Tag: statistics

The Stats We Use: Baserunning

Believe it or not, baserunning is a relatively simple aspect of the game to quantify. If you think for a moment about the possibilities in baserunning, there aren’t really that many of them. There are several of course, but it’s not as many as you may think. For example, a runner on 1st when a single is hit can move to 2nd or 3rd. On rare occasions he may move all around the bases. A runner could be on 2nd when a single is hit and he can go to 3rd or home. The baserunners could also be thrown out. There could be a baserunner at 1st when a double or triple is hit. There are stolen bases, caught stealings, pickoffs, and a number of others.

That probably sounds like a lot and it’s more than I made it sound like at first. However, we have a run expectancy table to help us quantify the value of these baserunning events. Before we go further, the run expectancy table is the heart and soul of advanced baseball metrics. The run expectancy table tells us exactly how many runs have scored in the various situations. The run expectancy table isn’t anything new. It’s not a newfangled set of statistics or anything like that at all. George Lindsey was writing about the run expectancy tables in the early 1960s. It’s been around for a long time.

Here’s the simplest of run expectancy tables using 1999 to 2002 data. Between those years at the start of an inning, teams averaged .555 runs that inning. If the first batter made an out, it decreased their RE to .297. That out cost the batting team the difference between those two in runs. With a runner on 1st and no outs, teams scored .953 runs. Imagine a single is hit. 1st and 2nd is 1.573 runs. Let’s say the baserunner goes 1st to 3rd on that single. The RE with runners on the corner and no outs is 1.904. The baserunner added the differene (1.904-1.573) in runs by going from 1st to 3rd rather than 1st to 2nd. That’s how many runs in that situation that baserunner provided his team. Imagine that same runner was thrown out at 3rd instead. The RE with 1st and 2nd is 1.573 runs again, but now there’s one out and only a runner on 1st base. The RE dropped to .573 runs. The baserunner cost his team exactly 1 run by trying to add the extra base. As you can see, the out is far more valuable than the extra base. It’s not even close. The extra base added .331 runs while the out was worth -1 run. The out in baseball is especially valuable once a batter reaches base.

It’s why a lot of people aren’t huge fans of the stolen base. Stealing 2nd with noboby out adds .236 runs while getting thrown out at 2nd costs you .68 runs. It’s why people say you have to be successful close to 70% of the time to make it worthwhile.

That table is the average. There are actually different run values depending on the state of the game. A successful stolen base in the 9th inning of a tie game is worth more. The same is true for other baserunning events. The close and later the game is, the more valuable those extra bases are. However, the outs are also more costly.

Anyway, you simply apply the RE table (in-season of course) to the various possibilities. Baseball Prospectus provides Equivalent Baserunning Runs (EqBRR). It includes the number of runs a player was worth on balls hit on the ground or ones hit in the air. It includes the value of runs after an out was made, stolen bases and of course the outs made in the process. There are actually five different categories that are added to get EqBRR: Ground Advancement, Air Advancement, Stolen Bases, Hit Advancement and Out Advancement. The first thing you do is comb through the play by play data and find the number of opportunities in each area. Then you calculate the average for all players and compare each to the average player in terms of runs added or subtracted.

If the average player went first to third on a hit X number of times while another player did it X+3 then he’s above average in that category. The same is true if a player advances on an out as well as the other categories more than the average player does so. Let’s say a player advanced from 1st to 3rd three more times than average. The value of those three additional bases is the difference between men on 1st and 2nd and men on 1st and 3rd times the number of times the player advanced above or below average. Making outs on the bases as well as stolen bases obviously affect baserunning. 

There’s a little more to EQBRR than I’ve made it sound like. If you have a subscription to Baseball Prospectus you can find many of the links discussing each category in this article. 

Few players each season are worth more than 6 runs on the bases. Last year there were only 10 and the year before just 5. Only 7 players have been worth -6 or fewer runs on the bases over the last two seasons. Most players are going to end up in the -2 to +2 range. Baserunning, at the team level especially, can be quite valuable. For a player like Carl Crawford, it’s certainly part of his value going forward (at least until he ages and the speed declines). It’s an important part of the offense that has to be added in when we’re looking for that one tell-all metric (WAR). 

