## Taking a Closer Look at Hitting with Runners in Scoring Position

In baseball, part of what is commonly debated is how important it is to hit with runners in scoring position. Viewers of their teams will often have their sad sigh when their team leaves runners stranded in scoring position and will look up how their team does in those situations and say, “this is why we don’t score runs” or “this is why we don’t win games.” They will also look at other teams and see how good of an offense the other team might have and immediately make the assumption that they are going to be better at hitting with runners in scoring position than most other teams if their offense is better. But just how much of a team’s success is based on hitting with runners in scoring position and how much of hitting with runners in scoring position is based on team success?

**I. Impact of Hitting with Runners in Scoring Position**

One of the old clichés in baseball is, “you can’t win without hitting with runners in scoring position.” Many people link that to why the Cardinals had done so well in the past and why they haven’t really been able to get going this year. In years past, they have consistently been not only one of the best teams in baseball, but also the best at hitting with runners in scoring position.

Many people in the game consider it also to be one of the most important stats when it comes to judging a player’s hitting ability. In a press conference at the beginning of the season, Matt Williams had sabermetricians finally thinking that someone with their ideology was becoming the manager of the Washington Nationals when he said, “If you don’t get with the times, bro, you better step aside.” When I heard that, I immediately thought that he would be talking about more advanced hitting metrics than batting average and home runs and RBI’s. He followed that comment up with, “My favorite stat right now and always has been the stat of hitting with runners in scoring position. Because batting average and on-base percentage and all of those things are great, but who is doing damage and how can they hit with guys in scoring position.” When I heard that, I immediately slunked back in my chair and placed him in the category of old-school.

And listening to one of the Reds games (as I always do), listening to Marty Brennaman (who I think is a good broadcaster for his catchy phrases and also because he’s from where I’m from), I heard him talk about Votto and he said, “Votto will take a 3-0 pitch an inch off the outside corner, when he could do with it what he did Wednesday. I believe in expanding your strike zone when you’ve got guys on base.” For those who don’t know, what he did on Wednesday (a while ago), was drive a 3-0 pitch from Matt Harvey (that shows how long ago it was) for a home run to left field in New York. Unfortunately, for a while now Marty Brennaman has been seemingly leading a war of the old-school against his own team’s star first baseman Joey Votto over hitting. Namely hitting with runners in scoring position or men on base. Again, while listening, I slide back in my chair, disappointed in Marty for being so illusioned and confused and broadcasting his wrong opinion to many of the people who listen to him on the radio.

Williams and Brennaman aren’t the only people that have this mindset though. The thing that they and many other people think is that if you can’t hit with runners in scoring position, you can’t win games and you can’t score runs. For these people, it is for the most part a blind hypothesis, just assuming it is true because it seems that it should be true.

For examining this data, I am going to look at the coefficient of determination, or R2 (I have below this the formula for R, correlation coefficient, that when squared equals the coefficient of determination). For those who don’t know, when looking at the data and calculating a formula of best fit, R2 shows a percentage value of how many of the samples of the x-value fit the line of best fit (the line that in perfect situations can calculate the y-values). I am going to call the dependent variable, or y-value, wins and runs and the independent variable, or x-value, the various offensive statistics that I will use to test my hypothesis (hitting with runners in scoring position does not have much to do with determining how many wins a team gets in a season or how many runs a team scores). Basically it is how dependent team wins and runs are on hitting with runners in scoring position. Before I look at hitting with runners in scoring position, it is important to establish which three offensive statistics are the best at determining wins and runs.

In terms of influencing the scoring of runs from 2002 to 2013, the three best offensive statistics are:

1. OPS with an R2 of .9132 (91% of the OPS x-values fit the formula: y = 2059.2x – 791.27)

2. ISO with an R2 of .5801 (58% of the ISO x-values fit the formula: y = 3279.75x + 238.02)

3. wOBA with an R2 of .3999 (40% of the wOBA x-values fit the formula: y = 3482.9x – 389.93).

