Project Trackflation: An Investigation into High School Track and Field Peformance Inflation

Abstract

In recent years, it's clear that high school athletes have blown our expectations. Athletes like Quincy Wilson, Jane Hendegren, Gout Gout, Sam Ruthe, Sadie Engelhardt, and Brayden Williams have each broken numerous records in each of their specialties. In general, more and more high schoolers are participating in the sport of Athletics (Track and Field and Cross Country) and it seems like high school athletes are improving at such a rate that they can compete with and even surpass professional athletes. Individuals on social media have termed this growth "Trackflation," a portmanteau of Track and inflation, due to the abnormally large rates of improvement. In this paper, I aim to determine whether high school athletics is improving at a rate greater than that of professional athletics. If so, when did it start and what are the causes? Keywords: Track and Field, Athletics, Cross Country, Athletic.net, Milesplit, Olympics, NCAA, CIF, Arcadia, Brooks PR, Nike Indoor Nationals, New Balance Indoor Nationals

Introduction

The traditional path to success in American Track and Field meant developing during one's adolescence, competing in the NCAAs, and gaining fame as they competed against the best in the Olympics and the international stage. And yet, high school athletics (Track and Field and Cross Country) athletes have blown our expectations in recent years. After being overlooked by spectators in favor of collegiate NCAA and professional athletes, some high school standouts can contend with and even surpass the best professional athletes. For context, Quincy Wilson as a high school sophomore became the youngest American to compete at the Olympics, Jane Hendegren has broken the outdoor NCAA mile record as a high school senior, Australian Sam Ruthe has become the youngest ever to break the 4-minute barrier, and Brayden Williams has ran 9.82 in the 100 meters, which would have won a gold medal at the Olympics as recently as 2004 (admittedly with a +6.0 wind reading). 22 out of the 27 total US high school athletes who've broken 4 in the mile have done it since 2015, and all five wind-legal (under +2.0 mph) sub 10 second 100 meters have occurred in the past 2 years. In this paper, I aim to determine whether high school athletics is improving at a rate greater than that of professional athletics. I will analyze both professional and high school performance data spanning more than a century, and then investigate the potential reasons for the outcome (including spikes and supershoes, track material, improvements in living conditions, a growing focus on high school athletes' success, easier access to the sport, and, on the other hand, an increased media focus on High School track and the approaching of the human limit and the inability to improve any further.)

Methodology

Here are the meets we decided to investigate:

The Olympics don't need an introduction. It's the competition where the best competitors come every 4 years to fight for the gold medal. However, some things to note are that women weren't allowed to compete until the 1928 Amsterdam Olympics. Even then, women were limited to few events. Additionally, the 5000m and the Triple Jump (two events we chose to investigate) were not competed in the women's division until 1996. Also, results from competitions during the Cold War are widely considered to be tainted by doping from both the United States and the Soviet Union (https://link.gale.com/apps/doc/A175877048/AONE?u=anon~a6be528b&sid=googleScholar&xid=2dd8fa62).

The CIF State Championships is the Championship meet for Californian High School Track and Field Athletes. For the most part, it's a two day meet, with prelims hosted on a Friday and the finals hosted the following day. Although we were able to find results as far back as the 1920s, It's important to note that most track events (100m, 200m, 400m, 1600m) were replaced by their imperial / english counterparts (100yd, 220yd, 440yd, 1 mile). Appropriate conversions were added (https://www.ustfccca.org/assets/track-event-conversions-standardized.pdf). Additionally, older meet results often lack depth, with only the fastest times being recorded. We make sure to note down (n), or the number of times in our sample.

Data Scraping Process

We decided to make a maximum sample size of 8 per sprint event (since standard tracks have 8 lanes), 12 per distance races (1600 and 3200), and 12 per field event (since 12 is the standard size of a field flight).

High School CIF Scraping

We used the athletichelper npm module created by ourselves to search and obtain the results page for each of the meets from 1915 to 2025. Then, we used NodeJS, CSV-parser, ChartJS, and other libraries to convert the results into CSV and JSON files, convert Imperial measurements to Metric Conversions, generate the charts that we needed, and finally generate the year to year improvement rates by percentage

Olympics Results Scraping

We used fetch to manually scrape the results from the official Olympics website. We were able to locate an open API route that provided us the JSON data of each of the results over the years. We then used a similar process to the CIF results scraping to convert the results into CSV and JSON files, convert Imperial measurements to Metric Conversions, generate the charts that we needed, and finally generate the year to year improvement rates by percentage.

Analysis

Distribution of Performances Over Time

The following boxplots show the distribution of performances comparing CIF Championships (left) and Olympics (right) for each event.

Men's 100m

CIF - Men's 100m

Olympics - Men's 100m

Women's 100m

CIF - Women's 100m

Olympics - Women's 100m

Men's 400m

CIF - Men's 400m

Olympics - Men's 400m

Women's 400m

CIF - Women's 400m

Olympics - Women's 400m

Men's 1600m/1500m

CIF - Men's 1600m

Olympics - Men's 1500m

Women's 1600m/1500m

CIF - Women's 1600m

Olympics - Women's 1500m

Men's 3200m/5000m

CIF - Men's 3200m

Olympics - Men's 5000m

Women's 3200m/5000m

CIF - Women's 3200m

Olympics - Women's 5000m

Men's High Jump

CIF - Men's High Jump

Olympics - Men's High Jump

Women's High Jump

CIF - Women's High Jump

Olympics - Women's High Jump

Men's Long Jump

CIF - Men's Long Jump

Olympics - Men's Long Jump

Women's Long Jump

CIF - Women's Long Jump

Olympics - Women's Long Jump

Triple Jump (Olympics Only)

