After a five-year slide in scoring landed on the lowest mark since 1981, everyone’s scrambling for offense. That’s a natural response, but maybe not a sensible one.
Stating the obvious, there’s no fixed scoring level that wins ballgames. You just have to score more runs than you allow. And the “pythagorean formula,” which predicts winning percentage from runs scored and allowed, has two corollaries that speak to the most efficient path to improving a team’s record:
- For most teams, a fixed gain in scoring margin is worth somewhat more if applied to run prevention.
- Prevention’s edge is larger at lower scoring levels.
I don’t mean to overstate this. The first point is strongest for teams that are already good, and the second is minor except on extreme ends of the scoring spectrum. But they’re real, measurable tendencies. And don’t they make the knee-jerk reaction to low scoring kind of funny?
I was going to run some pythagorean examples here to show the edge for run prevention over scoring, but those interested can easily work it out for themselves. Those who put little stock in the pythagorean formula can focus instead on the ratio of runs scored to runs allowed, or just jump ahead to the historical data.
Regarding ratios, here’s how a 60-run gain would affect four 2014 teams with varying run differentials:
- Athletics (+157): 729 RS, 572 RA, base ratio 1.27.
Scoring 60 more runs makes the ratio 1.38.
Stopping 60 more runs makes the ratio 1.42. - Giants (+51): 665 RS, 614 RA, base ratio 1.08.
Scoring 60 more runs makes the ratio 1.18.
Stopping 60 more runs makes the ratio 1.20. - Reds (-17): 595 RS, 612 RA, base ratio 0.97.
Scoring 60 more runs makes the ratio 1.07.
Stopping 60 more runs makes the ratio 1.08. - Yankees (-31): 633 RS, 664 RA, base ratio 0.95.
Scoring 60 more runs makes the ratio 1.04.
Stopping 60 more runs makes the ratio 1.05.
The edge for prevention isn’t always large. But wouldn’t you prefer the bigger ratio, other things being equal?
(Using a 60-run gain for the examples was somewhat arbitrary, but it’s not drastic. More than one-fourth of teams in the last 20 years gained at least 60 net runs over the year before, and one-sixth gained 100 or more. The average net change was +/- 82 runs. See method note below.)
The predicted edge for run prevention is greatest for teams that already outscore their foes by a solid margin. Per the formula, last year’s A’s would have gained 2 full wins by applying a 60-run gain to prevention instead of scoring. As the base ratio falls, the relative value of gains starts shifting from defense towards offense. But it takes a ratio below 0.90 to fully flip the edge in favor of scoring gains. Only six teams last year had a ratio that bad.
Whatever you think of the theory, it’s backed by historical data of real wins and runs. In the last 20 years:
- Teams that gained at least +5 wins from the year before improved an average of 43 runs scored, 55 runs allowed.
(198 teams, +12.2 wins) - Teams that gained at least +10 wins improved by 53 runs scored, 75 runs allowed.
(118 teams, +16.0 wins) - Teams that fell by at least -5 wins declined by 44 runs scored, 50 runs allowed.
(205 teams, -11.8 wins) - Teams that fell by at least -10 wins declined by 59 runs scored, 71 runs allowed.
(110 teams, -15.9 wins)
(Method note: Team runs gained or lost were pegged to their league’s scoring norms, so that yearly changes reflect team quality and not spikes in league context. The shortened years of 1994-95 were pro rated to a full slate, so that teams in the next years can’t show “wins gains” despite a worse won-lost percentage.)
No matter how I sort these data, run prevention shows a stronger impact. Suppose we start with changes in runs scored and allowed, and track the resulting wins. Here are the averages for teams with one-year gains of 50 to 100 runs scored or allowed, 1995 through 2014. (The groups had similar total gains in runs margin.)
