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swimming, athletics, speed skating and track cycling
it aimed to find the best combination of two linear regressions, which showed the largest difference between their slope – a significant decrease in gender gaps variations
for each event, a first model was performed with a minimum of eight points (eights years of records or performances) to fit the first linear regression
the second regression was fitted with the points that were left (from the ninth point to the last one)
an eight year segment representing two olympiads was chosen as the minimum time span over which a period could be considered as stable
then, a second model was created with the first slope now including one more point, and the second slope including the points left
all possible two part linear models were thus performed until all points were included in the first slope
for each model, coefficients of each slope, differences of slopes and global r2 of each regression are collected
finally, the model with the greatest r² and the most significant slope difference was kept
the breakpoint date was then determined by the cross point of the two linear regressions of the chosen model
gender gaps evolution according to time was modelled by
where l[t ≥ τ] is an indicator function taking value 0 if t < τ and 1 if t ≥ τ stated otherwise, the trends before and after the breakpoint date τ are linear, but the slope of the two lines may differ on either side of the breakpoint regression parameters α, β, α' and β' were estimated by maximum likelihood; the r2 coefficient measured the global fit of the model to the data analyses were conducted using sas software 9 2 version (sas institute, cary, nc, usa) after the breakpoint dates, the stabilized gender gaps ∆m were calculated and expressed as follows: with j being the date of stabilization the standard deviation of the ten best was measured for each event in order to calculate the yearly coefficient of variation cv(t): with representing the mean value of the 10 performances vector at year t and σp(t): standard deviation of the performances vector the coefficients of variation of the gender gaps for the two periods (before and after breakpoint date) were compared by a wilcoxon signed-rank test this non-parametric test was used since the data (performances values) could not be assumed to be normally distributed the annual cumulative proportions were used to describe the secular evolution of world records (guillaume et al factor ac,t is the annual ratio of the number of world records for the country c over the total number of world records: p is the annual cumulative proportion over the olympic era: for the first year t0, year t and the country c factor p defines the annual cumulative world records progression rate for each country for the evolution analysis of factor p, the mean slope of annual cumulative proportions (s) was calculated by linear regressions over 4 periods: 1921-1940, 1951-1970, 1971-1990, 1991-2008 statistical analysis was performed with the r software (r development core team, 2008) one way linear regressions were used to calculate the slopes of factors g and p statistical significance was considered at p < 0 in two disciplines (athletics and swimming), a graphic representation of the number of world records obtained each year through a 4-year smoothing mean illustrated the gender profile of their evolution since the beginning of the olympic era the breaking slopes method shows a breakpoint date separating the events into two periods the first one fits the fast reduction of the gender gaps, while the second one corresponds to a period of stability (after 1978, figure 1c ) results of the wilcoxon test show that the two periods are significantly different, with the coefficients of variation of the second period being inferior to the first one slope breaks results are listed in table 2 breakpoint dates; gg at stability; and variation coefficient before breakpoint date (cv bd) and after (cv ad) for all events * if a breakpoint date is not detectable (recently officialised women event), the given value is the last 2008 gg analysis of world records in athletics shows that global stability was reached in 1983 gender gaps have been stable in jumping and running events since 1982 gender gaps decreased in the

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