All the way back in May, I came up with a pretty simple way to calculate “expected strikes” based on data available at FanGraphs. I don’t know if I was the first person to do this, and it’s so simple I’d be surprised if I were, but I remember me so I’m linking to me. Once you have expected strikes, you can compare that total to actual strikes, and maybe then you can learn something about the pitcher(s) or the catcher(s) or about something else. I”ll explain further!

FanGraphs provides for you total pitches, total strikes, and plate-discipline data based on PITCHf/x data. By using zone rate, you can come up with pitches in the zone, which leads to knowing pitches out of the zone, which leads to knowing swings at pitches out of the zone. Based on those numbers, you can end up with an expected strikes total. You’re way ahead of me — I probably don’t need to explain this in great detail.

Read the rest of this entry »

In September, teams are allowed to expand their rosters and the Rockies did that in 2011 by calling up Wilin Rosario. Rosario showed a bit of pop, but had some problems making contact. Going into 2012, questions about his ability to not strike out existed. By using a small sample size of a hitter’s swing and contact values, a better estimate of his walk and strikeout rates can be estimated.

The Rockies began the 2012 season with Ramon Hernandez as their #1 catcher and Wilin Rosario was slated as the backup even though Rosario was a highly touted ranked prospect (#49 in 2011, #87 in 2012). The main reason the Rockies didn’t have any faith in Rosario was his plate discipline. In the minors, his BB% ranged between 4.5% and 8.7% and his K% between 19.2%-29.9%. In 57 MLB plate appearances, his BB% was 3.5% and his K% was 35.1%. These values forced people to have reservations about him being able to stick in the majors.

In the 2011 FG+ fantasy preview, Paul Swydan wrote the following on Rosario:

Swinging at every pitch thrown to you is only a good strategy for a hitter if you have enough bat control to hit or foul off nearly every pitch thrown to you (see Guerrero, Vladimir). Wilin Rosario is not this type of hitter, and his acceptable plate discipline in the low minors has steadily worsened as he has moved up the Rockies’ organizational ladder.  ….. Rosario still needs to fine tune his game — particularly his plate discipline — and is unlikely to contribute to your team no matter where he starts the season.

Instead of using BB% and K%, a player’s estimated K% and BB% can be determined by using swing and miss values. To get an idea of this value, I created a formula using (See Appendix) O-Swing%, K-Swing%, O-Contact% and K-Contact% plate discipline values.

By plugging Rosario’s 2011 plate discipline numbers into the spreadsheet, his 2011 plate discipline numbers would be 22% K% and 6% BB%. While the BB% is fairly close to his actual value (4%), the K% is off by 13 percentage points.

With questions surrounding his plate discipline in 2012, he saw is K% end up at 23%. This was within 1% point of what his 2011 estimated K%. With reasonable plate discipline, he was able to put up a decent season (1.8 WAR in 426 PA). Using a second method to calculate a Rosario’s K% and BB% helps to get a better picture of his true talent level.

Rookies, like Rosario, are called up and get a small number of plate appearances. By using a player’s plate discipline numbers, the player’s walk and strikeout rates can be estimated. The estimate can help determine if the player’s talent level is significantly different than their stats suggest.
.
.
.
Appendix

I wanted a formula to help estimate a player’s K% and BB% using the plate discipline values available at FanGraphs. The formula create wouldn’t be a prediction (as it contains no regression) or stat that stabilizes fast.

I took every player that had over 200 PAs in a season from 2002, when plate discipline numbers are first available at FanGraphs, to 2012. I ran a linear regression against over 3500 seasons and came up with the following two formulas:

BB% ((NIBB-IBB)/PA)
BB% = -0.228 x O-Swing% -0.139 x Z-Swing% – 0.030 x O-Contact% -0.257 x Z-Contact% + 0.437
R-Squared = 0.45

K% (K/PA)
K % = 0.248 x O-Swing% -0.345 x Z-Swing% – 0.153 x O-Contact% -0.837 x Z-Contact% + 1.169
R-Squared = 0.79

I have gone ahead and saved people some time and uploaded a spreadsheet to the Google Docs that will automatically do the calculations.

To use the sheet.

1. Download the spreadsheet by using the “Download As” feature under File.
Go to the players page at FanGraphs, minimize minor league data, go to the Standard stat area and copy the all the data going back to 2002.

2. Go to the downloaded spreadsheet and paste the data with the upper left corner being the left yellow box.

3. Go back to the player’s FanGraphs page and copy the (non-Pitch F/X) Plate Discipline values.

4. Go back to the downloaded spreadsheet and paste the data with the upper left corner being the right yellow box.

5. Once the data has been added to the spreadsheet, the player’s real and estimated K% and BB% will be calculated.

Here are the four rookie position players above 3.0 WAR in the 2012 season:
Read the rest of this entry »

Jeff Zimmerman and I have done lots of work on player aging curves in the past 12 to 18 months. Jeff started things off with a series of hitter aging curves, which focused mostly on standard outcomes and WAR components. Jeff and I then joined forces this year for a series focused on pitcher aging.