- Runs scored improved by 50-100: avg. +72 RS (+75 margin) … +6.2 wins … 12.1 marginal runs per win
(111 teams) - Runs allowed improved by 50-100: avg. +70 RA (+77 margin) … +7.2 wins … 10.7 marginal runs per win
(96 teams)
The difference also shows up in teams with small gains of 20 to 49 runs on either side:
- Runs scored improved by 20-49: avg. +35 RS (+42 margin) … +3.8 wins … 11.0 marginal runs per win
(89 teams) - Runs allowed improved by 20-49: avg. +35 RA (+41 margin) … +4.2 wins … 9.8 marginal runs per win
(82 teams)
If this isn’t widely understood, it may be due to human nature. We attack problems by breaking them into components, then targeting efforts to areas of biggest apparent need. Everyone involved with baseball — management, players, writers and fans — instinctively judges teams in terms of offense and defense, gauging each component against the competition. Balance is the unstated ideal.
But the problem of winning more ballgames is solved by improving the runs ratio. And for most teams, improving run prevention is the more efficient route.
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Two caveats to the primacy of run prevention:
(1) Pitching is more volatile than hitting.
… But maybe not as much as you would think: In the last 20 years, the average team change over the year before was +/- 56 runs scored, and +/- 60 runs allowed.
(2) Avenues of improvement depend on available talent. Teams with bad offense and good pitching (measured in context) will find it easier to add hitters who are better than what they have.
… But if everyone thinks there’s a hitter shortage, then that “shortage” seems artificial — more like a failure to accept the new normal in scoring.
The perception of a hitter shortage could make sense if the variance among current hitters was unusually small. That would make incremental gains harder to get by replacing one hitter with another. And by raw numbers alone, the variance is smaller now than a decade ago, because the numbers are down. But context is crucial: Just because it’s harder to add 10 homers to your first-base output, that doesn’t mean it’s harder to add something equal in current value to what 10 HRs did ten years ago.
Using OPS+ to gauge distance from the average, I don’t see much change in variance from ten years ago. For 2003-04 and 2013-14, I ranked the top 240 hitters by OPS+ — all the regulars, basically — and found the OPS+ averages for eight groups of 30, each representing one regular spot per team:
| OPS+ Rk. | 2003-04 OPS+ |
2013-14 OPS+ |
| #1-30 | 149 | 145 |
| #31-60 | 125 | 124 |
| #61-90 | 117 | 115 |
| #91-120 | 110 | 109 |
| #121-150 | 103 | 104 |
| #151-180 | 98 | 99 |
| #181-210 | 91 | 95 |
| #211-240 | 85 | 87 |
(Min. 600 PAs over the two years.
Group OPS+ weighted by PAs.)
The top tier in 2003-04 has a modest edge (149-145), but most of that comes from the crazy 247 OPS+ of Barry Bonds. The rest of the tiers are pretty similar for both periods.
Suppose we simply count the elite hitters: An OPS+ of at least 150 was logged by 8 guys over 2003-04, and 9 in 2013-14. At OPS+ 135 or better, the counts were 24 for 2003-04, and 23 for 2013-14. At 125 and up, each period had 44. What shortage?
The counterpart of the current hunt for hitters is a perceived glut of pitchers. All the big deals before the winter meetings were for hitters, as GMs tried to beat the rush by shopping early. But running the same exercise for starting pitchers, the variance in ERA+ seems as big or bigger now than ten years ago. I ranked by ERA+ the top 150 starters in IP for 2003-04 and 2013-14, and found the averages in tiers of 30, each representing one rotation slot:
| ERA+ Rk. | 2003-04 ERA+ |
2013-14 ERA+ |
| #1-30 | 132 | 136 |
| #31-60 | 110 | 113 |
| #61-90 | 102 | 102 |
| #91-120 | 93 | 93 |
| #121-150 | 84 | 81 |
(Min. 20 starts over the two years.
Group ERA+ weighted by IP.)
The top two slots in 2013-14 are 3-4 points above the prior period, while slots 3-4 are the same, and slot 5 is 3 points worse. If these numbers mean anything, don’t they suggest there’s more to gain now by upgrading a rotation’s back end?
The picture’s similar using Wins Above Average instead of ERA+:
| WAA Rk. | 2003-04 WAA |
2013-14 WAA |
| #1-30 | 5.9 | 5.6 |
| #31-60 | 2.2 | 2.2 |
| #61-90 | 0.6 | 0.6 |
| #91-120 | -0.7 | -0.8 |
| #121-150 | -2.3 | -2.6 |
Here, the top tier of 2003-04 fares a little better, mainly from averaging 40 more innings. But the spread from top to bottom is identical. It seems there’s still as much to gain as ever by improving a rotation.
