Beating Yourself: How Do Runners Improve Their Own Records?

Evolution of 1500-m Olympic Running Performance

PURPOSE

This study determined the evolution of performance and pacing for each winner of the men's Olympic 1500-m running track final from 1924 to 2020.

METHODS

Data were obtained from publicly available sources. When official splits were unavailable, times from sources such as YouTube were included and interpolated from video records. Final times, lap splits, and position in the peloton were included. The data are presented relative to 0 to 400 m, 400 to 800 m, 800 to 1200 m, and 1200 to 1500 m. Critical speed and D' were calculated using athletes' season's best times.

RESULTS

Performance improved ∼25 seconds from 1924 to 2020, with most improvement (∼19 s) occurring in the first 10 finals. However, only 2 performances were world records, and only one runner won the event twice. Pacing evolved from a fast start-slow middle-fast finish pattern (reverse J-shaped) to a slower start with steady acceleration in the second half (J-shaped). The coefficient of variation for lap speeds ranged from 1.4% to 15.3%, consistent with a highly tactical pacing pattern. With few exceptions, the eventual winners were near the front throughout, although rarely in the leading position. There is evidence of a general increase in both critical speed and D' that parallels performance.

CONCLUSIONS

An evolution in the pacing pattern occurred across several "eras" in the history of Olympic 1500-m racing, consistent with better trained athletes and improved technology. There has been a consistent tactical approach of following opponents until the latter stages, and athletes should develop tactical flexibility, related to their critical speed and D', in planning prerace strategy.

Short-Term Speed Variability as an Index of Pacing Stochasticity in Athletic Running Events

We aimed to compare differences in performance and pacing variability indices between 5000 m heats and finals during major championships in men and women. Data with 100 m time resolution were used to compare overall pacing variability (standard deviation of 100 m section times, SD; and coefficient of variation, CV%) and short-term pacing variability (root mean square of successive differences between 100 m section times, RMSSD). The changes in performance and pacing indices differed between races and competitions. For instance, the men's final in Beijing 2008 was quicker than the heat (p < 0.01) while the CV% was reduced (p = 0.03) and RMSSD increased (p < 0.01). For women, the heats and the final exhibited a similar mean time in London 2017 (p = 0.33) but with CV% (p < 0.001) and RMSSD (p < 0.001) showing opposite trends. Individual analyses of men's and women's champions revealed highly individual variability metrics. The use of RMSSD can complement overall variability indices for better characterization of pacing stochasticity.

Competition Between Desired Competitive Result, Tolerable Homeostatic Disturbance, and Psychophysiological Interpretation Determines Pacing Strategy

Scientific interest in pacing goes back >100 years. Contemporary interest, both as a feature of athletic competition and as a window into understanding fatigue, goes back >30 years. Pacing represents the pattern of energy use designed to produce a competitive result while managing fatigue of different origins. Pacing has been studied both against the clock and during head-to-head competition. Several models have been used to explain pacing, including the teleoanticipation model, the central governor model, the anticipatory-feedback-rating of perceived exertion model, the concept of a learned template, the affordance concept, the integrative governor theory, and as an explanation for "falling behind." Early studies, mostly using time-trial exercise, focused on the need to manage homeostatic disturbance. More recent studies, based on head-to-head competition, have focused on an improved understanding of how psychophysiology, beyond the gestalt concept of rating of perceived exertion, can be understood as a mediator of pacing and as an explanation for falling behind. More recent approaches to pacing have focused on the elements of decision making during sport and have expanded the role of psychophysiological responses including sensory-discriminatory, affective-motivational, and cognitive-evaluative dimensions. These approaches have expanded the understanding of variations in pacing, particularly during head-to-head competition.