This time around, I wanted to know how a hitter’s plate discipline changes over his career. We already know plate discipline statistics are easily the most stable, year over year. That said, I wondered whether I’d see meaningful patterns as players age. Often times, scouts and commentators mention how a hitter’s approach changes over time: less disciplined, less contact as a young player; better bat control and better strike-zone awareness as a hitter matures. But does the data confirm this thinking?

Read the rest of this entry »

Before all our attention is focused on the post-season, I thought I’s take a quick look back at free agent signings from the past year and how those deals worked out in 2012. The focus here is just on what teams got for their money. In other words: Did the players meet or exceed the expected value of the contracts they signed?

I focused on major league signings only, so the analysis does not include myriad minor league deals — many of which resulted in players accumulating playing time in the majors this year.

To get a sense of the how the deals turned out, I compared players’ expected values — which are based on their positions and the annual average value (AAV) of their contracts — to their actual values. I uses Matt Swartz’s research on the differences in dollars per Wins Above Replacement (WAR) by position, rather than assume an average dollar-per-WAR, as is typically done.

Read the rest of this entry »

I was previewing the Angels and Mariners series that began on Tuesday, kicking off the final nine games of the season for both teams, when I noticed how well the Angels ranked in my metrics. That the Angels are good is no surprise, but the magnitude to which they had improved since they last played the Mariners at the beginning of September caught my notice.

Since that last preview, the Angels went 15-7 with a massive 104-58 run differential. But where did has the dominance burst forth? Over those 22 games, the offense posted a .734 OPS which is only three points above the American League average. On the pitching side, the staff has a 21.7% strikeout rate, a 6.4% walk rate and 3.4% home run rate compared to league averages of 19.4%, 8.2% and 2.8%. That’s an above average line, but not an outright dominant one.

Lacking a breakout in either the bats or arms, it really highlights how well the defense has played. Read the rest of this entry »

There are rate stats for just about every kind of opportunity a hitter faces in a game. Batting average tells you how often a player reaches base via a hit. On-base percentage tells you how often a player avoids making an out per plate appearance. But what about swings as opportunities?

Last year, I played around with the idea of production per swing. The idea was to examine what hitters gave the most value when they took a swing. The methodology was pretty simple: calculate the Weighted On-base Average (wOBA) each hitter generated using their swings — instead of plate appearances — as the denominator*.

Of course, there is a healthy correlation between actual wOBA and wOBA per swing (.83 in 2012), but less so Isolated Power (ISO). (wOBA/swing and ISO share only a .53 correlation.) Some of the results may not be all that surprising, but many certainly are.

Let’s first look at the top-25 so far this year:

Read the rest of this entry »

Last Thursday, I took a stab at predicting how batter’s swing influences their ground ball / fly ball splits. One of the most important retorts to the research (a retort made both in the comments and on The Book blog) was that pitch location was the determining factor of bat angles — what I was attributing to hitter tendencies (at least for hitters who have big GB/FB platoon splits).

Consider today’s offering a second puzzle piece — hopefully an edge piece — in what is a 1000-piece puzzle of understanding GB/FB splits. Today I offer the case study of two (essentially randomly picked) hitters with large GB/FB platoon — Adam Dunn and Jay Bruce.

The results surprised me, twice, and in the end, it appears these two hitters employ different swing patterns, suggesting there may be traction with my original theory, even though pitch location does have a considerable affect on swing angles.
Read the rest of this entry »

What’s a pitcher’s ultimate goal? In the grand scheme, it’s to help win games. A pitcher needs to do his part to keep runs to a minimum — and strikeouts are the best way to accomplish that. Walking, or hitting a better, can’t help. Those outcomes (plus avoiding home runs) are the three rates, each with somewhat separate skills that most of us watch when evaluating pitchers.

And getting ahead in the count is at least partially responsible for all three outcomes. In my first look at pitching ahead to batters I defined a pitcher’s being ahead in the count as having it 0-1, 0-2 or 1-2. Conversely, batters were ahead in 1-0, 2-0, 3-0, 2-1 or 3-1 counts. Those demarcations were made by simply taking the greater number, aside from full counts.

The aggregate numbers support the difference between the two types. In my self-identified pitcher’s counts, batters are held to a .204/.211/.303 line this season. Shifting to a hitter’s count, the batting line more than doubles to .342/.472/.609. Clearly a pitcher benefits when he’s ahead, but I wanted to know about home runs, as well, and whether this was a good division of counts.

Read the rest of this entry »

Dustin Ackley has a strikeout problem. It’s not a problem new to him at this level. Last season in the American League, average hitters struck out on 18% of his trips to the plate. Dustin Ackley did so in 21%. However, it was new to him overall. In the minors, Ackley was terrific at avoiding strikeouts. With Tacoma in 2011, Ackley struck out on 12% of his PAs whilst the average PCL hitter would strike out 18% of the time.

The low strikeouts in the minors made sense. Ackley was billed as a polished hitter with good contact skills and a good eye for the strike zone. And indeed, Ackley has had fewer swinging strikeouts than average at every level, even including his two years now in the Majors.

Read the rest of this entry »