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March 24, 2014

Fantasy Freestyle

Early-Season Strategic Decisions

by Wilson Karaman


The old cliché that “you can’t win a championship in April, but you can lose it” is a good one to keep in mind when approaching early season team evaluation, particularly as it relates to trading. When you react to April and May performances, you react to inherently small sample sizes, and that’s a generally dangerous thing to do. Players get lucky, other players get unlucky, some other players try to play through undisclosed injuries… it’s a minefield of variables out there if you’re looking at a few weeks’ worth of performance data.

Still, the fantasy baseball season is only so long, and waiting too long to address your team’s Achilles heel(s) can mean digging a hole too deep to claw your way back out of later on. Waiting too long to make moves can also lead to increased limitations imposed externally by the established market; a seller’s market may develop around a player or players you need, driving up the costs of acquisition. Still, while early-season roster shake-ups are a necessary and sometimes-advantageous part of the game, approaching them with caution is of critical importance.

Know what to look for
Once the season starts, I tend to gravitate back to a landmark 2008 study published by Pizza Cutter at the now-defunct Statistically Speaking*, mostly because I’ve yet to find a subsequent piece that comes close to its depth. For math nerds, it’s a tremendous study on statistical variation and stabilization. For everyone else it offers an accessible series of punch lines for spotting trends in the making. Cutter found the following baselines for hitters and pitchers regarding sample size validity:

Hitters

  • K%: 150 PA
  • Contact%: 150 PA
  • LD%: 150 PA
  • BB%: 200 PA
  • GB%: 200 PA
  • GB/FB: 200 PA
  • FB%: 250 PA
  • HR%: 300 PA
  • HR/FB%: 300 PA
  • BABIP: Doesn’t reach a 0.50 r-squared at 650 PA or below.
  • AVG: Doesn’t reach a 0.50 r-squared at 650 PA or below.

Pitchers

  • K/PA: 150 PA
  • GB%: 150 PA
  • LD%: 150 PA
  • FB%: 200 PA
  • GB/FB: 200 PA
  • K/BB: 500 PA
  • IF FB%: 500 PA
  • BB/PA: 550 PA
  • BABIP: Doesn’t reach a 0.50 r-squared at 650 PA or below.
  • HR/FB: Doesn’t reach a 0.50 r-squared at 650 PA or below.

The above figures represent the turning point at which a sample size of plate appearances becomes more or less coherent enough and free of noise to be statistically meaningful. The most interesting takeaway from these findings is that all sample sizes are not created equal. Some small samples are smaller than others, insofar as certain performance indicators are more helpful in evaluating players over shorter timeframes while others are much less helpful without nearly a full season’s worth of data (at least). These benchmarks are extremely helpful to keep in mind, as it’s easy to get trapped into reductionist thinking along the lines of “Well, Player X’s BABIP is low, so it naturally stands to reason that it will likely regress positively as the sample grows.” The above numbers tell a cautionary tale, though, insisting instead that the component elements of BABIP provide a much more stable collection of data points for analysis. If you’re so inclined to attempt isolating real performance trends in the first half of the season you’ll be much better served turning to things like contact, strikeout, and walk rates for hitters and batted ball profile for pitchers.

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