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The results of 26 marathons covering 876,703 results for 754,851 runners were analyzed to look at the differences between positive and negative splits. Overall only 13% of finishes were negative splits, * We have 876703 finishes* We have 754851 runner* We have 72259 runners with multiple entries* We have 192585 entries for a mean split of positive 8.25%. Faster runners with multiple entries* We have 112309 a narrower distribution of splits and a mean closer to even. Looking at the 192,585 runners who did have more than one finish in the same city, only 10% of the best performances are with a negative split* We have 762868 , though a higher portion of the 2:00 to 2:30 and 4:00 to 5:00 finishes were negative. The subset of runners who did have both a positive and negative split* We have 93035 runners who did shows that 52% had their best performance with a negative split and the most common split for a best performance is negative (<0-1%.5) split* We Not surprisingly the biggest change in performance relates to extreme positive splits being much slower than more even splits. Overall, the data suggests that it's beneficial to have 739764 runners who did a positive (>0.5) split* We have 42378 runners who did an time that is close to even , with a slightly negative split* We have 12438 runners who had both +ve and -ve splitspossibly being optimal.
=Method=
The New York City and Chicago marathons use electronic timing that records the start, finish and half way times for runners. These races have 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.
* Runners were identified by using their age on race day to provide a year of birth. However, because the date of the race varies slightly, people with their birthday around this time may not get their results grouped together.
* The name provided on the race results for a given runner may vary depending on the use of nicknames or due to data entry errors.
=Conclusions=Obviously it is not possible to draw causal conclusions from this type of data. * Negative splits are relatively unusual, and typically a runner will only be negative by 0-3%.* Most runners have their best performance with a slight positive split.* Faster runners have more even splits, probably because they are better at pacing.* Slow runners have a broader spread of splits than faster runners, and their mean split percentage increases.* Not surprisingly, a large positive split reflects a massive slowdown in the second half of the race and is associated with the poorest performances. * Overall, negative splits are slightly (52%) more likely to result in a better performance than a positive split. However, for the fastest runners (sub-2:30), the negative split is 69% of best performances and 61% of over-5:00. * Of runners how tried both positive and negative splits, a slightly negative split is the most common best performance. =Appendix - Table of Split Times=
This table shows sample split percentages for various finish times.
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