Berselius and I have written about the Cubs awful baserunning in the past. He did so on this blog. The Cubs, as a team, are nearly -50 EqBRR since 2006. That’s an average of -10 runs per season. A win is 10 runs, or 10.5 to be precise. The Cubs have cost themselves 5 wins over the last 5 seasons. Not that big of a deal when you think about it, but the Cubs are also very poor defensively. In two ways teams can improve noticeably, the Cubs are very bad at those areas. The Cubs are left looking for positive contributions from their offense and pitching. It hasn’t gone so well on the offensive side of things since the end of World Ward II. Since the end of the war, the Cubs have had exactly one season in which they have been better than league average on offense. That was 2008 and they were only a few percentage points better than the league average.

Mike Quade made an intelligent decision at the end of spring training when he said the team would not be running as much. Earlier he had said they were going to be aggressive, but I’m guessing 7 stolen bases in all of spring training compared to 8 times being caught helped change mind. The best thing the Cubs can do to improve on the bases is to stop trying to steal them. They’re not good at it. No reason to insist on doing something you aren’t good at. I’m not good at surgery and I’m not insisting on performing one. Know your limitations. I’ve ripped on Quade before, but to me, that’s worth a lot.

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The Stats We Use: FIP

Evaluating pitchers is tough. Most of the traditonal batting stats are based on discrete, context-free events that mostly have to do with the offensive player’s skill – Home Runs, hits, stolen bases, batting eye, etc. There are still stats like RBI and R that depend on the players around the batter, but generally the quick and dirty thing that were tradtionally looked at with a batter were their triple crown stats: AVG, HR, and RBI. Nowadays you’re more likely to see a hitter’s slash line (AVG/OBP/SLG), OPS, or if you’re lucky wOBA, all of which are pretty much context-neutral.

For pitchers it’s much more difficult to tease out context-neutral stats. For a long time the primary stats used to evaluate a pitcher were Wins, ERA, and Strikeouts. I don’t even need to explain how useless Wins are as a stat to evaluate a pitcher’s performance. Much like RBI it’s a narrative stat rather than a particularly quantitative one, and the RBI stat is far less ambiguous. It depends heavily on a pitcher’s team’s offense scoring enough runs to give the team a win, as well as the pitcher’s bullpen not regularly imploding behind him. ERA is a little better — it’s a rate stat that quantifies the number of runs scored off the pitcher per 27 outs. But it also has issues. One problem is the bullpen issue mentioned above – if you leave a guy on when you’re pulled from the game and Jeff Samardzija gives up a HR on the first pitch you’re still dinged for the guy on the basepaths. One even bigger problem is defense. You don’t want to burn a pitcher for pitching in front of a team that’s a bunch of statues, similarly, a pitcher who plays in front of a team of Ozzie Smiths is obviously going to look a lot better. The fact that pitching is so intertwined with defense makes it harder to tease out some sort of context-free metric for how good a pitcher might be.

One big breakthrough in evaluating pitchers came when Voros McCracken introduced Defense Independent Pitching Statistics – namely, developing metrics that completely strip out fielding from the equation. He found that generally pitchers have no control over what happens to balls in play – in almost all cases pitchers defense independent stats such as strikeouts, walks, and home runs tended to be much more stable than their BABIP (batting average on balls in play). There have been more modifications and clarifications to this theory (which we’ll talk about in future stats posts), but overall it provided a new framework for evaluating pitchers.

Anyway, here’s the formula

FIP = (13*HR + 3*(BB+HBP – IBB) – 2*K)/IP  +  C

C is a constant that rescales FIP so it’s on the same scale as ERA, much like we do with wOBA (i.e. the average FIP is the same as the average ERA). For general purposes you can think of it as 3.2, and it is usually computed at a seasonal level.