Could it be that front offices still judge pitchers by raw ERA, perhaps unconsciously? Far more qualified starters met basic ERA thresholds in 2014 than 2004 — 22-7 for ERA below 3.00, 39-18 for ERA under 3.50, and 66-33 for ERA under 4.00. But the average ERA has dropped from 4.46 to 3.74, so a 3.50 ERA doesn’t mean the same thing nowadays.
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If the emphasis on hitters is a bit misguided, more so is the focus on home runs. Ben Cherington could have been speaking for all general managers when he lamented that “It’s harder to find power.” That viewpoint is ironic because, even after years of decline, home runs remain historically high. Although last year’s HR rate was the lowest since 1992, it’s still higher than all but two years from 1973-92. It’s 10% above the average for 1946-92, and 1% above average for the whole post-War period.
So why do GMs talk as if the steroids era was normal, or desirable? Does every team need a 30-HR man in order to win?
- From 1973-92, just half the World Series champs had a 30-HR hitter, and just over half the pennant winners.
- Even during the steroids era, five straight champions lacked a 30-HR man (1995-99).
- The 30 pennant winners from 1993-2008 featured just three league HR champs.
- The last five World Series featured just two of the 50 men who hit 33 HRs or more.
- Just three of last year’s eleven 30-HR men were on the ten playoff teams.
- Last year’s pennant winners were led by 22 and 19 HRs.
So why is the scramble aimed at home runs, and not the more critical shortage, high on-base percentages?
Last year’s .251 batting average and .314 on-base percentage were both the lowest of the DH era. Only three qualified hitters had a .400 OBP, matching the fewest since 1968. Leadoff and #2 hitters had a combined .324 OBP, second-worst of the DH era. Yet I’ve heard no GM bemoan the lack of table-setters.
OBP affects scoring more than home runs do. For 2014, I took each league‘s top five in scoring, in OBP and in HRs (excluding the Rockies from all rankings*), and cross-checked their league rank in the other two stats. Summarizing these combined top tens:
- Top 10 in scoring averaged #3.3 in OBP, #4.9 in HRs
- Top 10 in OBP averaged #3.6 in scoring
- Top 10 in HRs averaged #4.9 in scoring
(* Coors Field is such an extreme park that the Rockies’ stats only blur the picture of the league as a whole. They rank high in scoring whether they hit home runs or not, because their OBP is always high — above league average all 20 years at Coors.)
For OBP and HRs, the league ranks of the last ten pennant winners (ordered by highest ranking, again excluding Rockies):
- OBP: #1, #1, #1, #2, #3, #4, #4, #6, #8, #10 — Avg. rank #4.0
- HRs: #2, #4, #5, #5, #5, #6, #10, #12, #15, #15 — Avg. rank #7.9
Whether gauged by scoring or by team success, OBP seems more vital than HRs. So if front offices must target offense over pitching, they might at least focus on the key stat.
Now, maybe team power can be boosted more quickly than on-base percentage. And maybe GMs fear that OBP is a losing battle: With last year’s 7.6% walk rate tying 1968 for the lowest since 1922, and with the grip-and-rip batting style firmly entrenched, building sequential offense seems a tall task.
But it can be done. Of the 25 teams last year that hit fewer homers than the year before, 12 actually scored more runs. Those twelve shed an average of 14 HRs (155 to 141, -9%), but rose by 30 runs (645 to 677, +5%). Their slugging average dipped 3 points, but their OBP rose 3 points. (They also stole 10 more bases per team. The three that had non-trivial dips in OBP averaged +27 in SB.)
If there is a threshold below which the correlation of OBP with scoring and success breaks down, we haven’t reached it yet. Seven of ten playoff teams last year ranked in their league’s top five in OBP, and just one playoff team ranked in the bottom five. Meanwhile, the Astros ranked 3rd in AL HRs, better than four of five playoff teams. But they were 13th in OBP (worst in strikeouts), and 14th in scoring. The Cubs were 2nd in NL HRs, better than all five playoff teams — but they, too, were 13th in OBP, worst in strikeouts, and thus 12th in scoring.
