Tactical Behaviors in Men's 800-m Olympic and World-Championship Medalists: A Changing of the Guard.

PURPOSE: To assess the longitudinal evolution of tactical behaviors used to medal in men's 800-m Olympic Games (OG) or world-championship (WC) events in the recent competition era (2000-2016). METHODS: Thirteen OG and WC events were characterized for 1st- and 2nd-lap splits using available footage from YouTube. Positive pacing strategies were defined as a faster 1st lap. Season's best 800-m time and world ranking, reflective of an athlete's "peak condition," were obtained to determine relationships between adopted tactics and physical condition prior to the championships. Seven championship events provided coverage of all medalists to enable determination of average 100-m speed and sector pacing of medalists. RESULTS: From 2011 onward, 800-m OG and WC medalists showed a faster 1st lap by 2.2 ± 1.1 s (mean, ±90% confidence limits; large difference, very likely), contrasting a possibly faster 2nd lap from 2000 to 2009 (0.5, ±0.4 s; moderate difference). A positive pacing strategy was related to a higher world ranking prior to the championships (r = .94, .84-.98; extremely large, most likely). After 2011, the fastest 100-m sector from 800-m OG and WC medalists was faster than before 2009 by 0.5, ±0.2 m/s (large difference, most likely). CONCLUSIONS: A secular change in tactical racing behavior appears evident in 800-m championships; since 2011, medalists have largely run faster 1st laps and have faster 100-m sector-speed requirements. This finding may be pertinent for training, tactical preparation, and talent identification of athletes preparing for 800-m running at OGs and WCs.

The Boston Marathon versus the World Marathon Majors.

PURPOSE: To compare finish times across WMM races for Boston, London, Berlin, Chicago and New York Marathons. METHODS: Race times of the top 10 male and 10 female finishers were analyzed from 2005 to 2014 using the high-performance mixed linear model procedure in the Statistical Analysis System. Venue-to-venue comparisons, as well as comparisons between Boston and other WMM races, with and without factors of temperature, humidity and altitude change were examined. RESULTS: Performance from 2005 to 2014 in the WMM races was found to improve at a rate of ~1% each 7 years. Despite its higher variability, comparison between Boston's estimated mean finishing time and all other venues revealed moderate positive differences, indicating the Boston event to be typically slower than other venues. CONCLUSIONS: Across the 10-year study period, performance times improved ~1% each 7 years for both genders for the WMM, with the Boston Marathon being slower on average than other WMM venues. Weather rather than course metrics appeared to impact performance times most.

Variability of competitive performance of elite athletes: a systematic review.

BACKGROUND: Estimates of the variability that athletes show from competition to competition provide information about the relative contributions of environmental and other factors affecting competitive performance. Smallest and other important thresholds for assessing changes in performance in competitions and field or laboratory tests can also be derived from estimates of variability. OBJECTIVES: To systematically review estimates of within-athlete variability of competitive performance in various sports. METHODS: We searched SPORTDiscus and Google Scholar for studies providing estimates of within-athlete variability between competitions. Estimates are reported here as coefficients of variation (CV) only for the best athletes. Some studies also combined within-athlete variability with between-athlete differences into a measure of predictability expressed as an intraclass correlation coefficient, reported here for the full field of competition. RESULTS: Skeleton and 1,000-m speed-skating times have the lowest within-athlete variability (CV of 0.15% and 0.4%, respectively), apparently because of the effect of the initial phase of the race on race dynamics. Times in sprint and endurance sports also have relatively low variability (0.6-1.4%), reflecting the predominant contribution of mean power output to performance. The power-velocity relationship tends to make CV for time smaller in sports performed against water or wind resistance, but this effect is offset by variability in the effects of wind and water on individual athletes. Sports requiring explosive power in a single effort, such as field events and weightlifting, have larger CVs for their performance measures (1.4-3.3%), likely reflecting substantial contributions of skill. Sports with the greatest within-athlete variability (~50%) were those with subjective scores (e.g. surfing). Predictability correlations ranged from 0.17 (half-pipe snowboarding) to 0.93 (cross-country skiing). There was little difference in variability or predictability between men and women. Application of power-velocity and power-duration relationships allows transformation of the estimates of within-athlete variability of competitive performance into thresholds for smallest and other important changes in performance in laboratory and field tests of power output. CONCLUSION: Understanding the contributions of race dynamics, power output, environment, skill, and subjective scoring to the variability of athletic performance should help identify and evaluate strategies for performance enhancement.

Tracking career performance of successful triathletes.

