Archive for Meta Analysis

It’s pitching time! After comparing my hitter projections to Steamer, let’s now check in on the pitchers. I’ll start by comparing starting pitcher ERA from my Pod Projections with Steamer. I have decided to only list and discuss pitchers I have projected for a sub-4.00 ERA. Do you really care if I project a pitcher for a 4.40 ERA versus 5.50 by Steamer? Nope. I also left out two free agents, Jake Arrieta and Alex Cobb, who would have made this list.

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Last Thursday, I highlighted nine hitters my Pod Projections suggested enjoyed stolen base upside when compared to Steamer projections. Today, I’ll check in on the hitters who my forecasts hint at downside versus Steamer.

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Today we continue our comparison of my Pod Projections with Steamer projections. This time I’m going to check in on stolen bases and begin by identifying nine hitters that I’m projecting for a lower PA/SB rate. Stolen bases totals are primarily fueled by three factors — on base percentage (which itself is driven by a variety of components), how often the player attempts a steal once he reaches base, and how successful he is when attempting a theft. With so many moving parts, it’s sometimes very difficult to forecast stolen bases, especially for certain types of players, like the big sluggers who suddenly decide to run (Manny Machado in 2015 and Bryce Harper in 2016).

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Yesterday, I discussed nine hitters that my Pod Projections forecast for a lower AB/HR ratio than Steamer, giving them home run upside, in my opinion. Today, I’ll check in on a group of hitters I see as possessing significant downside compared with Steamer.

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Last year, I transformed my series pitting my Pod Projections against Steamer projections into a categorical comparison. I’m going to continue that this year, but instead of comparing counting stats extrapolated over my plate appearance projection, I’m going to go straight to ratios. As an ardent supporter of ratios over counting stats, I have no idea why I didn’t do this to begin with!

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In the latest episode of the Launch Angle Podcast, Rob Silver asked me how many Wins did I expect Chris Archer to accumulate this season. Basically, I came back with my normal response, I don’t chase Wins and don’t care. He pushed a little harder and wondered the actual difference. I just stammered out a horrible response because I didn’t know. I’m not one to not know so found out with the answer being a win or two.

For years, I’ve used the potential for more Wins as a tie breaker between pitchers with similar baseline stats (strikeouts, walks, and groundball rate). I focused on talent first. Usually, I found pitchers on projected better teams being drafted way ahead of those with similar skills on worse teams. I just assumed the better skills will lead the pitcher to as many Wins as the worse pitcher on a better team. There is no need for me to make that assumption anymore.

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Today marks the end of xBABIP week, after I shared and discussed 11 hitters potentially due for a BABIP surge and 10 hitters at risk of dramatic decline over the last two days. Today I’ll check in on hitters that at first glance, wouldn’t appear to be far off from their xBABIP marks, while the surgers and decliners list were quite a bit more obvious. If you posted a .230 BABIP in 2017, you’re probably going to find yourself on a potential surger list, while a .380 BABIP is likely going to get you onto the decliner list.

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Yesterday, I used my xBABIP equation to identify and discuss 11 hitters who might be in for a BABIP surge this season. Today, I’ll move on to the other side of the ledger — those hitters whose xBABIP marks were significantly below their actual BABIP marks, suggesting serious downside this year.

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Every year Jason Collette puts together a list of pitchers who are adding a new pitch. Last year, over 50 different pitchers said they were adding/changing at least one of their pitches. Once the season was over, the pitchers who made the biggest gains from adding a new pitch weren’t on the list. As much as I personally enjoy helping with the list during the spring, it doesn’t help fantasy owners. Instead of focusing on the list, I’m going to work propose a different method for finding pitchers to target.

Once every season ends, I go examine where the fantasy industry missed on players. Two top-15 pitchers who made the list were Robbie Ray and Luis Severino. In both cases, they began to effectively utilize a third pitch. For Severino, it was a changeup which generated a 13% SwStr% and his K/9 jumped from 8.4 to 10.7 and his ERA dropped from 5.83 to 2.98.

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A year ago, I introduced the latest and greatest version of my hitter xBABIP equation, this time incorporating shift data. Even though it was leaps ahead of any previous iterations and attempts at an xBABIP equation, it still only resulted in an adjusted R-squared of 0.5377. There’s still a whole lot more work to be done here! I would have liked to spend some time doing more research in the hopes of unveiling a further improved equation before the season begins, but alas, I haven’t had the time.

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