Overvaluing sluggers in this environment could exacerbate the falling OBP, making sequential offense even tougher, thus fueling a vicious cycle. I can’t help but think of another time that the major leagues shed 20 points of OBP in a five-year span, plus a big drop in homers, landing on the lowest scoring and OBP marks since the dead-ball era. That was 1963-67.
Be careful what you wish for, general managers. It can always get worse.
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As usual, all the raw data for these studies came from Baseball-Reference.com.
Thank you, John! I’ve been scratching my head listening to the hot stove commentary about the increasing value of sluggers, but your systematic analysis provides logic, fact and precision to the issues that have been bothering me vaguely.
“Yet I’ve heard no GM bemoan the lack of table-setters.”
Maybe not, but I’d think the analytics revolution has lead everyone to realize the importance of walks and OBP and I do not think they’re undervalued. Of course, the real point of Moneyball was to exploit market inefficiencies, not to promote OBP, and possibly GMs are making an over-correction due to the downturn in offense.
In any case, I get what you’re advocating and tend to agree with it. But where are these mythical high-OBP players? I’m not seeing them out on the FA market. Who is out there? Melky Cabrera?
If GMs are over-valuing sluggers and disregarding OBP, then why is Mark Reynolds sitting out there, along with Rickie Weeks and Mike Morse? How did 39-year-old Torii Hunter get $10M? Why did Nick Markakis get more money and years (despite upcoming neck surgery) than Adam LaRoche? LaRoche hits 10+ HR a year more and Markakis is a corner OF, so his defense is less valuable than an up-the-middle guy. Why is Chris Young getting 1 year for $2.5M when he’s the low-OBP, good-power type of player we’re talking about, but Billy Butler, a DH with little HR power, gets 3 for $30? Why does Yoenis Cespedes keep getting traded? Why did Russel Martin get more than 15M per season?
Where is the evidence that GMs are over-valuing slugging and under-valuing OBP based on this off-season’s moves?
(Note that this is a great piece and the analysis is excellent and I agree with the general point, and stated recently here that more teams will try to construct themselves like the Royals. Just taking umbrage to the statement you seem to be making that GMs aren’t paying attention to run prevention or OBP, when the contracts and moves made this offseason seem to show the opposite).
brp, thanks for the feedback. You’re 100% right to say that a claim of overvaluing homers vis-a-vis OBP needs more backing.
Touching on the guys you mentioned:
— Mark Reynolds actually was signed by the Cards last week, although ESPN hasn’t updated their Free-Agent Tracker.
— Torii Hunter is a point in my favor, I think. Comparing him to MLB totals for RF in 2013-14, he’s exactly average in OBP, but +.035 in slugging.
— The contract for Nick Markakis certainly doesn’t advance my point. But I’m not sure why you’d contrast him with Adam LaRoche, who is four years older. LaRoche is getting $12.5 million per year for 2 years, Markakis $11 million per year for 4 years. Arguably, the length of contract merely reflects age (LaRoche will be 35-36, Markakis 31-34), while the rate per year reflects valuation. Arguably.
— Chris Young (the OF) is 31 years old, is no longer a plus defender, and has looked like a replacement player the last two years except his brief stint with the Yanks. Power-wise, he’s no Mark Reynolds — in the last two years, Young averaged 20 HRs per 650 PAs, Reynolds 30. And even if teams aren’t highly focused on OBP, Young’s .289 in the last two years is hard to swallow.
— Billy Butler is 2 years younger than Young. And while I agree that his deal was puzzling, let’s not forget who signed him. The A’s seem to value OBP more than most teams, and despite an off year at age 28, Butler’s career OBP is .359, 32 points above the adjusted league mark in that span.
— Cespedes has been traded twice. The A’s dealt from strength (they were #2 in AL scoring at the break) to get an ace starter for a run at the title. (Hmm … trying to improve their run prevention. Who’s that clever chap?) The second trade sent him back to a team with serious right-now title hopes. I can’t see either deal as a point against my general argument.
— Russell Martin’s big deal, I believe, was more about his pitch framing than last year’s .402 OBP. I don’t think anyone expects him to match that, since his prior 3 years produced .321. And he does have some power, probably average power for a catcher.
