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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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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I’ll continue my look at players who our auction calculator and NFBC ADPs (average draft position) disagree on the most. Today, I will examine some top rated arms who have lower ADPs, Jose Quintana, Jeff Samardzija, and David Price than our Steamer projections may suggest. This trio has some common factors they share and some individual traits which could keep owners away.

These three starters really stood out on the rankings page with ADPs over 80 but supposedly top-20 starters. They each share these common traits.

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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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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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Last week, I began an examination of stolen base rates. The process is messy with too many variables and nuances to consider. I’m examining the information through several different lenses and seeing what applies. Today, I’m going to look at how success rate plays a role.

Team Level Analysis

As sabermetric principles are being utilized more and more by front offices, they quickly came around to the idea that for stolen bases to be helpful, the success rate needs to be high. In 2000, the success rate was 69% for the entire league and it has increased to 73% last season.

Knowing that each team is made of different players and their individual success rate are a factor, here are the three-year success rate along with total stolen base attempt percentage ((CS+SB)/(1B+HBP+BB)).

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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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With fantasy owner always searching for an edge, I may have found one buried deep in spring training stats. While looking for Yonder Alonso’s MLB.com player page, I noticed he had some batted ball data in the form of GO/AO (groundouts/air outs). In Alonso’s case, his GO/AO value had always been greater than 1.1 until last season when it dropped to 0.87. In the regular season, Yonder’s groundball rate plummeted from 44% to 34%. Yonder admitted to making a swing adjustment and that change should be detectable in spring training. By comparing spring batted ball data, fantasy owners can get an idea of those hitters who may be ready for the flyball revolution.

Note: While flyball and line drive rates are available for comparison, I will only use groundball rate (GB%) because it stabilizes quicker, has less stringer bias (tough call between some line drives and flyballs), and is only one set of benchmarks to memorize.

I’m going to have two foci for this article. Part 1 is all math and disclaimers. It’s the process I used to go from GO/AO values to ground ball rates to launch angles. Part 2 contains the results from Part 1 as a simple procedure for finding launch angle breakouts.

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Well, I used to be able to create starting pitcher disabled list (DL) projections with the average chance hovering around 40%. Until this past season when DL estimate was off by 18%.

Even though the rates have climbed the past couple of seasons, it was nothing like the jump this past season and I 100% blame the 10-day DL. All my work to this point is moot.

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Quite a bit has been written about batted ball data the past few seasons since the information has become publicly available. Fantasy owners have taken notice and are trying to find that next hitter who is raising his launch angle to be part of the “Flyball Revolution”. One major issue which is not being publicly discussed is the major effects backspin has on the ball. By knowing a hit’s spin rate, some of the anomalies seen between launch angle and exit velocity can be explained. The spin rate is a major batted ball component but is generally an unknown factor.

The importance of batted ball spin comes down to this simple table and explanation by Dr. Alan Nathan in a piece he wrote at the Hardball Times.

Finally, I want to take advantage of the fact that we have an aerodynamic model that accounts for most of the features of the data to investigate how flyball distance depends on the amount of backspin, here for a fixed exit speed of 103 mph and launch angle of 27 degrees. The results are given in the table below. They show that distance increases rapidly as the backspin increases from zero but eventually saturates, with very little gain in distance for spin rates exceeding about 1,500 rpm. The reason for the saturation is partly because air drag increases with increasing spin, essentially canceling the increase in lift.

Same launch angle. Same exit velocity. And the ball travels an additional 64 feet of distance because of backspin. Simply, how is a factor which can add an additional 60+ feet in travel distance not be part of our analysis?

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