number will likely improve after section finals, state meet and NXN/FL.
Right now, those numbers look a lot like this year's returning teams.
Great Oak 178.2 Martin Luther King 174.0 Buchanan 171.2 Glendora 169.4 Arnold O. Beckman 169.0 Jesuit 168.0 JSerra 167.4 Mira Costa 166.8 Tesoro 166.0 Oaks Christian 165.0 Trabuco Hills 164.0 Bellarmine College Prep 164.0
I don't have much data for 2022, but I have:
Newbury Park 193.8 Great Oak 172.8 Granada 171.8 San Clemente 167.8
Also, in turns of national strength, it isn't just comparing to previous Cal teams, because the last 2 years have seen (in my mind) a huge improvement in returning teams. So yes, this year might end up with the highest average speed ratings for returning runners, but where that puts them nationally compared to past years is hard to judge, and I don't have that data. But, it's a little early to be thinking about rankings for next year.
My ratings are based on the fact that the most common (and easiest) way to compare teams is the average speed rating from tullyrunners.com, and this works pretty well, but also is a little flawed. It has the same problem as team-times, in that the best team-times doesn't necessarily win (Clovis this year is an example, best team-time finished 3rd). And since the interest in doing ratings is to provide an estimate of possible NXN bid contenders, I thought it would be best to use a scale similar to what NXN bids are based on, and that is the California state meet power merge. So, 2 years ago, I created a mapping table based on tullyrunners speedratings from the previous 2 years state meets, i.e., a speed rating of X resulted in a point score of roughly Y in the merge. Now that things have been getting faster, I really should redo that mapping after this year if I want to keep using it.
So, at this point in the year, where I am only using speed ratings, it is that simple. Just take the best speed rating for each team's best 5, and use the mapping table to get each runner's hypothetical merge score, and add it up.
During track season and xc pre-season it is more complicated, because I also map 3200m times into a projected score and use that if it is better, and also allow for 1600m to improve the score a little.
Probably, since these number will likely improve after section finals, state meet and NXN/FL.
Right now, those numbers look a lot like this year's returning teams.
Great Oak 178.2 Martin Luther King 174.0 Buchanan 171.2 Glendora 169.4 Arnold O. Beckman 169.0 Jesuit 168.0 JSerra 167.4 Mira Costa 166.8 Tesoro 166.0 Oaks Christian 165.0 Trabuco Hills 164.0 Bellarmine College Prep 164.0
I don't have much data for 2022, but I have:
Newbury Park 193.8 Great Oak 172.8 Granada 171.8 San Clemente 167.8
Also, in turns of national strength, it isn't just comparing to previous Cal teams, because the last 2 years have seen (in my mind) a huge improvement in returning teams. So yes, this year might end up with the highest average speed ratings for returning runners, but where that puts them nationally compared to past years is hard to judge, and I don't have that data. But, it's a little early to be thinking about rankings for next year.
And to clarify, this post is just using the TullyRunners average speed rating, and not using my scale. In my rankings, it is doing a hypothetical meet, so lower score is better. In average speed ratings rankings, a higher score is better
One question you may ask, "Why don't you just use the highest speed rating individual data, and just create a hypothetical meet from that"? Well, this is exactly what TullyRunners does before NXN, and it can be done because you have a full data set for all the runners in the meet. In my case, I am collecting the data for all the top 40+ schools in California, and a hypothetical meet of that size would skew the scores (a 5th man around 16:15 would score about 100 extra points in that extra large merge), and therefore not be as effective a predictor of the scoring. But this year, the week before the state meet when the final team list is known, I plan to run a state meet simulation, like the NXN one.
One question you may ask, "Why don't you just use the highest speed rating individual data, and just create a hypothetical meet from that"? Well, this is exactly what TullyRunners does before NXN, and it can be done because you have a full data set for all the runners in the meet. In my case, I am collecting the data for all the top 40+ schools in California, and a hypothetical meet of that size would skew the scores (a 5th man around 16:15 would score about 100 extra points in that extra large merge), and therefore not be as effective a predictor of the scoring. But this year, the week before the state meet when the final team list is known, I plan to run a state meet simulation, like the NXN one.
Of course, anyone who has been collecting and saving all the data like I have could create the same meet results in minutes using Excel or similar tool. They probably would have to be doing what I do, and in meets where TullyRunners only speed rates down to a certain individual level, I extrapolate to get the speed ratings for every team's top 6., so I can track everyone's season best speed rating.
One question you may ask, "Why don't you just use the highest speed rating individual data, and just create a hypothetical meet from that"? Well, this is exactly what TullyRunners does before NXN, and it can be done because you have a full data set for all the runners in the meet. In my case, I am collecting the data for all the top 40+ schools in California, and a hypothetical meet of that size would skew the scores (a 5th man around 16:15 would score about 100 extra points in that extra large merge), and therefore not be as effective a predictor of the scoring. But this year, the week before the state meet when the final team list is known, I plan to run a state meet simulation, like the NXN one.
