Archive for Meta Analysis

I was pretty successful at developing an equation to estimate what a hitter’s HR/FB rate should be given his fly ball + home run distance, along with the average absolute angle and standard deviation of the distance of those batted balls. My formula resulted in an R-squared mark of 0.649, which seems pretty darn good to me, especially when it completely ignores park factors, which we know play a significant role. On Tuesday, I found that the year-to-year correlation of a pitcher’s batted ball distance is less than half that of a hitter’s. Then yesterday, I discovered there was some correlation between a pitcher’s batted ball distance and his HR/FB rate and ISO mark.

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Last week, I wrote about collusion. The comments are a reminder that collusion is mostly a straw man. When it does occur, it’s usually a league conspiring against an individual rather than two owners working out a lopsided traded. Those seemingly one-sided swaps happen because somebody has divergent opinions from the fantasy norm. Either they really like a player who isn’t drawing much attention, or they’re eager to sell high on an established veteran who they think is due for a flop.

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Yesterday, I continued batted ball distance week with our first look into the pitching side of the equation. Up until then, we had learned a lot about what a hitter’s distance meant, but essentially nothing about a pitcher’s. We now know that a pitcher’s batted ball distance allowed has a year-to-year correlation less than half that of a hitter’s. But, the correlation was still high enough to be meaningful.

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Yesterday, I shared the year-to-year correlations of the three variables that comprise my current xHR/FB rate formula. We now know that batted ball distance is fairly stable each season, which is excellent news for those using it to project future player performance. But what led me to gather the data and perform the calculations was actually a request to look into the pitcher side of the ledger.

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Allen Webster was on my “to investigate” list because his strikeout rate was low compared to the number of swings-and-misses he got in 2014. The numbers intrigued me enough to watch one of his starts for one of my Quick Looks (full report on Friday).

What I found is he can’t/doesn’t throw pitches in the strike zone. Hitters need to chase his pitches out of the strike zone if they want to make contact. Most of the time they don’t though.

In an effort to figure him out further, I found out how much to adjust a pitcher’s predicted strikeout rate knowing his strike zone percentage and swinging strike rate, but it doesn’t make a huge difference.

Time for a little math. A simple way to estimate a pitcher’s strikeout rate (K%) is to double their (SwStr%). Taking all of the 2014 pitchers (min 50 IP), the r-squared between K% and two times the SwStr% (pK%) is 0.66. Good, but not great. Usually early in the season, I look for pitchers with high differences between these two values to find potential break out or bust candidates.

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Two years ago, Chad Young and I embarked on a journey to analyze a hitter’s average home run and fly ball distance and ultimately formulate an expected HR/FB rate, or xHR/FB. While it wasn’t earth shattering news, we discovered that batted ball distance was highly correlated with HR/FB rate. But that correlation wasn’t nearly high enough to be worth using the equation that included just batted ball distance.

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Collusion is a topic I’ve wandered into a few times in the past. I tend to test the fringes of the fantasy equivalent of treason. Just yesterday, I wrote about selling my fellow owners on a strategy. If everything went according to plan, I’d get to dump some of my back-roster assets, and I’d get a shot at some elite talent after the draft. If I just change how the idea is presented, it can come across as an attempt to solicit collusion.

This topic is important because many leagues have anti-collusion rules. However, a widely held sentiment is that collusion is impossible to prove. The corollary: owners think collusion is occurring right under their noses, they just can’t prove it. Read the rest of this entry »

I’m not sure I’ve seen this before. As of this writing, the Padres have either completed or began finalizing five trades in the last 48 hours. I feel like we’re living inside a copy of MVP ’05. The Padres are positioned to make still more trades to alleviate a roster crunch. Seth Smith, Carlos Quentin, Cameron Maybin, Will Venable, and Yonder Alonso are currently looking at just three or four spots. Somebody has to go. Then there’s Tim Federowicz, whose role with the club may have been co-opted by the David Ross signing. The Friars have also shown a willingness to re-package players, so we could see an unexpected swap involving Wil Myers, Derek Norris, Justin Upton, Will Middlebrooks, or even Matt Kemp.

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In yesterday’s Corey Kluber article, a commenter pointed out a Steamer quirk – only three starting pitchers are projected for a sub-3.00 ERA. Last season, 22 qualified pitchers finished the year under the 3.00 benchmark. If you set the threshold at 50 innings pitched, 39 starters were below a 3.00 ERA. Clearly Steamer is crazy. Right? I mean, we have to expect a lot more than three pitchers to demonstrate excellence.

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Jose Fernandez was having a dominating 2014 season until he needed Tommy John surgery last May. The 22-year-old righty could be one of the more dominant arms in the game when he returns. Fantasy owners would love to have him available mid-season as a boost their team.  While he is on the disabled list (DL), some other pitcher must be filling in for him. Fernandez’s projection should include this replacement pitcher’s production. Here is a break down of how I put on value on players who will miss significant time.

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