The Japanese sensation Shohei Ohtani is finally coming to MLB (and more specifically to the Angels), and in doing so will become the trailblazer that sets a new expectation for the future of the (possible) “two-way” player.  Because salaries and injuries continue to escalate in the game, a true double threat major leaguer is still hard to imagine in baseball, but if the 23 year old Ohtani does become the first player since Babe Ruth to make a regular impact on both sides of the ball, he will change the landscape of fantasy baseball, too.

Ohtani 😱 pic.twitter.com/YXJJ8954Hs

— Vladimir Guerrero (@VladGuerrero27) December 8, 2017

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The projection systems love Miguel Cabrera. To them, he’s a hitter who performed decent in the first half and struggled in the second half. The projections don’t know that he has two herniated discs in his back. Because of the injury, his wOBA dropped from .339 in the first half to .274 in the 2nd half. Using projections, he’s the 54th highest ranked player but owners have pushed his ADP closer to 100th. It’s time to determine why the disconnect.

It was definitely a tale of two halves for Cabrera.

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About a week ago, I compared rankings with some actual ADP. One player which stood out was Rougned Odor. He ranked 47th overall (AVG vs OBP league) and it’s tough to rank a person so high who hit only .204 last season. Steamer projections currently have him back up to a .255 AVG. Acceptable but not great.

Additionally, Odor comes to the plate hacking and rarely walks (4.2% for his career) so almost all of his value comes from his BABIP. If his batted balls don’t fall for hits, he’s not getting on base. Since his value is so BABIP driven, I decided to see what the BABIP bounce-back chances were for low-walk hitters.

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Alas, we have finally reached our final 2017 preseason article recap! Welllll, this one shouldn’t have been the last one, but no one wants to read a recap of my David Dahl Pod Projection, right? So we wrap things up by reviewing my Pod Projection for curveball aficionado Lance McCullers, who was coming off around 200 innings of 3.22 ERA ball supported by strong skills over his first two seasons. Health was a question mark, but there was no doubting his talent. Let’s remind ourselves what I forecasted for his 2017 performance and how he actually performed.

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In my last article, I examined the potential value differences between the top-50 rank players and their average draft position (ADP). Today, I will examine the next 50. While the first list contained quite a few players moving up, today’s list is a little more balanced with over and undervalued players.

One of the biggest takeaways from the first article was the extra replacement value catchers receive in a 2-catcher format. To simply explain the idea, I will turn to Joe Bryant who goes through a fitting example but with football.

The league’s bottom catchers are so bad so any catcher who can hit has good value. Evan Gattis being ranked #17 got most of the scrutiny in the rankings. As was pointed out, the projection may be high on the plate appearances but the process was still sound. Here is how Gattis compares to the last catcher ranked (Yan Gomes) and Francisco Lindor compared with the last middle infielder (Kolten Wong).

Yan Gomes is such a sink, especially with a total of 55 Runs+RBIs. It’s imperative to understand and value catchers correctly for each league formats. It’s a potentially huge advantage for those owners who spend the time. Read the rest of this entry »

Today let’s continue recapping one of my 2017 Pod Projections, this time heading to Milwaukee to discuss Keon Broxton. Coming off an intriguing half-season in 2016 that featured an exciting blend of power and speed, along with some clear flaws, he was a popular sleeper for 2017 and one whose projections people couldn’t really settle on. So what was I projecting and how did that compare to his actual results? Let’s find out.

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“Long ago, Ben Graham taught me that ‘Price is what you pay; value is what you get.’ Whether we’re talking about socks or stocks,

… or fantasy baseball players

I like buying quality merchandise when it is marked down.” –Warren Buffett

Collecting as much value (talented players) from as little possible resources (draft picks or auction dollars) is the key to starting off a winning fantasy season. From now until each draft, owners should be trying to calculate player values and the possible range of outcomes. With these value ranges in mind, owners can use their draft resources to get the best deals. It’s time to start finding those deals.

To find the bargains, player values first need to be calculated. To create the values, I will use the average final standings from the 32 leagues in the 2017 NFBC Main Event (15 team, 5×5 roto with AVG).

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Moving along on our recaps of my 2017 Pod Projections, we stumble upon Trea Turner, who delivered a fantasy half-season back in 2016 that made him the talk of the town heading into 2017 drafts. He was so darn good, he was generally a first round pick. How much, if any, regression was I projecting and how did that compare to his actual results? Let’s find out.

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I was recently asked the following question by Werthless.

Jeff, what are you trying to accomplish here? Are you trying to estimate the volatility in an individual player’s projection? That’s an interesting question, and directly related to the risk of the player. Are you trying to do better than Steamer at predicting performance? That’s a big endeavor. Are you trying to predict injuries? Might be better to do that directly. Are you trying to better estimate number of plate appearances by estimating job security? Might be better to do that directly.

Then, you can combine the models to perhaps better quantify a player’s risk of meeting preseason performance objectives. You can apply your model onto a different year’s data to see how well your predictions match reality (ie. Do the higher risk players actually underperform more often than lower risk players).

I do have a plan I’m implementing but it wasn’t known to my readers. Sorry. I want to understand which hitter traits to concentrate on. If they don’t exist, I created some.

For a few season’s now, my hitters have steadily outperformed my pitchers. In my three main leagues, here are the pitching-hitting splits from this past season.
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It’s time to recap some of my 2017 Pod Projections! This preseason, I begun the series with one of 2016’s most surprising pitchers, Kyle Hendricks. We all figured that even backed by the historically strong Cubs defense, he was quite a bit fortunate en route to a sub-3.00 ERA. But how much regression was I projecting and how did that compare to his actual results? Let’s find out.

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