Category Archives: pirates

Team Blown Saves

Pirates 2014 — Bullpen

All the graphs are pulled from this Fangraphs leaderboard.

The Pirates bullpen has a been a source of problems and criticisms for the Pirates this year. At the beginning of 2014, the bullpen had almost the same personnel as the 2013 season. Bullpens can vary wildly from year to year, and the Pirates relievers pitched out of their minds for most of 2013, so you’d expect there to be some fall off. Currently [August 26, 2014], the Pirates lead MLB with 22 blown saves. Personally, I abhor saves and blown saves, but I needed to get this out of the way, since it’s the stat that will get thrown around the most. And for reference Tony Watson [the All-Star] leads the team with 6 blown saves. So there’s that.

Team Blown Saves

I wanted to look at some of the peripheral stats of the Pirates bullpen to understand the entire story. First, the Pirates starters have been terrible this year. They rank last in starter WAR, middle of the pack in FIP, and near the bottom in WPA. Analyzing that situation is for another day, but suffice it to say they give up a lot of runs before the bullpen gets into the game. The smaller the average lead the bullpen has to hold on to, the more often they will give up the lead [accrue a blown save]. Shutdowns and meltdowns are Fangraphs stats which are better for evaluating individual relievers than saves. They provide a broader evaluation of how a pitcher or bullpen has performed rather than just looking at save situations. For a shutdown a pitcher basically adds to the win probability while for a meltdown a pitcher subtracts from the win probability. For instance last night Jared Hughes had a meltdown allowing three runs and inverting the win probability.

Team SD

Team MD

The Pirates are in the middle of the pack for both of those stats. There really isn’t anything interesting here.

Finally, the Pirates’ reliever xFIP is not very good. It’s towards the lower end of MLB. xFIP is one of the better park-independent, context-independent predictors of pitching skill. It just uses BB, K, and flyballs [for HR/FB]. This will also ‘adjust’ for some of Grilli and Frieri’s HRs that they gave up when they were struggling earlier this year. Those struggles won’t affect the bullpen moving forward since they are no longer on the team.

Reliever xFIP

After this quick analysis to answer my initial question about the Pirates bullpen, they aren’t good. They aren’t terrible, but they aren’t good. They do have two really good pitchers with Melancon and Watson. Two decent pitchers in Wilson and Hughes. Then the rest aren’t great. Taking this analysis, what could the Pirates do to improve? Frieri was a gamble that didn’t pay off. But honestly, I think from a management stand point, you had to get rid of Grilli to get him out of the closer role. John Axford might help. He’s been good in the 5 appearances for the Pirates so far, and his career xFIP is 3.26 which is pretty good. As far as a trade, ‘proven’ relievers are overvalued in the free agent market, and the trade market was really expensive this year. Overall, one reliever isn’t going to affect your win total dramatically.

Base-Out State -- Game

MLB — Bases Loaded. No Outs. No Runs.

Bases loaded, no outs is one of the most tenuous points of a close baseball game. If you are rooting for the team at the plate, you feel confident your team will score here. Anything else, would be a huge disappointment. If you are rooting for the fielding team and your pitcher gets out of the jam, you are elated and praising the pitching staff for being able to handle pressure. Even though bases loaded, no outs (BLNO) seems like a sure thing, there is about a 15% chance the team DOESN’T score at all.

I’ve created this table of probability of scoring AT LEASE ONE RUN in the various base-out state situations using data from 2011-2013. The base-out states represent the 8 possible combinations of runners on base with the 3 out states that can exist [24 total]. 1- – means there’s only a runner on first, 1-3 means first and third, and 123 is bases loaded. Looking at the chart there is only an 85.18% chance that the team with BLNO scores a run. It’s one of the highest run probability situations, but there’s still a significance chance they won’t score a run.

Bases Loaded No Outs Probability

This table considers every play that started with this base-out configuration and looks at the remainder of the inning to see if the team scored. [It uses every play in baseball from 2011-2013 including playoff games.] In general these numbers fluctuate slightly over time and between teams. This table is also context neutral, specifically batter neutral, so having Mike Trout at bat would significantly change the probability versus a player like Clint Barmes.

Looking at the table, it’s apparent to score AT LEAST one run the lead runner is the most important factor, since all the base-out states have similar probabilities between the states when the lead runner is at third or second. So having a lead-off triple is about as valuable [in the context of scoring ONLY one run] as having the bases loaded, no out.

