Changes

The New York City and Chicago marathons use electronic timing that records the start, finish and half way times for runners. These races are reasonably flathave little elevation change; NYC has around 900 feet (300m) of ascent and descent, and Chicago is effectively flat. Data is publically available for the New York marathon from 2000 to 2011 and the Chicago marathon from 1998 to 2012. (The results of the 2007 Chicago marathon were excluded as that year the race was unusually hot.) The results were grouped within race location using name and age so that multiple finishes by the same individual could be compared. This gives data for 26 races, covering 876,703 results for 754,851 runners.

This section looks at the distribution of splits for all finishes. Overall, 87% finishes were positive splits and 13% were negative splits. This ratio is similar way when the splits are divided by finish time, with the exception of those finishing in over five hours, where the percentage of negative splits is somewhat lower. However, the distribution of splits varies significantly with finish time. For , with the fastest runners the distribution is having a quite narrowdistribution, and the distribution widening with each successively slower group has a wider spread of splits. The peak of the curve is also at a more positive split value for slower groups. This suggests that faster runners tend to run a more even pace than slower runners.

Below is a table of statistics for the breakdown of percentage splits, which give another view on the data. Again you can see that the faster runners have a narrower spread of positive, even splits (standard deviation) and negative their average splits by finish time. For this table a split is considered are closer to even if it is +/-0.5%.

This section looks at the finishes of runners with more than one finish in a city. This allows us to look at which split percentage resulted in their best time. This is a much smaller subset of the results, including ~22% of the overall results (192,585 of 876,703). For this subset of finishes the elite runners (sub 2:30) have a higher percentage of negative splits, but runners finishing between 2:30 and 5:00 have a lower percentage of negative splits when compared with all finishes shown above. The most significant difference however is that the best splits have a much narrower spread. The ; that is the best splits are generally closer to even, with fewer finishes in either the extreme negative or positive range.

Below Here is the breakdown of percentage of positivedata in tabular form, even and negative showing the lower standard deviation than the overall splits by finish time. For this table a split is considered While the splits are closer to even if it , the pattern of the mean becoming more positive and the standard deviation increasing as the runners become slower is +/-0.5%the same as the overall results.

! Interquartile Mean

For This section looks at the best performance from runners with multiple finishes, 90% did their best time on who had both a positive split and 10% on a negative splitresult. This mirrors is a much smaller subset of the overall distribution result, including just 12,425 finishes (1.4% of splitsthe finishes, so it's more interesting to look at 1.6% of the runners who had both a positive and negative split and see which gave their best result). Of those runners, 4452% did better on with a positive split, 16% did best on an even negative split (+/-0.5%) and 4048% did better on a negative positive split. If we look at the distribution by finish time, we see that not surprisingly the faster runners run close to even splits, with just a couple of percent variation. As the finish times get longer, so the distribution of the split percentage flattens out.

Here's a tabular breakdown of the same data. <span style='color:#FF0000'>

! </span>Finish Time

! Positive Split! Count ! Mean! Interquartile Mean! Median! Standard Deviation

| 3769%| 2931%| 35

| 2149%| 4351%| 190

| 3044%| 5256%| 3,182 3182

| 4153%| 4447%| 6,135 6135

| 5061%| 3739%| 2,883 2883

| totalTotal| 4052%| 4448%| 12,425 12425

! Even Split (-0.5% to 0.5%)! Positive Split (>0.5%)

Does the The distribution of splits does not vary much by age or gender? Not much.