Olympics - Men's Triple Jump

Olympics - Women's Triple Jump

Men's Shot Put

CIF - Men's Shot Put

Olympics - Men's Shot Put

Women's Shot Put

CIF - Women's Shot Put

Olympics - Women's Shot Put

Men's Discus

CIF - Men's Discus

Olympics - Men's Discus

Women's Discus

CIF - Women's Discus

Olympics - Women's Discus

Performance Statistics Comparison

CIF Statistics

Mean Performance Changes:

100m_F: -0.07%
100m_M: -0.06%
1600m_F: -0.09%
1600m_M: -0.09%
3200m_F: -0.10%
3200m_M: -0.04%
400m_F: -0.06%
400m_M: -0.07%
discus_F: -0.02%
discus_M: -0.04%
hj_F: 0.00%
hj_M: -0.00%
lj_F: -0.00%
lj_M: -0.00%
pv_F: -0.00%
pv_M: -0.00%
shot_F: -0.00%
shot_M: -0.01%

Best Performance Changes:

100m_F: -0.05%
100m_M: -0.08%
1600m_F: -0.07%
1600m_M: -0.11%
3200m_F: -0.06%
3200m_M: -0.03%
400m_F: -0.05%
400m_M: -0.10%
discus_F: -0.02%
discus_M: -0.03%
hj_F: -0.00%
hj_M: -0.00%
lj_F: -0.00%
lj_M: -0.00%
pv_F: -0.00%
pv_M: -0.00%
shot_F: -0.00%
shot_M: -0.00%

Median Performance Changes:

100m_F: -0.07%
100m_M: -0.06%
1600m_F: -0.07%
1600m_M: -0.09%
3200m_F: -0.09%
3200m_M: -0.04%
400m_F: -0.06%
400m_M: -0.07%
discus_F: -0.02%
discus_M: -0.04%
hj_F: 0.00%
hj_M: -0.00%
lj_F: -0.00%
lj_M: -0.00%
pv_F: -0.00%
pv_M: -0.00%
shot_F: -0.00%
shot_M: -0.01%

Olympics Statistics

Mean Performance Changes:

100m-men: -0.10%
100m-women: -0.11%
1500m-men: -0.12%
1500m-women: -0.01%
200m-men: -0.08%
200m-women: -0.13%
400m-men: -0.12%
400m-women: -0.10%
5000m-men: -0.13%
5000m-women: -0.09%
discus-throw-men: 0.65%
discus-throw-women: 0.74%
high-jump-men: 0.42%
high-jump-women: 0.38%
long-jump-men: 0.53%
long-jump-women: 0.27%
shot-put-men: 0.55%
shot-put-women: 0.44%
triple-jump-men: 0.21%
triple-jump-women: 0.00%

Best Performance Changes:

100m-men: -0.11%
100m-women: -0.11%
1500m-men: -0.13%
1500m-women: -0.01%
200m-men: -0.11%
200m-women: -0.13%
400m-men: -0.12%
400m-women: -0.08%
5000m-men: -0.11%
5000m-women: -0.11%
discus-throw-men: 0.68%
discus-throw-women: 0.64%
high-jump-men: 0.37%
high-jump-women: 0.31%
long-jump-men: 0.54%
long-jump-women: 0.21%
shot-put-men: 0.55%
shot-put-women: 0.38%
triple-jump-men: 0.23%
triple-jump-women: -0.01%

Median Performance Changes:

100m-men: -0.10%
100m-women: -0.12%
1500m-men: -0.13%
1500m-women: -0.00%
200m-men: -0.09%
200m-women: -0.13%
400m-men: -0.11%
400m-women: -0.11%
5000m-men: -0.13%
5000m-women: -0.10%
discus-throw-men: 0.66%
discus-throw-women: 0.73%
high-jump-men: 0.43%
high-jump-women: 0.38%
long-jump-men: 0.54%
long-jump-women: 0.25%
shot-put-men: 0.54%
shot-put-women: 0.43%
triple-jump-men: 0.21%
triple-jump-women: 0.07%

It seems like our hypothesis was incorrect. Olympic Athletes have improved, on average, a greater rate than CIF athletes in the 100 year period.

Further Research

Further areas to analyze include Indoor Track and Field Competition, Cross Country, and the NCAAs. Notably, the introduction of more and more international athletes and older athletes look to have impacted the competitiveness of Collegiate Track and Field.

Additionally, we would also like to compare just the last 30 years to see whether the time period makes a difference in the results.

Struggles & Comments

I originally wanted to compare the 100m, 200m, 400m, the mile (the 1500m can be converted to the 1600m and vice versa), the two mile (again, conversion), the high jump, long jump, triple jump, shotput, and the discus to have a balance between sprints, long distance, jumps, and throws. However, as it turns out, the triple jump wasn't held for the women's division as early as 1992, and the Olympics doesn't have the 3000m (although it does have the steeplechase). So the 3000m/3200m was removed from the list of events to compare. We will still compare the women's triple jump, however, with a grain of salt since the time period is so small.

Project Trackflation: An Investigation into High School Track and Field Peformance Inflation

Abstract

Introduction

Methodology

Here are the meets we decided to investigate:

Data Scraping Process

High School CIF Scraping

Olympics Results Scraping

Analysis

Distribution of Performances Over Time

Men's 100m

Women's 100m

Men's 400m

Women's 400m

Men's 1600m/1500m

Women's 1600m/1500m

Men's 3200m/5000m

Women's 3200m/5000m

Men's High Jump

Women's High Jump

Men's Long Jump

Women's Long Jump

Triple Jump (Olympics Only)

Men's Shot Put

Women's Shot Put

Men's Discus

Women's Discus

Performance Statistics Comparison

CIF Statistics

Olympics Statistics

Further Research

Struggles & Comments

References