There are different run and out possibilities that exist with each base-out state. For the lead-off triple, there is no force play on the bases, while a bases-loaded situation has a force play at every bag including home. Having bases loaded would turn a ground ball into a potential run robbing force play, while a single runner on third would require a tag. Conversely, BLNO allows for walks and hit by pitches to drive in a run. This table also looks uses the entire rest of the inning, not just the play that occurs with BLNO. So if the team got the bases loaded with no out, gets two outs, then scores a run, it still counts as a success. A double play, which is easier to get with bases loaded than just a runner on third, will dramatically reduce the run probability of the next play affecting the previous base-out state. In summary, there are trade offs that can occur effecting the overall, context-neutral probability of the base-out state.

Example — Pirates Game

Failing to score a run in the context of this post means after loading the bases, the team does not score any runs before the end of the inning. All the probabilities are determined empirically.

Something kind of cool happened during the Pirates game last night (8/8/2014). There were two instances that bases were loaded with no outs, and the teams weren’t able to score any runs. The not being able to score any runs with the bases loaded/no outs isn’t that uncommon. A run-probability table can tell you that ~14% of the time a team will fail to score any runs for the rest of the inning after achieving that base-out state.

A base-out state is one of the 24 possible combinations of baserunners and number of outs. So there are 8 base states, bases empty, runner on first, etc. to bases loaded, and three different out states, 0, 1, or 2 outs. 8 x 3 = 24.

In the control room at the Pirates game last night, we were debating how often you see two occasions in the same game where no runs are scored after the bases are loaded with no outs. It turns out it relatively rare, but it happened twice at PNC Park before 2014: May 12, 2002 and August 28, 2003.

Between 2003 and 2013, bases were loaded with no out and no runs scored 1,092 times. There were 25 games that this happened multiple times, which is 0.0923% of all games played during that time [27,094 games]. This is on par with the probability of seeing a no-hitter (0.111%) and less probable than seeing a walk-off walk to end the game (0.266%).

The probability of seeing a game with two or more non-scoring bases loaded/no outs situations is 0.0923%

Using the table below bases empty/no outs will occur in every game (this happens at the start of every inning), and all the other base-out states have varying frequencies with runners on third with low out-states being the rarest. Bases loaded/no outs is the rarest base-out state occurring in only 21.92% of all games and occurring twice in the same game only in 6.05% of all games.

Base-Out State -- Game

Just for reference here is a chart of how often the base-out state events occur relative all events. This would represent the probability that any random event (plate appearance, at-bat, stolen base, etc.) would have that base-out state.

Base-Out State -- Events

All data is from

Small Sample Size Comparison

Pirates Do Not Need Help Against LHP

Stats in this post are current up to right before the July 31, 2014 PIT-ARZ game.

The MLB non-waiver trade deadline just passed. I’m not interesting in debating what teams should or should not have done except to say the price for quality players was very high this year. The whole supply & demand, free market thing really worked in the favor of teams that were already out of the post season race. It was suggested that the Pirates needed a right-handed batter (RHB), since they don’t do well against left-handed pitching (LHP). I had my doubts this was really true, and adding a good RHB won’t improve the team beyond what general improvements you could expect from that batter. MLB teams generally do better against LHP, since most batters are RHB and the RHB/LHP split favors the batter.

Before getting into this, LHP make up only 21% of the Pirates’ season-to-date plate appearances, out of all the problems the Pirates could have making a roster move to address this isn’t necessary unless you are looking to platoon. More on that later.

Looking at the team batting splits, the Pirates have an overall .722 OPS and a LHP .670 OPS. On the surface, it appears they are performing worse against LHP, and I will concede the argument the Pirates HAVE performed worse against LHP so far in 2014, but this shouldn’t continue going forward.

The Pirates have 4,152 plate appearances racked up thru July 30th, but only 867 of them have occurred against LHP (~21%). To put this in perspective, that is equivalent to less than one month of games. How accurate are batting statistics at the end of April? They aren’t. Put simply the Pirates ‘struggles’ against LHP can mostly be attributed to a small sample size.

I went and laid out all the outcomes (1B, BB, 2B, etc.) in a vector of plate appearances and had the computer randomly draw 900 samples from the entire Pirates season and computed the OPS 1000 different times. Then I plotted them below.

Pirates LHP Central Limit Theorem

Due to the central limit theorem the mean should hover around .720 (the overall OPS) and the data should be normally distributed. Because of this I constructed the normal distribution curve and then used that to calculate the probability that a 900 plate-appearance sample can be drawn from the Pirates’ total plate appearances. It turns out 9% of the time the program will select plate appearances that total a < .670 OPS. 9% isn't that likely, but it is not outrageous to conclude the Pirates low vsLHP OPS is due to small sample size. This is not just applicable to LHP vs overall splits, but any low-count split including RISP. I wrote about this previously and came to a similar conclusion.

