RBI and Batting Order
I received some really interesting comments on my previous article on RBI luck, and that has steered me toward several new avenues of related research. Commenter Bill identified that several of the unlucky batters who had fewer RBI on home runs than I expected batted second in the order. Intuitively, it makes a lot of sense for batting order to influence a player’s opportunities for RBI, but that issue can be a bit difficult to disentangle from the quality of the offense he is a part of. Still, I think it is possible, and I’ve made an attempt to do so with a model that chains some league average rates to a hypothetical power hitter’s expected batting outcomes.
The easiest place to start is with baserunners. I mentioned in that last article that a batter can expect about 0.6 runners on base on average, which is a generalization calculated from historical seasons for all lineup positions. But when you calculate those averages based on the current run environment and split them by batting order, you can see how much of an impact that order has on a player’s opportunities to drive in runs.
| Bat Order | Runners On 1B | Runners On 2B | Runners On 3B | Runners On |
|---|---|---|---|---|
| 1 | 0.232 | 0.160 | 0.084 | 0.477 |
| 2 | 0.310 | 0.175 | 0.085 | 0.569 |
| 3 | 0.340 | 0.198 | 0.102 | 0.639 |
| 4 | 0.356 | 0.220 | 0.116 | 0.692 |
| 5 | 0.339 | 0.209 | 0.112 | 0.660 |
| 6 | 0.330 | 0.199 | 0.100 | 0.629 |
| 7 | 0.336 | 0.201 | 0.102 | 0.639 |
| 8 | 0.327 | 0.204 | 0.104 | 0.635 |
| 9 | 0.336 | 0.209 | 0.112 | 0.656 |
We’re working with fractional runners here, but over the course of a full season—which I’ll approximate as 600 at-bats—the difference between the prime RBI spot in the order, 4th, and even 3rd in the order is 32 baserunners. For players like Giancarlo Stanton and Kris Bryant who frequently bat second, it is a loss of 74 baserunners over their potential in the No. 4 hole. That’s not to say it is worse for the team. Quite the opposite. This is just one of the places where the real world and fantasy have incompatible interests.
Those RBI opportunities mean different things to different batters. A runner on first base is a great RBI opportunity for Stanton since he hits so many home runs. He is less so for a batter like Mookie Betts, who generates the bulk of his RBI on singles and doubles. So I opted to create a hypothetical batter to represent what I consider to be a typical run producer. I gave this batter an 18.0 percent chance of hitting a single, a 5.0 percent chance of hitting a double, a 0.5 percent chance of hitting a triple, and a 5.0 percent chance of hitting a home run. Those rates mean the batter hits for an average of .285 and a slugging percent of .495. Over 600 at-bats, he would produce 108 singles, 30 doubles, 3 triples, and 30 home runs.
Next, I calculated the model batter’s expected RBI totals based on the typical distribution of baserunners he should see in various lineup spots and given the average score percentages of baserunners on singles, doubles, triples, and home runs. In other words, this batter will have perfectly average baserunners with perfectly average frequencies of baserunners.