PURPOSE: Tracking athletes' performances over time is important but problematic for sports with large environmental effects. Here we have developed career performance trajectories for elite triathletes, investigating changes in swim, cycle, run stages, and total performance times while accounting for environmental and other external factors. METHODS: Performance times of 337 female and 427 male triathletes competing in 419 international races between 2000 and 2012 were obtained from triathlon.org. Athletes were categorized according to any top 16 placing at World Championships or Olympics between 2008 and 2012. A mixed linear model accounting for race distance (sprint and Olympic), level of competition, calendar-year trend, athlete's category, and clustering of times within athletes and races was used to derive athletes' individual quadratic performance trajectories. These trajectories provided estimates of age of peak performance and predictions for the 2012 London Olympic Games. RESULTS: By markedly reducing the scatter of individual race times, the model produced well-fitting trajectories suitable for comparison of triathletes. Trajectories for top 16 triathletes showed different patterns for race stages and differed more among women than among men, but ages of peak total performance were similar for men and women (28 ± 3 yr, mean ± SD). Correlations between observed and predicted placings at Olympics were slightly higher than those provided by placings in races before the Olympics. CONCLUSIONS: Athletes' trajectories will help identify talented athletes and their weakest and strongest stages. The wider range of trajectories among women should be taken into account when setting talent identification criteria. Trajectories offer a small advantage over usual race placings for predicting men's performance. Further refinements, such as accounting for individual responses to race conditions, may improve utility of performance trajectories.

Using athletes' world rankings to assess countries' performance.

UNLABELLED: There is a need for fair measures of country sport performance that include athletes who do not win medals. PURPOSE: To develop a measure of country performance based on athlete ranks in the sport of swimming. METHODS: Annual top-150 ranks in Olympic pool-swimming events were downloaded for 1990 through 2011. For each athlete of a given rank, a score representing the athlete's performance potential was estimated as the proportion of athletes of that rank who ever achieved top rank. A country's scores were calculated by summing its athletes' scores over all 32 events. Reliability and convergent validity were assessed via year-to-year correlations and correlations with medal counts at major competitions. The method was also applied to ranks at the 2012 Olympics to evaluate countries' swimming performance. RESULTS: The performance score of an athlete of a given rank was closely approximated by 1/rank. This simpler score has 1 practical interpretation: An athlete ranked 7th (for example) has a chance of 1/7 of ever achieving top rank; for purposes of evaluating country performance, 7 such athletes are equivalent to 1 athlete of the top rank. Country scores obtained by summing 1/rank of the country's athletes had high reliability and validity. This approach produced scores for 168 countries at the Olympics, whereas only 17 countries won medals. CONCLUSIONS: The authors used the sport of swimming to develop a fair and inclusive measure representing a country's performance potential. This measure should be suitable for assessing countries in any sports with world rankings or with athletes at major competitions.

Modelling the Progression of Competitive Performance of an Academy's Soccer Teams.

Progression of a team's performance is a key issue in competitive sport, but there appears to have been no published research on team progression for periods longer than a season. In this study we report the game-score progression of three teams of a youth talent-development academy over five seasons using a novel analytic approach based on generalised mixed modelling. The teams consisted of players born in 1991, 1992 and 1993; they played totals of 115, 107 and 122 games in Asia and Europe between 2005 and 2010 against teams differing in age by up to 3 years. Game scores predicted by the mixed model were assumed to have an over-dispersed Poisson distribution. The fixed effects in the model estimated an annual linear pro-gression for Aspire and for the other teams (grouped as a single opponent) with adjustment for home-ground advantage and for a linear effect of age difference between competing teams. A random effect allowed for different mean scores for Aspire and opposition teams. All effects were estimated as factors via log-transformation and presented as percent differences in scores. Inferences were based on the span of 90% confidence intervals in relation to thresholds for small factor effects of x/÷1.10 (+10%/-9%). Most effects were clear only when data for the three teams were combined. Older teams showed a small 27% increase in goals scored per year of age difference (90% confidence interval 13 to 42%). Aspire experienced a small home-ground advantage of 16% (-5 to 41%), whereas opposition teams experienced 31% (7 to 60%) on their own ground. After adjustment for these effects, the Aspire teams scored on average 1.5 goals per match, with little change in the five years of their existence, whereas their opponents' scores fell from 1.4 in their first year to 1.0 in their last. The difference in progression was trivial over one year (7%, -4 to 20%), small over two years (15%, -8 to 44%), but unclear over >2 years. In conclusion, the generalized mixed model has marginal utility for estimating progression of soccer scores, owing to the uncertainty arising from low game scores. The estimates are likely to be more precise and useful in sports with higher game scores. Key pointsA generalized linear mixed model is the approach for tracking game scores, key performance indicators or other measures of performance based on counts in sports where changes within and/or between games/seasons have to be considered.Game scores in soccer could be useful to track performance progression of teams, but hundreds of games are needed.Fewer games will be needed for tracking performance represented by counts with high scores, such as game scores in rugby or key performance indicators based on frequent events or player actions in any team sport.