Archive for Meta Analysis

When I analyze a starting pitcher’s strikeout rate spike, I want to see that it’s driven by an increase in swings and misses induced. It could come from other avenues — a higher rate of strikes thrown or more called or foul strikes. But in my eyes, the most believable way to sustain that strikeout rate surge is by generating additional whiffs. So below are the seven American League starting pitchers that have increased their SwStk% marks the most compared to last season.

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Three years ago, I introduced the best pitcher expected walk percentage formula yet. The formula uses a pair of strike type rates found at Baseball-Reference.com, including a pitcher’s in-play strike percentages, as well as his overall rate of strikes thrown. The beauty of the equation is that it uses components that stabilize quickly, as the rates are per pitch, rather than per inning or per batter. The equation isn’t nearly as strong as my xK% one, as it’s clearly missing sequencing, which may or may not be a consistent skill. But it’s pretty darn good and the best we have at the moment.

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I have finally finished Jeff Passan’s new book, “The Arm” and have a few pieces of information I found interesting.

• When the ulnar nerve is brought in injury discussion people get excited. Here is why, “… ulnar nerve, a tube of fibers that originates at the spine snakes down the arm and controls fine-motor movement in the hand.” The nerve may need to be moved to get away from bone chips.

Mike Marshall would even take it a step further

“… when a pitcher showed professional potential, he would go in for surgery to transpose the ulnar nerve.”

• The major issue with a second Tommy John surgery is the holes drilled in the arm weakening the bone like it has with Jarrod Parker. Here is a description when the book mentions Todd Coffey’s second surgery:

“Surgery dabbles in fractions of a millimeter. The drill holes from Coffey’s first surgery left his ulna in danger of cracking.”

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Two years ago, I modified the first equation I developed to yield an improved expected strikeout rate formula. The formula uses a trio of strike type rates found at Baseball-Reference.com, including a pitcher’s looking, swinging, and foul strike percentages, as well as his overall rate of strikes thrown. The beauty of the equation is that it uses components that stabilize quickly, as the rates as per pitch, rather than per inning or per batter.

Yesterday, I discussed the starting pitchers whose xK% most exceed their actual strikeout rates. Today, I’ll look at the other side of the list — those starting pitchers whose actual strikeout rates most exceed their xK% marks. These pitchers are at significant risk for regression.

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Two years ago, I modified the first equation I developed to yield an improved expected strikeout rate formula. The formula uses a trio of strike type rates found at Baseball-Reference.com, including a pitcher’s looking, swinging, and foul strike percentages, as well as his overall rate of strikes thrown. The beauty of the equation is that it uses components that stabilize quickly, as the rates as per pitch, rather than per inning or per batter. I calculated the xK% marks for all qualified starting pitchers, compared it to their xK% marks, and sorted. Let’s discuss those with the most significant potential upside, as suggested by xK%.

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Ottoneu Lineup

Here is a simple rule for daily lineups with game played limits:

If a player isn’t playing that day and you don’t need the games, move him to the bench.

I lost a game a few days back when a player who I knew wasn’t playing, hit for one at bat. It was for my utility slot which I can easily fill over the season, so I probably lost a few at-bats.

Boring Team

One fun part of a fantasy baseball auction is the ability to get the players an owner wants. Just like everyone else, I have my favorites. The problem is others like these players and they end up coming at a premium price.

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I find early season performances fascinating (or perhaps it’s our responses to these performances that’s fascinating.) With every owner competitive and paying attention in April – maybe for the only time all year – league wide perception of talent levels can be unrealistically altered. For example, Trevor Story hits 7 home runs in his first 8 games, then precedes to hit .200 over his next 60 plate appearances. That’s about as stark a difference as you can find in a player’s performances this early in the season. Given this, how do your league-mates value him? My wager is that they value him more as the 7 HR guy that the .200 hitter, but that’s just a guess. Would the view of Story be different if those 7 HR in 8 games occurred in July?

This is all hypothetical conjecture on my part, but it does help to show the crazy swings in performance that occur over small samples. If a player (especially a prospect) has a hot start, their trade value can be boosted by that helium all year. However, some hot starts still fall through the cracks, and many players who have performed well can still be bought relatively cheaply. Hopefully this will help you consider buying some of the players who’ve had great starts to 2016. Some of them appear to have made core improvements thus far.

One of the updates I run weekly is to look at the plate discipline and batted ball leaderboards here on Fangraphs. I look for players who are swinging less than last year, while also making more contact. Eno covered some of this in his post last week, but this early in the season it can be difficult to have enough data to make any concrete conclusions about player performances. A disclaimer – at the time I wrote this I didn’t realize how similar it was to Eno’s analysis. His piece goes into more detail and you should definitely read it.

The general premise: I want to find players who are being more patient than last year, but are also making contact at a higher rate. I also track changes in hard hit percentage, but admit that I don’t focus on this nearly as much. I plan to start incorporating exit velocity and launch angle, but have not been able to at this point. This is far from a full proof method or some statistical wizardry, but it’s a quick sniff test. Given that swing% becomes reliable around 50 PAs, and contact rate around 100 PAs, I would put a little more weight on those numbers at this time. I use this to help me bid in the early season Ottoneu FA auctions, or to help target players in trades. The last thing I want to do is waste precious dollars or trade assets on what is likely to be a mirage. (This is probably more important in Ottoneu than other formats where cap penalties can hamstring you if you aren’t careful). Anyways, here’s the list of all players swinging less, making more contact, and hitting the ball with a higher hard hit frequency in 2016. Read the rest of this entry »

Yesterday, I shared a list of the 11 hitters who have enjoyed the most sizable improvement in strikeout rate versus last season. If sustained, there’s a good chance that each of the hitters produce a sweet profit for their owners. On the other side of the coin are the hitters whose strikeout rates have increased the most. This is the danger zone. Let’s dive in.

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Last week, Eno discussed swing rate and ground ball rate changers as we finally started hitting the stabilization points of certain offensive metrics. Now, many hitters have reached the point at which strikeout rate become reliable, which has been found to be at 60 plate appearances. Obviously, all else being equal, a better strikeout rate will lead to better results. More balls in play equals a higher batting average, more home runs, and additional opportunities to drive in and score runs, and steal bases. So who has improved their strikeout rates the most so far compared to 2015?

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Raise your hand if any of these statements are true.

1. You have a hard time remembering all the players on your various fantasy teams.
2. You remember the players, but you have a hard time remembering exactly which team the players are on.
3. You get alerts that one of your players is out of the lineup and you need a quick way to find all the teams that player is on to swap him out.
4. You have more than four fantasy teams (… but maybe not this many).
5. You like to look at more than the base stats provided to you about each player on your league hosting site (R, HR, RBI, W, and SV are nice but you want K%, OPS, FIP, etc.)
6. You would like an easier way to research your own players on various sites like Fangraphs, Brooks Baseball, and Baseball Savant (the official MLB version!).

I have a poor understanding of the “fantasy baseball demographics” of our readership, but hopefully the majority of Fangraphs readers can relate to at least one of the issues above. Many believe that duct tape fixes all problems. I believe they’re wrong and that spreadsheets are the superior panacea.

I’ve been working on a prototype spreadsheet that will help keep track of all the players on your fantasy teams, provide easy access to all of your league sites, free agent listings, and standings pages, help you easily determine what teams a specific player is on, and will also let you easily research players on Fangraphs, see pitch velocities and usage mixes at Brooks Baseball, and see exit velocity data at Baseball Savant. Read the rest of this entry »