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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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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Yesterday, The Marlins traded Dee Gordon to the Mariners for three minor league prospects. The trade came out of nowhere since the Mariners already rostered a top second baseman, Robinson Cano. Then it became known that Mariners plan on playing Gordon in centerfield. Having both middle infield and outfield qualifications a couple weeks into the season could move up his average draft position. The question remains though, will Gordon be able to cut in centerfield? Probably.

Gordon’s offensive talent is easy to access. The 29-year-old makes a ton of groundball contact and gets on first base using his speed. Once on base, he uses those legs to steal as many bases as possible. That’s it.

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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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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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A few days ago, Jake Leech asked me if Zack Cozart’s 2017 improved plate discipline would stick into 2018.

https://twitter.com/Stroke_19/status/931525718667943936

Cozart saw quite a bit of improvement with his K%-BB% dropping by 6% points.

Note: I like using K%-B% to get an overall value for a hitters plate discipline. Earlier this year, I investigated what early season stats point to a true breakout. K%-BB%, along with launch angle (FB%), were the two key factors to focus on.

The Steamer projection has his K%-BB% regressing closer to his 2016 values than the ones from 2017. This is how projections work with previous season stats having some weight along with some regression.

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A few weeks ago, I began the process of determining an underperformance metric. In the article, I laid out the groundwork determining the drop off in plate appearances (PA) and production (wOBA). With these thresholds, I created several metrics, each with its own advantages and disadvantages. I’m not setting the values into stone yet but I’m getting closer to a solution. I’ve found a few value I like better than others.

In the original article, I found fantasy owners considered a drop in 220 PA from 600 PA (37% drop) and of 0.035 wOBA from .350 wOBA (10%) to be the thresholds. I didn’t mess with these two values. Besides the pair, I wanted to know when both occurred. Additionally, from a discussion in the comments, I found when either PA or wOBA thresholds where met and when both dropped close to, but not over, the thresholds. This value (called Minor Drop) I found to provide the most overall value.

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This past weekend, I was in Phoenix for Baseball HQ’s First Pitch forum. It’s an intensive few days of catching up with old friends and focusing on the upcoming fantasy baseball season. There was an underlying theme of the weekend, the fantasy baseball game is being forced to change. Some game facets have experienced some massive adjustments. The following are some of the meta-trends which have quickly popped up over the past few seasons.

Home runs are way up

A few days ago I wrote the following incorrect statement about Carlos Martinez for a 2018 player preview.

His 1.19 HR/9 will likely drop back below the league average.

It was pointed out to me, his home run rate was below league average. I was for sure it was not near 1.20 but I was wrong. Here are the recent league-wide HR/9 values.

2014: 0.86
2015: 1.02
2016: 1.17
2017: 1.27

I remember when the HR/9 hovered around 1.0. Not anymore. Some other pitching stats are feeling the effects of the jump like ERA, but the root cause is more home runs.

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Yesterday, the Justin Mason posted the ADP from four of the slowest drafts containing industry experts and myself. One thought I had after a handful of rounds was the lack pitching available and how the good were the available hitters. I decided to go back and examine draft results from last year and these draft to see if pitching was being taken early. While it wasn’t, some other information could be extracted.

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