Of course, anyone who has been collecting and saving all the data like I have could create the same meet results in minutes using Excel or similar tool. They probably would have to be doing what I do, and in meets where TullyRunners only speed rates down to a certain individual level, I extrapolate to get the speed ratings for every team's top 6., so I can track everyone's season best speed rating.
Wait, I'm a bit confused, how is creating a mapping table and then adding the corresponding point scores together different than just adding all the scores in a hypothetical 40+ school extra large merge?
Of course, anyone who has been collecting and saving all the data like I have could create the same meet results in minutes using Excel or similar tool. They probably would have to be doing what I do, and in meets where TullyRunners only speed rates down to a certain individual level, I extrapolate to get the speed ratings for every team's top 6., so I can track everyone's season best speed rating.
Wait, I'm a bit confused, how is creating a mapping table and then adding the corresponding point scores together different than just adding all the scores in a hypothetical 40+ school extra large merge?
The scoring differs somewhat based 2 main factors:
1) The hypothetical meet's mapping table is based on recent historical data for typical distribution of runners at the state meet. This distribution may differ slightly or significantly from the current year's set of runners, and thus produce a different score for a team, though the teams standings should be very similar.
2) In an extra large merge, the mid pack around 16:00-17:00 gets very, very dense, and a team with a 5th man that can avoid that does much better than in 20 team power merge. In the extreme example, in dual meet scoring, the 5th man sometimes doesn't even matter, the team can win just based on their top 4. In an extra large meet, the 5th man has a huge factor in determining the winner. If your #1 loses to Team B #1 by 10 seconds, 14:50 to 15:00, that might mean 5 points. If your #5 loses to Team B #5 by 10 seconds, 16:00 to 16:10, that means 30-40 points in the power merge and 50+ points in an extra large merge. In a larger merge of similar teams, the 5th man becomes enormously important.
Right now, Beckman wins by 41 points using a hypothetical power merge, but wins by 100+ points using this years data in an extra large merge (maybe even 150). The overall standings don't change much between the 2 scoring systems, but teams with a better 4th and 5th runner tend up move up a couple places in the extra large merge.
Using this year's current data, I ran a hypothetical extra large meet, then took the top 20 teams only and rescored a power merge (which I think is how they do it now). Note the scoring is lower in this power merge than in hypo-mapping merge, because teams run faster in the state meet than at this point in the current year, so the hypo-mapping merge is a point total against tougher competition. But this is how it would currently score (not entirely accurate because their is more southern section teams than will be in real state meet).
Beckman 136 Jesuit 222 ML King 267 Glendora 291 Great Oak 333 Menlo 334 Crescenta Valley 348 Buchanan 349 Matilda Torres 353 JSerra 358 Ayala 373 Oaks Christian 396 Saugus 410 Mira Costa 412 Clovis North 424 Trabuco Hills 426 Mountain View 441 Bellarmine 442 Clovis East 452 Hart 493
Using this year's current data, I ran a hypothetical extra large meet, then took the top 20 teams only and rescored a power merge (which I think is how they do it now). Note the scoring is lower in this power merge than in hypo-mapping merge, because teams run faster in the state meet than at this point in the current year, so the hypo-mapping merge is a point total against tougher competition. But this is how it would currently score (not entirely accurate because their is more southern section teams than will be in real state meet).
Beckman 136 Jesuit 222 ML King 267 Glendora 291 Great Oak 333
Wow. Great Oak up there this high without Westin Brown? With him and Franco in top shape, GO possibly could challenge for top 2 in the merge at CIF.
Before summer started, many of us considered them to be heavy favorites for this year.
Using this year's current data, I ran a hypothetical extra large meet, then took the top 20 teams only and rescored a power merge (which I think is how they do it now). Note the scoring is lower in this power merge than in hypo-mapping merge, because teams run faster in the state meet than at this point in the current year, so the hypo-mapping merge is a point total against tougher competition. But this is how it would currently score (not entirely accurate because their is more southern section teams than will be in real state meet).
Beckman 136 Jesuit 222 ML King 267 Glendora 291 Great Oak 333 Menlo 334 Crescenta Valley 348 Buchanan 349 Matilda Torres 353 JSerra 358 Ayala 373 Oaks Christian 396 Saugus 410 Mira Costa 412 Clovis North 424 Trabuco Hills 426 Mountain View 441 Bellarmine 442 Clovis East 452 Hart 493
Very cool nerd work....awesome. It will come down to which team can come up with 5 "A" races two days after Thanksgiving, two races at Mt. SAC and the inevitable sickness bug that floats around this time of the year.
