I’m less worried about 2 catchers b/c I think your system values them higher anyway so the Razzball teams wouldn’t pick them. Might have some impact against the other teams.

I liked the tests you did as well, although I think that ignoring positions makes it harder to draw conclusions. For my experiments, I really wanted a scenario that was as much as possible a “real” draft. This seemed like the most fair way to do it.

I’ll run one more simulation with Point Shares to see if I can address some of your concerns.

However, you are correct that the baseline will affect how one position relates to another. The basic idea is that the replacement-level baseline gives the biggest boost to the top players at a scarce position. An average-player baseline bumps the top-tier less, but also drops the worst players. (The players closest to average are affected the least.)

C: -6.61
SS: -3.68
2B: -3.60
3B: -2.86
OF: -2.88
1B: -2.46
MI: -3.68
CI: -2.39
Util: -2.88
P: -2.34

If, when computing the average above replacement for each position, we include multi-position players in all eligible positions, the positional averages become

C: -3.71
SS: -0.46
2B: -0.28
3B: 0.63
OF: 0.82
1B: 1.57

Pretty similar results, but they’re a bit more tightly bunched and the averages more closely follow the defensive spectrum.

The differences between replacement and average are then

C: -2.9
SS: -3.22
2B: -3.32
3B: -3.49
OF: -3.7
1B: -4.03

I’m not sure which method is “correct.” Or if it really matters. But I thought it was better to have some numbers when discussing.

Using a standard roto league and 2008 stats (same as in this simulation) these are the positional averages that I came up with.

C: -3.71
SS: -0.46
2B: 0.13
3B: 0.77
OF: 0.73
1B: 1.73

I did not “double count” players who were eligible for multiple positions when finding the mean. I arbitrarily assigned them to “most difficult to play” position according to the C, SS, 2B, 3B, OF, 1B spectrum. I realize that doing this assumes some things we’d rather not assume, but I had to do something. Also, I didn’t compute averages for CI, MI, and DH cause I was lazy. When computing the averages (I guess I should say “mean”) I used the cutoff given by the price guide for replacement level as the last player included in the calculation.

Looking at the differences between the replacement baseline and the avg baseline, we get

C: -2.9
SS: -3.22
2B: -3.73
3B: -3.63
OF: -3.61
1B: -4.19

I would conclude from this that since there is not a constant difference between the baselines, it does in fact matter which one we use. If we adjust the raw scores to be scores above average rather than replacement, we’ll see that the value of top 1B is decreased, while the top SS and C become more valuable.

Again, I think that deciding which position was “more scarce” to begin with may have significantly influenced these results (and for that matter I’m sure I made a math error somewhere), but it certainly does leave me with a lot of questions.

For a 12-team league, our Point Shares assume 1 catcher per team (vs. the 2 in your test) and a 108 pitcher universe of about 67/41 or 66/42 in starters/relievers (5.5 SP/3.5 RP per team). This netted an average of 1,265 innings per team.

I would argue that creating 4 800 IP teams is an artificial construct. You’re never going to see a league where 4 teams punt pitcher counting stats. I’d be interested to see how the test would go if you credited each team a realistic 5 starters and aim for a 1200 IP avg per team.

I’ve done point shares for 2 Catcher leagues vs 1. It does bump up Catcher values. If you want, I can send you Point Shares for a 12-team, 2-catcher league.

Keep up the good work -
Rudy