Every season, in addition to posting my standard bold predictions, I up the ante with my bold league leaders. If you thought nailing a bold prediction was tough, the bold league leaders is even more difficult! Just getting one right is worthy of celebration. Because these are bold, I automatically disqualify players I don’t personally believe should be considered bold. So I challenge myself and it typically causes me to bat .000 (though last year I actually hit one!).

We’ll start with the hitting league leaders in each of the five categories, split up by league.

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A couple of weeks ago, I examined the prospect Hit tool grade and how it provides useless information as it is currently being distributed. It’s time to dive back in. First, I am going to answer a couple questions which have come up on the topic and then get into my recommended changes.

Are there any systematic differences between Baseball America’s grades and those from MLB.com?

This study was easy. I grouped all players who had grades from both sources in the same season and I found the average differences.  The following table contain the averaged difference of the Baseball America grade minus the MLB.com grade for the 154 matched pairs.

The final differences are small with Baseball American being higher on power while MLB.com is higher on Speed and Defense.

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A few weeks ago I rolled out a large update to my 2017 estimated stats page. You could call it a projection, but that word sounds too official for me. These stats take all of the Statcast data available for each player, weights it by season, and regresses to the mean with respect to plate appearances. So, a guy with more plate appearances will have less regression than a guy with fewer plate appearances, and plate appearances in 2016 weigh more than those in 2015.

These numbers, theoretically, represent the quality of contact generated by each player. There are some caveats, of course. There are players who have large amounts of missing Statcast data, which I will try to skip over for the purposes of this piece. There are some who have relatively small sample sizes.  Some played through injury which may have cast a large shadow on their numbers the past two years. Try to keep these issues in mind.

I’m going to be comparing these 2017 xStats estimations/projections to Steamer projections, and I’ll be displaying the difference. The difference is calculated using xStat – Steamer. So positive numbers show xStats are more optimistic, and negative numbers show Steamer is more optimistic.

With all that said, I’m focusing on mid to late round picks, ADP between 100 and 400. These are the sorts of guys I personally find easier to trade after a draft, assuming your drafts have already concluded (and at this point that seems like a fair assumption). Read the rest of this entry »

Let’s finish up the hitting side of Pod’s Picks and Pans with a look at the outfielders. Since we draft so many of them, there are far more opportunities for disagreement. For this position, I’ll only consider Picks to be those in my top 60 and Pans to be those in the consensus top 60.

Outfielders March Rankings Update

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Let’s continue my picks and pans with a look at the second base and shortstop positions. Like for the corner guys, I’ll only consider Picks to be those in my top 20 and Pans to be those in the consensus top 20.

March Rankings Updates:
Second Base
Shortstop

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Earlier in the offseason, I examined out how reported Hit tool grades compared to actual MLB batting averages. I called the process a “mess” but figured it had some value. When I implemented the formula on MLB.com’s 2017 grades, commenters had the following to say about the projected batting average values:

“… not enough differentiation there in my opinion”
“… adjust your outputs to create more difference..”
“… hoping the table would be more conclusive…”
“…way too tightly grouped to the mean…”
“…it’s better to have no projection than to project everyone to be average…”
“… regressing too much to the mean…”
“… hit tool grades should be ignored…”
“…hit tool is undervalued in prospect analysis…”

I have no issue with the hit values being regressed to the mean. What I do have a problem with is if the hit tool is not measuring the correct factors. I needed to find out if reported hit grades provide any value. The following is a detailed look at how the hit tool is graded and how it fails to predict one simple factor, a hitter’s ability to get hits.

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Earlier in the year, I introduced the newest versions of batter xBABIP and xHR/FB. Finally, after many, many requests, I share with you every batter’s 2016 marks that I have calculated. Enjoy!

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The 2017 World Baseball Classic has been riveting thus far. Many of the teams are loaded, and the players and fans have been wildly into it. Saturday’s game between the Dominican Republic and the United States was perhaps the greatest heavyweight match-up the game has ever seen. The lineups on both sides were absurd, and the game lived up to the hype. The Dominicans overcame a 5-0 deficit to win in dramatic style, 7-5.

The Dominican lineup could be the best ever, but the United States gives them a run for their money. Since the majority of FanGraphs readers are, presumably, American, and pulling for Team U.S.A., it struck me that it would be fun to analyze the roster from a fantasy perspective. Although many on the roster are undisputed stars, there are overrated players, players to avoid for other reasons, and potential bargains mixed in. Let’s get right into it, analyzing the starting position players on Team U.S.A.: Read the rest of this entry »

Last week, we took a look at some pitchers whose Fantasy-irrelevant stats in 2016 suggested they pitched better (or worse) than appeared. There was some reason, beyond our need for something to write about, to think this approach might actually identify under- and overvalued players. There’s no reason at all, other than intuition, to think that the same approach works with hitters. But let’s take it for a spin and see how it handles.

To review the underlying theory and the stats in question: the harder a batter hits a ball, the likelier he is to get a hit. So the less frequently a pitcher gets hit hard, the better he will do. But sometimes, the inscrutable gods of baseball decree otherwise, so that weakly-hit balls go for hits, popups go for home runs, and guys who throw effective pitches have nothing to show for it. Vagaries, however, balance out in the long run. So last week we looked for pitchers whose Batting Average on Balls in Play and Home Run to Fly Ball Ratio were immoderately high, even as their Hard-Hit Ball Percentage was low, on the theory that the universe would right itself this time around. And, of course, we looked for pitchers whose stats suggested the same outcome in the opposite direction. Read the rest of this entry »

After a short break to tend to family matters, let’s return to the comparison of my Pod Projections to the Steamer forecasts. A week ago, I identified six hitters I was more bullish on for stolen bases, so today, I’ll discuss the hitters I’m more bearish on. To ensure we’re comparing apples to apples, I extrapolated Steamer’s stolen base projections to the same number of plate appearances I’m forecasting for each player.

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