No doubt, you could find other counterpoints. The trend I posited is not necessarily followed by all teams, and there are multiple factors in any player acquisition. Still, I don’t think there’s any one counterpoint that’s stronger than Nelson Cruz getting $57 million for age 34-37 to go hit home runs in a park that quashes right-handed power.
Now, again, you’re right to ask where are the high-OBP guys that GMs might pursue instead. I agree that they’re relatively scarce — as are the true 30-HR men. But here are a few suggestions:
— Nori Aoki, free agent, .353 OBP over 3 years, averaging 2.5 WAR with a max salary of $2 million. No idea what he’s looking for, but he’s out there.
— Daniel Nava, career .362 OBP. Boston still owns him, and he made roughly the MLB minimum the last two years. They still have a crowded outfield, so he could be available in trade.
— Jon Jay, career .359 OBP. Last winter, you probably could have gotten him fairly cheap. He was coming off his worst year, and the Cards bumped him to the bench for Peter Bourjos. That move failed miserably, and Jay regained the job and flourished. So he wouldn’t be cheap now, but there are bound to be similar opportunities this year.
— Allen Craig for 3 years was a high-OBP guy with only medium power for his position. Last year he stunk. I don’t know what his health is, and his salary is no longer cheap. But I think Boston would listen to offers, and he could be worth a chance at the right price.
Of course, the top OBP guys are securely under contract and don’t seem likely to be traded. But you never know until you ask. Maybe the Dodgers are weary of Yasiel’s antics and would accept another young player with more power and less OBP.
Although power and OBP are in similar short supply over all, it probably is easier to find a power guy at a palatable price, because there are more players who have that skill in isolation, while the high-OBP guys tend to be better all-around players. But flip that around: It might be harder to land the high-OBP guy, but if you can, you’re probably getting a better player. So maybe it’s worth making a bold run at someone like Jayson Werth.
Super stuff John,
The Blue Jays front office would do well to take a read. They’ve made two high-profile moves to upgrade their offense at two positions. But, nothing yet on the pitching front where they’re relying on two aging vets (at least one of which probably isn’t rotation-grade anymore) and hoping for improvement from the rest. Going after Lester or Scherzer ought to be a higher priority.
Re: Toronto’s acquisition of Russell Martin, it’s fitting that he will be playing for a Canadian franchise when (barring injury) he passes George Gibson next season for most career games caught by a Canadian. Also convenient for Toronto to have a high profile native son now that Lawrie has been dealt.
The Cubs have the right idea. Nice pickup (obviously) with Lester who will presumably displace Edwin Jackson (worst starter ERA+ in baseball over the past two seasons) in the rotation and complement and up and coming staff (4 starters in 2014 age 31 or younger with 125 ERA+). With a bounce back year from Travis Wood, Chicago could have an enviable rotation.
Great piece. It touches on what I think is the biggest problem with WAR other than defense: Linear components in runs don’t add up to a linear win total. I think the basic statistical phenomenon you’re getting at is the marginal value of a run scored or saved goes up or down in value depending on the run scoring environment. The lower the run scoring environment, the more value a run has. Allowing the same number of runs but scoring a little more raises the run scoring environment and decreases the value of each run. Scoring the same number of runs but allowing a few fewer lowers the run scoring environment and increases the value of each run.
I think this has a large effect on how we allocate value on the defensive side of the inning. In an historically low scoring era with a great defense behind you and in a pitcher’s park, giving up a solo shot is going to correlate with a win much much more directly than, say, a 32 year old Larry Walker hitting a solo shot in coors. WAR uses essentially a linear compensation for this due to the way it does components. Park factor is a linear scalar, defense is a linear scalar, average run scoring is a linear scalar, etc. All these together net out to the correct average but they assume essentially full independence from one another.
What you’re showing here to me is that the marginal value of a run has a bit of a curve that aught to be considered when adding up marginal components. That curve would tell you that the total piece combination for the extreme cases like Larry Walkers and Sandy Koufax’s are not quite right. In effect, you can plot the shape of that curve by looking at W/L rates with scoring.
More on your topic, I think you’re right that we haven’t come close to the point where avoiding an out isn’t the most important factor in run scoring. It seems like if you extrapolate run scoring to be so scarce enough it’s possible but it would have to be much lower than we’ve ever seen. I like the current game except perhaps for the time between pitches and reliever substitutions. People like home runs, they get that. People like the excitement of a close game in the 9th, they get that too.
