1 Altitude Adjustments and NCAA Track and Field Qualifying: A Critical Evaluation by Conner Cox A project submitted in partial fulfillment of the requirements for the degree of MASTER OF EDUCATION with an emphasis in SPORTS COACHING LEADERSHIP WEBER STATE UNIVERSITY Ogden, Utah November 2024 2 Altitude Adjustments and NCAA Track & Field Qualifying: A Critical Evaluation Nature of the Problem One could argue that the implementation of altitude adjustments by the NCAA within track and field mainly serves the purpose of increasing equity and fairness. After all, their sole function is to equalize performances by accounting for a significant condition such as altitude. Armed with the understanding of altitude, and its interactions with physiological systems, as well as biomechanics, the NCAA saw fit as an institution to undergo the task of standardization across the many events that are contested on the track. This is undoubtedly a noble task that is in line with their mission statement, which is to “coordinate and deliver safe, fair and inclusive competition directly and by Association members (NCAA, 2024).” This, however, has turned out to be easier said than done, raising nearly as many questions as it has answered. One of the problems that has arisen as a result of the implementation of altitude conversions is one of accuracy and modernity. While there is no shortage of literature on the subject of altitude and the effects it has on the human body, that is certainly not the case when it comes to analyzing it within the specific context of elite track and field athletes and their performances. There exists right now a significant lack of retroactive research conducting a critical analysis on how these formulas have held up over the years in terms of consistency and fulfillment of their intended purpose, as well as a lack of large-scale studies using populations of relevance, such as elite athletes across multiple track and field events. Due to this barrier, the formulas and tables themselves hold the potential of having inaccuracies or failing to be updated in a manner that is consistent with the quickly evolving and improving understanding within the field. For example, it could very well be the case that these conversion tables contain biases 3 that could result in the over adjustment of some events, while under adjusting in others. There are a host of factors and inputs that may not be considered in the adjustments at this time. Another potential problem that lies at the core of this research is the breadth of events in the sport itself. Not only are there distinctions between sprints and distance, but there are also subcategories within each. There are short sprints, long sprints, hurdles, middle distance, and long-distance events, creating several sports within the larger sport. Each of these events likely interact with altitude in subtly different ways, to a degree that is significant enough to potentially change outcomes dramatically when it comes to the utilization of adjustments for altitude. At this point, there is a hole in the research when it comes to stress testing the outcomes of these adjustments thus far. Particularly, how each event has been affected using a lens of equity and fairness. To address this problem, this paper retroactively collects and analyzes large amounts of performance data. 4 Literature Review This literature review will cover a few aspects of relevance regarding altitude within track and field. The first section will focus on the core reason for the implementation of altitude adjustments in the first place, thoroughly explaining the physiology of altitude. Following that, the next section will focus on the NCAA and its current practices, specifically on the qualifying process when it comes to championship competition, as it holds implications for the purposes of this paper. Lastly, this literature review will discuss a phenomenon known as density altitude, bringing to light its potential importance and implementation into conversions and adjustments. Track and field is one of the oldest sports in the world. Human beings have been testing their running, jumping, and throwing capabilities for centuries. As science has advanced, so too has the understanding of the factors that influence performance in running, jumping, and throwing events. Among these, altitude has emerged as a key determinant, affecting both sprint and distance events in measurable ways. Substantial evidence within scientific literature has established that altitude has a consistent, measurable impact on the running events within track and field (Peronnet et al., 1991). This phenomenon is true for short sprints, as well as long distance races. For example, according to Prampero et al. (2021), performance in the 100m dash can be improved by 1-2% when running at high altitudes versus sea level. Conversely, athletes competing in events greater than 800 meters in length can expect to run slower times at higher altitudes, although by how much varies depending on the event (Gundersen et al., 2001). To address these discrepancies, the NCAA developed altitude conversion tables, ensuring fair comparisons of marks achieved at different elevations (U.S. Track & Field and Cross Country Coaches Association, 2009). Online calculators such as those provided by RTS Performance 5 Testing (n.d.), apply these formulas to standardize times, maintaining equity in collegiate competition. At the collegiate and professional levels, even minor differences in performance can have significant ramifications for athletes. Individuals of this caliber are competing for scholarships, sponsorships, contracts, medals, and even their livelihood. A single percentage point difference in performance can completely alter the course of an athlete’s career. For example, at the 2024 Summer Olympic Games in Paris, the difference between a gold and no medal in the Women’s 1500m was decided by just 1.46 seconds, or 0.007%. Most relevant for the purposes of this paper, however, is the fact that the NCAA currently allows the use of altitude conversion formulas for qualifying purposes at the National Championships. As another example, using TFRRS data collected for this paper, the difference between qualifying for the 2018 NCAA Division 1 Outdoor National Championships in the Men’s 800m and watching from home was 0.01 seconds (TFRRS, 2018). These stakes highlight the importance of critically evaluating the accuracy and application of these formulas, the scientific theories supporting them, and identifying gaps in the existing research. The Physiology