The composite distribution curves below illustrate what happens with sample size increases and why small small sizes are problematic. The vertical line is the .670 OPS mark. On the 900-sample distribution (vs LHP) there is a 9% probability of drawing a .670 OPS from the Pirates’ total plate appearances. This is the area underneath the curve to the left of the red line. Using the 3000-sample distribution curve, it’s 0.0016%. There is barely any area under the 3000-PA curve at that point, and this is a huge difference. (3000 samples are approximately how many the team has against RHP.)

Small Sample Size Comparison

One more graph! This is a histogram of the differences between the LHP OPS and the overall OPS. The Pirates are on the low end of it. Not great, but there’s a lot of variation there.

Team OPS Difference

Switching from statistics to baseball, the Pirates have the second fewest plate appearances against LHP in MLB. They are 11-9 in games started by a LHP. That alone should discount the poor-performance-against-LHP argument, but obviously the team batting stats suggests that they are and it has been woven into a narrative.

Looking closely at the Pirates’ roster there are many solid RHBs, McCutchen [their best hitter], Martin, Marte, Sanchez, and Mercer/Harrison are pretty good against lefties. Now, some of these player are underperforming against LHP this year, but this is where the small sample size comes in again. You wouldn’t determine any of these batters lost their platoon advantage after only 80 plate appearances. Going forward almost all of these bats should regress to their normal platoon splits.

Pedro Alvarez, Gregory Polanco, Ike Davis. Their platoon splits are pretty atrocious both for 2014 and career-wise. For example, Alvarez has a .787 OPS vs RHP and a .517 OPS against LHP this year. I don’t want to get into analyzing what’s wrong with the Pirates’ left-handed bats, except to say they are terrible against LHP. The argument should change from the Pirates don’t do well against LHP to the Pirates’ left-handed batters are terrible against LHP.

What can be done about this? The simple answer is to get better left-handed batters. Since that’s not really possible, the next best option would be platooning the left-handed batters. Ike Davis is already platooned with Gaby Sanchez, and Pedro Alvarez is barely starting any games. Polanco has regressed from his debut, but I think the best idea is for him to play everyday and deal with LOOGY relievers. I also don’t know how many fans actually want to see or are suggesting that he’s should be platooned. With all this in mind I’m not quite sure what acquiring a right-handed bat would accomplish. The Pirates are already trying to find a place for RHB Josh Harrison to play. He’s been having a good season, no matter what you think about Harrison. Furthermore, the Pirates have a guy who’s been killing LHP this year and has decent splits against them for his career. And that’s Jose Tabata.

Bottom line, adding a RHB wouldn’t help much because the team splits are still a small sample size against LHP. Beyond the statistics, the two big left-handed bats have terrible splits against LHP, and these problems have been already addressed by platooning and benching.

Pirates Run Probability

Probability and Sunday Night Baseball

There’s nothing I like more than a bases-loaded, no-outs situation in baseball. This might be my favorite situation/stat no one realizes. There’s around a 15% chance that the team who has the bases loaded will not score at all that inning! 15% might not seem like much, but over the course of the season it happens often.

Let’s set the scene: Bottom of the ninth, down by two, the Pirates knock in a run and get McCutchen on 1st with no outs to move within one run of the Cardinals.

This is a win probability graph FanGraphs has for every game. I’m not entirely sure what all they consider when calculating a win probability, but it mirrors the data I have, so there’s not much to discuss there. Clearly, the closest they came to winning the game was after Barmes walked putting Alvarez, the winning run on 2nd.

FanGraphs Win Expectancy Pirates 5/11/2014
source: FanGraphs

According my run probability calculations for 2013, the probability to score at least one run with bases loaded and no outs was lower than the Pirates batting with a runner on second/third or first/third and no outs [Probabilities –123: 77.9%, 1_3: 82.4%, _23: 90.9%] The advantage of having the bases loaded is a walk or HBP brings a runner home, but the downside is there is an easy force at home. That would hurt the Pirates in this instance because Mercer didn’t hit the ball past the pitcher’s mound making for an easy 1-2-3 double play.

Screen Shot 2014-04-26 at 12.02.38 PM

Pirates 2014 — Take Your Finger Off The Panic Button

Pirates Panic Button


The Pirates did really, really well last year. They won 94 games, the NLWCG, and took the Cardinals to 5 games in the NLDS. Expectations for right or wrong reasons have been raised for the following year. With April coming to a close the Pirates are looking at a 9-15 record. I’ve seen a lot of criticism about the offense, Jason Grilli blowing saves, and Gregory Polanco not being called up. I’m not an expert on talent development, so I can’t fully address the pros and cons about calling up Polanco. I can say that just like all the criticisms at trade deadlines, one player can’t save a team. Let’s say Polanco helped them win 2 games in April beyond what Tabata/Snider could do. The Pirates would be sitting at a 11-13 record. Now what? You are still sitting in 3rd place in the NL Central. Now, here’s the other side, what if he doesn’t help much? You mess with player development and long term plans because you panicked over a month of baseball. I’m fine with Polanco getting 2+ months in AAA before being called up. McCutchen got a year and two months.

