In a previous post, we looked at Ottoneu head-to-head (H2H) leagues and how starting pitching values shift due to different rules. Because season-long points leagues use an innings-pitched cap, innings pitched are the scarce resource you expend (and need to maximize) during the season, putting a premium on SP who post high points per IP. But because H2H leagues use a games-started-per-week cap instead of IP, IP are no longer a scarce resource and the premium is on pitchers who score a high number of points per start, regardless of their points per IP. Today, we’ll see if we can identify pitchers to target or avoid based on that.
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The standard pitching metric of production in Ottoneu points leagues is points per inning pitched. With a 1500 IP cap, innings are currency and you need to maximize the return for every inning pitched. But in head-to-head leagues, innings are no longer the scarce resource managers have to manage. Instead, each team can throw an unlimited number of innings, but there’s a limit on games started per week. For Ottoneu head-to-head, or any other league with a games-started cap for pitchers, the pitching metric that matters for SP is not points per inning pitched, but points per game started. And that can swing values. The question is, how much does it matter?
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In ottoneu (or really any keeper/dynasty format) you generally want to keep players with surplus – guys whose production will be worth more than the cost to keep them – and cut players without. Of course, it isn’t always that simple. With less than a week before the Ottoneu keeper deadline, we’ll look at three players who I am cutting or trading, despite their projected surplus.

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Last time, I suggested three players who I would sell or cut in Ottoneu, despite seemingly having surplus. Today, we’ll look at three players who I would buy or keep, despite projections telling me they are over-priced.

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“Part I with a question mark?” you ask.

“Yes,” I answer. Today, I am going through every trade I made in one ottoneu league. I have no idea how this will play out, if it will lead to interesting conversation, or if you will all hate it. If you enjoy it, I will do the same for other leagues and there will be subsequent parts. If not, I won’t, and there won’t.
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Back in March, I compiled the bold predictions from all the RotoGraphs writers to figure out which were our most common. Now we can look back and see if we did any better as a group than we did as individuals.

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One would expect that a fantasy writer’s success on something like bold predictions would correlate nicely with their success overall that year. After all, if you pay the low, low price to try out your bold predictions and you hit big, your teams should be excellent, right?

Well, maybe not. I had probably my least successful fantasy season in the last few years in 2016, yet easily my personal best performance on these here bold predictions. And looking back, I still think these were bold, but perhaps you disagree?
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I promised myself I would make a mix of positive and negative predictions this year, and I aimed to make them really bold, refusing to make any prediction that was less bold than one made by another writer about the same player.

Let’s see how that is working out as of the All-Star break.
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Almost three years ago, Ryan Braun was suspended and he got cut in a number of ottoneu leagues. I wrote about the chain reaction that occurs when a guy like Braun becomes a free agent.

Just this week, an arguably more interesting scenario played out in the FanGraphs Staff League Two, when a $62 Giancarlo Stanton was kicked to the curb, and a there are a number of lessons to learn from it.
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Over the last couple weeks, we’ve been releasing our bold predictions, and I noticed some patterns emerging. Before I wrote mine, I made a number of edits to my list because I didn’t want to cover the same player as my fellow Rotographs writers, unless I was going to be more bold than they were.

To facilitate that, I was tracking the other writers’ predictions. Because of that, I now have a list of the players where we overlapped – the 30 players who were the focal point of more than one bold prediction.

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