Runs Batted In is a tough statistic to track for fantasy purposes. It’s not entirely in the control of the hitters on your team, which can make picking the players that will finish with lofty RBI totals a bit troublesome for the canny fantasy GM. For example, if you have a power hitter who is sending 40 home runs out of the park each year while playing on a team with a poor overall offense, you run the risk of that player not picking up as many RBI as a lesser hitter might in a lineup that’s stacked. It turns into a balancing act, as you try to decide which stats are more important and reliable while drafting or selecting which players to stick with.
This kind of team- and lineup-dependence upon forces beyond any one player’s control contributes to the RBI not being seen as a serious stat in actual baseball analysis when it comes to evaluating player value, but since it is part of the fantasy baseball culture, it’s not something we can ignore. Luckily, there are more advanced ways to study RBI in order to help your team, and you need look no further than the RBI Opportunities Report for this kind of assist.
Looking at the report, starting from the left you have the basic identifying information for the player, followed by a column labeled PA_ROB, which is plate appearances with runners on base. When you find players on bad offensive teams who are up near the top of these rankings, it’s time to take a deeper look to see if their performances are sustainable. Likewise, hitters on good teams that aren’t seeing as many opportunities as they should can be found here. This is broken down further, though, with R1, R2, and R3, showing the number of baserunners on each base for the hitter as they came to the plate. Next, you see the number of those runners driven in from each base in R1_BI, R2_BI, and R3_BI, and the total number of runners on base (not included in the table below).
The key column is OBI, for Others Batted In-essentially, runs batted in minus the player’s number of home runs hit. This helps you see how many opportunities the hitter’s cashing in, rather than that number combined with the opportunities he creates for himself by hitting one out of the park. This, along with the information for each individual base, is available in a rate form at the far right of the report so that you can see a hitter’s success in the form of a percentage. You can learn a lot by fiddling with this sortable data; for example, Joe Mauer, with 30.4 percent of runners on base driven in, has the highest percentage of baserunners plated among hitters with 100 plate appearances, with Brad Hawpe (28.0), Jarrod Saltalamacchia (25.0), and Luke Scott (22.8) trailing him. None of those are names you would expect to see having this level of success at driving in runners, but that’s nothing, as Juan Pierre (22.2 percent) comes in at ninth. Surely, the apocalypse is upon us.
Of course, your league is not scoring the percentage of baserunners driven in, but instead is looking for the cumulative real deal. Today, we’ll take a look at some players that are over their heads or below expectations in relation to their RBI totals.
Name R1 R2 R3 R1BI R2BI R3BI OBI OBI% Evan Longoria 62 71 41 8 16 18 42 24.1% James Loney 92 64 36 6 10 20 36 18.8% Prince Fielder 89 49 31 9 11 16 36 21.3% Jason Bay 87 51 30 11 11 12 34 20.2% Justin Morneau 86 55 26 9 8 16 33 19.8% Nick Markakis 65 59 29 7 13 13 33 21.6% Brad Hawpe 58 32 26 6 13 13 32 27.6% Adam Lind 72 64 26 7 13 10 30 18.5% Aubrey Huff 76 47 22 8 10 12 30 20.7% Torii Hunter 71 53 22 9 12 9 30 20.5% Jorge Cantu 74 53 22 6 10 13 29 19.5% Raul Ibanez 63 50 27 6 10 13 29 20.7% Victor Martinez 67 51 31 7 7 15 29 19.5% Mark Teixeira 78 48 27 8 7 13 28 18.3% Orlando Hudson 83 52 28 5 12 11 28 17.2% Brandon Phillips 68 43 26 4 11 12 27 19.7% David Wright 82 63 35 4 13 10 27 15.0% Miguel Cabrera 79 35 21 7 12 8 27 20.0% Mike Lowell 82 53 26 8 11 8 27 16.8% Todd Helton 49 42 30 2 7 18 27 22.3%
Evan Longoria leads in OBI (and RBI) for a few reasons, besides his being a fantastic hitter. He has had the benefit of having 71 runners on second base during his 227 plate appearances, which is the most of anyone in the majors. Given he has 20 doubles along with 13 homers, it’s easy to see why 22.5 percent of those runners on second have been driven in while he’s at bat. He’s ranked just 30th in that percentage, but given the high volume of baserunners he’s getting to try to plate, it hasn’t mattered to his totals. Tampa Bay is currently leading the league in runs, and has a .354 OBP (the American League average is .338), so we should expect Longoria to see this many baserunners now and into the future; remember, the team’s current mark is with B.J. Upton leading off, and he has slumped horribly since coming back from his injury. When Upton returns to form, he should help Longoria up his RBI totals even more, and even if he doesn’t for whatever reason, then Longoria’s already in a pretty good place with the support he does have from the men getting on base ahead of him as is.
Hawpe has been a very good hitter for a few years now, but he’s hitting like crazy in 2009 with a .348/.417/.624 line. This has resulted in his making an appearance on the OBI leader board, though in both regards he’s due to take a step back. Hawpe is hitting liners at a rather absurdly high rate for him right now-his career average is 21.1, but he’s approaching 25 percent this year. He’s also cut down on his ground balls considerably while increasing his fly balls, which is more likely to be sustained-the issue is that his rate of homers per fly balls is down, so even though he has more fly balls to hit home runs on, he’s not hitting them as often. He is hitting plenty of doubles though, in no small part thanks to those extra liners, and all of those extra hits are helping him drive in 40.6 percent of baserunners from second, the second-best mark in the majors behind Mauer’s. Among players with 450 plate appearances in 2008, the highest R2BI% was Pittsburgh’s Ryan Doumit, at 25.5 percent. Once Hawpe’s line begins to dip back toward normalcy, we should see his R2BI rate drop, which would also mean a drop in his overall OBI and RBI totals. Since he may end up with less home-run power from here on out (based on his falling HR/FB), you may want to sell high on Hawpe while he still looks like a multi-category machine.
