Rohann Asfaw (+4.36), Noah Fisher (+2.93), Charlotte Turesson (+2.45), Sam Martin (+2.19), Lucas Corea (+1.97), Joachim El-Masry (+1.79), Stephen Alexander (+1.20), Garrett Suhr (+1.07), Grace Hanger (+1.04), Henry Kaye (+1.03), Emmanuel Porquin (+0.81), Nefret El-Masry (+0.60), Lauren Zhou (+0.24), Lauren Gotting (-0.10), Sophie El-Masry (-0.18), Phillip Golotyuk (-0.36), Alana Moore (-0.64), Adelaide Keller (-0.71), Grace Finnegan (-0.75), Nicholas Olano (-0.90)
Colin La (+2.35), Alexandra Giedd (+2.03), Tsach Mackey (+2.00), Nathaniel Swanson (+1.96), Margaret Sagan (+1.87), Sean King (+1.71), Justin Rheingold (+1.49), Korinne Austin (+1.40), Will Conway (+1.37), Breanna Boggan (+1.24), Julia Reicin (+0.79), Luke Shannon (+0.73), Santiago Gutierrez (+0.70), Henry Burd (+0.58), Zachary Weinstein (+0.56), Maryam Fikri (+0.20), David Fitzgerald (+0.06), Sophie Jacobson (+0.04), Lucy Srour (-0.15), Abigail Hill (-0.23), Stepan Volkov (-1.01), Noor Aly (-1.74)
Mind the toggle! Comparing boys and girls is not usually how we think about things, but this page offers boys and girls as a combined query option because it is very interesting to those who cherish school program performance.
AAll-Time Highest End-of-Season (EOS) Z-Scores - Z-score is a universally accepted statistic used to measure how far an individual performance deviates from the group baseline, normalized by the overall volatility or spread of the group. The Z-score is the number of standard deviations a specific data point is above or below the mean of its dataset or population. A z-score is a way to measure how normal or unusual a specific number is compared to the rest of the group. In this cross country ranking, it is how much a runner excels compared to other ranked runners (who are all considered very good). A negative Z-score is below average, which is fine in this context since there is a high bar to become ranked at all. A z-score of zero is average for a ranked runner. Z-Score = 1 is "Above Average": The value is 1 standard deviation above the mean. Z-Score = 2 is "Unusual / Exceptional": The value is 2 standard deviations above the mean. The runner has outperformed 98% of the group. Z-Score = 3 is "Extremely Rare / An Outlier": The value is 3 standard deviations above the mean. This is an extreme rarity. The runner outperformed 99.9% of the group. In data science and statistics, any data point with a Z-score of 3 or higher is officially flagged as an outlier - a number so high it almost looks like a typo. Z-Score = 4 is "Legendary": The value is 4 standard deviations above the mean. This is a "Black Swan" event. The odds of a Z-score reaching 4 naturally in a normal distribution are about 1 in 30,000. If you see a 4, it means the performance completely shattered the standard scale of competition.
More: Z-score is calculated using end-of-season (EOS) points. The ranking's point scale has shifted over the decades which makes a simple point total metric an unreliable indicator of best runners; however, Z-score remains a reliable metric for comparing the dominance of runners across the decades. Z-score is still limited by the same factors that limit this ranking system as a whole. Did the elite runner run their hardest in every race, or did they jog a few races to get to the post season? Did the elite runner log enough honest races by the state championship meet for their score to reflect their maximum ability? For the truly elite who value post-season races above all else, not always. Conversely, it is possible for elite athletes to "pad the stats" by beating up on a "weak" crop of ranked runners during any given year. Hence, why Z-score is a dominance metric.
BHypothetical All-Time All-Star Meet Based on Z-Score - This section uses only the highest end-of-season Z-score per athlete. The scoring table ranks runners by highest end-of-season (EOS) Z-score and uses standard cross country scoring of five scoring runners and two displacers. It calculates an all-star team score for each school from across the decades. Click the scoring breakdown to see the names of the athletes who comprise the top five from across the decades for that school. Most people will probably prefer to see the best boys teams or girls teams. If you happened to select "Boys and Girls" from the toggle menu, it will indeed select an all-star team of boys and girls. Boys and girls can compete on a level playing field of standard deviations. Take it or leave it! Remember from above that Z-score is a dominance metric which is a statistical proxy for best runners compared to the mean if every runner runs their hardest in every race.
CHypothetical All-Time Team Clash Based on Z-Score - This section uses only the highest end-of-season Z-score per athlete. The scoring table ranks runners by highest end-of-season (EOS) Z-score and uses standard cross country scoring of five scoring runners and two displacers. In this table, athletes are grouped by season so it essentially ranks the best single season teams in the ranking's history based on Z-score. Click the scoring breakdown to see the names of the athletes who comprise the top five from that school for that given year. Most people will probably prefer to see the best boys teams or girls teams. If you happened to select "Boys and Girls" from the toggle menu, it will indeed select a team of boys and girls from a given year. Boys and girls can compete on a level playing field of standard deviations. Take it or leave it! Remember from above that Z-score is a dominance metric which is a statistical proxy for best runners compared to the mean if every runner runs their hardest in every race.
