Ottoneu, like most fantasy leagues, uses a soft innings cap. This is pretty standard. You have an innings limit of 1,500 IP, and the day that you go over that innings limit, you are able to exceed it by as many innings as possible. How can we use this rule in our pursuit of a league championship?

Looking at Ottoneu specifically, though easily adjustable to any league format, you would want to get as close as possible to your innings limit, then on the last day, fill out your pitching lineup with players who are pitching that day. For example, start the day you plan to go over with 1,499 IP and throw 5 starters that day, with 5 RP in your lineup as well. Practically, this could get you another ~25-40 innings. Even if these innings fell below replacement level, they are still valuable as they would not be points you would otherwise accrue. Even if your pitchers were terrible across the board you would be looking at a lower bound of around 50 points, with an upper limit in the 250 point range. That’s a big gain on competitors who may not deploy the same tactic.

This is the main reason I recommend pacing your innings at 1,550 to 1,600 all year. It allows the flexibility to wait a week or two to pick your last day to blow up your pitching totals. Read the rest of this entry »

It seems that since Yoan Moncada was signed by the Boston Redsox in March of 2015 for $31.5 million, he has been a staple atop MLB prospect lists. Top-5 by Baseball America in 2016 and 2017. Number 1 by Fangraphs’ Eric Longenhagen this season with KATOH+ also ranking him in the top-10. This list goes on. He is an elite prospect. If you play Ottoneu, you already know that.

However, given the pedigree (and the likely fact that he has been owned since his signing in nearly all leagues) it is likely that Moncada’s performance is viewed as a disappointment to Ottoneu owners. I mean this purely on a performance basis. With an average price across in all Fangraph Points leagues of nearly $12, owners are likely expecting production from a premier prospect. With the relative ease at which young players have transitioned to the major leagues over the past few seasons, who can blame them?

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It appears to me, that since Mike Trout became, well, Mike Trout, the standard that young players are held to has risen dramatically. Maybe I’m showing my youthfulness, or maybe it has always been this way.

In 2017, we can look at the performances of young hitters like Aaron Judge, Cody Bellinger, or Rhy Hoskins to see current versions of this phenomena, or we can look at Byron Buxton over the past few seasons to see how perceptions can change (either warranted or not) when a top prospect comes up and isn’t an immediately elite talent (though Buxton has been doing quite well recently).

Today, I want to take some time to look at another prospect who have recently debuted, and see what his initial 100 or so plate appearances can tell us.

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If you think about rookie outfielders who have taken the league by storm in 2017, chances are Aaron Judge and Cody Bellinger immediately jump to mind. Both have been excellent sources of offensive production, posting wOBAs of .419 and .392, respectively. However, if you’re reading this, chances are that the excellence of Judge and Bellinger doesn’t come as a surprise. They have been excellent. Each of us knows they have been excellent.

However, there is a more recent promotion who belongs in a similar tier to Judge and Bellinger, when examining sources of surplus value in Ottoneu, and that player is Rhys Hoskins.

Dave and Carson have both covered Hoskins in the past week, with Dave likening the offensive skillset to that of Daniel Murphy, Matt Carpenter, Justin Turner, Ian Kinsler, or Anthony Rendon. Carson, examining the players in the minor leagues currently showing similar skill sets. Today, I want try to look at the production Hoskins has shown and see if we can potentially get a better idea of his future Ottoneu FG points value. (For those in non-ottoneu leagues, this should still translate relatively well).

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If you’ve followed baseball over the course of of the past few seasons, you’ve probably noticed the new data available to us with the advent of Statcast. This has led to the development of new metrics to measure player performance, with xwOBA being one of the most notable. If you’re familiar with xwOBA, you have likely seen it used to examine the quality of contact made or induced by hitters or pitchers.

Today, I want to look at the pitcher side of things. While it is generally accepted that some pitchers are better at inducing weak contact than others, to this point, the baseball community is still working through the best ways to process the implications of the relatively new data available to us.  As Craig Edwards wrote yesterday on the main site, there isn’t a strong relationship between weak contact year to year.

Acknowledging all of this, I want to look at pitchers who have recently improved the quality of contact they have allowed. There are a couple assumptions to acknowledge here (included at the bottom of the following table). First, I am only looking at pitchers with over 1000 pitches in 2017 before the All-Star Game. Additionally, I am only including pitchers who have thrown 500 pitches since the All-Star Game.

My intent with this is to try to get a better look at starting pitchers, who have made more than a couple of starts, and remove relief pitchers. I have also limited the group to players who’s post All-Star Game expected wOBA is less than the sample average at the time of the break (this works out to be around .315, for reference). The last stipulation I have included is that I am only showing pitchers who have seen an improvement of .010 or greater in their expected results (10 points or greater). The reason for this is simple, I would rather show 25 results than 45.