of Altitude The focus of this paper is not on debating whether altitude affects track and field performances—a point well-established in scientific literature (Hamlin et al., 2015). Instead, the emphasis lies in examining how the current altitude adjustments used by the NCAA impact the qualification of athletes from elevation schools and performances at elevation that earn an adjustment. However, in order to understand why the NCAA has considered altitude adjustments to be justified and necessary up to this point, one must first gather at least a basic level of knowledge into the physiology of altitude in terms of how it interacts with the human body. 6 More specifically, it’s important to note what we currently understand about air density, oxygen availability, and air resistance. These factors not only influence athletic performance but also underpin the scientific validity of conversion formulas, forming the foundation for equitable competition. The atmosphere always contains approximately 20% oxygen, regardless of altitude. However, the key factor that changes as altitude increases is partial pressure. As one goes up in altitude, air pressure decreases, meaning that for every breath inhaled, there are less oxygen molecules, since the air is less dense. Consequently, the lungs have less oxygen available for gas exchange, and the blood delivers less oxygen to working muscles (Wyatt, 2014). This reduction in oxygen availability significantly impacts several physiological systems, including cardiovascular and pulmonary function. Although the specifics are beyond the scope of this paper, these changes ultimately alter energy production, endurance, and recovery during exercise (Bärtsch et al., 2007). The reduced availability of oxygen at high altitudes poses a significant challenge for endurance athletes, particularly distance runners. Events over 400m primarily rely on the body's aerobic energy systems, which require oxygen to sustain energy production over extended periods (Hill, 1999). In high-altitude conditions, the hypoxic effect limits oxygen delivery to working muscles, making these activities more difficult. Recognizing this challenge, the scientific community worked with the NCAA to design altitude conversion formulas for distance running events within track and field. These adjustments aim to account for the physiological disadvantage faced by athletes competing at elevation, ensuring fair qualification standards. How does altitude impact short-distance sprinting events like the 100m or 200m? Races 400m or less primarily utilize the anaerobic energy systems, which do not require oxygen for 7 energy production during the activity. If oxygen isn’t a factor, why would these events also receive altitude conversions? As stated in the introduction, altitude has an inverse effect on sprint events, allowing athletes to run faster at higher elevations. This occurs because the drop in air pressure reduces air resistance, making it easier for sprinters to propel themselves forward. As a result, sprint times are adjusted by adding time to account for this altitude-based advantage and ensure fair competition. Qualifying Process & Conversion Formulas The NCAA currently utilizes a somewhat complicated qualifying system for the Division 1 Track and Field National Championships. First, the entirety of the NCAA is broken into two regions, east and west, based on geographical location within the US. At the conclusion of the regular season and Conference Championships, the top 48 marks in each event from each region qualify for the first round of the National Championship meet, also known as the Regional Championships (NCAA, 2024). From there, athletes compete for a spot at the final round, which is considered to be the National Championship. Since converted altitude marks are permitted to qualify for the first round, it is crucial to assess the accuracy and validity of these conversion formulas. However, there is limited transparency about the origins of these formulas used by the NCAA. Moreover, access to them is restricted to tables, charts, and calculators converting marks from a variety of locations. This raises questions about whether they reflect the latest scientific understanding of altitude's effects on track and field performances, highlighting the need for a more modern and evidence-based approach. Fortunately, researchers at the University of Indiana (Escalera et al., 2023) have developed a modern conversion calculator using 13 years of performance data and advanced data science techniques. This tool focuses on long-distance events of 800m and up, while providing 8 an alternative to the NCAA’s formulas. Escalera et al. (2023) found performance differences, due to altitude, likely begin below 1,000m and scale linearly, unlike the NCAA’s formulas, which only apply to altitudes above 3,000 feet. It’s unclear whether the NCAA came to the same conclusions when creating their conversions, but we do know that their calculator does not allow for a conversion using marks that occur below 3,000ft above sea level. While extensive testing and comparisons could estimate the exact formulas used by the NCAA, this lies beyond the scope of this paper. However, preliminary comparisons suggest that the NCAA has not updated its formulas to reflect modern findings. For example, the NCAA calculator converts a 5,000m time of 15:00 at 4,825 feet to 14:37.21 at sea level, while Escalera’s calculator produces 14:33.46. Although the difference may not be statistically significant, it could determine whether an athlete qualifies for the first round of nationals. For example, in 2015, a four second difference in the outdoor Men’s 5,000m is enough to drop from 48th to 61st in the West Region (TFRRS, 2015). The variety of events in track and field, combined with physiological differences between male and female athletes, suggests that altitude impacts each event uniquely. Current research provides varying estimates of altitude’s effects on sprint events. Hamlin et al. (2015), found slight but measurable improvements of 0.2% in male and female 100m-400m sprint events at altitudes below 1,000m. However, at altitudes of 1500m or higher, improvements increased to as much as 0.7% for all sprints except the 100m and 110m hurdles. More significant gains were reported by Prampero (2021), who observed up to a 2% improvement in 100m dash performance at altitudes above 2,000m. Although altitude’s effect on sprint performance is less frequently discussed in the track and field community, it exists to an extent that warrants further research