Successful participation in competitive endurance activities requires continual regulation of muscular work rate in order to maximise physiological performance capacities, meaning that individuals must make numerous decisions with regards to the muscular work rate selected at any point in time. Decisions relating to the setting of appropriate goals and the overall strategic approach to be utilised are made prior to the commencement of an event, whereas tactical decisions are made during the event itself. This review examines current theories of decision-making in an attempt to explain the manner in which regulation of muscular work is achieved during athletic activity. We describe rational and heuristic theories, and relate these to current models of regulatory processes during self-paced exercise in an attempt to explain observations made in both laboratory and competitive environments. Additionally, we use rational and heuristic theories in an attempt to explain the influence of the presence of direct competitors on the quality of the decisions made during these activities. We hypothesise that although both rational and heuristic models can plausibly explain many observed behaviours in competitive endurance activities, the complexity of the environment in which such activities occur would imply that effective rational decision-making is unlikely. However, at present, many proposed models of the regulatory process share similarities with rational models. We suggest enhanced understanding of the decision-making process during self-paced activities is crucial in order to improve the ability to understand regulation of performance and performance outcomes during athletic activity.

Purpose:To analyze pacing strategies displayed by athletes achieving differing levels of performance during an elite-level marathon race.Methods:Competitors in the 2009 IAAF Women’s Marathon Championship were split into groups 1, 2, 3, and 4 comprising the first, second, third, and fourth 25% of finishers, respectively. Final, intermediate, and personal-best (PB) times of finishers were converted to mean speeds, and relative speed (% of PB speed) was calculated for intermediate segments.Results:Mean PB speed decreased from groups 1 to 4, and speeds maintained in the race were 98.5% ± 1.8%, 97.4% ± 3.2%, 95.0% ± 3.1%, and 92.4% ± 4.4% of PB speed for groups 1–4 respectively. Group 1 was fastest in all segments, and differences in speed between groups increased throughout the race. Group 1 ran at lower relative speeds than other groups for the first two 5-km segments but higher relative speeds after 35 km. Significant differences (P < .01) in the percentage of PB speed maintained were observed between groups 1 and 4 and groups 2 and 4 in all segments after 20 km and groups 3 and 4 from 20 to 25 km and 30 to 35 km.Conclusions:Group 1 athletes achieved better finishing times relative to their PB than athletes in other groups, who selected unsustainable initial speeds resulting in subsequent significant losses of speed. It is suggested that psychological factors specific to a major competitive event influenced decision making by athletes, and poor decisions resulted in final performances inferior to those expected based on PB times.

The aim of this study is to analyse the influence of performance level, age and gender on pacing during a 100-km ultramarathon. Results of a 100-km race incorporating the World Masters Championships were used to identify differences in relative speeds in each 10-km segment between participants finishing in the first, second, third and fourth quartiles of overall positions (Groups 1, 2, 3 and 4, respectively). Similar analyses were performed between the top and bottom 50% of finishers in each age category, as well as within male and female categories. Pacing varied between athletes achieving different absolute performance levels. Group 1 ran at significantly lower relative speeds than all other groups in the first three 10-km segments (all P < 0.01), and significantly higher relative speeds than Group 4 in the 6th and 10th (both P < 0.01), and Group 2 in the 8th (P = 0.04). Group 4 displayed significantly higher relative speeds than Group 2 and 3 in the first three segments (all P < 0.01). Overall strategies remained consistent across age categories, although a similar phenomenon was observed within each category whereby 'top' competitors displayed lower relative speeds than 'bottom' competitors in the early stages, but higher relative speeds in the later stages. Females showed lower relative starting speeds and higher finishing speeds than males. 'Top' and 'bottom' finishing males displayed differing strategies, but this was not the case within females. Although pacing remained consistent across age categories, it differed with level of performance within each, possibly suggesting strategies are anchored on direct competitors. Strategy differs between genders and differs depending on performance level achieved in males but not females.