When it comes to which statistics determine wins the most, the three best statistics are:

1. WAR with an R2 of .5329 (53% of the WAR x-values fit the formula: y = 1.1243x + 59.614)

2. wRC+ with an R2 of .4302 (43% of the wRC+ x-values fit the formula: y = 0.8977x – 5.4636)

3. wRAA with an R2 of .3632 (36% of the wRAA x-values fit the formula: y = 0.1033x + 81.239)

There are a couple things to notice when looking at this data. One of those things is that most offensive statistics have a much weaker coefficient of determination when looking at wins, largely in part to the fact that pitching is kept completely out of the equation. Another thing to know is that if there was a bigger sample size, the R2 values would be different but using this sample size (which I will use for RISP), these are the R2 values that show up.

The purpose behind collecting those statistics in terms of offense in general as opposed to just RISP is because this way there will be statistics to use when looking at how much RISP influences offense. Looking at determining runs scored in an overall season with RISP numbers:

1. OPS has an R2 of .3099 (31% of the OPS x-values fit the formula: y = 948.7x + 19.173)

2. ISO has an R2 of .2395 (24% of the ISO x-values fit the formula: y = 1812.2x + 470.92)

3. wOBA has an R2 of .2898 (29% of the wOBA x-values fit the formula: y = 2391.5x – 35.754)

It is quite a dramatic change, especially when looking at OPS that clearly had a big hand in determining runs scored in a season. While some of them still have some modest effect in determining runs scored, it is still not quite at the same level as those that covered a full season and not just a given scenario. Now looking at how those other statistics determine wins with runners in scoring position:

1. WAR has an R2 of .29 (29% of the WAR x-values fit the formula: y = 2.5609x + 68.94)

2. wRC+ has an R2 of .2739 (27% of the wRC+ x-values fit the formula: y = 0.5518x + 27.727)

3. wRAA has an R2 of .2366 (24% of the wRAA x-values fit the formula: y = 0.2366x + 80.996)

As I had mentioned before, it should be expected that these numbers ought to be low because there is much more that goes into a win than just offensive ability. There has to be great pitching too that is not put into account. With that said, these numbers are quite far from being great in determining wins as is evidenced by their still being far away from even the 50% mark that they should be close to.

For Matt Williams’ sake, I also looked at how much batting average with runners in scoring position determines wins and runs:

1. For scoring runs, AVG has R2 value of .181 (18% of AVG x-values fit the formula: y = 2005.8x + 213.05)

2. For wins, AVG has R2 of .1427 (14% of AVG x-values fit the formula: y = 257.76x + 13.255)

So Matt, not to rain on your parade, but batting average with runners in scoring position has very little to do with determining runs or wins. And Marty, it’s just limiting Votto’s overall production to a small sample size that doesn’t have a whole lot to do with winning games. No one will argue that hitting with runners in scoring position can help to win games because it does often result in scoring a run but it should not be looked at as one of the key stats in a player’s production.

**II. Is it dependent on overall strength of offense?**

Now back to those St. Louis Cardinals. Last year, with runners in scoring position, they put up not only unreal numbers, they put up numbers that are really just plain stupid. I mean, they batted .330 with runners in scoring position, had a .370 wOBA, and a 138 wRC+, and won 97 games, 32 games over .500. Like I have previously established, those numbers are intrinsically worthless considering that it is such a small sample size but those are still just gaudy numbers. This year, for lack of a better word, they’re awful with runners in scoring position. A .244 batting average, .293 wOBA, and 86 wRC+ all those with runners on second or third and have won 39 games, only 4 over .500.