Where do these numbers come from? It’s the same idea as in wOBA – those weights on the events are derived from the average run value of each event. And in fact you could even say that FIP does include balls in play because of the + C factor at the end. By scaling it to the league ERA, you’re basically saying that FIP evaluates a pitcher for skills that he has control of relative to facing an average offense and with an average defense behind him. Aside from neutralizing the context, one of the advantages of FIP is that it is a better indicator of future performance than ERA. Colin Wyers did a study a few years ago that looked at FIP (as well as a few other DIPS type stats that we may look at here) as a predictor of ERA and found that it does roughly a 20% better job than ERA alone.

FIP does have its flaws, which other stats have sought to overcome. One of the ones that has always jumped out to me anecdotally is things like GB rate, which a pitcher also has some control over. Some other systems such as tRA and Baseball Prospectus’s SIERA factor in batted ball types. Another problem is that HRs can obviously have huge impact on the FIP formula but are relatively rare events, so some bad luck on HRs leaving the park can affect a pitcher. xFIP (for expected FIP) improves FIP by trying to normalize out pitchers’ HR/FB rates, though on average it’s not much better of a predictor than FIP because if you look at the entire population of pitchers, xFIP should be about the same as FIP.

FIP is used in Fangraphs’s calculation of Wins Above Replacement, which we will discuss in the future. One debate in the saberist world is what pitching stat one should actually use to value a pitcher’s performance. Fangraphs uses FIP because as mentioned it neutralizes defense and offense faced. However Rally, the creator of the now-propreitary CHONE projections, used ERA when creating his historical Wins Above Replacement database, and it is also used at Baseball-Reference. The main question here is whether you prefer FIP, which is more of a predictive stat, i.e. what should have happened, vs ERA which is a narrative stat, i.e. what did happen. We refer to these as fWAR and rWAR. 

Credit where credit is due

FIP was originally created by Tom Tango, based on McCracken’s DIPS theory.

This FIP primer at 3-D Baseball by The Book Blog regular Kincaid was a good reference, as well as the one that pointed out that balls in play are taken into account via our rescaling to ERA.

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The Stats We Use: wOBA

Several people on Another Cubs Blog had asked us to write something that explained some of the stats that we often reference in articles and comments. It was always something that I wanted to do, but never got around to it. Since several have asked before and now that we’ve created a new blog with Tim and Adam, it seems more appropriate than ever to finally stop being lazy and get it done.  Maybe we’ll even convince a few more of you of the value of advanced statistics compared to the traditional ones. No big deal if we don’t.

To keep things as simple as possible, Berselius and I are going to break this down into several parts.  We can’t possibly cover all the stats that may show up here at times, but we can cover the majority of them.

The most oft-cited offensive statistic is going to be weighted on-base average (wOBA). It’s a fantastic statistic, but to explain why it’s needed let’s back up and look at OBP and SLG. Each of those stats values a certain aspect of hitting. OBP measures the rate at which a player has reached base safely via hit, walk and hit by pitch. Some people, usually myself if I take time to calculate it, will also include reached on errors while excluding intentional walks. It’s the rate at which batters reach base. It has its flaws. It considers a walk and a home run equally. We know they are not.

Slugging measures the total bases a batter has hit for per at-bat. It’s the measure of how many bases were advanced on the base hits. However, SLG does not even consider how often a batter gets on base. We have two stats that provide two valuable pieces of information, but each piece by itself ignores much about hitting.

To make up for these flaws, people began adding them together to create OPS. The problem with OPS is that it treats OBP and SLG equally, but the most valuable aspect of hitting is not making outs. Football, basketball and hockey are measured in time. After a set amount of time the game is over. Each minute is hugely important. Baseball’s clock is outs. Each team gets 27 of them and each one brings you closer to the end of the game. OBP is more important than SLG yet OPS considers them equal.  This is why we needed a new statistic and thanks to The Book authors tangotiger, MGL and Dolphin, we have that stat.

It’s called wOBA. It weights the value of reaching base and the number of bases advanced to create a rate statistic that is then scaled to OBP because we’re so familiar with what are good and bad OBP’s.  wOBA starts by calculating the run value of each offensive event in baseball. No, not all hits are going to result in runs while sometimes they may result in 2 or 3 runs, but each single helps produce runs while each out does not. The same thing is true for any event.

