In February, I wrote an article evaluating multi-year dealsgiven out to players with at least six years of service time, and I discovered something interesting. I found that players who re-signed with their current teams aged better than players who signed contracts with new teams, and not by a small margin. This finding gained some extra attention (and extra scrutiny) when I used it to question whether the Phillies might not have erred as terriblyas sabermetricians had suggested when they extended Ryan Howard's contract for five years and $125 million last month. The primary question that people asked was whether there was any bias in the ages of players who signed multi-year contracts with their current teams versus the ages of players who signed multi-year contracts with new teams. In fact, there is some difference in the ages of these groups of players.
# |
Avg. Age |
Two-Year Deals' WARP3 |
Year 1 |
Year 2 |
---|---|---|---|---|
39 |
34.1 |
Re-Signings |
39% |
61% |
32 |
35.3 |
Signed w/New Teams |
63% |
37% |
# |
Avg. Age |
Three-Year Deals' WARP3 |
Year 1 |
Year 2 |
Year 3 |
---|---|---|---|---|---|
15 |
30.7 |
Re-Signings |
36% |
34% |
30% |
35 |
32.3 |
Signed w/New Teams |
45% |
39% |
16% |
# |
Avg. Age |
Four-Year Deals' WARP3 |
Year 1 |
Year 2 |
Year 3 |
Year 4 |
---|---|---|---|---|---|---|
6 |
31.3 |
Re-Signings |
20% |
33% |
29% |
17% |
13 |
30.9 |
Signed w/New Teams |
29% |
25% |
21% |
24% |
While not a particularly large difference, the re-signed players were younger on average in the two-year and three-year deals, which were the contracts that showed major differences in aging. Does this explain the effect?
TWO-YEAR DEALS
Let’s first focus on two-year contracts. This was the group where the aging difference was the most lopsided, with the re-signed players actually improving during their deals. I split this group of two-year contracts into different age ranges. Most players who signed two-year deals were older, so it was difficult to get very large sample sizes. I split them into a group of 14 players who were between 26-31 years old during the first year of their two-year contracts, a group of 23 players who were between 32-34 years old, a group of 20 players who were between 35-37 years old, and a group of 14 players who were between 38-47 years old.
# |
Avg. Age |
Two-Year Deals' WARP3, ages 26-31 |
Year 1 |
Year 2 |
---|---|---|---|---|
9 |
29.0 |
Re-Signings |
40% |
60% |
5 |
29.8 |
Signed w/New Teams |
65% |
35% |
# |
Avg. Age |
Two-Year Deals' WARP3, ages 32-34 |
Year 1 |
Year 2 |
---|---|---|---|---|
12 |
33.1 |
Re-Signings |
41% |
59% |
11 |
33.4 |
Signed w/New Teams |
46% |
54% |
# |
Avg. Age |
Two-Year Deals' WARP3, ages 35-37 |
Year 1 |
Year 2 |
---|---|---|---|---|
13 |
35.8 |
Re-Signings |
38% |
62% |
7 |
35.7 |
Signed w/New Teams |
62% |
38% |
# |
Avg. Age |
Two-Year Deals' WARP3, age 38-47 |
Year 1 |
Year 2 |
---|---|---|---|---|
5 |
41.0 |
Re-Signings |
36% |
64% |
9 |
40.4 |
Signed w/New Teams |
72% |
28% |
Even in very small samples, we see a very large difference. Players who were re-signed by their current teams aged better than those players who signed deals with new clubs. In fact, each re-signed subgroup actually improved during their contracts, while three of four sub-groups of the newly signed players regressed during their contracts.
THREE-YEAR DEALS
Does this hold for three-year contracts? The answer appears to be yes.
# |
Avg. Age |
Three-Year Deals' WARP3, ages 27-30 |
Year 1 |
Year 2 |
Year 3 |
---|---|---|---|---|---|
9 |
28.9 |
Re-Signings |
35% |
37% |
28% |
12 |
29.2 |
Signed w/New Teams |
34% |
43% |
22% |
# |
Avg. Age |
Three-Year Deals' WARP3, ages 31-34 |
Year 1 |
Year 2 |
Year 3 |
---|---|---|---|---|---|
4 |
32.8 |
Re-Signings |
36% |
32% |
32% |
15 |
32.3 |
Signed w/New Teams |
41% |
48% |
10% |
# |
Avg. Age |
Three-Year Deals' WARP3, ages 35-38 |
Year 1 |
Year 2 |
Year 3 |
---|---|---|---|---|---|
2 |
35.0 |
Re-Signings |
40% |
31% |
29% |
8 |
36.6 |
Signed w/New Teams |
67% |
17% |
16% |
Even in a small sample size, we still see the same effects. Additionally, the older players who received three-year contracts appeared to drop off even more suddenly at the end.
