My initial goal was to determine the amount of variation in pitching stats in a short season. What I found was a stipulation filled mess. It should have been simple. Just take the first two months and compare how the pitchers performed to a full season. The short answer is that they did great because they pitched in cooler weather and were 100% healthy. Instead, should the results from August and Septemeber be used, by that point in the season, many had broken down and the breakouts (e.g. Lucas Giolito) emerged. There is no perfect way to answer my original idea, so I’ll try to provide several possible answers.
To limit the focus, I’m going to implement the following guidelines. It’s a lot and when I was setting them, I was questioning any possible findings. By changing any one of them, the process to find the results and the actual final results differ.
- Assumed a 12-team league and used SGP (Standing Gain Points) equation from The Process.
- I used historic Steamer projections to set the preseason valuation.
- I only examined WHIP and ERA. Most of the hot takes I’ve heard involve not wanting to deal with the possible variation in these rate stats.
- Ignored closers. They are their own beast.
- Focused on the 7 starters for 12 teams.
- Used April to May data and then August to September. Both aren’t ideal but the differences can then be analyzed.
- Anyone who didn’t pitch during the two-month time frame got zeros across the board.
- I just did 2019 and kept the mess to one season.
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