and consideration. Notably, no publicly available published papers establish an alternative 9 conversion calculator or formula to be used in comparison to the NCAA’s when it comes to events less than 800 meters in length. This gap in the literature highlights the need for modern, evidence-based conversion methods to ensure fair and consistent adjustments across all events. Density Altitude’s Potential Use in Conversions Not only are current altitude conversion formulas utilized by the NCAA for qualifying purposes potentially out of date and unreflective of real-world outcomes, but they may also be failing to account for a highly influential factor that could possibly reframe our current implementation entirely. Weiss et al. (2022), conclude that running speed is significantly and positively correlated with barometric pressure in elite runners. Barometric pressure is simply a measure of the weight of the atmosphere, or air, pressing downwards at a given time (National Geographic Society, n.d.). Factors such as current weather conditions, geographical features, and time of year can all influence and change the barometric pressure measurement at a given location. The primary reason for the performance differences that altitude creates within track and field is due to the changes in air pressure as you go further up from sea level. As air pressure decreases, so does resistance, and oxygen molecule density (WildSafe, n.d.) . However, what no current altitude conversion formula or calculator considers, is the day-to-day variation of barometric pressure at the same location. Density altitude is a commonly used measurement within the aviation industry. It essentially accounts for and normalizes altitude measurements relative to temperature and humidity, as well as barometric pressure. According to Grigorie et al. (2010), "Density altitude (Hρ) is the barometric altitude corrected for non-standard temperature variations. This corresponds to the altitude at which the density of air is equal to ISA air density evaluated for the current flight conditions" (p. 45). This is important because it has a substantial impact on the 10 behavior and performance of the aircraft. Density altitude is a more accurate reflection of what the altitude “feels like” on a given day, similar to how things like wind chill can make a 40degree day feel like 30. Standard altitude measurements that simply tell you how high above sea level a place is don’t accurately reflect the actual oxygen availability or air resistance at that location at a given time. While none of this seems relevant to a runner doing a 400 m or 5000 m race, considering that running is nothing more than moving a physical object through the air, as well as being completely dependent upon the conditions, just like an aircraft, it can potentially impact performance enough to warrant consideration. In terms of physiological justification for the use of density altitude measurements in track and field performance, we know that oxygen availability is directly impacted by barometric pressure (hence the entire reasoning for altitude conversions in the first place), so it seems reasonable to take this to its logical conclusion and replace standard altitude with density altitude. Bringing this to a more practical level, we can utilize a density altitude calculator (Shelquist, 2023) to see in simple terms how temperature, humidity, and barometric pressure can drastically alter the density altitude on a given day. The exact formula used to calculate this is beyond the scope of this paper but suffice it to say that it is a well-established practice with plenty of literature backing its merit. The National Weather Service constantly tracks and records all the inputs necessary, publishing them online for public use (National Weather Service, n.d.). Using the Ogden-Hinckley airport data, we can demonstrate the point. On November 10th, 2024, the recorded temperature, relative humidity, and barometric pressure at this location was 43 degrees Fahrenheit, 51%, and 30.09 in. of pressure, respectively. This comes out to a density altitude measurement of 4.184 ft. Just one day later, at the same location, the temperature was 60.2 degrees Fahrenheit, with a relative humidity of 26% and pressure of 29.99. This comes out 11 to a density altitude measurement of 5,407 feet. In a space of 24 hours, the “real” altitude at the same location changed by over 1,200 feet. This means that during a standard two-day track and field competition, athletes competing on the first day would see significant variation in results compared to those competing on the second day. None of the currently utilized conversion formulas would account for this. This has implications in both sprinting and distance events, as well as athletes competing at very low/sea level altitude. Using Escalera’s conversion calculator, if a female athlete were to run the 5000 m in a time of 16:45 at the Ogden-Hinckley airport on November 10th, 2024, which measured a density altitude of 4,184 ft, her time would be converted to a 16:21 at sea level. However, if she were to run the same mark a day later at that location, which measured a density altitude of 5,407 ft, her time would be converted to a 16:15. While it has long been known within the track and field community that there are “fast” and “slow” days, based on conditions, as well as some locations having tracks that seem to produce faster marks, no published literature has explored the potentially sport-changing impact that measuring density altitude at competitions could have. Research in this area is extremely limited and requires much more testing before potentially being implemented into the NCAA’s qualifying system and mark conversion formulas. It is not abnormal in the sporting world for scientific literature to come to findings or conclusions after coaches or athletes themselves. Oftentimes, common practices within the sport are not necessarily reflected within the research yet. This is due to a multitude of reasons, one being that conducting research on elite athletes is challenging, and funding can be hard to obtain. With that in mind, the importance and contributions of coaches shouldn’t be ignored. While there is currently no published literature available that analyzes density altitude as it relates to track and field, there is a thorough article written by a prominent high school coach published online. 