Don’t be afraid of the formula though. It may seem overwhelming at first: wOBA = (.72*(BB – IBB) + .75*HPB + .90*S + .92*ROE + 1.24*D + 1.56*T + 1.95*HR)/PA. The first thing you may be wondering is why aren’t the value of the walk and hit by pitch the same? It has to do with the control of the pitcher. The single is more valuable than the walk because singles can score runners from 2nd and sometimes there are errors after a single. Everything else is rather straightforward in terms of the values of each event.

The reason I say not to be afraid of that formula, is that it’s actually a more simple formula than something like OPS, which almost all baseball fans are familiar with at this point. OPS is based on two stats and each of those stats has a formula. Below are the formula for each.

OBP=(Hits+BB+HBP)/(AB+BB+HBP+SF)

SLG=(1B+2B*2+3B*3+HR*4)/AB

That makes the formula for OPS (see below):

OPS=((Hits+BB+HBP)/(AB+BB+HBP+SF)) + ((1B+2B*2+3B*3+HR*4)/AB)

Compare that to wOBA

wOBA=(.72*BB+.75*HBP+.9*1B+.92*ROE+1.24*2B+1.56*3B+1.95*HR)/PA

There are also versions of wOBA that incorporate stolen bases and caught stealings. Fangraphs wOBA figures include each. It’s important to note that the values above change slightly from year to year based on the run evironment during the season. A .335 league average OBP (also league average wOBA) could be .328 the following year or .323. Maybe it will be .338. This changes the value of each event.

If the formula is still overwhelming, focus more on the processes of the the three metrics I’ve referenced. When calculating OBP, HR=1, BB=1 and so on. All the stats used in OBP are equal to 1 even though some of those events are less than others. For SLG, single=1, double=2, triple=3, and home run=4. SLG assumes each additional base is twice as valuable as the previous one, which is also not true. A home run is not four times as valuable as a single.

wOBA uses the more accurate weights for each event, relative to the out, and combines both aspects of hitting (reaching base and bases gained) into a rate statistic we’re all familiar with. The stat is then adjusted so that the leaguve average wOBA is equal to whatever the league average OBP is.

Why should you care about wOBA? Because it has a direct relationship with the the number of runs produced. Runs, as you know, lead to wins. So rather than being a number like OBP that only tells us the rate at which a player reached base safely, wOBA tells us exaclty how many runs the player was worth. We can then convert those runs to wins, which is really what we want to know.

To convert wOBA into runs we simply subtract league average wOBA (equal to whatever league OBP is) from the player’s wOBA, divide the total by 1.15, add in league runs per plate appearance and then multiply it all by the number of PA the player had. In simpler terms, the formula is below.

wRC (weighted runs created)=((wOBA-lgwOBA)/1.15+lgR/PA)*PA

We now have the number of runs the player contributed based on his wOBA. We can also compare the player’s wOBA to the average player in terms of runs above average.

wRAA (weighted runs above average)=(wOBA-lgwOBA)/1.15*PA

The 1.15 is the scale used to adjust wOBA so that it’s on the OBP scale and it changes slightly from one year to the next, but is always around 1.15.

Still having trouble swallowing all this? Try to think of the various events in terms of how excited you get when the Cubs are batting. Imagine a close game in the 7th, 8th or 9th inning with 1 out. A single will get you to think that they have something going, but they’re a long way from scoring that run. A double though, now you’re got a guy in scoring position who can score on a single. You’re more excited because that run is more likely to score. Imagine a triple. You’re very excited now because that runner can come home on a base hit, wild pitch, passed ball, infield grounder or sac fly. The chance of scoring that much needed run is pretty good. A home run. You’re as happy with that plate appearance as possible. Now add in the emotions you may feel for a walk, hit by pitch, and so forth. In that particular situation a hit and BB or HBP woud be the same, but over the course of a game that’s obviously not true.

The weights used in wOBA reflect how excited you got after each event.

As mentioned, the great thing about having the value in runs is that we can easily convert it to wins. That’s another post and we have to look at defense and baserunning before that one anyway.

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