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Primate Studies — Where BTF's Members Investigate the Grand Old Game ## Tuesday, October 29, 2002## Adjusted Average AgePaul examines the effects of team age. I got the idea to analyze team performance in the context of team [1] First things first, I need to describe my methods. Now, not having the Win Shares numbers in a convenient fashion (and having some philosophical issues with James’ method[2]), I decided to use a different formula to arrive at Team Age[3]. It’s pretty simple and fairly easy to compute quickly (well, with the help of a computer, anyway). For each hitter, multiply his baseball age for that season by his plate appearances. This gives you BatterAgeContribution. Then divide the sum of these for all players who played with a team in that season by that teams’ total PA to get AverageHitterAge. Do the same for pitchers, except to substitute IP for PA. Then, to combine the two, count the number of different hitters and pitchers to appear. The final average for the team is then arrived at by multiplying AvgHitterAge by the number of hitters, and AvgPitcherAge by the number of pitchers, adding the two and dividing by the total number of players[4]. Now, we have what I will refer to as Adjusted Average Age. So, now with all these Adjusted Average Ages computed for all teams since 1903[5], I decide to just look around at the numbers. For the course of the 98 seasons (1903-2001 inclusive), the average team age was 29.32 years. The chart that follows is the average age by decade[6]:
The average age has stayed pretty steady across baseball history. It dipped in the 1960s and 1970s, and was pretty low in the teens. I can’t think of any particular reason for this. Perhaps the 1960s and the 1970s were eras predicated around speed, a “young player’s skill”, as opposed to the 90’s and 00’s which are largely power and walks, “old player’s skills”. That doesn’t explain the 1980’s though. Here’s a chart that shows team age movement by year: There’s a huge dip at the end of the first decade, and a rise in the postwar era that gave the 5 “oldest” seasons in history. I cannot, however, explain the huge drop-off last year. There WAS a influx of young talent playing last year, but I didn’t think it was substantial enough to be reflected in the average team age. There was also a steady climb starting in the late 1970s that pushed the average team age up over 29.5 for most of the 1980s and 1990s. So the next natural question to ask is, “What effect does this have on team performance? Are older teams better? Are younger teams worse?” Well, that’s three questions. I looked first at the correlations between team age and winning percentage. For the period studied, there was an overall correlation of .213. There’s a slight upward trend to the data, but nothing that jumps out at you on first glance. Here’s another graph, the year to year correlation of team age to winning percentage:
As you can see, it’s a mess. There’s no clear patterns or relationships emerging. The data fluctuates from a .7 correlation (for two consecutive years in the late 1970s) to a -.4 correlation in the mid 1930s. I think it’s more a small sample size issue than anything else (one team can influence the numbers in a drastic fashion). So let’s look at the numbers, in table form, of correlations by decade:
Excepting the 1980’s, there’s a trend in the free agent era towards older teams being better (I should rephrase that, older teams being better than their historical counterparts). Of course, medical science may also have something to do with that phenomenon as well. So now I’m going to focus on extreme teams: the oldest and the youngest teams in history. First, let’s look at the teams that fall into each category. The oldest and youngest:
As you can see, there’s a lot of expected teams on these lists. The recent Jeffrey Loria debacles rank as some of the youngest, while the “Geezerbacks” of last year rank as one of the oldest. These old teams as a whole posted a .528 winning percentage, while the young teams posted a .431 winning percentage. You can begin to see that having an old team is not a recipe for success, but having a young one is definitely inviting failure. This becomes more apparent when we examine division placement, grouped the young and old teams:
The younger
teams routinely finished amongst the weaker teams, whereas the older teams
finishes were more evenly distributed. The two “young” first place finishers
were the 1970