12 It summarizes the findings that he presented at the New Mexico High School Coaches Association Clinic in 2012. Versaw (2012), after applying exclusion criteria and inclusion standards, found that high school 3200 m race times during the 2011 outdoor track season within the state of New Mexico increased 14.4 seconds for each 1000 ft increase in density altitude for boys, and 5.6 seconds for girls. Versaw gathered hundreds of data points, including density altitude readings at each race site, before applying statistical analysis in the form of multiple regression. He found that the difference was statistically significant for boys, but not girls, and that both scale in a non-linear fashion. While this is not a peer-reviewed, published study, it certainly gives insight into the potential for practical implementation of density altitude within distance running performance. Notably, since the methodology and results are shared in the article, this study is potentially replicable. Further research is strongly recommended in this area to determine the efficacy of Coach Versaw’s findings. Summary While modern science and extensive research has shed significant light on the effects of altitude on track and field performances across various events, there remains considerable room for progress. The altitude conversion formulas currently used by the NCAA provide a valuable framework but lack critical context that could reshape the landscape of collegiate track and field. Specifically, it remains unclear whether these conversions are being applied equitably, or if certain athletes or regions benefit disproportionately. To date, no research has examined who may gain or lose as a result of these formulas, or whether adjustments are needed. This paper aims to fill that gap by offering fresh insights and actionable proposals. This study takes a novel approach by conducting the first-ever retrospective analysis of how altitude conversion formulas are applied within the NCAA Division 1 Track and Field 13 National Championships qualifying process. Previous research on altitude's impact has been limited in scope, focusing on small experiments or theoretical recommendations rooted in physiological findings. This prospective nature of past studies has created a significant barrier to understanding how these formulas operate in real-world scenarios across diverse populations. What sets this research apart is its use of fifteen years of data, representing an unprecedented sample size within a relevant population. By leveraging this comprehensive dataset, the study conducts a thorough covariate analysis that examines multiple variables and their potential impact. This approach allows for deeper insights into the practical implications of altitude conversion formulas, particularly when applied to individual events. No prior research has independently analyzed how altitude affects outcomes across specific track events—a gap this study seeks to address. Furthermore, the retrospective methodology of this research introduces a practical advantage: it is easily replicable, requiring no active participants or funding, as it relies entirely on publicly available data. By analyzing outcomes event-by-event, this study reveals the unique ways altitude influences different disciplines, offering critical insights that have been absent from the literature until now. In the digital age, compiling and analyzing vast amounts of data has never been more accessible. This study capitalizes on that opportunity to initiate a new dialogue about the proper implementation of altitude conversion tables and formulas in collegiate and professional track and field. By providing a data-driven foundation, this paper aims to inspire future research and inform potential reforms that ensure the equitable application of these formulas, ultimately advancing fairness and transparency in the sport. 14 Methods Purpose of the Study The purpose of this research is to analyze the current use of altitude conversions for national championship qualifying purposes across track and field events at the NCAA Division 1 level, and to gain insight into what changes, if any, need to be made to ensure fairness, equity, as well as scientific merit. Specifically, this study retroactively analyzes the outcomes of the last fifteen years of track and field performances as a result of the use of altitude conversions across multiple events. Research Design This study utilizes a non-experimental, quantitative design. It is retrospective in nature, collecting and analyzing publicly available data from past nationally qualifying NCAA Division 1 track and field performances in order to isolate and measure the impact of several variables, including altitude. Particularly, this study will compile the top forty-eight NCAA Division 1 outdoor track and field performances, in both the East and West region, across ten different events, from the years 2010-2024. This will be done for both the male and female sex categories. Not only that, but this study will also complete the same process for eight events in indoor track and field, which only requires the top sixteen marks, rather than forty-eight. Data recorded will include details such as athlete name, year, sex, event, region, and whether the athlete attended a university that is located at altitude or not. Most importantly, every single performance will have a record of whether or not an altitude conversion was utilized to make the list. This study only collected and analyzed data from track and field events that the NCAA currently accepts the use of altitude conversion formulas in for qualifying purposes. In outdoor track and field, these events include the 100m, 100m hurdles for females, 110m hurdles for 15 males, 200m, 400m, 400m hurdles, 800m, 1500m, 3000m steeplechase, 5000m, and 10000m. For indoor track and field, the NCAA allows altitude conversion for the following events: 60m, 60m hurdles, 200m, 400m, 800m, 1 mile, 3000m, and 5000m. These are the events used for the purposes of this study. The rationalization for using the top ranked forty-eight outdoor, and sixteen indoor marks for each sex, region, and year is because that is in line with the NCAA limit on how many athletes automatically qualify for the first round of the national championship competition. Furthermore, recording factors such as university altitude, event, etc., allows for thousands of data points to be analyzed in a multivariate fashion. A sample size consisting of fifteen years’ worth of performances between every notable event is sufficient. Study Population This study contains a very specific population. As mentioned in the research design section, data was collected on nationally qualifying track and field performances at the NCAA Division 1 level. This qualifies the population as elite athletes. Specifically, this study features the top forty-eight NCAA Division 1 male & female performers across ten outdoor track events, as well as the top sixteen indoor performers in events that utilize altitude conversion formula. The question this study seeks to address, that being the impact of altitude and the use of conversion formulas on the national qualifying process, requires a population such as this. Sampling and Recruitment Considering this study is non-experimental and retrospective in design, there will be no recruitment or sampling process. There are no active participants, as this study is a collection and analysis of pre-existing data points. This is the only feasible and practical option when it comes to gaining any meaningful research insights on the topic. 