Prediction of performance in a 100-km run from a simple equation

Coquart, Jérémy

doi:10.1371/journal.pone.0279662

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Simple tools, like the Cooper Test for running as far as possible in 12 min, are gaining popularity [ 19 , 31 ]. Previous studies have employed linear regression to formulate prediction equations, prompting us to explore this approach in our study [ 14 , 16 , 32 , 33 ]. Additionally, a recent study employed artificial intelligence methodologies to predict marathon completion times [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of half-marathon performance of male recreational marathon runners using nomogram

Shu,

Wang,

Zhou

et al. 2024

BMC Sports Sci Med Rehabil

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Background Long-distance running is a popular competitive sport. We performed the current research as to develop an easily accessible and applicable model to predict half-marathon performance in male recreational half-marathon runners by nomogram. Methods Male recreational half-marathon runners in Zhejiang Province, China were recruited. A set of literature-based and panel-reviewed questionnaires were used to assess the epidemiological conditions of the recruited runners. Descriptive and binary regression analyses were done for the profiling and identification of predictors related to higher half-marathon performance (completing time ≤ 105 min). Participants were assigned to the training set (n = 141) and the testing set (n = 61) randomly. A nomogram was used to visually predict the half-marathon performance, and the receiver operating characteristic (ROC) was used to evaluate the predictive ability of the nomogram. Results A total of 202 participants (median age: 49 years; higher half-marathon performance: 33.7%) were included. After multivariate analysis, three variables remained as significant predictors: longer monthly running distance [adjusted odds ratio (AOR) = 0.992, 95% confidence interval (CI): 0.988 to 0.996, p < 0.001], faster mean training pace (AOR = 2.151, 95% CI: 1.275 to 3.630, p < 0.001), and better sleep quality [the Pittsburgh Sleep Quality Index (PSQI), AOR = 2.390, 95% CI: 1.164 to 4.907, p = 0.018]. The AUC of the training and testing sets in nomogram were 0.750 and 0.743, respectively. Further ternary and linear regression analyses corroborated the primary findings. Conclusions This study developed a nomogram with good potential to predict the half-marathon performance of recreational runners. Our results suggest that longer monthly running distance, faster mean training pace and better sleep quality notably contribute to better half-marathon performance.

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Section: Discussionmentioning

confidence: 99%

Prediction of half-marathon performance of male recreational marathon runners using nomogram

Shu,

Wang,

Zhou

et al. 2024

BMC Sports Sci Med Rehabil

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Win Your Race Goal: A Generalized Approach to Prediction of Running Performance

Dash

2024

Sports Med Int Open

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We introduce a novel approach for predicting running performance, designed to apply across a wide range of race distances (from marathons to ultras), elevation gains, and runner types (front-pack to back of the pack). To achieve this, the entire running logs of 15 runners, encompassing a total of 15,686 runs, were analyzed using two approaches: (1) regression and (2) time series regression (TSR). First, the prediction accuracy of a long short-term memory (LSTM) network was compared using both approaches. The regression approach demonstrated superior performance, achieving an accuracy of 89.13% in contrast, the TSR approach reached an accuracy of 85.21%. Both methods were evaluated using a test dataset that included the last 15 runs from each running log. Secondly, the performance of the LSTM model was compared against two benchmark models: Riegel formula and UltraSignup formula for a total of 60 races. The Riegel formula achieves an accuracy of 80%, UltraSignup 87.5%, and the LSTM model exhibits 90.4% accuracy. This work holds potential for integration into popular running apps and wearables, offering runners data-driven insights during their race preparations.

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Valid and accurate simple equation to predict 3,000-m steeplechase performance

Barbry,

Lerebourg,

Racil

et al. 2024

Front. Sports Act. Living

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IntroductionPredict running performances is very important for athletes and trainers. Sport researchers have therefore developed certain tools to predict running performances, but only in non-obstacle races. This study aimed to develop and test the validity and accuracy of an equation for predicting 3,000-m steeplechase performance (PerfSteeple).MethodsThe official rankings of French runners for the 3,000-m track-running (Perf3000) and 3,000-m steeplechase events were examined. Age, height and body mass were collected. From 146 included athletes, two groups were randomly composed: one comprising 80% of the sample (n = 117) to develop a simple equation to predict PerfSteeple (i.e., development group) and the other comprising the remaining 20% (n = 29) to test the validity and accuracy of the developed prediction equation (i.e., cross-validation group).ResultsThe simple prediction equation included Perf3000 and age: PerfSteeple=−57,165+1,147×Perf3000+0,955×age. No significant difference was noted between the actual and predicted performances. Predicted performances were significantly correlated with the actual ones, with a very high correlation coefficient (p < 0.001; r = 0.929). Bias and 95% limits of agreement were −5 ± 24 s, i.e., −0.8 ± 7.6%. In 95 of 100 new predictions, the difference between actual and predicted performance would be less or equal to—5 ± 24 s.DiscussionThe study confirms the validity and accuracy of the equation for predicting PerfSteeple. Predictions using this simple equation may be used in training and competitions for athletes and coaches. PerfSteeple = −57,165 + 1,147 X Perf3000 + 0,955 X age.

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Prediction of performance in a 100-km run from a simple equation

Cited by 3 publications

References 17 publications

Prediction of half-marathon performance of male recreational marathon runners using nomogram

Prediction of half-marathon performance of male recreational marathon runners using nomogram

Win Your Race Goal: A Generalized Approach to Prediction of Running Performance

Valid and accurate simple equation to predict 3,000-m steeplechase performance

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