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Sports Science Applied (SSA) 1: Set Rate Improvement

This may or may not be obvious to members of this site, but I have believed for a while that talent is twofold. It is not only the ability to perform an action or skill, but also the ability to improve that action or skill. It is my belief that you could actually measure an ‘improvement factor’ and use it to adjust training plans. Actually identifying that factor, however, is well beyond the scope of this post or this thread. Instead, I took the step of trying to figure out what the average rate of improvement was from year to year.

I went on athletic.net and made a database of every male high school senior in Oregon from 2011 that ran under 12.00 FAT in the 100m dash (there were a couple non seniors as well). I then took their personal bests from each year (FAT only, all conversions were thrown out) and tried to use them to predict their personal bests from the next year. Hence I ended up with a linear regression equation. That equation is as follows:

Next year personal best = (This year personal best) * .6182 + 4.2577

This equation is valid for times between 10.42 and 14.57

N = 331, r = .7451

There are a few things to note here. For one, the fact r = .7451 makes this equation more or less useless for actually predicting the performance of your future athletes. If you aren’t statistics savvy I’ll show you why in a minute. But you can use it as a guideline to see how well your athletes are improving compared to the norm. And this can tell you something about their talent level, or your program’s effectiveness. There is also something else peculiar about this equation. If you took an athlete who ran 10.9 this year and put it into the equation… we get 11.0! This equation actually states a limit where an athlete is no longer expected to improve! That limit happens to be 11.15. So a secondary way of evaluating your sprint program is to see how often in produces runners faster than 11.15. Any time an athlete runs faster than 11.15, they have surpassed what they were expected to run.

The second thing you can use this equation for is incoming talent evaluation. While all of us may wish that every incoming athlete is a future superstar that is most likely not the case. What we might look for, however, is athletes that are serviceably fast to contribute in other events such as relays, jumps, or hurdles. So you can backwards engineer the equation to get a number that you think will work. For now I will just say anyone that runs under 13 as a freshman could be a substantial contributor as a senior. I have further data to substantiate this claim, but it’s too much effort for one post.

The final thing to note is prediction intervals. I stated earlier that this equation is nigh useless for actually predicting the performance of an athlete from year to year. And I say this because the prediction interval equation is as follows:

y = (.6182x + 4.2577) +/- .4894

That basically says that the range you can expect the athlete to run in with 90% accuracy is .9788 seconds. Simply unacceptable for a track coach. But nevertheless, the data does have interesting implications as stated above. And when it’s combined with good record keeping, I think it can be a very powerful tool.