It may not be exactly the same as the pre ‘roid era, but I think it will be similar. Pitching and hitting league wide tend to go in cycles. I fell like overall, we will have an uptick in pitching and a downward correction in hitting over the next few years. Then, about 2023, we’ll see several guys make a run at Barry’s single season HR record. Just the natural ebb and flow of the game.

If your analysis of the Giant’s (or any team’s) struggling offense is “they don’t hit enough home runs” it only shows that you don’t really understand baseball very well. Its the same as analyzing a struggling pitching staff and saying “they need to strike more batters out.” It shows a very limited understanding of how to win in baseball. They are plenty of ways to get runners across home plate that don’t involve home runs, and plenty of ways to get batters out w/o striking them out. Just because it is the most direct and publically visible doesn’t mean it is the driver.

The home run craze of the last 6-7 years (or I guess almost ten) was great at first, but now it’s just left a sour tase in my mouth.

Certainly, they are a modifier. It’s not so much the stolen bases that are making/breaking this offense (altho they are certainly helping).

I guess I have a hard time seeing how HR’s are a very important part of the game – while they do account for the most runs of any single play, the frequency is so low that they don’t account for nearly as large of a part of an offense as some see to think.

Saying a home run is a very important part of the game is like saying the triple play is a very important part of the game. Yes, it is the best way to score runs / get outs in any given situation. However, the frequency is low enough (obviously the triple play is much much lower) that it’s relavence in terms of the whole is diminished.

On other variables. If you have a pitching staff that on average cannot hold the opposing team to less than 4 runs a game, then you need to take an offensive approach that will on average score more than 4 runs a game. In that case, Sacraficing outs for runs wouldn’t make much sense ‘cause your just going to lose anyway. losing 7-4 is the same as losing 7-0 ‘cause you waited around for the big inning

If your pitching staff, like the 2008 giants, is very likely to hold the opposing team to less than 4 runs, then you can manufacture runs to get your 4 runs and call it good. Losing 2-0 because you had RISP but didn’t manufacture, and instead waited for the big inning (that rarely comes with an inept offense) is retarded when you could have won that game by sacraficing a few outs for runs.

With the Giants pitching staff, they will typically allow less than 5 runs a game, which means that the giants need to figure out a way to score 4-5 runs a game to win. In that circumstance given their offense, sacraficing outs for runs is perfectly acceptable.

not easy for me, at least.

You are probably aware of this, so I apologise if you are, there are linear formulas that are tweaked for the modern run environment, such as David Smyth’s BaseRuns, or Tango’s Linear Weights by the 24 base/out states, 1999-2002, Linear Weights, by Runner on Base and Outs etc.

OTOH, formulas like Baseball Prospectus’ EQA, or just the basic Batting Runs formular, are derived so that they apply better to all environments.

You’ve probably seen it, but if you haven’t, Clay Davenport’s article on BPro introducing EQA gives a good idea on the accuracy of some of the various run estimation formulas.

Metric Correl RMSE
Equivalent Average .928 24.13
BaseRuns .930 24.38
eXtrapolated Runs (per PA) .920 24.83
Runs Created (per PA) .928 24.96
Total Average .926 25.33

f you only looked at the years from 1971 to 2003, eXtrapolated Runs would have a virtually identical RMSE to EqA (20.98 for EqA, 21.06 for XR), while BaseRuns actually does a little better (20.77). However, if you look at the 30 years from 1871 to 1900, the same XR and BsR equations are getting more than 60% worse—their RMSEs shoot up to 34.16 and 33.41, respectively. EqA, in contrast, “only” loses about 50%, scoring a 31.69. EqA is less sensitive to the conditions of the times than many of the other metrics which have been “tuned” to fit recent performances, so it’s especially good for historical performances.