FOUR-YEAR DEALS
Last time, we found that four-year contracts appeared to show little evidence of re-signed players aging better. Does breaking this down into players in different age groups reveal an affect?
# |
Avg. Age |
Four-Year Deals' WARP3, ages 27-30 |
Year 1 |
Year 2 |
Year 3 |
Year 4 |
---|---|---|---|---|---|---|
3 |
30.0 |
Re-Signings |
15% |
44% |
30% |
11% |
6 |
29.2 |
Signed w/New Teams |
22% |
28% |
26% |
23% |
# |
Avg. Age |
Four-Year Deals' WARP3, ages 31-34 |
Year 1 |
Year 2 |
Year 3 |
Year 4 |
---|---|---|---|---|---|---|
3 |
32.7 |
Re-Signings |
25% |
23% |
28% |
24% |
7 |
32.4 |
Signed w/New Teams |
33% |
24% |
19% |
24% |
This does not appear to provide any evidence either way, as we are now dealing with such small sample sizes that we cannot find any effect beyond the noise. Of course, this does not mean that there is no such effect on four-year deals, but it is certainly does not prove that there is.
CONCLUSION
Recently, well-respected sabermetrician Mitchel “MGL” Lichtman has stated that if this effect were true, it would be the “one of the most interesting and significant things to come out of sabermetrics since DIPS.” Of course, Lichtman also professed a profound skepticism of whether the results could be interpreted as true.
To me, this is not a matter of true and false. The results above are reflective of past events. They happened. Inasmuch as the contracts’ terms are reported to the media truthfully and WARP3 is computed in the way it is (and the results were the same for FanGraphs’ WAR), these results happened. The teams that re-signed their players did think that their knowledge of the players indicated they would be good bets, and they turned out to be just that. Thus, it is true that re-signed players aged well, as their teams believed. That does not imply that the future will necessarily show this same effect. In fact, the very publication of this series of articles might change this outcome, as other teams might target players more aggressively if their know those players' current teams are bidding for their services. All I can say conclusively is that this happened on average for the 140 contracts that ranged from two-four years long and concluded after the 2007-09 seasons.
Whether it will continue is not clear, because it is a market outcome, not a baseball outcome. When Lichtman alludes to DIPS, he is referring to a baseball outcome. If pitchers are unable to exert much control over BABIP, that will remain true as long as pitchers are trying to get hitters out (of course, if pitchers minimized their FIPs, this might not remain true). Once a market effect is discovered, it can be changed. While this market inefficiency is at least based on inside information and therefore stands a larger chance to stand the test of time, general managers can react to the market and make different decisions about signing other teams’ free agents.
In fact, this effect may not have existed in earlier years as re-signings may have been based on factors that no longer control clubs’ decision-making process. All we can say for sure is that these outcomes in the tables above are real for the years tested, and the effect appears to be large enough to suggest it is not random.
I will continue looking at this topic, and invite others to do the same. Determining this effect is no small issue, and may be a huge step in understanding a side of baseball economics that is not based on information you can find on Retrosheet. Sabermetrics has largely focused on aiding teams to make better decisions based on data generated on the field, but has remained unenlightened about data generated off the field. This could be a significant bridge to that information.
Thank you for reading
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If each group had WARP of 4 on average before resigning and the group the team resigned put up WARP of 0.39 and 0.61 in years 1 and 2 while the group that signed with new teams put up WARP of 6.3 and 3.7 in years 1 and 2 I wouldn't concluded that the group that resigned with the team aged better, but your study would.
The split over the lifetime of the contract is not the most important thing by a long shot.
As always, great stuff. I still question if the results would vary if you excluded players who signed extensions and only used free agents. Do you think you could have that variation come down the pipe?
Thanks.
J
playerid,ageSigned,yearSigned,DollarsSigned,NumYears,WARP0,WARP1,WARP2,WARP3,WARP4
(for each year they were signed, with WARP0 being the WARP prior to signing)
Instead of, or maybe along with, controlling for age, you could control for WARP in the years leading up to free agency.
Great work though.
It's hard to understand how meaningful these results are without seeing the actual WARP numbers. I'd like to echo the readers who are asking to see WARP for year 1, 2, etc. for each group.