DLargest Consecutive Season Z-Score Improvement calculates each runner's end-of-season Z-score year by year and compares each season to the immediately previous season only. It keeps only positive gains, then ranks the largest increases first to highlight the biggest single-year jumps in dominance.
EGreatest Career Z-Score Improvement evaluates multi-year development across an athlete's full tracked career window by comparing debut season end-of-season (EOS) Z-score to career end-of-season Z-score. It includes only positive growth with at least two varsity seasons and ranks the largest career gains first.
FZ-Score Development by School compares each athlete's debut season EOS Z-score to their final chronological EOS Z-score, includes both positive and negative growth for athletes with three or more seasons ranked, then averages those growth values by school. A minimum of five qualifying athletes is required for a school to appear.
¹The Most Weeks Ranked stat is based on the # of rankings published. It is not based on calendar weeks. It is not consecutive weeks. The number of rankings published each season varied. There are typically 10 to 12 rankings published each season.
²The Highest All-Time Point Totals stat is not meant to infer a hierarchy of best runners. The point system evolves and cannot be compared across seasons. 2006 had inflated point totals relative to subsequent seasons due scoring changes after the first full season. The pandemic also shook things up with just a few races run in spring 2021. The ranking shown in this list is the rank at the time of the peak point total and not necessarily the peak ranking of that runner.
³The All-Time Highest Ranked Athlete by School stat displays the highest peak ranking by an athlete for every school (one per school unless there is a tie). In the event of a tie, it displays all athletes to achieve that highest ranking for his or her school. The 'final season ranking' only includes rankings from the final ranking published each season. The final ranking of each season incorporates the most data and emphasizes championship races. EOS = End of Season.
⁴The All-Time Highest Ranked Freshmen stat lists the peak ranking for athletes in grade 9. The 'final season ranking' only includes rankings from the final ranking published each season. The final ranking of each season incorporates the most data and emphasizes championship races. EOS = End of Season.
⁵The Highest Debut Ranking stat shows the highest rankings at which individual athletes first appeared in the rankings. The original 2006 preseason rankings are excluded.
⁶The Greatest Individual Ranking Improvement in a Week identifies and ranks the individuals with the greatest ranking improvement in a single week. The athlete must be ranked both weeks to be considered. It excludes newly ranked runners. It excludes the ranking transition from final season rankings to the next preseason rankings.
⁷Progress is not linear for everyone. The Most Ranking Entries and Re-Entries stat shows athletes that had setbacks and kept working their way back onto the ranking at least three times after their initial debut. It is very common for athletes to fall off the rankings and get re-added later.
⁸The Most Ranked Athletes Per School stat adds the total number of unique athletes per school to ever achieve a #1 ranking, a top 10 ranking, or to ever appear on the ranking.
⁹The Most Athlete-Weeks Per School stat adds the total number of weeks that all individual athletes for each school were ranked #1, top 10, or ever ranked. For example, if athlete 'X' was ranked for 30 weeks in their high school career, and athlete 'Y' from the same school was ranked for 15 weeks in their high school career, that school would have accumulated 45 all-time athlete-weeks.
¹⁰The Most Athletes Ranked From One Team in a Week identifies and ranks the teams that have had the most runners ranked simultaneously. A team may be listed once for each year. The earliest week in the season where the team achieved the most runners for the season is shown.
¹¹The Most Athletes on the Ranking Each Season identifies and ranks the peak number of runners ranked on the ranking system in a week each season. Only one week for each season is shown.
¹²The Most Athletes Added in a Week identifies and ranks the weeks where the most new runners were added to the ranking.
¹³Top-Heavy Seasons averages the end-of-season Z-scores of the top 10 runners in each season to highlight years where front-runners most separated from the field.
¹⁴Depth Strength Seasons measures field depth - how tightly packed all the competitors were from the top to the bottom. Field density score, also known as coefficient of variation, shows the point gap between athletes as a percentage. A lower percentage means the field was compact. A higher percentage means the field was spread far apart. If the average score for a season is 100 points, a 30% metric means 70% of your entire field finished tightly packed between 70 and 130 points. If the average score for a season is 100 points, an 80% metric means the standard variation spans all the way from 20 to 180 points. The field is spread out. This usually happens when superstars dominate.
¹⁵There is no magic formula to determine which season of cross country runners was the greatest of all time, but Season Quality Index takes a crack at it. Top end strength and field depth tend to oppose one another, but what if one particular season had both? The metric combines two signals in one metric: field density (more compact full-field competition) and higher top-10 average Z-score (strong front-end quality). The combined value is normalized to a 0-100 seasonal scale. The season quality index uses an advanced mathematical curve that measures seasons against theoretical perfection. 50% represents a perfectly average historical year. 90% or higher represents a rare, legendary era where the front-runners were strong and a deep field followed. 100% is mathematically impossible to achieve. The 2020 season is excluded due to small sample size of races.
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