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Are hitters consciously deciding to hit more balls in the air, now that more data is available? I don’t know. There is certainly evidence that more players have hit the ball in the air over the past 2 years. Maybe it’s the ball. Maybe it’s the availability of StatCast, though teams have had more batted ball data than what is publicly available for some time. Would more widely available data be all that was needed in order for a player to see the merits of hitting the ball in the air?

I’m skeptical. Perhaps, hitters have always known that hitting the ball in the air is valuable, but now we have a standardized way of articulating these changes? These are just a couple of questions that sift through my head as we sort our way through changes in league wide batted ball tendencies over the past 2 years.

We know that expected production based on qualify of contact changes as we change launch angles and increase exit velocities, with the least valuable batted balls being those hit under 0 degrees and over 39 degrees. Balls in play fall into these buckets nearly 50% of the time.

Given this, I was curious if players increasing their launch angle are, on average, increasing their expected production. Much could be said about Yonder Alonso deciding to hit more fly balls. It’s worked out well for him, but there’s some survivor bias involved here. We remember Alonso because he has succeeded. Has everyone who has added more lift to their swing seen similar improvements? Read the rest of this entry »

A few weeks ago, I took an initial (and someone rudimentary) dive into tying Statcast data to Ottoneu. Specifically, looking at the Fangraphs Points (FGpts) format. Like in real baseball, when the ball leaves the bat with a higher exit velocity, it is more likely to produce more favorable results. This was to be expected. The way the scoring settings for FGpts was developed was through linear weights, so it would make sense that the distribution of points per batted ball ties closely to reality. Today, I am going to look at the batted balls hit though the all star break (actually, just before the break as I pulled this July 6th) and examine the expected points per ball in play on specific batted ball types. Let’s get started.

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With the All-Star break upon us, I wanted to take some time to notice several players who are putting up seasons worth your attention across Ottoneu. I guess we should probably consider what we mean by “worth attention.” Certainly many players are having great seasons. Chris Sale is pitching at career bests, Aaron Judge and Cody Bellinger have combined for 53 HRs, Justin Turner is walking more than he strikes out, and Jose Ramirez has picked up 2B eligibility when producing a career high ISO. None of these players will be included… Wait, what? I thought you said this was an All-Star team?

A couple key points that drive the Ottoneu format. First, it is an economic game with a defined budget. Second, players get kept year to year. For this reason, and because others can/have spilled virtual ink over the players listed above, I will exclude them. A couple additional stipulations in filling out this roster Read the rest of this entry »

Monday, Tom Tango took to twitter, releasing two graphs.

Explanation here: https://t.co/fSoKBGymlH pic.twitter.com/lXpHEkTy7s

— Tangotiger (@tangotiger) June 26, 2017

This might be interesting, especially at the high-end speeds. @pobguy pic.twitter.com/LJcmO7l7RQ

— Tangotiger (@tangotiger) June 27, 2017

Like many of us who benefit greatly from Tom’s work, these got me thinking (specifically the second graph). What we have here is wOBA on each event type plotted against each event’s exit velocity. Like we would expect, the harder you hit the ball, generally, the better outcomes you have. So my thought is, how do we apply this to Ottoneu? While hardly revolutionary, the idea of converting all metrics into Fangraphs Points is an adjustment all players must make.

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Last month, I went through pitchers who had improved their projections the most since the start of the season. It was May 5th, so we were looking at close to a months worth of games. To revisit the methodology, I wanted to look at pitchers who had improved their projections the most from the start of the season, while also examining their underlying skills.  What I specifically was considering was pitchers who were inducing less contact in the zone – the area where batters are more likely to make contact – while also seeing if they were throwing in the zone more or less than they had previously. I was using zone contact rate in conjunction with frequency of pitches in the zone as a proxy for improved stuff. Of these two variables, improvement zone-contact rate being the more important.

Over the first month, this list would have been small: Chris Sale, Danny Salazar, Jeff Samardzija, Gerrit Cole, Lance McCullers, Michael Pineda, and Ivan Nova were the only starters who improved in both capacities. If we expanded the bounds on zone frequency, we would add Jacob deGrom, James Paxton, Zack Greinke, Taijuan Walker, and Carlos Martinez to the list. Not a bad list of pitchers if you’re trying to look for SP who are on everyone’s radar, but also could be showing real improvements in 2017.

Today, I want to revisit this exercise. However, I will only be looking at improvements over the last month. Over the last month, here are all SP who have improved their Ottoneu FGpts projections by more than .05 points per inning. (If you don’t play Ottoneu, this should be generalize enough to help you in standard leagues). Read the rest of this entry »