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A Mine of Information: Can Sports Analytics Provide Wisdom From Your Data?

Louis Passfield and James G. Hopker

This paper explores the notion that the availability and analysis of large data sets have the capacity to improve practice and change the nature of science in the sport and exercise setting. The increasing use of data and information technology in sport is giving rise to this change. Web sites hold large data repositories, and the development of wearable technology, mobile phone applications, and related instruments for monitoring physical activity, training, and competition provide large data sets of extensive and detailed measurements. Innovative approaches conceived to more fully exploit these large data sets could provide a basis for more objective evaluation of coaching strategies and new approaches to how science is conducted. An emerging discipline, sports analytics, could help overcome some of the challenges involved in obtaining knowledge and wisdom from these large data sets. Examples of where large data sets have been analyzed, to evaluate the career development of elite cyclists and to characterize and optimize the training load of well-trained runners, are discussed. Careful verification of large data sets is time consuming and imperative before useful conclusions can be drawn. Consequently, it is recommended that prospective studies be preferred over retrospective analyses of data. It is concluded that rigorous analysis of large data sets could enhance our knowledge in the sport and exercise sciences, inform competitive strategies, and allow innovative new research and findings.

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Cycling Performance and Training Load: Effects of Intensity and Duration

Antonis Kesisoglou, Andrea Nicolò, and Louis Passfield

Purpose: To examine the effect of cycling exercise intensity and duration on subsequent performance and to compare the resulting acute performance decrement (APD) with total work done (TWD) and corresponding training-load (TL) metrics. Methods: A total of 14 male cyclists performed a 5-minute time trial (TT) as a baseline and after 4 initial exercise bouts of varying exercise intensity and duration. The initial exercise bouts were performed in a random order and consisted of a 5- and a 20-minute TT and a 20- and a 40-minute submaximal ride. The resulting APD was calculated as the percentage change in 5-minute TT from baseline, and this was compared with the TWD and TL metrics for the corresponding initial exercise bout. Results: Average power output was different for each of the 4 initial exercise bouts ( η p 2 = .971 ; P < .001), and all bouts resulted in an APD. But APD was only different when comparing maximal with submaximal bouts ( η p 2 = .862 ; P < .001). The APD contradicted TWD and TL metrics and was not different when comparing 5- and 20-minute maximal TTs or the 20- and 40-minute submaximal bouts. In contrast, TL metrics were different for all training sessions ( η p 2 = .970 ; P < .001). Conclusion: An APD is found after initial exercise bouts consisting of 5- and 20-minute TTs and after 20- and 40-minute of submaximal exercise that is not consistent with the corresponding values for TWD or TL. This discrepancy highlights important shortcomings when using TWD and TL to compare exercise bouts of different intensity and duration.

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The Effect of Cycling Intensity on Cycling Economy During Seated and Standing Cycling

Marco Arkesteijn, Simon Jobson, James Hopker, and Louis Passfield

Background:

Previous research has shown that cycling in a standing position reduces cycling economy compared with seated cycling. It is unknown whether the cycling intensity moderates the reduction in cycling economy while standing.

Purpose:

The aim was to determine whether the negative effect of standing on cycling economy would be decreased at a higher intensity.

Methods:

Ten cyclists cycled in 8 different conditions. Each condition was either at an intensity of 50% or 70% of maximal aerobic power at a gradient of 4% or 8% and in the seated or standing cycling position. Cycling economy and muscle activation level of 8 leg muscles were recorded.

Results:

There was an interaction between cycling intensity and position for cycling economy (P = .03), the overall activation of the leg muscles (P = .02), and the activation of the lower leg muscles (P = .05). The interaction showed decreased cycling economy when standing compared with seated cycling, but the difference was reduced at higher intensity. The overall activation of the leg muscles and the lower leg muscles, respectively, increased and decreased, but the differences between standing and seated cycling were reduced at higher intensity.

Conclusions:

Cycling economy was lower during standing cycling than seated cycling, but the difference in economy diminishes when cycling intensity increases. Activation of the lower leg muscles did not explain the lower cycling economy while standing. The increased overall activation, therefore, suggests that increased activation of the upper leg muscles explains part of the lower cycling economy while standing.