16 Data Collection & Instruments TFRRS is an NCAA track and field performance database that archives marks dating back to the year 2010. All data used in this study came from this website, as it is the most reliable source for accurate recording of performances, and all the factors related to them such as name, university, region, etc. This database organizes performances by year, sex, and event. This allows for easy collection and sorting. It lists in descending order by rank, so we can see the exact top sixteen/forty-eight marks for each year in both the indoor and outdoor seasons. The data collected from TFRRS was the top forty-eight outdoor marks from each sex and region category each year in the 100m, 200m, 400m, 100m Hurdles, 110m Hurdles, 400m Hurdles, 800m, 1500m, 5,000m, 10,000m, and 3,000m Steeplechase. For indoor track, the events collected were as follows; top sixteen marks each year in the 60m, 200m, 400m, 60m Hurdles, 800m, Mile, 3000m, and 5,000m. This was also done for both sex and region categories. Data collection for both indoor and outdoor track and field began in the year 2010, and included every season to the present day, with the exception of the 2020 outdoor season, which was canceled due to the COVID-19 Pandemic. This data was then coded using SPSS software, with specific values assigned to each event, and a binary system of the numbers 1 and 2 indicating factors such as sex, event, event type differentiating between sprint and distance, if the university the athlete competed for is located at altitude, whether the athlete’s mark utilized an altitude conversion, and whether this was an indoor or outdoor mark. Each event corresponded with a value from 1-18. Variables The independent variables in this study are altitude, and the use of altitude conversion formulas for qualifying purposes. The dependent variables are sex, event, rank, region, event type between sprint and distance, and whether it was outdoor or indoors. There are minimal 17 confounding factors to control for, since no experiment is taking place, and it is purely objective, quantitative data collection from a pre-existing database. However, multivariate analysis should allow for accurate takeaways regarding how these variables and factors interact with each other. Procedures To increase efficiency and efficacy of the data collected, four researchers were involved in organizing and recording it. Each researcher was assigned three to four years to complete for all events and genders. After collection, verification was done to ensure inter-rater, and intra-rater reliability. Once the collection phase was complete, the analysis was conducted by myself and committee chair Kurt Ward. 18 Results Chi-Square for Independence WEST We used chi-square test for independence to determine if altitude vs non-altitude schools and conversion vs non-conversion qualifiers were independent in the West. Altitude and qualifying type (conversion versus non-conversion) were dependent in the present sample (χ21 = 206.67, p < .001). We used standardized residuals to determine how cells contributed to the significant chi-square. Athletes at altitude schools significantly overrepresented those who qualified via conversion qualifiers (SR = 14.3) but were significantly underrepresented by those that qualified without a conversion qualifier (SR = -1.3). Alternately, athletes at non-altitude schools were significantly underrepresented by those who qualified by conversion qualifiers (SR = -1.2) but were also not overrepresented by those that did not qualify via a conversion qualifier (SR = 0.1). Based on this data, athletes at altitude schools were most likely to qualify for NCAA Regionals via a conversion qualifier, whereas athletes at non-altitude schools were more likely to qualify at a non-conversion qualifying event. EAST We used chi-square test for independence to determine if altitude vs non-altitude schools and conversion vs non-conversion qualifiers were independent in the East. Altitude and qualifying type (conversion versus non-conversion) were dependent in the present sample (χ21 = 1433.80, p < .001). We used standardized residuals to determine how cells contributed to the significant chi-square. Athletes at altitude schools significantly overrepresented those who qualified via conversion qualifiers (SR = 31.2) but were significantly underrepresented by those that qualified without a conversion qualifier (SR = -9.9). Alternately, athletes at non-altitude 19 schools were significantly underrepresented by those who qualified by conversion qualifiers (SR = -16.5) and were significantly overrepresented by those that did not qualify via a conversion qualifier (SR = 5.1). Based on this data, athletes at altitude schools were most likely to qualify for NCAA Regionals via a conversion qualifier, whereas athletes at non-altitude schools were more likely to qualify at a non-conversion qualifying event. Factorial ANOVA We used a factorial ANOVA to determine if NCAA first round rank (lower rank indicates better qualifying position) varied based on the interaction of altitude school status (athletes at altitude schools versus those not at altitude schools) and whether the athlete qualified for nationals via conversion qualifier or not. Regional ranking was negatively skewed (skew -3.413, SEskew .014) and leptokurtic (kurtosis 11.570, SEkurtosis .028) likely because only top 48 qualified for the first round of NCAA and therefore were the only ones included in this analysis. There was a significant difference in rank based on the interaction (F1,30997 = 72.105, p <.001). About 54% of the variance in rank was explained by the combination of altitude school status and qualifying type (2 = .45). To explore the significant