One of the major lifestyle changes of the 20th Century has been the considerable increase in voluntary relocation. Of course, war, famine and plague have historically resulted in massive relocations of entire populations. However, as the technology of transportation has improved, many individuals and small family units relocate frequently, often on a whim. Until recently, it was automatically assumed that the natal chart was adequate for predictive purposes. Within the 20th Century, this idea has been questioned. This has happened on several fronts. First, there has been much discussion of the coordinates to use for solar returns: the birth, residence, or actual location at the time of the solar return. Second, a debate has ensued about which coordinates to use for a horary question: those of the person asking the question, or those of the astrologer answering it. The latter only could become a question when the technology for simultaneous communication over a distance became possible. The third development is the rise of locational astrology. There are three major types of locational astrology. The most widely used method, and the oldest, is to simply take the birth chart and recompute it for the relocated coordinates, leaving the date, time, and time zone intact. This produces a chart with new house cusps. The second is what is now generally known as Astro*Carto*Graphy, a system based on right ascension, popularized by Jim Lewis, but evidently developed by Brigadier Firebrace.(1) The third is the relocation azimuth system, also known as local space, proposed by Michael Erlewine, and popularized by Steve Cozzi.(2) There is some interesting anecdotal evidence to support all three systems. Beyond the question of whether these systems work at all, the question that we put here is, how important are any of them, compared to the natal chart? Essentially, if they prove important, then provision may need to be made for using a relocated chart for any desired electional work. How do we test relocation? There is actually a wonderful system for testing this very thing: baseball statistics. We have virtually complete statistics for individual players going back to 1875. Since players are often traded to different teams during the course of their careers, we should be able to track performance before and after the trade. This has the advantage of not even requiring birth data! Why? It is possible to set up a model for testing relocation which doesn’t require birth data. This can be done as follows. If we assign a cut-off point in mileage – in this case, we used 900 miles – and compare trades where the team city-to-city distance was over or under 900 miles, then if any of the systems of astro-location produced significant effects, it would follow that a greater effect should be discernible with trades of a greater geographical distance. This is because all of the systems show a difference between natal and relocated chart that increases as the distance increases from the birth location. In other words, the greater the distance between the two team’s home cities, the greater the relocational effect, and thus, the greater change in job performance should be. How do we measure performance? There is an entire branch of statistics known as sabermetrics which is devoted to baseball statistics. But our purpose here is not to rate the relative merits of Ty Cobb and Babe Ruth as hitters against left handed pitchers with two outs. Therefore we can keep it simple. I took the deviation of the batting average in any one year from the lifetime average in the case of the hitters, and yearly deviation for lifetime Earned Run Average (ERA) in the case of the pitchers. The reason for studying deviation from a lifetime average is that we are interested in relative performance for a particular year. By invoking an analysis tool which simply calculates the standard deviation from these deviations, we have captured the amount of variance from year-to-year regardless of whether the player has a better year or a worse year, in other words, the deviation is summed as a square, so a -4 does not cancel a +4 the next year. This is necessary because we are not attempting to predict whether a particular player should do better in the new location, because we are not invoking an astrological model directly to study it. What we are attempting to characterize is the magnitude of the change, not the direction of it. As I considered how to design the study, several factors stood out. The first is that there is some astrological opinion that it takes a few years to adjust to a location. The second is the astrological consideration that we are actually mixing a couple phenomena in this model. When a player is traded, not only is the new location different, so is the team. Teams have charts, and so do leagues. Therefore, I also collected other data: whether a trade was between leagues, and whether an entire team relocated, as when the Boston Braves moved to Milwaukee, and the Milwaukee Braves moved to Atlanta, or when the Brooklyn Dodgers moved to Los Angeles, or the New York Giants to San Francisco. The third factor is a bit of baseball wisdom: trades during the season may work differently than trades between seasons, when the player has the time to adjust to the new team and players at Spring Training. Taking these factors into consideration, I established the following model.
Conditions for Inclusion
Player career 1875 – 1992 inclusive
Player must have played for first team for at least 3 years.
Only trades that took place between seasons are included.
Player must have played for second team for at least 3 years.
I took the statistical information from Total Baseball.(3) The counts of hitters and pitchers who matched the above criteria are shown in Tables One and Two.
Table One. Hitters who match the model criteria by category.
Team relocation: 14
League switch: 95
Less than 900 mi: 398
More than 900 mi: 198
Table two. Pitchers who match the model criteria by category.
Team relocation: 11
League switch: 83
Less than 900 mi: 175
More than 900 mi: 108
The results in tabulating standard deviation for pitchers and hitters is given in Tables 3 and 4, and the same results are presented graphically in Figures 1 and 2. Notice that I am not applying any statistical tests to these results. It would be inappropriate. Statistical tests of significance are a measure of the probability that a given result is a result of a particular sample really representing a different population from the “population as a whole.” This is a complete data set of all the players who match my criteria. Thus, the question would be, “different from what?” Accordingly, we can simply study the results, and in fifty years or so, see whether the results obtained through 1992 still hold up through 2042: if there still is professional baseball! There is no clear pattern before and after the trade for each mileage group. In every case except the third year after relocation, the group comprising the players whose team relocated shows a distinctly lower variance. How do we interpret this? The players on the relocating team are a different set of players from the rest because they were not traded. Thus, the fact that they show less variation from their own averages in these years simply supports the hypothesis that players that play consistently are less likely to be traded. The fact that there is a peak in deviation three years after the team relocation may suggest that there is a relocation effect that produces a crisis in the third year. However, this hypothesis is not supported by the pitchers. The performance of the pitchers is fairly flat, except for the fifth year after trade. We may speculate that this shows that pitchers are traded when the trading team sees something “wrong,” and if this is the case, by the fifth year that pitcher is either ready to become a star in the new place, or retire! Thus, we see little evidence for a strong relocation effect in a measurement of job performance in either the hitters or the pitchers. Thus, we are probably justified in minimizing the need to work with a relocated chart as an indicator of everyday performance and possibility. This does not negate all possible meanings to relocation analysis. Many of us who have had the experience of traveling to other places have directly experienced the relocation effect. However, this study does question whether the intuitively, spiritually or psychologically experienced difference between places translates into job performance. It may not.
1. Firebrace, Brigadier R.C. “The Cancer Ingress.” Spica 1(1): 37-39.
2. Cozzi, Steve. 1988. Planets in Locality. Exploring Local Space Astrology. Llewellyn Publications: St. Paul, MN.
3. Thorn, John & Pete Palmer. 1993. Total Baseball. Third Edition. Harper Perennial: New York.