Thanks, mosc. You raise interesting points on the implications for WAR … though I must admit, the WAR formula (and justification behind it) are things I’d need a classroom, blackboard, and very patient tutor to grasp firmly.
After reading your comment, I did a quick study of games in Petco and Coors in the last two years. Far more runs scored in Coors, obviously — 5.45 to 3.40 per team/game. Average winning margin also much bigger in Coors, 3.88-2.97. But the *ratio* of winning score to losing score was higher in Petco, 2.55-2.11. I can’t think what that means, if anything. (FWIW, the average winning scores were 4.88-1.91 in Petco, 7.39-3.51 in Coors.)
But here’s something that kind of surprised me: The number of games settled by 2, 3 or 4 runs was nearly the same: 64-63 Petco, with breakdowns of 31-29, 20-22 and 13-12 (listing Petco first). All the difference in winning margin was packed into 1-run games (64-39 Petco) and blowouts by 5+ runs (60-34 Coors). But of course, a 3-run margin means much more in Petco. So I’m just chasing my tail, I think.
Interesting analysis as usual, John. I have a working theory that the decline in scoring is largely due to improved work from team’s bullpens, combined with an up-tick in overall defense. Both of these changes come from improved scouting and breakdown of batters’ tendencies, which has likely also triggered the corresponding increase in strikeouts over the past decade. As you said, the current crop of starters are not throwing as many innings as their counterparts from ten years, so their overall impact on the game has lessened.
I’d love to see the ERA+ table that you created for each slot in the starting rotation applied to the top five relievers (by innings pitched) on each team. That might complete the puzzle, at least in my mind.
Daniel, good question about the reliever comparison. Here’s what I found, using the same basic method as the SP table, but I’ll start with an overview:
Top 150 RPs by two-year IP totals, yearly average:
2003-04 — 61.4 IP … 128 ERA+ … 0.26 WAA
2013-14 — 58.8 IP … 128 ERA+ … 0.28 WAA
(Min. 80% of games in relief *and* max. 15 total starts)
Slightly fewer IP in recent years, same ERA+, insignificant gain in WAA.
Now the tiers of 30 RPs each, based on ERA+:
Tier 1:
2003-04 — 66.6 IP … 188 ERA+ … 1.29 WAA
2013-14 — 60.6 IP … 180 ERA+ … 1.00 WAA
Tier 2:
2003-04 — 65.5 IP … 138 ERA+ … 0.63 WAA
2013-14 — 62.6 IP … 136 ERA+ … 0.60 WAA
Tier 3:
2003-04 — 63.5 IP … 116 ERA+ … 0.23 WAA
2013-14 — 57.4 IP … 120 ERA+ … 0.26 WAA
Tier 4:
2003-04 — 55.7 IP … 101 ERA+ … -0.16 WAA
2013-14 — 57.4 IP … 108 ERA+ … 0.00 WAA
Tier 5:
2003-04 — 55.5 IP … 85 ERA+ … -0.68 WAA
2013-14 — 55.9 IP … 90 ERA+ … -0.47 WAA
This looks like a downward distribution of IP, effectiveness and value in the recent period. Ten years ago, Tier 1 had a pretty big edge across the board, especially in WAA. But recent bullpens are deeper, making up the value gap with their back ends.
Then I took a slightly different approach: Instead of the top 150 in total innings over 2 years, I took the top 300 one-year IP marks. Big difference in the summary here:
Method 2: Top 300 RPs by one-year IP, yearly average:
2003-04 — 65.7 IP … 133 ERA+ … 0.27 WAA
2013-14 — 62.4 IP … 138 ERA+ … 0.34 WAA
(Min. 80% of games in relief *and* max. 5 starts)
Recent years have slightly fewer IP, but better performance per IP and more total WAA.