disordinal interaction and determine how the cells differed from one another, we used simple effects analysis. Among those that qualified via a conversion qualifier, there was a significant difference in rank between those from an altitude school versus those that were not from an altitude school (F1,30997 = 71.665, p <.001). However, there was not a significant difference in rank between athletes at altitude schools versus those not at altitude schools among those that qualified via a conversion qualifier or not (F1,30997 = 2.860, p <.091). Among the present sample, athletes at altitude schools had a higher mean rank when qualifying via a conversion qualifier versus qualifying at a sea level event. Alternately athletes at non-altitude schools had a lower mean rank (aka better ranking) 20 among those that qualified by a conversion qualifying event compared to a sea level qualifying time. Overall, athletes from non-altitude schools qualifying via a conversion qualifying event had the best rankings going into the NCAA first round. We used a factorial ANOVA to determine if NCAA first round rank in the WEST (lower rank indicates better qualifying position) varied based on the interaction of altitude school status (athletes at altitude schools versus those not at altitude schools) and whether the athlete qualified for nationals via conversion qualifier or not. This analysis was also split for distance events versus sprint events. For distance events, there was a significant difference in rank based on the interaction of altitude school and conversion qualifiers (F1, 7746 = 6.298, p = .012). The interaction accounted for about .06% of the variance in regional rankings (2 = .0006). To follow up on the significant disordinal interaction and determine how the cells differed from one another, we used simple effects analysis. Among those at altitude schools, there was a significant difference between those that qualified via a conversion qualifier versus those who did not (F1,7746 = 6.189, p = .013). There was not a significant difference between those that qualified via a conversion qualifier versus those who did not at non-altitude schools (F1,7746 = 6.189, p = .128). See Table 1 for descriptive statistics and Figure 1 for a plot of cell means. Among the present sample, those at altitude schools had lower mean regional ranking scores when qualifying without a conversion qualifier whereas those at non-altitude schools had lower regional rankings when qualifying via a conversion qualifier. This indicates that more athletes from altitude schools that went to sea level events to get qualifiers had better regional rankings. For the sprint events, there was also a significant difference in rank based on the interaction of altitude school and conversion qualifiers (F1, 7421 = 27.046, p < .001). The 21 interaction accounted for about 0.3% of the variance in regional rankings (2 = .003). To follow up on the significant ordinal interaction and determine how the cells differed from one another, we used simple effects analysis. Among those at altitude schools, there was not a significant difference between those that qualified via a conversion qualifier versus those who did not (F1,7421 = .149, p = .699). There was a significant difference between those that qualified via a conversion qualifier versus those who did not at non-altitude schools (F1,7421 = 63.775, p < .001). See Table 2 for descriptive statistics and Figure 2 for a plot of cell means. Among the present sample, Non-altitude schools had the lowest mean regional rankings when qualifying via a conversion qualifier. This indicates that in the sprint events, athletes at non-altitude schools who qualified via a conversion qualifier were likely ranked better at the first round of NCAA in the West. 22 Discussion Key Takeaways In order to extract meaningful recommendations because of this statistical analysis, it must first be discussed in a practical manner that translates to real world understanding. The tests used to understand the vast amount of data in this paper are intentionally complicated, so discussing it in layman’s terms serves an important purpose when it comes to reflecting on standard practices and potential changes within the NCAA. One of the things that shows in the results data is that interactions with the altitude conversion formulas vary widely based on geographical regions. Unsurprisingly, we see that in both regions, athletes at high altitude schools are most likely to qualify for the first round of nationals by utilizing altitude conversion formulas, and athletes at low altitude schools are much less likely to qualify using the conversion. This seems to paint a picture of dependence, demonstrating that athletes at altitude schools, since they are generally underrepresented when it comes to qualifying without the use of conversions, rely on these formulas to get an opportunity to compete at the first round of the national championships. This could be due to several factors, but it likely has to do with logistical restraints such as access to low altitude facilities and events located within a reasonable travel distance. Many universities that reside in high altitude areas simply don’t have the resources to frequently travel to low altitude competitions. This works in the inverse as well. Low altitude universities are not likely to travel to high altitude venues for competition. Another factor leading to this could be that athletes at altitude schools generally compete at high altitude facilities for their Conference Championships meets. This results in a high concentration of talent, leading to marks that meet the qualifying standards via conversion more frequently. 