# Opposition Quality Seems Hardly a Factor

Remember when you thought baseball statistics were easy? Now, analytically, a statistic is hardly worth anything if it’s left unadjusted. You know all the adjustments that go into numbers. Adjustments for ballpark environment. Adjustments for era. Sometimes adjustments for league. As far as WAR is concerned, there are adjustments for position. There’s one adjustment we still don’t make, though: that’s adjustment for quality of opposition. In theory, if there were a pitcher who only ever faced the best teams, and a pitcher who only ever faced the worst teams, that wouldn’t be accounted for. That’s something you’d have to figure out yourself.

Related to this, James Shields has obvious selling points: first and foremost, he’s been good. He’s been durable, and he’s experienced, and he’s pitched in the playoffs, and everything. Then there’s one other thing I don’t think has gotten much attention: Shields has, relative to the average, faced a fairly tough slate of opponents. In the past, I’ve manually calculated average opponent wRC+. Baseball Prospectus has its own version, oppRPA+, and the big advantage of oppRPA+ is it’s already been calculated for me. The average, as usual, is 100. Last year, Shields’ opponents came in at 105. The year before that, 105. The year before that, 105.

Seems like this should be a good thing for Shields’ market; seems like, if you adjust for this, Shields’ numbers would get a boost. But how much does this matter, really? Coming up soon, a first attempt at an answer.

The research itself wasn’t difficult. I set a season minimum of 100 innings. I looked for pitchers who threw 100 innings in consecutive years, and I tracked their season-to-season oppRPA+. Simple math yielded the change in oppRPA+, and I sorted by the change, in descending order. Then I linked the pitchers to various FanGraphs stats: ERA-, FIP-, and xFIP-. In the end, I had the full slate of numbers for Year 1, and the full slate of numbers for Year 2. I had a sample exceeding 1,100, so I split that into five groups. This table shows you the performances, on average. Group 1 includes the pitchers who, in Year 2, faced considerably tougher opponents. Group 5 includes the pitchers who, in Year 2, faced considerably weaker opponents. The oppRPA+ for Group 1, on average, increased six points. The opposite occurred for Group 5.

Group Y1 RPA+ Y2 RPA+ Change Y1 ERA- Y1 FIP- Y1 xFIP- Y2 ERA- Y2 FIP- Y2 xFIP-
Group 1 97 103 6 95 96 96 100 99 98
Group 2 99 101 2 95 95 96 98 98 98
Group 3 100 100 0 95 94 95 96 97 97
Group 4 101 99 -2 96 98 98 99 99 99
Group 5 103 97 -6 95 97 97 97 97 97

To make that easier to understand:

Group Y1 RPA+ Y2 RPA+ Change Change, ERA- Change, FIP- Change, xFIP-
Group 1 97 103 6 5 3 3
Group 2 99 101 2 3 3 2
Group 3 100 100 0 2 3 2
Group 4 101 99 -2 3 2 1
Group 5 103 97 -6 2 0 0

Absolutely, you see different numbers in the columns. They’re just not extremely different numbers. All the groups saw a little Year 2 ERA-related regression to the mean. Those pitchers who faced tougher opponents saw their average ERA- go up almost five points. Those pitchers who faced weaker opponents saw their average ERA- go up just over two points. You observe similar changes with FIP- and xFIP-. Differences do exist. Differences ought to exist — it’s not like opposition quality is completely irrelevant. But at least based on this, it’s a decidedly small factor. Maybe a run. Maybe two or three. And that’s toward the extremes. Most of the time, this isn’t worth thinking about for even a couple of minutes.

In case you’re curious, the most difficult season-to-season adjustment: 2012-13 Joe Saunders. In 2012, Saunders faced maybe the weakest group of opposing hitters in baseball. The next year saw an increase in oppRPA+ of 15 points. Sure enough, Saunders was far less effective the second year. On the other hand, Bud Norris saw his opponents also get a lot better between 2012-13, and he lowered his ERA and FIP.

And the least difficult season-to-season adjustment: 2010-11 Madison Bumgarner. Somehow, in 2010, Bumgarner led all of baseball in oppRPA+. The next year, he finished second from the bottom, a massive drop of 16 points. What happened? His ERA- actually got worse, but his FIP- and xFIP- got significantly better. Jordan Lyles also saw an improvement between 2013-14, as his oppRPA+ fell by 12 points.

Bigger differences might emerge as you isolate the very biggest season-to-season changes in oppRPA+. We can set a threshold of a change of at least 10 points. There are 14 examples of pitchers who saw oppRPA+ hikes of at least 10 points. In Year 2, their average ERA- got worse by 14 points. Their average FIP-, meanwhile, got worse by 10 points, and xFIP- by 4 points.

And there are just nine examples of pitchers who saw oppRPA+ drops of at least 10 points. In Year 2, their average ERA- got better by 12 points. Their average FIP-, meanwhile, got better by one point, while xFIP- got worse by 3 points.

So you wonder how much that means. That’s a huge ERA implication, but it’s more mild by FIP, and it’s virtually non-existent by xFIP. It’s also a small sample of just 23 total pitcher season-pairs, and such changes are uncommon, with an average of fewer than two per year. Of course, it only makes sense that the biggest changes in opposition quality would lead to the biggest changes in pitcher performance. But we don’t have enough data yet to really be able to say how significant this is. Last season’s lowest oppRPA+ figures belonged to Brad Hand, Clayton Kershaw, and Gerrit Cole. Among available pitchers, Cole Hamels was close to the bottom. So there’s that small amount of risk in an AL team dealing for Hamels. But a team like San Diego would have relatively little to worry about.

I suppose all this probably just confirms what you would’ve already thought: opponent quality does make a difference, but almost all of the time it makes a small difference, so leaving it unadjusted for doesn’t throw anything way out of whack. On a case-by-case basis, it’s worth checking if a given player is in for an unusually extreme adjustment, but those cases are rare. I should also acknowledge there are shortcomings with the method. This was already selective for pitchers who threw 100 innings in consecutive years, so guys who bombed in Year 2 are excluded. Also, this gives no consideration to platoon issues, and it doesn’t have the hitter numbers adjusted for pitcher quality. It turns out adjusting for competition is surprisingly hard. Thankfully, it’s also usually unnecessary. That’s the convenient bit.

Jeff made Lookout Landing a thing, but he does not still write there about the Mariners. He does write here, sometimes about the Mariners, but usually not.

Inline Feedbacks
everdiso
8 years ago

interestingly, the burgeoning advanced stats movement in hockey has found a similarly negligible effect of quality of competition.

Craig Tylemember
8 years ago

Interesting about hockey – can you site any examples?

everdiso
8 years ago
everdiso
8 years ago

in general the consensus in hovkey is that ehile qualcomp is significant in theory, in reality the actual differences in qualcomp are too small to matter all that much.

Jason B
8 years ago

On your phone you say? ðŸ™‚

(I kid, appreciate the links. Good info.)