Archive for Meta Analysis

My fellow RotoGraphs writers and I have been working through our 2016 player evaluations. I was tasked with the declining J.J. Hardy. I noticed that two of his core hitting traits, the ability to make contact and pull the ball, had degraded from 2013 to 2014. While Hardy saw an overall decline in his skills when these two skills declined, I wanted to see it was common among other players. I was able to find that decline in these two areas is a sign of a player on a fast decline.

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For the previous Steamer and I reviews, I separated the two players into their own recaps, as there was simply too much to discuss for one post. However, for the motley pair of Shane Greene and Jesse Hahn, this is unfortunately not going to be a problem. You see, both of them saw their seasons cut short by injury and one of them was so bad that including his name as part of the title of an article will do little to motivate potential readers from clicking. So, combined they are.

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Thirty-year old Chris Coghlan just had his best season of his career. He’s only surpassed 500 plate appearances twice in his seven-year career, and as a guy without a standout tool and some established platoon issues, he obviously owns a substantial ‘bust rate.’ As in, he could be unusable next year, even though he was worth $6 in mixed leagues this past season.

And so, even though we liked some boring veterans at third base, it’s time to take inspiration in another direction — where’s the line, when you should bypass the veteran for the unproven prospect? Is it right around a guy like Chris Coghlan?

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Back in May, I introduced an equation that would calculate expected isolated power (thus, “xISO”) numbers for hitters based on their batted ball profile. The idea was to generate an equation that could accurately describe for how much power a hitter should be hitting based entirely on publicly available data (provided to FanGraphs by Baseball Info Solutions), as opposed to proprietary data, so all fantasy baseball enthusiasts could use it.

I won’t get into the nitty gritty again — you can click on the link in the first sentence if you want to open that can of worms — but I will provide the equation again for posterity:
a
xISO = –.1396 + .1814*Pull% + .5136*Hard% + .2344*FB%

I’ll provide a table of xISOs for all qualified hitters below and deliver some insight regarding potential buy-lows and sell-highs.

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Every year after the RotoGraphs consensus positional rankings are published, I compare my column of ranks to a recalculated consensus after excluding mine. It’s a fun little activity and it allows me to figure out why I might be more bullish or bearish on a particular player than the rest of the crew. I published all infielders in one post in the preseason, but have already recapped the catchers and first basemen. So today I’ll finish up with the infield, reviewing the second basemen, shortstops and third basemen.

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Well, my pain may be your gain. For a future Hardball Times article I am working on, I needed to create some historic pitcher groundball rate (GB%) projections. I decided to go ahead and run the values for 2016 and make them publicly available.

Here is the basic background behind creating the projections.

• I used a three-year weighting of .7 for 2015, .2 for 2014 and .1 for 2013.
• The data was regressed some to a league average rate.
• I didn’t use any aging factors.
• The final r-squared from the projection to actual results is around .67.

With that small amount of background information, here are the projections.

Yesterday, I reviewed my short list of pitchers that my xBB% equation suggested had significant room for improvement. Today I check in on those pitchers I identified as having downside, as this group posted xBB% marks well above their actual walk rates in 2014.

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The preseason reviews continue to trickle in and today I’ll recap my list of xBB% underachievers. Using my xBB% equation, I identified pitchers whose walk rates were well above their xBB%, suggesting likely improvement in 2015. Let’s see how the equation did for the names I discussed.

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Yesterday, I recapped my preseason starting pitcher strikeout rate upside target list using my xK% equation. So naturally today I’ll review the other side of that list, those pitchers who posted strikeout rates well below their xK% marks, which suggested potential downside for the 2015 season.

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Review season continues this week and I begin by analyzing how well my xK% equation performed at identifying starting pitchers with strikeout rate upside in 2015. When I originally created my upside target group, it wasn’t a straight list of the starters whose xK% marks most exceeded their actual K% marks, but rather the more interesting names that were fantasy relevant that appeared at or near the top.

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