Evidence That Rating of Perceived Exertion Growth During Fatiguing Tasks is Scalar and Independent of Exercise Mode

Introduction: The relationship between the percentage of a fatiguing ambulatory task completed and rating of perceived exertion (RPE) appears to be linear and scalar, with a relatively narrow “window.” Recent evidence has suggested that a similar relationship may exist for muscularly demanding tasks. Methods: To determine whether muscularly demanding tasks fit within this “ambulatory window,” we tested resistance-trained athletes performing bench press and leg press with different loadings predicted to allow 5, 10, 20, and 30 repetitions and measured RPE (category ratio scale) at the end of the concentric action for each repetition. Results: There was a regular, and strongly linear, pattern of growth of RPE for both bench press (r = .89) and leg press (r = .90) during the tasks that allowed 5.2 (1.2), 11.6 (1.9), 22.7 (2.0), and 30.8 (3.2) repetitions for bench press and 5.5 (1.5), 11.4 (1.6), 20.2 (3.0), and 32.4 (4.2) repetitions for leg press, respectively. Conclusions: The path of the RPE growth versus percentage task fit within the window evident for ambulatory tasks. The results suggest that the RPE versus percentage task completed relationship is scalar, relatively linear, and apparently independent of exercise mode.

Pacing Profiles of Middle-Distance Running World Records in Men and Women

The aims of the current study were to compare the pacing patterns of all-time 800 m, 1500 m and mile running world records (WRs) and to determine whether differences exist between sexes, and if 800 m and 1500 m WRs were broken during championship or meet races. Overall and lap times for men and women’s 800 m, 1500 m, and mile WRs from World Athletics were collected when available and subsequently compared. A fast initial 200 m segment and a decrease in speed throughout was found during 800 m WRs. Accordingly, the first 200 m and 400 m were faster than the last 200 m and 400 m, respectively (p < 0.001, 0.77 ≤ ES ≤ 1.86). The first 400 m and 409 m for 1500 m and mile WRs, respectively, were faster than the second lap (p < 0.001, 0.74 ≤ ES ≤ 1.46). The third 400 m lap was slower than the last 300 m lap and 400 m lap for 1500 m and mile WRs, respectively (p < 0.001, 0.48 ≤ ES ≤ 1.09). No relevant sex-based differences in pacing strategy were found in any event. However, the first 409 m lap was faster than the last 400 m lap for men but not for women during mile WRs. Women achieved a greater % of WRs than men during championships (80% vs. 45.83% in the 800 m, and 63.63% vs. 31.58% in the 1500 m, respectively). In conclusion, positive, reverse J-shaped and U-shaped pacing profiles were used to break 800 m, men’s mile and 1500 m, and women’s mile WRs, respectively. WRs are more prone to be broken during championships by women than men.

Pace and motor control optimization for a runner

We present a model which encompasses pace optimization and motor control effort for a runner on a fixed distance. We see that for long races, the long term behaviour is well approximated by a turnpike problem, that allows to define an approximate optimal velocity. We provide numerical simulations quite consistent with this approximation which leads to a simplified problem. The advantage of this simplified formulation for the velocity is that if we have velocity data of a runner on a race, and have access to his [Formula: see text], then we can infer the values of all the physiological parameters. We are also able to estimate the effect of slopes and ramps.

Frequency and type of adverse analytical findings in athletics: Differences among disciplines

Athletics is a highly diverse sport that contains a set of disciplines grouped into jumps, throws, races of varying distances, and combined events. From a physiological standpoint, the physical capabilities linked to success are quite different among disciplines, with varying involvements of muscle strength, muscle power, and endurance. Thus, the use of banned substances in athletics might be dictated by physical dimensions of each discipline. Thus, the aim of this investigation was to analyse the number and distribution of adverse analytical findings per drug class in athletic disciplines. The data included in this investigation were gathered from the Anti-Doping Testing Figure Report made available by the World Anti-Doping Agency (from 2016 to 2018). Interestingly, there were no differences in the frequency of adverse findings (overall,~0.95%, range from 0.77 to 1.70%) among disciplines despite long distance runners having the highest number of samples analysed per year (~9812 samples/year). Sprinters and throwers presented abnormally high proportions of adverse analytical findings within the group of anabolic agents (p < 0.01); middle- and long-distance runners presented atypically high proportions of findings related to peptide hormones and growth factors (p < 0.01); racewalkers presented atypically high proportions of banned diuretics and masking agents (p = 0.05). These results suggest that the proportion of athletes that are using banned substances is similar among the different disciplines of athletics. However, there are substantial differences in the class of drugs more commonly used in each discipline. This information can be used to effectively enhance anti-doping testing protocols in athletics.