| Result Type | Runners On 1B | Runners On 2B | Runners On 3B |
|---|---|---|---|
| Single | 0.5% | 53.6% | 90.2% |
| Double | 39.2% | 92.7% | 92.9% |
| Triple | 95.9% | 95.0% | 95.5% |
| Home Run | 100.0% | 100.0% | 100.0% |
Then, I just chained it all together. The batter would hit 108 singles multiplied by 0.232 runners on first base when he bats first in the lineup multiplied by the 0.005 chance that runner would score on a single equals 0.125 RBI. I did that for each of his batting outcomes for each of his expected baserunners in every lineup spot, assuming he had 600 at-bats regardless of his lineup spot. Here are the results:
| Bat Order | Runners On 1B | Runners On 2B | Runners On 3B | Batter | Total |
|---|---|---|---|---|---|
| 1 | 10.5 | 19.0 | 13.3 | 30.0 | 72.7 |
| 2 | 14.0 | 20.7 | 13.4 | 30.0 | 78.2 |
| 3 | 15.4 | 23.5 | 16.1 | 30.0 | 85.0 |
| 4 | 16.1 | 26.1 | 18.3 | 30.0 | 90.5 |
| 5 | 15.3 | 24.8 | 17.7 | 30.0 | 87.8 |
| 6 | 14.9 | 23.6 | 15.8 | 30.0 | 84.3 |
| 7 | 15.2 | 23.8 | 16.1 | 30.0 | 85.1 |
| 8 | 14.8 | 24.2 | 16.4 | 30.0 | 85.4 |
| 9 | 15.2 | 24.8 | 17.7 | 30.0 | 87.7 |
I was surprised to see that the RBI opportunities were fairly flat between the No. 3 spot and No. 9 spot. In that range, a hitter is losing no more than 6.2 RBI over his maximum potential hitting 4th. However, batting second really hurts a hitter’s RBI potential. There, he can expect to lose 12.3 RBI over the course of a season. The relative drop from 3rd to 2nd is more significant than even the drop from 2nd to 1st.
As an aside, I think the No. 9 hole has a higher total here because teams intentionally put runners on base for the pitcher in the NL. I didn’t bother to split the leagues out since it’s unrealistic for our model batter to be batting ninth in any case.
Those calculations omit one important piece of the puzzle, which is that a batter who hits first in the order can expect more at-bats over the course of a year than one who hits fourth. That increased volume of at-bats should help to offset his lesser RBI potential in each specific at-bat. To make that adjustment, I calculated the total number of plate appearances for each lineup spot in recent seasons and then adjusted it to the No. 4 hole, assigning it the expected 600 at-bats. Here are the adjusted at-bat and corresponding RBI totals:
| Bat Order | At-Bats | Runners On 1B | Runners On 2B | Runners On 3B | Batter | Total |
|---|---|---|---|---|---|---|
| 1 | 636 | 11.1 | 20.1 | 14.1 | 31.8 | 77.1 |
| 2 | 636 | 14.8 | 22.0 | 14.2 | 31.8 | 82.9 |
| 3 | 616 | 15.8 | 24.1 | 16.6 | 30.8 | 87.3 |
| 4 | 600 | 16.1 | 26.1 | 18.3 | 30.0 | 90.5 |
| 5 | 581 | 14.8 | 24.0 | 17.1 | 29.0 | 85.0 |
| 6 | 566 | 14.1 | 22.3 | 14.9 | 28.3 | 79.5 |
| 7 | 553 | 14.0 | 21.9 | 14.9 | 27.6 | 78.4 |
| 8 | 537 | 13.2 | 21.6 | 14.7 | 26.9 | 76.4 |
| 9 | 518 | 13.1 | 21.4 | 15.3 | 25.9 | 75.7 |
Relative to the fixed-600 at-bat calculations, the No. 2 hitter gains back 4.7 RBI, but he still falls short of his potential RBI in the No. 4 hole by 7.6 RBI. That’s a big deal, and it neatly explains many of the perceived shortfalls in RBI that No. 2 hitters had in my previous article. Meanwhile, the No. 5-9 spots in the order now see a much steeper decline in expected RBI as they lose upward of 82 at-bats over the course of the season.
Scott Spratt is a fantasy sports writer for FanGraphs and Pro Football Focus. He is a Sloan Sports Conference Research Paper Competition and FSWA award winner. Feel free to ask him questions on Twitter – @Scott_Spratt
This is gooderly. I mean sure, it’s intuitive, but it’s nice to see the numbers laid right out there.
Now do it for runs scored please 🙂
Could I firstly agree that this is awesome? Thanks. and then also I would like to request these numbers for PA instead of AB’s. Please. Makes a big difference. AB’s for Votto in the 2 aren’t the same as AB’s for Player X in the 2, but PA’s are roughly the same.
