If a player performs well in every second cycle and poorly in every alternate cycle(let the cycle in this case be one season), then their production will look similar to an ordinary sin curve or cos curve or something liek this y={1 for x=1,3,5,7,9….etc 0 for x=2,4,6,8…etc}

If you change the beginning/end of the cycle to the very middle of the current cycles and take an average between the new cutoffs, of course the average will be even, because half of 1 and half of 0=.5 every time.

Wether a season is an arbitrary endpoint or not that answer would be true with this test in any circumstance.

]]>From a manager’s perspective, you choose players *in the off season* with the intent of finding out who will give you the best chance to get to the playoffs and win the world series. To do so, knowing the stats that a given player has during that period of baseball (a season) is absolutely important and useful to you. It is not the only type of stats that you care about, but it is anything but arbitrary.

Other time frames may become important in other scenarios. For instance, knowing a batter’s performance between july-sept vs april – june could be very important to a manager nearing the trade deadline, looking for a player that will give an added boost to their team at the end of the season. These stats, which could much more easily be called arbitrary than full season stats, have their value and place as well. They do not make season stats random or arbitrary though.

]]>That is what you’re doing. You’re writing a ex post narrative fallacy. Many writers did this about the magical ability of the Angels to out perform third order wins. And recently Nate Silver did this talking about Michigan St (when he wasn’t shilling a political agenda).

Please stop. Fangraphs has enough issues with nonsense and trivial articles to have deceitful statistical “analysis” added to the mix.

PS: Learn what random means. You did use a “random” endpoint. A random endpoint would be to choose a player then have an RNG choose a number out of 162 and you would then use that to analyze the data as the end point. You used a carefully selected and arbitrary (what it actually means) to lend credence to your post.

]]>In other words, is it more likely that Prince Fielder and Aubrey Huff have some sort of power on/off switch that flips from year to year, or that they’ve had very normal careers with traditional statistical arcs?

I would say that in these two cases, using seasonal end points have failed us a little.

And there’s been research that has suggested that certain stats like ISO and BABIP don’t ever become reliable over a season… Perhaps there’s a strong sample size somewhere between a season and a career that we haven’t found yet.

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