So far in 2018, the hot stove has been more of a cool counter, leading to a dearth of enjoyable “change of scenery” fantasy baseball topics. Thankfully, there’s still plenty of fantasy fun to be had, because the National Fantasy Baseball Championship (NFBC) has draft data ready to roll. I’m going position by position through this slow offseason, picking out players with an average draft position (ADP) significantly higher or lower than I expected.

Today, it’s time to discuss the shortstop position — namely Chris Taylor and Marwin Gonzalez. Neither guy is a full-time shortstop in real life, but in fantasy that’s where the vast majority of owners will play them in 2018. (The player pool I used included all players with at least 10 starts at short in 2017.) One of the main commonalities between Gonzalez and Taylor is that they’re both eligible to play several positions (1B/2B/3B/SS/OF for Marwin, 2B/SS/OF for Taylor).

Both players also experienced shocking breakouts in 2017, so it’s probably a good idea to see where they stand heading into the 2018 fantasy season, correct? I sure thought so.

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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 Friday, I wrote about four of the most obvious closer sleepers. If I’m looking for the lowest hanging fruit, my eyes are on the Diamondbacks, Twins, White Sox, and Angels. That leaves us with plenty of other low hanging fruit. You’ll just need to overlook some minor blemishes.

In case you need justification to bargain hunt for closers, here’s my cold take on the subject:

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This is the first iteration of a new regular column here at Rotographs about baseball video games, focused primarily on MLB The Show and Out of the Park Baseball. I’m looking at every other week here to start and then weekly during the regular season. Why video games? Well, because frankly these two games are legitimate iterations of fantasy baseball.

It is different than a 5×5 rotisserie league, obviously, but it’s still constructing teams under a set of parameters in order to be the best. OOTP is a pure sim that allows an incredible level of depth into creating your organization from the ground up. More on that brilliant game in future pieces dedicated to it. MLB The Show is dropping the first trailer for the 2018 version on Monday so I thought it’d be a great time roll out the debut. By the way, I’m looking to name this column so if you have any ideas of what to name a baseball video game piece, drop it in the comments!

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With the ATC projection data is now available on the website, it’s time to examine where the auction values from the projections and the NFBC ADP disagree. For these comparisons, I used the normal NFBC league settings.

Carlos Correa (10th Hitter by his ADP, 19th by ATC values in auction calculator)

I did not expect to see him on this list but here he is. Correa’s going seven picks ahead of Francisco Lindor while Lindor has the better projection.

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Check out the transcript below!

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Last year I posted an article summarizing the keeper results for all ottoneu FanGraphs points leagues, and now that the ottoneu keeper deadline has come and gone once again I am back with an update.

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Paying full price for saves can make it challenging to build a dominant roster. Closers are among the most inconsistent of baseball assets. Not only do they have the injury and performance risks typically associated with pitchers, but they also have to remain the best reliever on their particular team. A guy like Hector Neris could stay exactly the same – i.e. acceptable but not exceptional – and lose his job.

Last season, 23 relievers had the closer role and then lost it. That’s excluding the handful of players who had the job, lost the job, and later recovered the job. In 2016, 25 closers were booted from the ninth. Only 21 got the ax in 2015. The good news is that many of those guys – like Tyler Clippard – were always meant to be temporary solutions. A handful of struggling teams often account for over half of the demoted closers.

Entering 2018, most of the league looks pretty set in the ninth inning. Even so, we’ll still almost certainly see the usual turnover at the position. Over the next couple days, I’ll cover some of my preferred closer sleepers, starting with the most obvious.

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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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Swing plane has always been an important metric for evaluating batters, but there has never been an objective measure of swing plane in the public. Privately, through wearable technologies or video analysis one could obtain information regarding swing path, which has since gone on to become a valuable tool for coaching and training. Even still, we lacked data from actual games.

Over the past two years many writers and researchers have turned to using Exit Velocity vs Vertical Angle charts to analyze batter performance. Ranging from Rob Arthur to David Kagan to Alan Nathan. It is a great way to visualize the data. I have wondered in the past whether you could use these information to estimate the average plane of the bat on impact. If you were to plot a second order polynomial regression on top of such a chart and take the derivative of the function you could find the peak launch angle, that is the angle with the highest average exit velocity. Perhaps. I’m not entirely sure this is the case, but it is a good place to start. Read the rest of this entry »