Now about those Pirates. How bad are they? They have pretty much the same team coming back this year from the team that won 94 games last year. They lost Burnett, who was stellar last season, and they had Marlon Byrd down the stretch who made a big impact in September. Other than those losses, the Pirates have the exact same team.

The basis of my analysis is boiled down to this: the Pirates weren’t really as good as you thought they were last year, and they aren’t nearly as bad you thought they are now. Why do I think this? Numbers! I used the current 2014 numbers compared to 2013 overall, and the first two months of the 2013 season. June, July, August, and September numbers are really good for the Pirates. I’m going to point out that bad months can happen.

Pirates Year to Year 2014

Compared to last year, this offense right now is not as good no matter what sample of 2013 you look at. But here’s the take away…the Pirates had a very mediocre offense all year in 2013. They have a below average offense right now. Based on past performances, the Pirates will regress upward toward where they were in 2013. So the bigger problem for the organization as a whole is that they have a mediocre offense. This is a long term problem, not a short term aberration happening right now. Long term problems require better solutions than knee jerk reactions.

My assessment of the Pirates is that their poor April performance, is part luck, part poor hitting, and mostly regressed pitching. The pitchers pitched out of their mind last year. Jeff Locke went to the All-Star game. Things were crazy. Grilli isn’t going to Mariano Rivera every year. (Especially because he didn’t touch Rivera-type numbers till his 12th year in MLB.) Even AJ Burnett isn’t pitching as well for the Phillies as he did for the Pirates in 2012/2013. The Pirates’ FIP has been below average instead of a stellar like it was for most of 2013.

Everyone keep their fingers off the panic buttons, things are going to be alright.





Data is pulled from

Morton PitchFX April 2014

Charlie Morton — PitchFX

I’m in a predictive modeling class for my grad program at NU, and we are learning a statistical programming language called SAS. One of the things we are trying early on is cluster analysis to determine if variables are related. I decided to play around with data that’s a little more interesting than housing prices. Charlie Morton has been on of my favorite pitchers to watch pitch. His curveball is just sexy. Cluster analysis can help us separate Morton’s pitches into different pitch types using PitchFX data I’ve been scraping.

I’ve plotted two charts, one is the vertical movement vs. the release speed. The second is the vertical movement vs the horizontal movement. [The movement parameters are calculated from the deviation of the ball from a straight path with no spin. And the horizontal movement is from the perspective of the catcher/batter. So imagine that Morton is throwing toward you.] So fastballs with backspin will have a positive vertical movement. Curveballs with top spin will have negative vertical movement. I used SAS to look at the speed, vertical, and horizontal movement and cluster similar pitches together. Without much tweaking, I was able to identify Morton’s fastballs and curveballs. He also has a third group which is a splitter according to

Morton PitchFX April 2014

Morton PitchFX April 2014

Morton is famous for his sinker, which is a two-seam fastball that ‘sinks’ relative to a four-seam fastball thrown at the same angle. I’ve annotated the sinker on the vertical movement to release speed chart below. Morton’s sinker is hard to differentiate because it’s almost as fast as his four-seamer. (low-90s) It doesn’t stay as high due to the different spin compared to the four-seam fastball. The advantage here is that a batter will swing as to hit the four-seam fastball, but the sinker will be an inch or two lower than what the batter adjusted for. Since the bat is round, the ball will come off the bat at a low angle, and bam! Ground ball.

Morton PitchFX April 2014 Annotated has updated and historical PitchFX data presented very nicely. I suggest checking them out if you want to see visualizations like this for other games or pitchers. Their visualization tools are easy to use and updated right after games end.

Pirates Run Probability

Pirates — Run Probability

Presented without much commentary or analysis. This is how the Pirates fared last year given a certain number of outs and with runners on specific bases. So for example with no ones and nobody on base the Pirates had a 26% chance of a scoring a run from that point in the inning on till the end. So that would score a run once in about every 4 innings. The stat I always reference is bases loaded and no outs. It should be the highest, for the Pirates, it’s not. Runners on 2nd and 3rd with no outs is the highest.

For a point of comparison the black reference lines on the bar graph are the MLB average for that specific base-out state.

Pirates Run Probability