It seems as if David Wright has busted out of his early slump, but he hasn’t had everything come together yet. He’s on this list more because he’s had the fourth-most baserunners on base among all hitters in the majors, not because he’s been especially effective at driving them in-look no further than his 15 percent OBI percentage for evidence of that. Part of the problem is that he slugged just .390 in April, but he did turn things up this past month (.561). Wright has just three homers, though, and based on how difficult Citi Field is going to be for home runs-the park is averaging just 1.5 per game right now-that’s not a number that is going to shoot up with any certainty. Even without the homers he’s capable of being a dangerous hitter, and we should see his OBI and RBI rates climbing as he gets further from his awful April.
Mike Lowell ranks 19th in total runners on base with 161, which is why he’s in the top 20 for raw OBI despite an OBI percentage that is only good for 76th in the majors. He has plenty of baserunners to drive in thanks to the Red Sox posting a team OBP of .356, so even if Lowell doesn’t drive in a high percentage of runners, he’s still going to have plenty of opportunities to make up for it in the counting stats. Well, during home games anyway: the Sox have a .378 OBP at home, but that drops to just.339 on the road. They’re also hitting for less power on the road (.220 ISO against .153), and have a lower batting average (.302 against .252), so there are fewer extra-base hits putting runners into scoring position, as well as fewer hits moving runners along. All in all, that’s fewer opportunities for Lowell, who is part of the road problem himself (.349/.378/.640 at Fenway, .277/.296/.438 everywhere else). This is a player who is much more useful in the half of his games played at Fenway, and you can already see the somewhat skewed results this early in the season, as Lowell has 22 RBI at home, against 13 on the road. Even if the Red Sox pick up the slack on the road-which they may not, given they’ve hit .292/.372/.468 at Fenway and .268/.344/.428 away from 2006-2008-he himself is known to struggle more on the road while putting together most of his offense in Boston. In roto leagues, you may not care, as this all adds up in the end, but in head-to-head leagues, where Lowell’s splits means more to you on a week-to-week basis, flipping him might not be a bad idea.
A version of this story originally appeared on ESPN Insider .
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I also fail to see the importance of 'Others Batted In'. Batting yourself in counts just as much, and strikes me as more reliable a skill to count on.
If you have 42 RBI and 21 HR, then it's already well known that 50% of your RBI total is completely reliant on your HR total, so as long as your HR skill set doesn't decrease you have the skill to maintain that part of your RBI total going forward.
So then the question becomes, how reliable is the other 50% of your RBI total? And the OBI stat (and the other numbers mentioned here) are the attempt to answer that.
"Once Hawpe's line begins to dip back toward normalcy, we should see his R2BI rate drop, which would also mean a drop in his overall OBI and RBI totals"
Wait, so Hawpe's individual performance is due to revert, and once it does, it is likely that he will drive in fewer runs from 2nd base, which means he will also be driving in fewer non-self runs overall, which means he will be getting fewer total RBI. Therefore, sell high.
But is there anyone who would have expected his RBI production to stay constant as his performance leveled off?
Sorry for all the negativity -- as I think in-season secondary stat projection is an area ripe for development -- but this article fails to keep its analysis clean and useful. If OBI is the topic, then try to identify which players have abnormally low or high OBI%s and make a case that these rates will persist (due to great/awful surrounding offense) or revert to lg. avg....rather than obscure the discusssion with general talk of which players are off to hot/cold starts.
This (getting at the core of RBI creation) is an interesting and neglected topic in fantasy. I hope the author or someone else can do some more in-depth research on it.
FWIW, when I did some research several years ago on in-season RBI predictors, team OBP had only a near-zero correlation to RBI -- which was surprising and may have been an anomaly due to limited sample. It needs to be evaluated more thoroughly. Also slighty interesting: 1st-half slugging pct. was not the best single-stat predictor of 2nd-half RBI. A modified slugging pct. (altering the weights of 2B, 3B and HR) was more accurate by a non-trivial amount.
I don't know the proper way to predict future RBI, but I was disappointed this article didn't tackle the issue.
Obviously, you have done more than I have on this, since I have done practically none, but I have wondered if OBI%, or more correctly R1BI%, R2BI% and R3BI%, will regress to the league average or can we expect some players to have a higher percentages. But if they do, would not that qualify as a clutch hitter?
"A version of this story originally appeared on ESPN Insider."
What the heck! I am paying top dollar to get the info FIRST from you guys. Not second hand info that ESPN insiders have already seen. At the very least the articles should be published simultanously. I only have so much money to spend on this hobby each year and I chose to spend it on your site. Mabey I should subscribe to ESPN next year to get your info fresh and ALL of the other good info that ESPN provides. It's starting to seem that my money would be better spent there, that way I get both.
Even then, I had an ESPN Insider account and didn't renew it since it got to the point that the only article-writer I enjoyed reading over there was Stark and I found I could get most of the other info and analysis from CNNSI which was free.