Archive for Meta Analysis

The 2018 Pod Projections are now available! For the first time, the package includes NFBC ADP, along with all historical Pod-developed xMetrics. My projections are based on the methodology shared in my eBook Projecting X 2.0, and the process continues to evolve and improve (thanks Statcast!). Given the hype and the difficulties of translating performance from a foreign league, it was obvious who the first player for this series should be — Japanese uber-athlete Shohei Ohtani.

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Last week, I profiled Jose Quintana as a pitcher’s whose ADP and our auction value differed. In my analysis, I noticed that his swinging strike rate (SwStr%) and strikeout rate (K/9) didn’t move in tandem. Here are the pair plotted against each other.

And here are his historic values showing he doesn’t have a propensity to have a higher strikeout rate than his swinging strike rate suggests.

Quintana’s projections seemed based off his strikeout jump, not his swing-and-miss numbers. Steamer has him pegged for a 9.1 K/9 for 2018. Today, I’m going to look to see if just looking at strikeouts rates and not swing-and-miss totals is the correct procedure moving forward.

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Yesterday, I used my new xHR/FB rate to identify and discuss 15 hitters whose xHR/FB rates actually validate their HR/FB rate spikes in 2017. Today, I’m going to check in on the opposite end of the validations — those hitters who suffered severe declines in HR/FB rate that was confirmed by xHR/FB rate. Without xHR/FB rate, we cannot be sure if it’s luck or just skill changes driving the swings in HR/FB, so the metric assists in making that determination.

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As for now, I can’t find any find any predictability to year-to-year hitter consistency once adjusting for plate appearances. For the readers looking for a short article, stop now and move on to Paul’s thesis on starting pitchers. For the stubborn ones, here is what I’ve additionally found out after previously investigating the subject.

On Monday, I could not find any predictability for hitter being consistency. That is not entirely true, I did find that the more plate appearances a hitter accumulates, the more likely they are to reach their true talent level. And if given the opportunity to be closer to their talent level, the more consistent their output.

The one factor I thought might point to year-to-year consistent play in a player’s statistical profile. Are power hitters inconsistent because a few gusts of wind could make a difference in a half-dozen home runs? Do high-walk hitters see their stats as being more consistent since walks stabilize faster? Basically, are certain hitter types more consistent on a yearly basis.

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As suggested by commenter Konoldo in yesterday’s post discussing 10 2018 HR/FB rate decliners, today I am going to use my new xHR/FB rate to identify surprise 2017 power sources validated by the metric. These are the hitters that either came out of nowhere to post big HR/FB rates, or really upped their games, blowing past even the most optimistic of projections. The knee-jerk reaction is always to expect severe regression, but xHR/FB rates might make you think twice. Perhaps a repeat is more likely than you think for these hitters.

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Yesterday, I used my new and improved xHR/FB rate equation to discuss 10 hitters whose xHR/FB rates sat significantly above their actual marks in 2017, suggesting serious 2018 upside. Today, I’ll talk about the other end of the spectrum, hitters whose xHR/FB rates were well below their actual marks, hinting at real downside risk in 2018, assuming the same underlying skills.

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Whether I like it or not, I’ve opened Pandora’s box on year-to-year consistency. The concept states that if a hitter’s overall year-to-year production is consistent, the consistent production will continue. Therefore, good, consistent hitters should be valued more highly since owners know what they’ll be getting on draft day. The problem is that consistent overall production doesn’t lead to future consistency.

The discussion started last week when I wrote that Eric Hosmer and Edwin Encarnacion had similar fantasy values but Hosmer’s NFBC ADP (average draft position) was quite a bit lower. Reader’s stated in the comments they devalued Hosmer because of his year-to-year inconsistency.

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You might recall that just three weeks ago, I shared 10 HR/FB rate surgers for 2018. At the time, I was still using my original Statcast-driven xHR/FB rate. Since then, I have developed a new and improved version of the metric, dubbed xHR/FB 2.0. So it’s time to redo the HR/FB rate surgers. A couple of the names are the same as on the original list, but there are some new faces as well.

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Yesterday, I shared and discussed nine fantasy relevant hitters whose Statcast Average Fly Ball Distance (Avg FB Dist) marks surged the most versus 2016. Not surprisingly, every single one of them enjoyed HR/FB rate gains, most of them significant jumps. Now let’s dive into the batters on the opposite end of the spectrum, those fantasy relevant hitters whose Avg FB Dist marks dropped most versus 2016.

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… they need Production. That’s it. If anyone says differently, they’re wrong. I’m tired of hearing owners say they want 35+ home runs from first base. It doesn’t matter where the production comes from. Owners don’t get extra points because the home run was from first base or from their shortstop. Home runs are just one category. Other hits, besides home runs, keep the AVG high and can also generate Runs, RBIs, and stolen base opportunities. Home runs don’t have monopoly on run scoring. Owners need to stop tying home runs (or any other stat) to a position and just pick the most productive players.

Today’s rant is being brought to everyone by my Twitter followers. Yesterday I asked them why Eric Hosmer was getting no love with his low NFBC ADP.

I have Eric Hosmer valued as the 28th or 32nd best hitter (z-score and SGP methods). His NFBC ADP is the 46th hitter off the board. Here are my theories for the difference. Which one do you believe is correct?

I have no idea.

— Jeff Zimmerman (@jeffwzimmerman) January 23, 2018

The big winner is power from first base. I’ve never gotten this philosophy of targeting a single stat, like stolen bases or home runs, from a set position. This is especially true early in a draft. In the first 100 picks or so, all the players are average or better. Accumulate as many of these above average talents as possible and then fill in the voids. If the team needs stolen bases, find them now later. Or batting average. Or heaven forbid, home runs.

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