q/e/w/c = qualifying, east/west prelims, championships; d1/d2/d3/n1/j1 = NCAA Division I, II, III, NAIA, and NJCAA; i/o = indoor/outdoor; for example, type qd1o to see qualifying NCAA Div I outdoor results

© Dr. Joseph Myers

United States 2018 National College Track and Field Best/Rarest 0.1% Athlete Performances (Entire Indoor and Outdoor Seasons, All Divisions, All Events)

Track and Field National Championships Statistical Bests

The top 25 athletes of the nation's 25 biggest track and field teams, sorted by most unique athletes

Comparison of average national rank of performance levels for each division in U.S. college track and field

About Athlete.City

Athlete.City ranks college track and field's athletic performances by an advanced statistical calculation of the rareness of human athleticism, which is defined as the statistically-modeled ratio of college athletes (or relay squads) who are able to achieve a certain result compared to those who cannot.

Statistical modeling and machine learning are used to precisely assess how rare every track and field result is in a systematically standardized way that can be compared across all athletes and all events and all divisions. The goal is to truly find out which was legitimately more incredible and fantastic, an amazing long jump or a terrific 100 meter dash (or anything else), despite the fact one may be measured in distance and the other in time.

Let's give an example! Just how awesome and amazing and fantastic was the USC men's 4x400 squad when it ran 3:59.00 in the national championships? It was so fantastic that it would take 1,257 college men's 4x400 squads all trying their best before you would ever be likely to finally have another 4x400 squad that could potentially match that time, accounting for the weather, training conditions, competition level, etc. in the 2017-2018 season.

The Athlete.City statistics are not based on fairy tales or word of mouth, but based on robust, outlier-invariant statistical modeling derived from painstakingly-gathered data and countless precise variables.

Most importantly, all the data are from this season only. It is wrong to use statistical models based on other seasons to assess this season's results, which is why these statistics are not created from invalid assumptions, but by live statistical analysis and deep learning from tens of thousands of data points and precise analysis of countless variables exclusively from this season only.

Rankings will and should change from season to season, because the competition levels change, the weather changes, everything changes! Even the other athletes in an event affect the outcome. All this is taken into account exclusively within our statistical modeling at Athlete.City.

About Joseph Myers

Dr. Joseph K. Myers, Professor of Science and Math, is an applied mathematician specializing in inverse problems for partial differential equations. Under Distinguished Professor Victor Isakov, his completed Ph.D. dissertation, Inverse Doping Profile Analysis for Semiconductor Quality Control, was nominated for the CGS/UMI Distinguished Dissertation Award by the Council of Graduate Schools. His papers have been published in the peer-reviewed journals of Inverse Problems (IOP) and Inverse Problems and Imaging (AIMS). At Friends University, he coordinated 87 math, science, business, and technology classes for the Program for Adult College Education (PACE), created 26 new undergraduate courses and 14 graduate courses, developed the MBA Business Analytics concentration, and as project manager created the Master of Science in Cyber Security program and designed nine of its ten courses. Since 1999, Dr. Myers has written public domain computer programs driven by a passion for algorithms and optimization theory, including JavaScript engine code used by Mozilla Firefox, high-speed cryptographic functions used world-wide including by Accenture (Oct 27, 2017), MIT (Nov 7, 2015), Facebook (Dec. 2, 2012), etc., conformal mapping for true-hue color brightening, athletic analytics for all national divisions of college track and field, CGI software for web app development, and high-speed sorting and encoding algorithms for servers.