23 Discussions taking place throughout the NCAA track & field community regarding altitude conversions have oftentimes centered around a narrative that they are exploitable to a degree. The idea being that athletes who wouldn’t otherwise be capable of qualifying for the first round of the national championships, especially those from high altitude schools, are able to benefit from these conversions to a degree that is significant enough to be considered unfair. To see whether the data bears this out, factorial ANOVA tests were performed to see how these factors affect regional rankings heading into nationals. What we see doesn’t necessarily reflect this narrative. For example, athletes from altitude schools that qualify via conversion rank worse on average when compared to athletes from altitude schools that qualify at a sea level event without conversion. If altitude conversions were inflating performances unfairly, we would expect athletes qualifying via conversion to have lower average ranks (i.e., better qualifying positions) than their peers. However, the data reveals the opposite trend. Converted qualifiers, particularly from altitude schools, often have higher average ranks, suggesting they are not benefiting from an unfair competitive edge through the use of conversions. This does get complicated, however, because one potential explanation for this seemingly inverse phenomenon is that coaches tend to send their top athletes to sea level events to compete in deeper, more talented meets, which leads to them not needing to hit a conversion mark at an altitude event. In order to glean further insight regarding this area, one would have to examine performance at sea level vs altitude from the same athlete over multiple instances, which is beyond the scope of this paper. To further complicate this question, we see that everything stated above regarding mean rank between athletes at altitude schools is opposite when it comes to analysis of athletes from low altitude schools. In fact, athletes from low altitude schools that qualify via conversion rank 24 better on average than athletes from low altitude schools qualifying via sea level marks. This suggests that exploitation, if any, could surprisingly be occurring at low altitude schools that happen to compete at high altitude venues. This is further bolstered by the fact that among all qualifiers, athletes from non-altitude schools who qualified via conversion have the best mean ranking heading into the first round of the national championships. There are limitations that must be considered here before any strong recommendations regarding the use of conversion formulas are made. None of this data is analyzing or considering how these athletes ended up performing or placing at the national championships meet itself, rather just where they ranked heading into NCAA Regionals. Further research could see whether the performance was largely in line or even deviated from these rankings. To further break it down and understand how these interactions take place across event types, as well as regions, another factorial ANOVA test was performed separately for both the sprint and distance events in the West region. Regarding distance events, what we see is largely in line with the broader data, and that is that qualifying athletes from altitude schools are likely to rank better when they don’t use the conversion. In other words, they rank better when going to sea level and achieving a non-conversion qualifying mark. This further supports the counter against the aforementioned exploitation narrative by altitude athletes. When it comes to athletes in the West from non-altitude schools, the sprint events are a bit different. They are likely to rank best when qualifying via a conversion mark. One potential explanation for this is the rare case where non-altitude schools must compete at a Conference Championship meet that takes place at a high-altitude venue. Even though their marks get penalized for altitude, they still rank higher even after the conversion. Again, it’s difficult to determine whether the current use of conversion formulas for performances at altitude should remain in place, as this study does not assess how 25 those athletes ultimately performed at the regional or national championship meets. However, the data suggests a nuanced picture: while distance conversions do not appear to give athletes from altitude schools a ranking advantage, sprint athletes from non-altitude schools who receive an altitude-based adjustment may benefit more significantly. This raises the possibility that the current sprint conversion formulas may under-adjust and could warrant further scrutiny, perhaps by adding more time to these conversions. Limitations/Further Research Considerations Although this study represents the largest retrospective analysis of altitude conversion outcomes in collegiate track and field and provides a strong foundation for future research, it is not without limitations. While the analyses offer valuable insights and contribute meaningfully to the ongoing conversation, the findings are not sufficient to warrant direct recommendations or changes to the NCAA qualifying process. That said, the results can help inform broader discussions around whether the NCAA should revisit its altitude conversion criteria, improve transparency of its formulas, or explore the use of modern approaches such as density altitude. To support concrete policy decisions, further research is needed, particularly studies that examine whether the mean rankings reported here align with actual performance outcomes at the championship meets. This would require the collection and analysis of NCAA Championship result data. Additionally, future research could benefit from breaking down performance and conversion impacts at the level of individual events, providing a more granular understanding of fairness across disciplines. Essentially, analyzing the data between every single event. Conducting this level of analysis for every individual event would require a scale far beyond the scope of this paper. While this study broke down the factorial ANOVA by sprint and distance event types, it did not analyze each specific event within those categories. Since the NCAA’s 26 altitude conversion formulas are uniquely tailored to each event and sex, meaningful conclusions about fairness or accuracy would require event-specific analyses. For example, does the conversion formula for the Women’s 800-meter run result in a higher or lower average rank compared to non-converted performances? Questions like these could be addressed through more granular, targeted research. Another limitation is that this study focused exclusively on NCAA Division I athletes. It did not account for other competitive levels such as Divisions II and III, junior college, NAIA, high school, or professional track and field. Future research could expand the dataset to include these populations or examine them independently to assess how altitude conversions may operate differently across institutions and competitive levels. Another potential limitation of this study is that it only looked at performances that met qualification standards for the national championships. While the merit in that is evident, considering the implications, there is also merit in using a dataset that includes non-qualifying marks. For the purposes of this paper, the population was sufficient, however, further research may seek to answer