I am planning on publishing this piece on a weekly basis. I’ll be looking at a few interesting players. I try to work out for 30-45 minutes a day, so I will just watch a different player each time and give my thoughts on their game at the end of the week. This will not be a complete breakdown of the player, just what I saw. Also, I will probably be looking at average to below-average players to see if anything sticks out with them.

David Buchanan

Why I watched: I noticed him while looking for pitcher with big curveballs. Looking at little deeper and found this:

Month:K%-BB%,GB%
May:4%,42%
Jun:9%,49%
Jul:12%,46%
Aug:10%,55%

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A few days ago I released a way too early set of hitter projection values. Today, it is the pitchers’ turn. Truthfully, I wasn’t 100% sure I would release them. It requires a person using them to use their brain somewhat. I decided to go ahead and release and hope most people read a few lines of the article to understand how the spreadsheet is set up.

Notes on the data (PLEASE READ)

• I averaged the rest of season Steamer and/or ZIPS projections. Sometimes only one or the other was available so only one was used at times. The rest of season the projections are a good attempt at getting the player’s talent level right now. The values are close to the 2015 projection with the exception of the September numbers.

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Right or wrong, significantly greater weight is given to how a player finishes a season than how he began it. If a hitter endures a second half swoon, he’ll be considered a prime bust candidate the following year, regardless of what he did during the first several months. The same goes for pitchers. Let’s discuss a trio of those second half starting pitcher busts in the American League.

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No matter your league’s settings for starting pitchers, you’re forced to make decisions about whether to start or bench pitchers each time their turn in the rotation comes up. If you’re in a league with an innings or games started cap, you have to try to maximize the limited opportunities you have to start a pitcher. If you play in a league with no such limitations, the natural friction between the counting categories and the ratio categories forces you to make similar decisions. Sure, you can start Jake Odorizzi and take advantage of the tenth best strikeout rate among qualified starters. But his 4.23 ERA and 1.31 WHIP count, too.

The number one factor I consider when making such a decision is who the pitcher is facing that day. The strength of the other team’s offense against pitchers of my starter’s handedness usually determines whether I start or bench a pitcher I’m on the fence about. Matchup is probably the biggest factor for most fantasy owners when it’s not a must-start pitcher. I asked Twitter where the cutoff is for must-start guys. I got answers ranging from only Felix and Kershaw all the way to the top 25-30 starters.

Top 30 was sort of the number I had in my head. I recently wrote a fantasy football piece in which I examined how matchups affected fantasy production for the top 25 wide receivers last year. I just tested the correlation between a player’s weekly production and the strength of the opposing defense measured by pass defense DVOA from Football Outsiders. Some interesting results came from that exercise, but one thing that wasn’t a surprise was the lack of correlation between production and matchup for the top receivers. To amass enough points to finish the season among the best, a receiver has to accumulate points each week regardless of the opponent. Likewise, I thought the top starting pitchers would be matchup proof with the correlation growing stronger as we moved away from the elite guys. Read the rest of this entry »

It’s the last week before September call-ups, that frustrating nook of the fantasy season when playing time for DLWW candidates is subject to change. But in cases of our two veterans this week, one has just received what could be an extended opportunity to make an impact in NL-only leagues, while the other’s hot bat could make him an option in the junior circuit.
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Many of you are preparing for the head to head playoffs, which often involves looking at the worst couple of pitchers on your roster and thinking about what you want those spots to look like in two weeks. Even those of you in roto leagues are getting down to it and wondering how best to use the remaining innings you have available. In either case, a look at the schedule can be huge.

My method is to look at the free agents in my shallowest league, sort them by ownership, and move through the pitchers. It’s not the most rigorous method. I fail the people around me often.

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Last Wednesday I took a look at the pitchers who have generated the most weak contact this year. Today I want to take a look at the other side of the coin and examine the guys getting hit the hardest. Let me start by saying I haven’t updated the data since I calculated it last Tuesday. Calculating the relevant data takes a whole lot of time, and I doubt it’s changed all that much after one turn through the rotation.

The reason the data takes so long to calculate is because I’m calculating sOPS+ for batted balls. You could find each pitcher’s sOPS+ on their b-ref page, but that takes walks into consideration, and walks obviously have nothing to do with batted ball quality. I have taken each pitcher’s sOPS+ for each specific batted ball type, factored in how often a pitcher generates each batted ball type and calculated their sOPS+ just on balls that are put into play.

If you’re unfamiliar with sOPS+, it is just the OPS allowed by each pitcher relative to league average. It’s similar to wRC+ or ERA-. In this case, numbers below 100 are better than average while those above 100 are worse.

The idea is that this is supposed to tell us more about the quality of the hits a pitcher is giving up. In this way the stat is much more relevant than something like BABIP because BABIP gives all hits equal weight. I like this stat for the same reason Michael Salfino likes ISO allowed. BABIP includes singles, which are the most common outcome and doesn’t even consider home runs. But sOPS+ considers all hit types and weights them. This means sOPS+ is much more likely to tell us if a pitcher has been ‘lucky’ than BABIP is. If you see a pitcher whose ERA is much lower than ERA estimators, sOPS+ will give you a much better indication of whether the pitcher is due to regress than BABIP will.

That said, here are the qualified starters with the worst sOPS+ so far this year. Read the rest of this entry »

We looked at pitcher mixes over the last month in order to see who’s made the biggest changes to their arsenals. First up were the pitchers that had gone to the slider more often.

Now let’s look at the pitchers that are throwing more changeups over the last month than they did earlier in the season. The changeup has none of the negative health ramifications of the slider, so in this case the question is: sure, you’re throwing it more, but is it a good pitch? And, also, why they might have made the change.

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We know that fastball velocity tends to increase as the season progresses. We all generally panic in April when our favorite sleeper’s velocity is down a mile per hour from last year, but usually by the end of the month, his velocity has returned and you’re able to sleep well at night again. But when we see a downtrend in velocity during the season, it’s a troubling sign. And the velocity charts on these three pitchers are concerning.

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On Monday I touched on Tony Blengino’s presentation on contact management that he gave this past weekend at Saber Seminar in Boston. Tony measured the pitchers who were best able to manage contact by looking at HITf/x data, data to which us normal people have no access. But I theorized that something like sOPS+ (like ERA- but with OPS allowed) could be a replacement for the HITf/x data. I thought that sOPS+ might be able to tell us which guys are better at inducing weak contact and thus which guys can have an ERA that beats their ERA estimators.

When I say “tell us” I should clarify that could mean two different things. It’s the classic explanatory versus predictive problem. Yes, sOPS+ can explain why a pitcher’s ERA is lower than his ERA estimators in a given season, but, as it turns out, it can’t tell us whether he’ll be able to do so the next season. I spent some time last night looking at whether sOPS+ in year one correlated well with the gap between ERA and SIERA in year two. The answer is no. While the correlation was stronger than it was between ISO allowed (another weak contact indicator) and the ERA-SIERA gap, it was still far too small to be meaningful.

It’s obvious that things like sOPS+ and ISO allowed don’t stabilize quickly. And that should not come as a surprise given that we know certain batted ball data takes a long time to stabilize. That doesn’t mean it’s not a skill. It simply means you can’t look at any pitcher’s sOPS+ or ISO allowed in the offseason and rely on it too much in projecting them for the next season. But when I say you can’t look at “any” pitcher, I mean you can’t just look at any old pitcher. I do think there are some you can look at. Read the rest of this entry »