Many people look at that and think that clearly, their inability to hit with runners in scoring position this year has caused the drop off in production. Of course, the low .303 wOBA, 92 wRC+, OPS of .681, and AVG of .250 are a bit of a drop off from the .322 wOBA, 106 wRC+, .733 OPS, and .269 AVG of last year might have something to do with that drop off in offense too. The Cardinals offense is also scoring about a run less this year than they did last year (4.83 Runs/9 innings in 2013 and 3.67 Runs/9 innings in 2014) meanwhile their pitching has practically been identical to last year with a FIP of 3.31, xFIP of 3.66, and SIERA of 3.60 this season compared to last year’s 3.39 FIP, 3.63 xFIP, and SIERA of 3.57. But is hitting with runners in scoring position dependent on how the offense overall is? I’m sure you can already see what coefficient we’re going back to.

The process was similar to last time, with the dependent variable, or y-value, being hitting with runners in scoring position, and the independent variable, or x-value, being the same statistic only looking at the value over the course of a full season. I found that wRC in a year has by far the strongest effect in determining how a team hits with RISP with an R2 of .7527 with 75% of the x-values fitting into the equation of y = 0.3364x – 51.232. OPS is after that with an R2 of .6487 and 65% of the x-values fitting the equation of y = 1.0184x + 0.0025. And then there is wOBA that has an R2 of .6258 and 63% of the x-values fitting the equation of y = 0.9807x + 0.0062. Some other values are:

• wRAA that has an R2 of .5811 (58% of the x-values fit into the equation: y = 0.2586 + 0.5721)

• wRC+ that has an R2 of .5558 (56% of the x-values fit into the equation: y = 0.9678x + 3.3038)

• WAR that has an R2 of .3831 (38% of the x-values fit into the equation: y = 0.2005x + 0.8901)

So a case could be made that the strength of a team’s offense overall does dictate how that same team hits with runners in scoring position. While by no means is it an overwhelmingly strong coefficient of determination in any of the cases, in most cases the strength of an offense determines at least 50% of hitting with runners in scoring position which is good enough to at the very least say that better offensive teams are more likely to hit better with runners in scoring position than weak offensive teams.

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Thank you! I get really irritated when sports talk show hosts say players or teams are bad players/teams because they don’t hit well with RISP or leave a lot of runners on base. To expand on your point, I’ve always thought this was a good example of the central limit theorem where you randomly sample your batting average data, and some of the samples are higher while some are lower, and most are right around the overall value.

I did that for the difference in AVG overall and RISP for both teams and players and you get a very nice looking normal distribution curve: http://stats.seandolinar.com/risp/.

I hadn’t even thought of the central limit theorem but you’re absolutely right. And my compliments to you on a great article that you wrote as well on your website about runners in scoring position! Great use of distribution curves with batting averages.

Wait a minute. You debunk Matt Williams and then at the end of the article you say this, “….which is good enough to at the very least say that better offensive teams are more likely to hit better with runners in scoring position than weak offensive teams.”

Isn’t that the point Williams was trying to make?

Average with RISP makes a difference. It is not the end-all in determining a team’s offensive strength.

Now, if you could write something on Productive Outs that would be great.

RISP does matter for individual players because it’s a collection of those several individuals that contribute to the team’s success. For example, the Detroit Tigers hit 282 as a team with RISP in 2013. Why was this the case? It’s very simple, they made the most of the RISP opportunities they had regardless of the sample size. Here are the general numbers with RISP for some of the hitters in Detroit’s lineup in 2013:

Cabrera Avg 397 OBP 529

Fielder Avg 282 OBP 371

Infante Avg 275 OBP 304

Martinez Avg 264 OBP 340

Peralta Avg 344 OBP 414

Hunter Avg 281 OBP 298

Detroit had 4 guys hitting 280 or better with RISP and yes some of this is brought up greatly by Cabrera and Peralta. However when you line up Cabrera, Fielder, Martinez, Hunter, and Peralta in a row, you can see why the 2013 Tigers didn’t have RISP slumps that lasted for an longer period of time compared to the average team. If Detroit had struggled with RISP immensely for the first 2-3 months of the season, they would have not hit 282 with RISP.

Detroit may not have had as many opportunities with RISP as they could have, but they didn’t disappoint fans because they came through to an amount that was satisfying enough because they maximized the given opportunities presented. This is why fans get on players for stinking with RISP, because you benefit from multiple guys stacked in a row in a lineup that are hitting well for the season with RISP.