Effect of Running Velocity Variation on the Aerobic Cost of Running

The aerobic cost of running (CR), an important determinant of running performance, is usually measured during constant speed running. However, constant speed does not adequately reflect the nature of human locomotion, particularly competitive races, which include stochastic variations in pace. Studies in non-athletic individuals suggest that stochastic variations in running velocity produce little change in CR. This study was designed to evaluate whether variations in running speed influence CR in trained runners. Twenty competitive runners (12 m, VO2max = 73 ± 7 mL/kg; 8f, VO2max = 57 ± 6 mL/kg) ran four 6-minute bouts at an average speed calculated to require ~90% ventilatory threshold (VT) (measured using both v-slope and ventilatory equivalent). Each interval was run with minute-to-minute pace variation around average speed. CR was measured over the last 2 min. The coefficient of variation (CV) of running speed was calculated to quantify pace variations: ±0.0 m∙s-1 (CV = 0%), ±0.04 m∙s-1 (CV = 1.4%), ±0.13 m∙s-1(CV = 4.2%), and ±0.22 m∙s-1(CV = 7%). No differences in CR, HR, or blood lactate (BLa) were found amongst the variations in running pace. Rating of perceived exertion (RPE) was significantly higher only in the 7% CV condition. The results support earlier studies with short term (3s) pace variations, that pace variation within the limits often seen in competitive races did not affect CR when measured at running speeds below VT.

Summated Hazard Score as a Powerful Predictor of Fatigue in Relation to Pacing Strategy

During competitive events, the pacing strategy depends upon how an athlete feels at a specific moment and the distance remaining. It may be expressed as the Hazard Score (HS) with momentary HS being shown to provide a measure of the likelihood of changing power output (PO) within an event and summated HS as a marker of how difficult an event is likely to be perceived to be. This study aimed to manipulate time trial (TT) starting strategies to establish whether the summated HS, as opposed to momentary HS, will improve understanding of performance during a simulated cycling competition. Seven subjects (peak PO: 286 ± 49.7 W) performed two practice 10-km cycling TTs followed by three 10-km TTs with imposed PO (±5% of mean PO achieved during second practice TT and a self-paced TT). PO, rating of perceived exertion (RPE), lactate, heart rate (HR), HS, summated HS, session RPE (sRPE) were collected. Finishing time and mean PO for self-paced (time: 17.51 ± 1.41 min; PO: 234 ± 62.6 W), fast-start (time: 17.72 ± 1.87 min; PO: 230 ± 62.0 W), and slow-start (time: 17.77 ± 1.74 min; PO: 230 ± 62.7) TT were not different. There was a significant interaction between each secondary outcome variable (PO, RPE, lactate, HR, HS, and summated HS) for starting strategy and distance. The evolution of HS reflected the imposed starting strategy, with a reduction in PO following a fast-start, an increased PO following a slow-start with similar HS during the last part of all TTs. The summated HS was strongly correlated with the sRPE of the TTs (r = 0.88). The summated HS was higher with a fast start, indicating greater effort, with limited time advantage. Thus, the HS appears to regulate both PO within a TT, but also the overall impression of the difficulty of a TT.

How to build a new athletic track to break records

We introduce a new optimal control model which encompasses pace optimization and motor control effort for a runner on a fixed distance. The system couples mechanics, energetics, neural drive to an economic decision theory of cost and benefit. We find how effort is minimized to produce the best running strategy, in particular, in the bend. This allows us to discriminate between different types of tracks and estimate the discrepancy between lanes. Relating this model to the optimal path problem called the Dubins path, we are able to determine the geometry of the optimal track and estimate record times.