Over the year or two many people have noted that the Statcast numbers in Comerica Park seem inflated compared to the real world results for the batted balls. From cursory research it appeared that this trend may be focused in right field, but perhaps not. Either way, you see Miguel Cabrera topping the xwOBA leaderboards on Baseball Savant, and xOBA leaderboards on xStats while his true results are lagging behind.

In one hand, that may appear to be a positive sign for a possible recovery, given his down season in 2017. In the other hand, it shouldn’t sit that well. His xwOBA is about 60 points higher than his real wOBA. The xStats fair a little better, and the difference is closer to 40 points. Either way, that is an absolutely enormous discrepancy.

There have been some hints that this effect may be focused in right field, which I noticed with Nick Castellanos. When I tell you something weird is going on in right field of Comerica Park, you might instinctively shed blame on that ludicrously deep right center field fence. I certainly did. That fence is about 430 feet deep and 11 feet high. It is one of the deepest areas in any MLB ballpark, rivaling AT&T Park.

But, I no longer believe that is the explanation. More on that in a moment, first I want to explain my methods. Read the rest of this entry »

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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It’s been Statcast’s average fly ball distance (Avg FB Dist) week, as I introduced the metric on Monday and then listed and discussed names whose Avg FB Dist marks suggest there is either extra stuff going on or some missing ingredients in their batted balls. Today, let’s dive into fantasy relevant players whose Avg FB Dist marks surged the most versus 2016.

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With the addition of NFBC ADP (average draft position) to our projection pages. I went and set our auction calculator settings for an NFBC roto team (14 Hitters, 9 pitchers). I just started going down the hitter rankings to find any major discrepancies. I didn’t make it off the first page. Here is an examination of why the values differ for three players.

Note: I’d prefer to use plate appearances to compare playing time but all the print publications use at-bats so I’m stuck using at-bats as a comparison.

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Yesterday, I introduced you to Statcast’s batter average fly ball distance (Avg FB Dist), which is one of the new components of my recently updated and improved xHR/FB rate. Today, I’ll continue to swim in the Avg FB Dist pool, this time listing and discussing specific players.

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Last week, I unveiled the newest version of my xHR/FB rate, which incorporates two Statcast metrics. The first of those components was Barrels per True Fly Ball Rate (Brls/True FB), which I introduced last Wednesday, and then discussed further on Thursday. Today, I’m going to dive into the other Statcast metric, Average Fly Ball Distance (Avg FB Dist), which is found on the Statcast Search page.

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Hard to believe January is already half finished.  Maybe even harder to accept that there are a significant number of talented MLB free agents left unsigned.  As we wait through the long winter days, I’m looking forward to seeing these players in action in 2018.

Part One

Garrett Richards

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Several years ago Alan Nathan, Jeff Kensrud, Lloyd Smith, and Eric Lang brought an air cannon and a few boxes of brand new baseballs to Minute Maid Park. If you’re anything like me, you like where this is going. They set up their cannon to fire balls roughly 96mph on a 28° angle and used Trackman to measure their distance and spin rate. They tested four groups of balls, two groups composed of MLB balls, one MiLB, and one NCAA. One group of MLB balls, group A, were tested using reasonably low spin rates, about 1800. The other, group B, had variable spin rates, ranging from 2100 to 3300. The results of their study were published in an article titled  How Far Did That Fly Ball Travel (Redux)? on Baseball Prospectus, although it can also be found here. I encourage you to read the piece, but today I want to focus on the MLB-A and MiLB groups.

The major league ball traveled 28 feet further than the minor league ball. Albeit with a higher spin rate. Presumably, the higher spin rate should translate to increased distance, but it is difficult to imagine that a difference of 200 rpm could bridge a gap of 28 feet. More on this in a moment. Read the rest of this entry »

Earlier this month, I asked our readers for any aspects of the fantasy game which are missing. Okra stepped up and said:

“I feel like we still do a poor job of predicting stole bases. I think we could better utilize the new Sprint Speed data and speed scores to predict SBs. Taking it one step further would be to try and quantify each managers propensity for SB attempts.”

This statement is 100% true. We really don’t know which measurable factors fantasy owners should focus on when looking for stolen base breakouts. I’ve gone ahead and dived into the topic of just the manager influence with positive results.

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