- David Gassko
AVERAGE WARP EACH YEAR FOR EACH DATA SET (N = SAMPLE SIZE IN PARENTHESIS):
TWO YEARS
26-31 YEARS OLD
Re-signed: 1.64, 2.48 (N=9)
Newly signed: -0.34, -0.18 (N=5)
32-34 YEARS OLD
Re-signed: 0.37, 0.53 (N=12)
Newly signed: 0.28, 0.33 (N=11)
35-37 YEARS OLD
Re-signed: 0.80, 1.31 (N=13)
Newly signed: 0.73, 0.44 (N=7)
38-47 YEARS OLD
Re-signed: 1.00, 1.80 (N=5)
Newly signed: 1.16, 0.44 (N=9)
THREE YEARS DEALS
27-30 YEARS OLD
Re-signed: 1.99, 2.11, 1.59 (N=9)
Newly signed: 1.13, 1.43, 0.73 (N=12)
31-34 YEARS OLD
Re-signed: 4.13, 3.73, 3.68 (N=4)
Newly signed: 1.17, 1.35, 0.29 (N=15)
35-38 YEARS OLD
Re-signed: 0.40, 0.31, 0.29 (N=2)
Newly signed: 1.96, 0.49, 0.48 (N=3)
FOUR YEAR DEALS
27-30 YEARS OLD
Re-signed: 1.47, 4.17, 2.87, 1.03 (N=3)
Newly signed: 1.50, 1.85, 1.77, 1.57 (N=6)
31-34 YEARS OLD
Re-signed: 2.27, 2.03, 2.53, 2.17 (N=3)
Newly signed: 3.89, 2.87, 2.23, 2.86 (N=7)
So, all in all, it doesn't seem like there is any real pattern to the overall quality of the players signed. The sample sizes obviously get pretty small, but on the aggregate, they were statistically significant, and nearly every one of the individual samples seems to point in the same direction. I'll continue to look for different ways to splice this data set and please make suggestions if you have any. Thanks for these comments.
Looking at the data, it becomes imperative to find matching pairs to avoid selection bias. For example:
26-31 YEARS OLD
Re-signed: 1.64, 2.48 (N=9)
Newly signed: -0.34, -0.18 (N=5)
Those newly signed are pretty much useless. I don't see what their WARP was in the year prior to being signed (which I hope you can also include), but I'm going to guess it's going to be also low. They were probably injured. Something about them explains how they can sign a two-year contract and become replacement level.
Now, if you tell me that BOTH groups were a WARP of say 1.80 in the year prior to signing, then that would be VERY interesting. (Again, presuming no health issues.)
Look at this data:
THREE YEARS DEALS
27-30 YEARS OLD
Re-signed: 1.99, 2.11, 1.59 (N=9)
Newly signed: 1.13, 1.43, 0.73 (N=12)
Again, there's no way these are two similar groups of players. The newly signed must have been alot of platoon players, while the re-signs are regulars. Unless of course you tell me that their WARP in the preceding seasons were both 1.80 or something, which I doubt would be the case.
And here:
FOUR YEAR DEALS
27-30 YEARS OLD
Re-signed: 1.47, 4.17, 2.87, 1.03 (N=3)
Clearly, 3 players, with that kind of jump, that means that there was one superstar season in there that skews it all.
***
If I do a weighted average of all the 27-30 year olds in your list, this is what I get:
21 players re-signed, with first year WARP of 1.77 and 2nd year WARP of 2.56. That's pretty interesting, for sure. The question is why. Is it because of insider knowledge? Was it that they were 2.10 players who DROPPED to 1.77 and then rose to 2.56? We desperately need more context, specifically their WARP in the preceding season. And PA (plate appearances) would be good too.
23 players signed with new teams, with a first year WARP of 0.91 and 2nd year WARP of 1.20. It looks to me that players who signed multi-year deals with new teams are alot of platoon players.
Given the small number of players (44 players 30 and under) who signed multi-year deals, a couple of big seasons could skew things.
So, more information please, more context, as noted above, and I think we can move forward better.
Thanks, and I love it when others do all the hard work and roll up their sleeves. Everyone benefits.
If so, then the list of players and salaries from 2006-2010 can be obtained here: http://sports.espn.go.com/mlb/features/freeagents?season=2006
The pain in the butt is always in the linking of the ESPN ID to the BPro ID. Obviously, you linked your source's ID to BPro's ID.
Anyway, I'm trying to understand how far you can give out the data within whatever arrangement you agreed to. It seems that for us to replicate and extend your work, someone would need to simply link ESPN to BPro IDs.
BTW, I also have a problem with this methodology, using the percentages to argue that players that re-signed with their teams "aged" better. Unfortunately, I can't really articulate why--I'll see if I can come up with something constructive.
OTOH, I find your general conclusion (teams that re-sign their own players get better results cause they know their players best) intuitively correct. It would be great to really "prove" it.
Or, include dummy parameters (for WARP class and age class), run a regression, and let's see what you get. I'd like to see the regression equation, especially if it shows a 0.5 or higher for "re-signed with same team".