Tier 1 (method 2):
2003-04 — 71.7 IP … 213 ERA+ … 1.54 WAA
2013-14 — 65.5 IP … 222 ERA+ … 1.39 WAA
Tier 2 (method 2):
2003-04 — 68.0 IP … 142 ERA+ … 0.74 WAA
2013-14 — 62.9 IP … 147 ERA+ … 0.73 WAA
Tier 3 (method 2):
2003-04 — 65.4 IP … 118 ERA+ … 0.20 WAA
2013-14 — 64.3 IP … 125 ERA+ … 0.40 WAA
Tier 4 (method 2):
2003-04 — 62.4 IP … 100 ERA+ … -0.19 WAA
2013-14 — 60.9 IP … 104 ERA+ … -0.13 WAA
Tier 5 (method 2):
2003-04 — 60.8 IP … 81 ERA+ … -0.95 WAA
2013-14 — 58.3 IP … 84 ERA+ … -0.70 WAA
The tiers picture is pretty similar by this method. I’m not sure why the summary is different. My first guess was that RPs are more fungible now, less likely to repeat a good year — so that selecting on a one-year basis would favor recent years. But I found the opposite: 2013-14 had 100 guys appearing twice, while 2003-04 had 87.
Although most teams carry 7+ relievers, I only did 5 tiers because the guys filling those last two slots are extremely fungible. The realism of matching individuals to bullpen slots probably breaks down after 3-4 slots, but certainly by #6.
FWIW, the SP and RP tables account for a very different percentage of total innings in those roles. The SP figures for both periods average 81% (84% for 2003-04, 81% for 2013-14). The RP figures average 61% (62%, 60%).
P.S. Reading your question again, I realize you asked for top-5 in IP for each team. Maybe I’ll get to that if I can do it without 30 separate searches. 🙂
JA: It’s possible to search for the top 5 in IP on each team without dong 30 separate searches but it’s too hard too explain. You would have to use Fangraphs.
Richard, my specific problem is the guys who changed teams during the season. They show up as “TOT” in a non-team-based P-I search, and “—” on Fangraphs. How can I get their team-specific stats without a team-based search? Give me a hint!
On Fangraphs there is a box titled Split Teams. Check it and you get the record of each player for each team that he played on. The problem is separating the starters from relievers. After you export the data from Fangraphs sort by IP and delete all pitchers whose IP exceeds the reliever maximum IP for the year. That gets rid of most of the starters. Somehow you have to identify the remaining starters.
Actually it’s easy to sort out the starters as GS is listed in the output.
Thanks for all of that research, John. You actually provided what I was asking for (but worded the request in a clumsy fashion): the top 150 relievers, averaging 5 per team. That way, we have an apples-to-apples comparison with the starters’ info, at least in terms of sample size.
The data are surprising; I was expecting to see an elite tier in the current crop, with smaller innings totals and better ERA+ than a decade ago, but the first search you ran defeats that theory. Perhaps the fact that bullpens are deeper than ever leads to better pitchers being slotted into those lower tiers? This would explain the improvement in Tiers 3-5.
Maybe I can explain the discrepancy in results between your two searches. As a hurler puts together a great year, they get leaned on heavily in that first season, then possibly promoted to closer or starter for the next season. Their two-year IP total would then either be lower than you would expect, which could explain the overall trend of improvement in the one-year IP search, or would violate your set criteria (starting games in Year 2), so some of those great single-year performances would vanish in your two-year search.
What a great post. Thanks.
Speaking of sluggers, from 1988-1995, there were only 3 players who hit 28 or more HR from the catching position. Mike Piazza did it twice (1993,1995), and Chris Hoiles did it in 1993. Can anyone name the other player?
A Tigers fan knows this. 🙂
I was sure you meant Matt Nokes until I checked to be sure. He hit 32 in ’87 and it was in ’88 that I paid $20 for him in my Rotisserie League draft. It took until I saw him play for the St. Paul Saints in ’98 or ’99 for me to forgive him.
Then I thought about it for a few moments longer and figured out who it was.
My two guesses were off by a year. Todd Hundley and Terry Steinbach both had their big HR year in ’96.
Thanks for all the guesses. For those who haven’t looked it up, the answer is Rick Wilkins for the Cubs in 1993. I was shocked when I ran across this earlier today, because I honestly don’t even remember the guy. He actually hit 30 total HR that year, 28 as a catcher, the other two as a pinch-hitter. Mickey Tettleton did hit 31 in 1991, and 32 in 1992 and 1993, but never more than 23 as a catcher.