Cincinnati Reds, who lost in the World Series to the Orioles, and were the
28 It turns out that they are. Of the course of all the World Series that have been played so far, the average World Series champion is 29.322 years old, and averages being 0.36 years older than that team’s average, a 1.21% increase. Here’s a table of the “oldest” WS winners, listed by Difference From League Average:
Notice a trend? Three of the top four champions are the last three WS winners, last year’s “Geezerbacks”, who were a whopping 4.1 years “older” than the league average, and the NY Yankees of 1999 and 2000, as all mentioned in the previous section. (For reference, last year’s Yankee team wouldn’t have placed on this list, only creeping in at +.03). Let’s look at the youngest teams to take the top prize:
Both NY Met championships are on here (hint, hint Steve Phillips). One that surprises me is the 1995 Atlanta Braves. That’s probably colored by my perception of them now, for back in 1995 the Three Aces were still in their 20s. The only entries on here in the last twenty years are that team, the surprise 1990 Cincinnati Reds team, the Buckner Mets, and 1982’s St. Louis team that relied on speed, speed, and speed (they hit less HRs as a team that year than Barry Bonds did last year). Since there’s obviously a range of WS winners, let me get back to the original questions: “Do old teams sink? Do young teams rise?” Taking our young group, they averaged 0.006 improvement on their winning percentages in their next season. So it doesn’t seem like young teams necessarily have to improve. Players may not pan out, injuries, and inconsistent play may do these growing teams in. The older group averaged a loss of 0.025 percentage points in the following season. This is a lot more significant, but still not very significant. It may make the difference between winning your division and some good golfing in October, though. Part of the problem is the old teams already are up high in the standings for the most part, and thus are more likely to fall instead of improve, whereas the young teams tend to be distributed more towards the bottom making it easier to move up. The overall correlation between age and next year’s performance is -0.158, and here’s the numbers by decade:
Note I left 2000 off the chart. There’s only one season pair, so I
decided to do without it. Bill James, in the section of Some conclusions: having a veteran laden team isn’t a recipe for success, but completely lacking any veterans is dangerous for your team’s health, the game is getting older[8], and last year’s Diamondbacks were truly a “historical” team, in more than one sense of the word.[9] [1] James, [2] James’ methodology multiplies WS by Age. I feel that we shouldn’t be looking at performance as measure of team contribution in this sense; rather, I felt that measures of playing time were more appropriate. [3] It turns out my method is similar to the method used by Sean Forman in calculating Team Ages on baseballreference.com. His method differs from mine, in that he keeps hitter and pitcher averages separate, and the formulas used, although the batting formula is very similar, to arrive at these team ages. I was unaware of this method when I started my work. His weighted averages can be found here. [4] Yes, pitchers in non-DH leagues are double counted under this method. They are counted for their PAs and IPs. I originally took out their hitting contributions, but I feel more comfortable leaving them in. [5] Spotty DOB information caused problems doing calculations for any season before this. Besides, it happily coincides with the first year the World Series was played. [6] 1900s=1903-1909, 2000s=2000 and 2001. [7] Two standard deviations less than the all time average. For older teams it was the top 37 to match the fact that were 37 “young” teams. [8] As noted by Clay
Davenport in his essay, “Graying the Game”, printed in [9] A quick acknowledgement to Sean Lahman, for his wonderful database, which I used in all these calculations.
Paul Mazurkiewicz
Posted: October 29, 2002 at 05:00 AM | 11 comment(s)
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1. Sean Forman Posted: October 30, 2002 at 12:59 AM (#606972)I know a guy who believes the baby boom is the main explanation for the pitching performances of the sixties and early 70's. With fewer prospective players the top athletes want to be batters. When the competition is higher some of them "settle" for being pitchers in order to get opportunities to play. He believes this starts in little league and high school.

