Last week, the Athletics traded for Stephen Piscotty, alleviating a bit of the great depth in the Cardinals outfield. Piscotty is coming off a forgettable offensive performance, in which he dealt with injuries, a minor league demotion, and the terrible news that his mother was diagnosed with ALS. Typically, the knee-jerk reaction is a move to Oakland will likely hamper a hitter’s offensive results. But surprisingly, this appears to be one of those rare instances in which the park switch may actually provide a boost. Let’s dive in.

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So the Angels are apparently going for it all, eh? First, they signed Shohei Otani, then traded for Ian Kinsler, and have now signed Zack Cozart, who figures to play third base with defensive stud Andrelton Simmons entrenched at shortstop. Moving out of the perceived hitter friendly Great American Ballpark in Cincinnati and into an environment perceived to be far more favorable for pitchers, let’s find out how the relevant park factors may impact Cozart’s performance.

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In leagues with Runs and RBIs as categories, predicting how a player’s mix will change with a new team can be guessing. Some clown at Fantrax yesterday wrote the following:

“As for Headley, his value drops by going to a worse offensive team in a pitcher-friendly park. Part of the decline could be offset by a move up in the lineup since he mainly batted seventh for the Yankees last season.”

It could go up, it could go down, who really knows? While writing the statement, I needed a better answer so I created a couple quick and simple tool. If an owner can estimate a few stats, they can predict changes in plate appearances, Runs, RBI when a hitter moves from one team to another.

The key was to be simple and quick. For simplicity, only the following stats are needed.

• New likely lineup location
• Estimate of projected home runs
• Estimated games played such 150 out of 162 games as a percentage.
• Estimated Runs scored by a team. Used over on-base percentage because team level runs scored is easier to find and remember.

The estimated runs scored is the toughest value to come up with. I’d just go to FanGraphs team projection page to get a decent idea. Just take that year’s RS/G and multiply it by 162. Another method is to take the previous season value and plug it into the following regression equation:

`RS in Y2` = .575 * `RS in Y1` + 311

The goal is just to get a basic idea of possible changes.

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I think Ryan Schimpf is my favorite player. He takes the word extreme to an entirely new level, ranking at or near both the top and bottom of various statistical categories, for the better and for the worse. That’s what makes him such a fascinating hitter. He debuted with the Padres in 2016 to excellent results over about a half a season’s worth of plate appearances. He was a new breed of hitter – a five true outcomes type, as his plate appearances generally ended with either a walk, strikeout, fly out, pop-up, or home run. The approach worked that season, but failed miserably in 2017. His performance earned him a demotion to the minors, and ultimately a ticket out of San Diego.

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The best inspiration for an article is one in which you find a meaningful leaderboard, sort away, and identify who doesn’t belong. The surprise of the group, if you will. That player is most certainly going to be fun to discuss! That brings me to Eddie Rosario. If you perform a Statcast search and select only “Barrel” for “Quality of Contact” from August through the end of the season, you will be presented with a leaderboard of top sluggers ranked by number of barreled balls they hit during that time period. The top 10 is littered with your standard who’s who of the game’s best power hitters. Then you get down to #14 and who do you find, none other than Eddie Rosario.

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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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For all of the talk of the increase in power in 2017, the number of 20-homer hitters among shortstops decreased from 13 to 10. Yet a few players at the position did have dramatic power surges, and none was more notable than Francisco Lindor’s. He led all shortstops with 33 home runs, besting Didi Gregorius and Paul DeJong by eight (though DeJong and Carlos Correa could have made it a contest if they had approached Lindor’s 723 plate appearances). Not only did he more than double his prior season’s total of 15 home runs, but Lindor increased his doubles from 30 to 44 and his Iso from .134 to .232.

There is not much mystery as to the source of Lindor’s newfound power. While his average exit velocity ticked slightly downward from 88.5 to 88.1 mph (per Baseball Savant), his average launch angle soared from 7.7 to 13.7 degrees. More airborne balls meant that Lindor was hitting for greater distance, but he also made more frequent outs on balls in play. After batting .313 and .301 in 2015 an 2016, respectively, his overall batting average dropped to .273, even though he struck out at the lowest rate of his career. His career BABIP trend reads as follows: .348, .324, 275.
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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 »

“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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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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