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Continuous Versus Intermittent Running: Acute Performance Decrement and Training Load

Antonis Kesisoglou, Andrea Nicolò, Lucinda Howland, and Louis Passfield

Purpose: To examine the effect of continuous (CON) and intermittent (INT) running training sessions of different durations and intensities on subsequent performance and calculated training load (TL). Methods: Runners (N = 11) performed a 1500-m time trial as a baseline and after completing 4 different running training sessions. The training sessions were performed in a randomized order and were either maximal for 10 minutes (10CON and 10INT) or submaximal for 25 minutes (25CON and 25INT). An acute performance decrement (APD) was calculated as the percentage change in 1500-m time-trial speed measured after training compared with baseline. The pattern of APD response was compared with that for several TL metrics (bTRIMP, eTRIMP, iTRIMP, running training stress score, and session rating of perceived exertion) for the respective training sessions. Results: Average speed (P < .001, η p 2 = .924 ) was different for each of the initial training sessions, which all resulted in a significant APD. This APD was similar when compared across the sessions except for a greater APD found after 10INT versus 25CON (P = .02). In contrast, most TL metrics were different and showed the opposite response to APD, being higher for CON versus INT and lower for 10- versus 25-minute sessions (P < .001, η p 2 > .563 ). Conclusion: An APD was observed consistently after running training sessions, but it was not consistent with most of the calculated TL metrics. The lack of agreement found between APD and TL suggests that current methods for quantifying TL are flawed when used to compare CON and INT running training sessions of different durations and intensities.

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Variability in Submaximal Self-Paced Exercise Bouts of Different Intensity and Duration

Ciaran O’Grady, Louis Passfield, and James G. Hopker

Purpose: Rating of perceived exertion (RPE) as a training-intensity prescription has been extensively used by athletes and coaches. However, individual variability in the physiological response to exercise prescribed using RPE has not been investigated. Methods: Twenty well-trained competitive cyclists (male = 18, female = 2, maximum oxygen consumption = 55.07 [11.06] mL·kg−1·min−1) completed 3 exercise trials each consisting of 9 randomized self-paced exercise bouts of either 1, 4, or 8 minutes at RPEs of 9, 13, and 17. Within-athlete variability (WAV) and between-athletes variability (BAV) in power and physiological responses were calculated using the coefficient of variation. Total variability was calculated as the ratio of WAV to BAV. Results: Increased RPEs were associated with higher power, heart rate, work, volume of expired oxygen (VO2), volume of expired carbon dioxide (VCO2), minute ventilation (V E), deoxyhemoglobin (ΔHHb) (P < .001), and lower tissue saturation index (ΔTSI%) and ΔO2Hb (oxyhaemoglobin; P < .001). At an RPE of 9, shorter durations resulted in lower VO2 (P < .05) and decreased ΔTSI%, and the ΔHHb increased as the duration increased (P < .05). At an RPE of 13, shorter durations resulted in lower VO2, V E, and percentage of maximum oxygen consumption (P < .001), as well as higher power, heart rate, ΔHHb (P < .001), and ΔTSI% (P < .05). At an RPE of 17, power (P < .001) and ΔTSI% (P < .05) increased as duration decreased. As intensity and duration increased, WAV and BAV in power, work, heart rate, VO2, VCO2, and VE decreased, and WAV and BAV in near-infrared spectroscopy increased. Conclusions: Self-paced intensity prescriptions of high effort and long duration result in the greatest consistency on both a within- and between-athletes basis.

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A 1-Year Study of Endurance Runners: Training, Laboratory Tests, and Field Tests

Andy Galbraith, James Hopker, Marco Cardinale, Brian Cunniffe, and Louis Passfield

Purpose:

To examine the training and concomitant changes in laboratory- and field-test performance of highly trained endurance runners.

Methods:

Fourteen highly trained male endurance runners (mean ± SD maximal oxygen uptake [VO2max] 69.8 ± 6.3 mL · kg−1 · min−1) completed this 1-y training study commencing in April. During the study the runners undertook 5 laboratory tests of VO2max, lactate threshold (LT), and running economy and 9 field tests to determine critical speed (CS) and the modeled maximum distance performed above CS (D′). The data for different periods of the year were compared using repeated-measures ANOVA. The influence of training on laboratory- and field-test changes was analyzed by multiple regression.

Results:

Total training distance varied during the year and was lower in May–July (333 ± 206 km, P = .01) and July–August (339 ± 206 km, P = .02) than in the subsequent January–February period (474 ± 188 km). VO2max increased from the April baseline (4.7 ± 0.4 L/min) in October and January periods (5.0 ± 0.4 L/min, P ≤ .01). Other laboratory measures did not change. Runners’ CS was lowest in August (4.90 ± 0.32 m/s) and highest in February (4.99 ± 0.30 m/s, P = .02). Total training distance and the percentage of training time spent above LT velocity explained 33% of the variation in CS.

