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Fun With Sample Sizes

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One thing you never want to do when playing DFS is rely on too small of a sample size. Or do you?

If you’re entirely unwilling to put any faith in a small sample size, you’re likely to be left in the dust if what everyone is seeing is real. Furthermore, we don’t have time to wait to see if it’s real or not, as in baseball hitters are out there every day and pitchers every fifth day. If the newly performing player is going to be as good as he’s flashed, you’d better get in now before his price rises and your opponents catch on. Or if the old veteran ace is showing signs he may be struggling, it behooves you to let everyone else continue to use him as he puts up ugly numbers.

But how do we differentiate between flukes and trends? And which numbers can we trust in small samples to help us make that differentiation? Well those are the key questions that deserve exploring.

Differentiating between flukes and trends this early in the MLB season is of course a little tricky. Many odd things can happen in a single month of baseball, so putting stock into everything you’re seeing is a big no no. However, if you know which stats to look for, you can possibly gain some insight into which early season trends may be signs of something to expect going forward.

Take Felix Hernandez, for instance. After yesterday’s nightmare outing against the Oakland A’s his ERA jumped to 2.21. Before the outing, his ERA was in the 1s. Knowing that, you’d think he was a hot commodity in DFS. Not so fast. Most good DFS players or baseball handicappers are well aware that something is up with Felix Hernandez. Nearly every single peripheral stat you look at paints the picture of a guy who is struggling mightily. His xFIP is at a career high 4.30. His K/9 is at a career low 7.12. The Velocity is a career low 89.7 average miles per hour on his fastball. The 8.4% swinging strike rate is a career low. So while he was sporting the ERA in the 1s, all of these other indicators aligned to show a guy who was in a decline phase or possibly isn’t fully healthy.

The same process can be done when looking at hitters. Recently, I wrote about Colby Rasmus and how his approach seemingly did a 180 degree turn at the plate. So far this year he’s walking way more and striking out way less than his career norms. His performance has been amazing. That’s the kind of change you can put some stock into. If a guy is sporting a .437 batting average and has a .397 batting average on balls in play and his strikeout and walk rates remain as they’ve always been, who cares? We know that he’s likely to regress to the mean some and can rightfully just take that with a grain of salt like we should.

This is how you can use small sample sizes to your advantage in DFS. You have to look beyond the obvious numbers like batting average and ERA and try to get a feel for what the performance has looked like, not what the results have been.

To relate it to poker, we all know that any two cards can win any given hand. That doesn’t make playing bad hands in suboptimal situations correct. It simply highlights the randomness involved. Sports are the same way. Randomness abounds. Guys get hot. Guys get cold.

To predict what will happen successfully, we have to dig deeper and find out if what we’re seeing is real, or just randomness happening. Sometimes a small sample size means nothing. Other times it can be the new normal.

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Thanks for reading. Find me on Twitter @IanJ300 with any questions.


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