I decided to use ABs instead of PAs because that’s the fixed portion of this. I think that, more or less, any player with this contact and power profile could expect these RBI totals over 600 ABs, whether they walk 5% of the time, 10% of the time, or anything else. So if you wanted to adjust these numbers for someone like Votto, you’d start by projected his PAs (say 700 in the No. 2 hole), subtract out his projected walks (maybe 125), and then scale his RBI projections down to that estimate of ABs (in this example, 574 / 636 * 82.9 = 75 or so (assuming Votto hits similarly to the model player).
This is great stuff. Thank you.
This might be my new favorite FanGraphs/RotoGraphs article.
Here’s an interesting point though – is there some way to control for time through the lineup? The leadoff hitter is guaranteed that his first PA has no one on. Assuming a .400 OBP, then the two-hole hitter is essentially guaranteed a 60% chance that no one is on. As you get further into the lineup, these chances slim.
I think where your line of reasoning is going here is a simulation model, which is really how this analysis would best be used. It’s something I’d like to do long-term, but it’s obviously a big project.
Two things. Have you used Statcast data to get the actual runners on bases for each player for each PA?
Second, if I were forming modern day Roto scoring I think I’d remove RBI. The balance between player types, lineup slots and statistical category correlations would make for s slighty better game with just OBP/AVG TB/HR with R and SB. After all, your job as a hitter is to get on base, driving in runners is as you’ve stated the product of a noisy system with opportunities combined with your offensive skill against the defenses skill.
Jim
Jim, you’re certainly free to pursue whichever type of Roto setup brings you the most satisfaction, and it sounds as if you do. A large percentage of players will always enjoy 5×5 Roto, and We appreciate the continued 5×5 content. After all, isn’t it your only job as a Roto format to provide the most enjoyment for your users? Is that kinda Tron-ish?
Sorry, yeah, my point is just that RBI seems like a superfluous stat now, in hindsight. Keith Laws book made the same sort of assessment in another context.
I wasn’t saying abandon the 5×5 (I play in one), just that the RBI really isn’t needed. I have the same opinion about the win and save.
I am using FanGraphs’ plate appearance data, which I’m pretty sure comes from Baseball Info Solutions in this case.
So it’s the macro level data, you didn’t break this down batter by batter to see the impacts across the various offenses and such? Not saying you should, just curious what level of granularity you worked with before you aggregated.
I calculated all of the rates using plate appearance-level data, but I assembled the RBI projections for one hypothetical batter. You could do it for anyone, but that would be a difficult thing to present in an article. That is what you’d want to do in real projections, though.
Yes, agreed. Just testing my adult learning. Thanks Scott.
Why would only 95.5% of runners in 3B score on a triple? What am I missing?
Sometimes Jack Cust falls down 🙂
This is all based on the baserunner situation at the start of the plate appearance. I haven’t specifically looked it up, but I’m guessing the 5% here is mostly runners on 3rd base who score on wild pitches, eliminating the RBI chance.
So it’s not the triple itself it is more the beginning and end of a base out situation. That makes sense, thanks!!
I think it’s worth splitting the leagues out, not just because of the higher chances of RBI for 9th slot, but there could be a decent difference for leadoff hitters between following a pitcher versus a hitter.
I don’t mind a power hitter batting second, but batting power hitters leadoff definitely cuts into their fantasy value – especially for players who don’t steal bases out of that leadoff spot.
Thanks for the plug and the follow up!
Great piece! How is it that the score rate for someone on 1st is higher than someone on 2nd when a triple is hit? (95.9% vs. 95.0%)
Scott explained why the man on 3rd score rate is less than 100%, but I don’t get why man on 2nd score rate isn’t 100% as well! Shame on those runners! Shame!!
Scott, I can’t take this “RBI” as the plural. Please call it RBIs. It’s so uncomfortable to read.
Has anyone ever said “WMD”? Of course not. Every leader in the world says WMDs.
If it’s WMDs,then it has to be RBIs.
Good job overall. I’d recommend using the number of PA with men on base, for each batting slot. This takes care of both the main issues: (1) the 1st hitter comes to bat more often then the 2nd and so on down to the 9th and (2) the middle of the order has a higher proportion of their PA with men on.
Thanks, Tom. That makes a lot of sense.