other questions regarding accuracy and fairness of conversion formulas, for less elite populations. Lastly, although completely outside of the control of its design and methods, is that this paper does not analyze throwing and jumping events. This is due to the fact that the NCAA does not currently employ the use of altitude conversions for any throwing and jumping events within track & field. Since the purposes of this paper largely focus on the qualifying process, and the use of these conversions within it, factoring in throwing and jumping events was not necessary. However, due to the findings in the literature review, as well as an understanding of altitude as a whole, it stands to reason that altitude may also have a significant impact on performance in 27 throwing and jumping events as well. One of the goals of this study is to spark further dialogue and potential policy changes within the NCAA to promote equity and fairness within the sport. With that being said, further research needs to be done to understand the interactions of altitude within throwing and jumping events, which can then be brought to light and implemented into the qualifying process if necessary. If altitude does in fact impact throws and jumps, there is no reason that those athletes should not have the same mechanisms to account for it as sprinters and distance runners. 28 References Bärtsch, P., & Gibbs, J. S. R. (2007). Effect of altitude on the heart and the lungs. Circulation, 116(19), 2191-2202. DOI: 10.1161/CIRCULATIONAHA.106.650796 di Prampero, P. E., Osgnach, C., Morin, J. B., Slawinski, J., Pavei, G., & Samozino, P. (2021). Running at altitude: the 100-m dash. European journal of applied physiology, 121(10), 2837–2848. https://doi.org/10.1007/s00421-021-04752-y Escalera, A. (2023). Evaluating the effect of competition elevation and residential elevation on endurance race performance: A prediction model (Doctoral dissertation, Indiana University). ProQuest Dissertations & Theses. https://doi.org/30249968 Escalera et al. (2023). Track and field race time calculator. Track Calculator. Retrieved November 12, 2024, from https://trackcalculator.com/ Grigorie, T. L., Dinca, L., Corcau, J., & Grigorie, O. (2010, May). The density altitude. influence factors and evaluation. In 6th WSEAS International Conference on Dynamical Systems and Control, Kantaoui, Sousse, Tunisia. Hamlin, M. J., Hopkins, W. G., & Hollings, S. C. (2015). Effects of altitude on performance of elite track-and-field athletes. International Journal of Sports Physiology and Performance, 10(6), 781-788. https://doi.org/10.1123/ijspp.2014-0261 Hill, D. W. (1999). Energy system contributions in middle-distance running events. Journal of sports sciences, 17(6), 477-483. https://doi.org/10.1080/026404199365786 https://www.ncaa.org/sports/2021/6/28/mission-and-priorities.aspx 29 National Geographic Society. (n.d.). Barometer. National Geographic. Retrieved November 12, 2024, from https://education.nationalgeographic.org/resource/barometer/ National Weather Service. (n.d.). Observations for Ogden, Utah (KOGD). National Oceanic and Atmospheric Administration. U.S. Department of Commerce. Retrieved November 12, 2024, from https://forecast.weather.gov/data/obhistory/KOGD.html NCAA. (2024). Road to the championships: Division I men's and women's outdoor track & field. NCAA. Retrieved November 12, 2024, from https://www.ncaa.com/championships/trackfield-outdoor/d1/road-to-thechampionships Peronnet, F., Thibault, G., & Cousineau, D. L. (1991). A theoretical analysis of the effect of altitude on running performance. Journal of Applied Physiology, 70(1), 399-404. https://doi.org/10.1152/jappl.1991.70.1.399 RTS Performance Testing. (n.d.). NCAA altitude adjustment calculator. Retrieved November 12, 2024, from https://www.rtspt.com/ncaa/altitude.exe Shelquist, R. (2023, January 22). Density altitude and relative humidity. Shelquist Engineering. Retrieved November 12, 2024, from https://wahiduddin.net/calc/calc_da_rh.htm Stray-Gundersen, J., Chapman, R. F., & Levine, B. D. (2001). "Living high-training low" altitude training improves sea level performance in male and female elite runners. Journal of Applied Physiology, 91(3), 1113-1120. https://doi.org/10.1152/jappl.2001.91.3.1113 Track & Field Results Reporting System. (2024). TFRRS | College track & field rankings and meet results. https://www.tfrrs.org/ 30 U.S. Track & Field and Cross Country Coaches Association. (2009). Altitude adjustment for NCAA championship qualifying. https://www.ustfccca.org/assets/ncaa-info/2009-outdooralt-adjust.pdf Versaw, A. (2012, July 31). It’s Not Just Altitude, Folks. Milesplit. https://nm.milesplit.com/articles/90135/its-not-just-altitude-folks Weiss, K., Valero, D., Villiger, E., Thuany, M., Scheer, V., Cuk, I., & Knechtle, B. (2022). Temperature and barometric pressure are related to running speed and pacing of the fastest runners in the Berlin Marathon'. European Review for Medical & Pharmacological Sciences, 26(12). DOI: 10.26355/eurrev_202206_29054 WildSafe. (n.d.). Altitude safety 101: Oxygen levels. WildSafe. Retrieved November 12, 2024, from https://wildsafe.org/resources/ask-the-experts/altitude-safety-101/oxygenlevels/#:~:text=At%20high%20a ltitude%2C%20barometric%20pressure,19.6%20kPa%20at%20sea%20level Wyatt, F. B. (2014). Physiological Responses to Altitude: A Brief Review. Journal Of Exercise Physiology Online, 17(1). 31 Tables and Figures Mean Regional Rankings by Group Altitude vs Non-Altitude School Conversion Qualifier vs Non-Conversion Qualifier M Altitude Conversion Qualifer 24.38 Altitude Non-Conversion Qualifier 22.11 Non-Altitude Conversion Qualifer 18.07 Non-Altitude Non-Conversion Qualifier 22.61 32 Table 1 Descriptive Statistics for Regional Rankings in WEST Distance Events Altitude Status Qualifier Type M Altitude Conversion Qualifier 23.18 14.08 346 Non-Conversion Qualifier 21.13 14.00 1869 Conversion Qualifier 20.36 14.21 83 Non-Altitude SD N Non- Conversion Qualifier 22.74 14.15 5452 Total Conversion Qualifier 22.64 14.13 429 Non-Conversion Qualifier 22.34 14.13 7750 Figure 1 Plot of Cell Means for Distance Events in the West 33 Table 2 Descriptive Statistics for Regional Rankings in WEST Sprint Events Altitude Status Qualifier Type M Altitude Conversion Qualifier 25.13 13.82 460 Non-Conversion Qualifier 24.81 13.75 491 Conversion Qualifier 16.97 13.31 422 Non-Altitude SD N Non- Conversion Qualifier 22.60 14.06 6052 Total Conversion Qualifier 21.24 14.18 882 Non-Conversion Qualifier 22.76 14.05 6543 Figure 2 Plot of Cell Means for Sprint Events in the West 34