Bad teams tend to be younger teams for this reason: if you don't have any good veterans, there's no point in stocking up on poorer veterans, as Orioles fans well know. It's better to play a bunch of kids and hope that some of them will improve.

* How well does team age make a forecast of later wins? Young teams win few games and old teams win many games. If young teams become better teams the next year and old teams become worse, this may be one of the reasons teams always regress to .500.

To look at this right, you must look at all teams in a winning percentage group, say all teams with winning percentages between .400 and .450. Look at how many games each team won the next year, and see how many games young teams and old teams won.

* I guess you know about Don Malcolm's look at age in the 2000 BBBA. What Don's showed was the span of age. The 1991 Texas Rangers had the young Ivan Rodriguez and the old Nolan Ryan. As Bobby Bragan would say, the mean shows that team as in the middle, but it is the mean of two outliers. Do these teams win more or fewer games than other teams of the same mean age?

If the former is true, then aren't there only two significant digits in your calculations?

I also wonder what the win-age correlation would be if one omitted teams such as the recent Marlins, who have clearly and purposely gotten young and bad. Of course there are non-competetive teams at all points in history, and I suspect those teams may comprise a significant fraction of that positive correlation shown.

Sean--that's an interesting theory about the pitching (I know it's not yours). It's hard to prove--although maybe with the multitude of options in sports today, this explains the hitter binge we had?

Charles--you caught me, I am not familiar with Malcolm's work...that's interesting stuff to look at. And the groupings by WINPCT are a good idea...I'm disappointed I didn't think of it myself.

Lujack--my team ages are as of July 1st of the season. My knowledge of significant digits is lacking right now...someone who remembers can please help me with this. I could go back and rewrite queries to calculate a more precise age...although at first blush I'd be worried about cutoff dates and making assumptions about season length/start.

A quick note on presentation. The first table would read better (and mean just as much), with just two digits after the decimal (hundredths of years).

Suspect the 1917-19 seasons and 1942-46 seasons should be out of the study because of demographic skew arising from factors external to baseball. Maybe that would have no effect on the trends, but it's worth considering anyway..

I think the age dip is, as Sean Forman suggests, a big uptick in the players available. He cites the baby-boom phenomenon, but I'd like to suggest racial integration <u>might</u> be as significant a difference-maker as the baby-boom. I think there's even a way to track it, which is to chart the average age of *assumed-white* ballplayers from '50- about '73 (i proffer '73 because it's the 15th season of full integration since the last Jim Crow major-league team integrated in '59).

Also think Saeger's suggestion is important. My memory of James' age study (though maybe a later add-on of the original) was he had a shape for age distribution based on 'young', 'prime years', and 'old'. Maybe you could consider creating clusters and having the computer assign teams to those clusters and see if that clarifies both "success" and "direction".

The increase starting in the 70s is most likely due to the DH.

The drop in the 90s may be due to the MIN/MON philosophy of not competing on the field for financial reasons, and running cheap kids out there instead. There were a lot of seasons of AA players in some of the "small markets" that would have done a lot to offset the normal, older, rosters.

My point about the significant digits was that the difference in comparing decades seems to be less than a year in most cases and by using only two significant digits any number that you report as 28.3 may actually be closer to 28.8 in reality.

* You might want to run the Win Share age means to see the differences. I do not think there will be large differences, but I think there might be some, and it may be worthwhile to look at those teams.

* As I see it, there are three main ways to weigh it: opportunity (PA/BFP), stolen opportunity (Outs/IP), and success (Win Shares or some way like it). As I said above, I wonder if any teams have huge differences in the three ways, who they are and what happened to them.

* Are there differences between young and old hitters and young and old pitchers? I guess age means less for pitchers, but I do not know.

* I know Bill James looked at matched pairs of young and old teams in an old Baseball Abstract.

I suspect that old teams get young quickly, with a correlating drop in success (as the older stars get dumped or retire and are replaced with kids), while young teams age slowly (roster stays the same, with everybody one year older and one or two veterans added) and improve slowly. But that's a casual-fan observation, and I'd be curious to see if it is supported by the stats.

Thank you,

-Vardibidian.

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