Conclusion:

Highly trained endurance runners achieve small but significant changes in VO2max and CS in a year. Increases in training distance and time above LT velocity were related to increases in CS.

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A Single-Visit Field Test of Critical Speed

Andy Galbraith, James Hopker, Stephen Lelliott, Louise Diddams, and Louis Passfield

Purpose:

To compare critical speed (CS) measured from a single-visit field test of the distance–time relationship with the “traditional” treadmill time-to-exhaustion multivisit protocol.

Methods:

Ten male distance runners completed treadmill and field tests to calculate CS and the maximum distance performed above CS (D′). The field test involved 3 runs on a single visit to an outdoor athletics track over 3600, 2400, and 1200 m. Two field-test protocols were evaluated using either a 30-min recovery or a 60-min recovery between runs. The treadmill test involved runs to exhaustion at 100%, 105%, and 110% of velocity at VO2max, with 24 h recovery between runs.

Results:

There was no difference in CS measured with the treadmill and 30-min- and 60-minrecovery field tests (P < .05). CS from the treadmill test was highly correlated with CS from the 30- and 60-min-recovery field tests (r = .89, r = .82; P < .05). However there was a difference and no correlation in D′ between the treadmill test and the 30 and 60-min-recovery field tests (r = .13; r = .33, P > .05). A typical error of the estimate of 0.14 m/s (95% confidence limits 0.09–0.26 m/s) was seen for CS and 88 m (95% confidence limits 60–169 m) for D′. A coefficient of variation of 0.4% (95% confidence limits: 0.3–0.8%) was found for repeat tests of CS and 13% (95% confidence limits 10–27%) for D′.

Conclusion:

The single-visit method provides a useful alternative for assessing CS in the field.

Open access

Validity of the Training-Load Concept

Louis Passfield, Juan M. Murias, Massimo Sacchetti, and Andrea Nicolò

Training load (TL) is a widely used concept in training prescription and monitoring and is also recognized as as an important tool for avoiding athlete injury, illness, and overtraining. With the widespread adoption of wearable devices, TL metrics are used increasingly by researchers and practitioners worldwide. Conceptually, TL was proposed as a means to quantify a dose of training and used to predict its resulting training effect. However, TL has never been validated as a measure of training dose, and there is a risk that fundamental problems related to its calculation are preventing advances in training prescription and monitoring. Specifically, we highlight recent studies from our research groups where we compare the acute performance decrement measured following a session with its TL metrics. These studies suggest that most TL metrics are not consistent with their notional training dose and that the exercise duration confounds their calculation. These studies also show that total work done is not an appropriate way to compare training interventions that differ in duration and intensity. We encourage scientists and practitioners to critically evaluate the validity of current TL metrics and suggest that new TL metrics need to be developed.

Open access

Training Load: Differentiating Training Volume and Training Dose

Louis Passfield, Juan M. Murias, Massimo Sacchetti, and Andrea Nicolò

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Pacing Strategy and Tactical Positioning During Cyclo-Cross Races

Arthur H. Bossi, Ciaran O’Grady, Richard Ebreo, Louis Passfield, and James G. Hopker

Purpose : To describe pacing strategy and competitive behavior in elite-level cyclo-cross races. Methods: Data from 329 men and women competing in 5 editions (2012–2016) of Union Cycliste Internationale Cyclo-Cross World Championships were compiled. Individual mean racing speeds from each lap were normalized to the mean speeds of the whole race. Lap and overall rankings were also explored. Pacing strategy was compared between sexes and between top- and bottom-placed cyclists. Results: A significant main effect of laps was found in 8 out of 10 races (4 positive, 3 variable, 2 even, and 1 negative pacing strategies), and an interaction effect of ranking-based groups was found in 2 (2016, male and female races). Kendall tau-b correlations revealed an increasingly positive relationship between intermediate and overall rankings throughout the races. The number of overtakes during races decreased from start to finish, as suggested by significant Friedman tests. In the first lap, normalized cycling speeds were different in 3 out of 5 editions—men were faster in 1 and slower in 2 editions. In the last lap, however, normalized cycling speeds of men were lower than those of women in 4 editions. Conclusions : Elite cyclo-cross competitors adopt slightly distinct pacing strategies in each race, but positive pacing strategies are highly probable in most events, with more changes in rankings during the first laps. Sporadically, top- and bottom-placed groups might adopt different pacing strategies during either men’s or women’s races. Men and women seem to distribute their efforts differently, but this effect is of small magnitude.