Purpose: This study examined the determinants of pacing strategy and performance during self-paced maximal exercise. Methods: Eight well-trained cyclists completed two 20-km time trials. Power output, rating of perceived exertion (RPE), positive and negative affect, and iEMG activity of the active musculature were recorded every 0.5 km, confidence in achieving preexercise goals was assessed every 5 km, and blood lactate and pH were measured postexercise. Differences in all parameters were assessed between fastest (FAST) and slowest (SLOW) trials performed. Results: Mean power output was significantly higher during the initial 90% of FAST, but not the final 10%, and blood lactate concentration was significantly higher and pH significantly lower following FAST. Mean iEMG activity was significantly higher throughout SLOW. Rating of perceived exertion was similar throughout both trials, but participants had significantly more positive affect and less negative affect throughout FAST. Participants grew less confident in their ability to achieve their goals throughout SLOW. Conclusions: The results suggest that affect may be the primary psychological regulator of pacing strategy and that higher levels of positivity and lower levels of negativity may have been associated with a more aggressive strategy during FAST. Although the exact mechanisms through which affect acts to influence performance are unclear, it may determine the degree of physiological disruption that can be tolerated, or be reflective of peripheral physiological status in relation to the still to be completed exercise task.

Purpose:Previous literature has presented pacing data of groups of competition finalists. The aim of this study was to analyze the pacing patterns displayed by medalists and nonmedalists in international competitive 400-m swimming and 1500-m running finals.Methods:Split times were collected from 48 swimming finalists (four 100-m laps) and 60 running finalists (4 laps) in international competitions from 2004 to 2012. Using a cross-sectional design, lap speeds were normalized to whole-race speed and compared to identify variations of pace between groups of medalists and nonmedalists. Lap-speed variations relative to the gold medalist were compared for the whole field.Results:In 400-m swimming the medalist group demonstrated greater variation in speed than the nonmedalist group, being relatively faster in the final lap (P < .001; moderate effect) and slower in laps 1 (P = .03; moderate effect) and 2 (P > .001; moderate effect). There were also greater variations of pace in the 1500-m running medalist group than in the nonmedalist group, with a relatively faster final lap (P = .03; moderate effect) and slower second lap (P = .01; small effect). Swimming gold medalists were relatively faster than all other finalists in lap 4 (P = .04), and running gold medalists were relatively faster than the 5th- to 12th-placed athletes in the final lap (P = .02).Conclusions:Athletes who win medals in 1500-m running and 400-m swimming competitions show different pacing patterns than nonmedalists. End-spurtspeed increases are greater with medalists, who demonstrate a slower relative speed in the early part of races but a faster speed during the final part of races than nonmedalists.

Initial pace is associated with an individual's perception of risk, with low perceptions of risk being associated with a faster starting pace. Large differences between predicted and actual pace suggest that the performance template lacks accuracy, perhaps indicating greater reliance on momentary pacing decisions rather than preplanned strategy.

The data demonstrate that tactical positioning at intermediate points in qualifying rounds of middle-distance races is a strong determinant of qualification. In 800-m races it is important to be in a qualifying position by 400 m. In the 1500-m event, although more changes in position are apparent, position at intermediate distances is still strongly related to successful qualification.

Vaquera, A, Suárez-Iglesias, D, Guiu, X, Barroso, R, Thomas, G, and Renfree, A. Physiological responses to and athlete and coach perceptions of exertion during small-sided basketball games. J Strength Cond Res 32(10): 2949-2953, 2018-This study describes heart rate (HR) responses during different small-sided games (SSGs) in junior basketball players and identifies the level of agreement between athlete and coach perceptions of internal training load calculated using the in-task rating of perceived exertion (RPE) method. Over a 6-week period, 12 male junior basketball players, who played in the Spanish national under-18 League, played 7 games of one-a-side (1v1), 6 games of 2-a-side (2v2), 8 games of 5-a-side (5v5), and 5 games of superiority (3v2) situations. During 1v1, 2v2, 5v5, and 3v2, peak HRs were 90.27 ± 3.37%, 92.68 ± 3.29%, 92.01 ± 3.48%, and 88.74 ± 5.77% of HRmax, respectively. These differences were statistically significant between 1v1 and 2v2 (p < 0.01), 1v1 and 5v5 (p ≤ 0.05), 2v2 and 3v2 (p < 0.001), and 5v5 and 3v2 (p < 0.001). Mean HR was 79.5 ± 4.4%, 83.1 ± 4.2%, 91.2 ± 4.7%, and 78.5 ± 7.5% of HRmax during 1v1, 2v2, 5v5, and 3v2, respectively, and differences were observed between 1v1 and 2v2 (p < 0.001), 2v2 and 3v2 (p < 0.001), and 5v5 and 3v2 (p ≤ 0.05). There were differences in athletes and coaches in-task RPE in all SSGs (all p < 0.0001 apart from 5 × 5 p = 0.0019). The 2v2 format elicited a higher mean in-task RPE in comparison with all other SSGs (p < 0.001), possibly because 2v2 imposes a greater cognitive load.