**** Think about it though. It only makes sense regardless of a hitter’s overall numbers and sample size with RISP for a pitcher to pitch around a guy hitting over 300 with RISP than it is to pitch to a pull hitting guy who is at 240 with RISP. I mean between having to face Edgar Martinez and Mark Texeira with RISP, I’ll take Texeira all day because he’s the type of hitter that if you locate, he won’t get a hit whereas Martinez can flick a perfectly spotted fastball on the outside corner to right field for a hit to drive in a run. Why would I want to deal with that headache?

Sorry, the reply function didn’t work for me there. My most recent comment (seen below) was supposed to be a reply to your comment.

I understand what you are saying to a degree. Sure, I can definitely understand where it might be important to individual hitters but at the same time the numbers that you brought actually seem to work more for my argument that the numbers don’t matter. Regardless of what their numbers with RISP are, I will take pitching to Omar Infante or Torii Hunter any day over pitching to Victor Martinez. I would even rather pitch to Johnny Peralta than pitch to Prince Fielder or pitch to Victor Martinez because at the end of the day, Peralta is not as good of a hitter as Fielder and he’s not as good of a hitter as Martinez. The sample size of runners in scoring position is more of examining batting average in a select few at bats and those numbers can change at any time because it is just too small a sampling of the plate appearances.

Just a few points I would add to what you said though. RISP sample size isn’t really all that low of a sample anymore if you look at a hitter’s entire career with RISP. Some people will look at RISP in a particular season that is the end all be all to if they have been a successful hitter with RISP. I don’t really look at it that way though. Like I’ll give you an example, Allen Craig is having a terrible season at least for him with RISP, if you look at Craig’s career with RISP, it suggests he will get back to his career numbers with RISP, because of his history of hitting with RISP. A bad hitter that is having an average season with RISP will go back (expectedly) to be being a bad hitter with RISP, because of his history of being bad with RISP. What defines you with RISP is what your history says you are. RISP is just not repeatable from year to year because it’s just inherently difficult to hit a baseball and generally speaking requires you to hit 300 every year, which nobody except Miguel Cabrera and maybe Trout does.

I don’t necessarily believe what a hitter’s numbers are with RISP is byproduct of sample size, because if we could assume every hitter had the same exact sample size, there would be nothing to suggest everyone would be equally successful. There would still be several players better than others with RISP if we could assume the sample size for everyone right??? Especially when you consider just how many position players are in the league. Undoubtedly there would be several better than others. Like for example, there wouldn’t be any evidence to suggest that Mark Texiera would be more successful with RISP than Mike Trout or a Stanton if he had the exact same sample size with RISP as those hitters.

Now as far as team success goes with RISP and scoring runs. Well I’ll say this. The reason it doesn’t seem like RISP correlates with winning games has some to do with the fact that power is down with RISP. That means if you go 0 for 10 with RISP in a game that generally means you must hit several homeruns if you assumed your pitching staff gave up 3 runs in the game. In hindsight this sounds easy, but it’s even harder now to hit homeruns with men on base no less with nobody on base. Starting pitchers and especially relievers simply don’t give up the long ball. So I would argue that going 0 for 10 with RISP can be damaging to a team especially when you consider that strikeouts limit a team’s ability to get a runner over and then get him in. I mean hits with RISP can change the tenure of a game. I mean bases loaded with nobody out, if you score 4-5 runs the game is all but over in this day and age, but if you don’t score it has a tendency to weigh heavily in what happens the rest of the game. I mean the best opportunities wasted in this day and age can weigh heavily on the outcome of games, because more good then great opportunities happen more often. I mean bases loaded with nobody out or runner on 3rd with nobody out doesn’t happen as often, which means you can’t just ignore it even if it happens early in the game. I mean I’ve seen it several times where a team doesn’t score with bases loaded and nobody out and end up losing.